Development of a novel mechatronic system for mechanical weed ...

Institut für Landtechnik
der Rheinischen Friedrich-Wilhelms-Universität Bonn
Development of a novel mechatronic system for
mechanical weed control of the intra-row area in row
crops based on detection of single plants and adequate
controlling of the hoeing tool in real-time
Inaugural-Dissertation
Zur
Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
der
Hohen Landwirtschaftlichen Fakultät
der Rheinischen Friedrich-Wilhelms-Universität
zu Bonn
vorgelegt im November 2007
von
Dipl.-Ing. Maschinenbau M.Sc Zoltan Gobor
aus Apatin (Serbien)

Referent: Prof. Dr.-Ing. P. Schulze Lammers
Korreferent: Prof. Dr.-Ing. habil. Jan-Welm Biermann
Koreferent: Prof. Dr. Milan Martinov
Tag der mündlichen Prüfung: 17.12.2007
© im Selbstverlag
Bezugsquelle: Institut für Landtechnik
Rheinsche Fridrich-Wilhelms-Universität
Nussallee 5
531115 Bonn
Alle Rechte, auch die der Übersetzung und des Nachdruckes sowie jede Art der
photomechanischen Wiedergabe, auch auszugsweise, bleibt vorbehalten.

Development of a novel mechatronic system for
mechanical weed control of the intra-row area in row
crops based on detection of single plants and adequate
controlling of the hoeing tool in real-time
Abstract
As a component of successful non-chemical weed control intra-row weeding shall be
considered as a final weed elimination procedure and not as a primary method.
Conventional methods for inter-row weed control can handle with approximately 80% of
the field area in row planted crops and vegetables. However, the weeds occur in the
remaining area between (intra-row) and around the plants (close-to-crop) have a much
bigger impact on the development and yield of the plants.
Online detection of the single plant position and the plant/weed distinction are the
bottlenecks of intra-row weeding but concerning the expeditious research and
development in this field it is to expect that appropriate systems would be available on
the market in near future. In the meantime, construction and adjustment possibilities of
implements considering the role of soil properties and mechanics need to be optimised
toward universal intra-row weeding tools, which can be used in different plant spacing
systems, different plant intra-row distances and growth stages.
A virtual prototype of a system for intra-row weeding imitating the manual hoeing
motions under the soil surface was designed. The hoeing tool consists of an arm holder
and three or more integrated arms rotating around the horizontal axis above the crop
row. Tests and simulations of the hoeing trajectories carried out with the virtual
prototype have increasingly facilitated the design process and significantly shortened
the path from the idea to the prototype. The physical prototype was realised using a
servo motor with direct software control providing rotational speed adjustment
according to the forward speed of the carrier, intra-row distance between plants and
the observed position of the arms.
A simplified methodology and system for plant position detection based on the spectral
characteristics of crop plants combined with the context information of the planting
pattern was developed and tested. The experimental results showed that the
combination of the RGB sensor for determination of the spectral characteristics and
area covering laser sensor for determination of height and reflectance can be used for
accurate detection of the plant centre position independently from illumination
conditions. The maximum deviation of the estimated centre positions from the plant

Abstract
real centre positions, in experiments conducted with RGB sensor, was 31 mm whereby
50% of the samples were inside the interval 0 to 5 mm and 90% of the samples were
inside the interval 0 to 16.9 mm. The maximum deviation of the estimated centre
positions from the plant real centre positions, in experiments conducted with laser
sensor, was 25 mm whereby 50% of the samples were inside the interval 0 to 3 mm
and 90% of the samples were inside the interval 0 to 6.9 mm.
The servo system built in the physical prototype was operated in a mode with direct
software control providing rotational speed adjustment according to the forward speed
of the carrier, intra-row distance between successive crop plants and the observed
angular position of the arms. The controlling algorithm and software solution were
developed in the Labview® environment. The main task of the controlling software was
permanent calculation, checking and change of the recent rotational speed of the
hoeing tool in real time. The software solution used an extended version of the
software previously developed for detection of the plants’ centre position.
Tests have proved that depending on the angular adjustment of the arms carrying the
duckfoot knives an uncultivated area big enough to avoid damaging of the plants can
be left around the plants during the intra-row weeding with the developed system.
The system is able to autonomously adapt the rotational speed of the hoeing tool in
case of non-intensive forward speed change. After rapid forward speed change,
several plants can be damaged while the system stabilises its work, but stable state
can be reached immediately after the stabilisation period.
The presented concept of the intra-row hoeing system can fulfil the requirements; it has
sufficient degrees of freedom to allow full adaptation to different crop and vegetable
species, different plant intra-row distances and plant growth stages. In combination
with an inter-row hoe or installed on an autonomous vehicle, the developed robotic
system could be a solution for accurate and rapid mechanical weed control.

Kurzfassung
Entwicklung eines neuartigen mechatronischen Systems
für die mechanische Unkrautbekämpfung in der Reihe
basierend auf der Erkennung von Einzelpflanzen und
adäquaten Regelung der Hackwerkzeuge in Echtzeit
Als ein Bestandteil einer erfolgreichen nicht chemischen Unkrautbekämpfung soll die
Unkrautbekämpfung in der Reihe das Verfahren vervollständigen und nicht als eine
primäre Methode angesehen werden. Die konventionelle Maschinenhacke zwischen
den Reihen deckt ca. 80 % der Fläche in Reihenfrüchten ab. Unkräuter treten jedoch in
der Reihe zwischen den Pflanzen (intra-row) und nahe bei den Pflanzen auf (close-to-
crop) und haben dort einen größeren Einfluss auf die Entwicklung der Nutzpflanzen.
Die Online Erkennung der Position einzelner Pflanzen und die Unterscheidung
Nutzpflanze-Unkraut sind die Hauptprobleme einer mechanischen Unkrautbekämpfung
in der Reihe. Auf Grund der umfangreichen Forschungs- und Entwicklungsaktivitäten in
diesem Bereich wird jedoch erwartet, dass solche Systeme in naher Zukunft verfügbar
sein werden. In der Zwischenzeit kann die Weiterentwicklung von universellen
Hackgeräten für den Bereich in der Reihe, die für unterschiedliche Pflanzenabstände
und Entwicklungsstadien eingesetzt werden können, stattfinden.
Es wurde ein virtueller Prototyp für die Unkrautbekämpfung in der Reihe entwickelt, der
die Bewegung der manuellen Hacke im Boden imitiert. Das Hackwerkzeug besteht aus
einem Armträger und drei oder mehr daran befestigten Armen, die um die horizontale
Achse, die sich oberhalb der Pflanzenreihe befindet, rotieren. Tests und Simulationen
der Bewegungsbahnen der Hackwerkzeuge, die mit dem virtuellen Prototyp
durchgeführt wurden, haben den Entwicklungsprozess zunehmend verbessert und den
Entwicklungsweg von der Idee zum Prototyp erheblich beschleunigt. Ein physikalischer
Prototyp wurde mit einem Servomotor, der über die Betriebssoftware die Drehzahl in
Abhängigkeit von der Vorfahrtsgeschwindigkeit, dem Pflanzenabstand in der Reihe und
der Winkelposition der Hackarme regelt, realisiert.
Zusätzlich wurde eine Methode und ein System zur Erkennung der Position der
Nutzpflanzen basierend auf deren spektralen Eigenschaften, kombiniert mit der
Information über die Geometrie der Saatgutablage entwickelt und getestet. Die
experimentellen Ergebnisse zeigen, dass die Kombination eines RGB Sensors für
Ermittlung den spektralen Eigenschaften Laser Sensor für Ermittlung der
Pflanzenhöhe, für eine präzise Erkennung der Mittelpunktsposition der Nutzpflanzen

Kurzfassung
verwendet werden kann und unabhängig von den Lichtverhältnissen arbeitet. In den
Versuchen betrug die maximale Abweichung des geschätzten Pflanzenmittelpunktes
von dem gemessenen Mittelpunkt, ermittelt von dem RGB Sensor, 31 mm, wobei 50 %
der Werte in einem Intervall von 0 bis 5 mm und 90 % der Werte in einem Intervall von
0 bis 16,9 mm lagen. Für den Laserabstandssensor lag die maximale Abweichung der
geschätzten von den gemessenen Werten bei 25 mm, wobei in einem Intervall von 0
bis 3 mm 50 % der Werte und 90 % der Werte in einem Intervall von 0 bis 6, 9 mm
lagen.
Der Servomotor, der in dem Prototyp eingebaut war, wurde in einer Betriebsweise mit
direkter Softwareregelung betrieben, die die Anpassung der Drehgeschwindigkeit
entsprechend der Vorfahrtsgeschwindigkeit, dem Abstand der Pflanzen in der Reihe
und der aktuellen Position der Hackarme vornimmt. Der Regelalgorithmus und die
Software wurden mit Labview® erstellt. Die Hauptaufgabe des Regelalgorithmus ist die
permanente Berechnung und Prüfung sowie Änderung der aktuellen
Drehgeschwindigkeit des Hackorgans in Echtzeit. Die Software basiert auf einer
Erweiterung der Software, die vorher für die Pflanzenerkennung benutzt wurde.
Tests haben bestätigt, dass abhängig von Winkeleinstellung der Hackarme ein
ausreichend unbearbeiteter Bereich um die Nutzpflanzen während des Hackvorganges
in der Reihe belassen wird, um die Schädigung von Wurzeln durch das Hacksystem zu
vermeiden.
Das System ist in der Lage selbständig die Drehgeschwindigkeit des Hackwerkzeuges
für den Fall nicht abrupter Geschwindigkeitsänderungen anzupassen. Nach einer
schnellen Geschwindigkeitsänderung können einige Nutzpflanzen beschädigt werden
bis das System wieder stabil arbeitet. Ein stabiler Zustand kann unmittelbar nach der
Stabilisierungsperiode erreicht werden.
Das dargestellte Konzept einer INTRA-Reihenhacke kann die gestellten Anforderungen
erfüllen, es verfügt über ausreichend Freiheitsgrade, um die volle Anpassung an
verschiedene Pflanzenarten, verschiedene Pflanzenabstände in der Reihe und
Entwicklungsstadien der Nutzpflanzen zu erreichen.

Acknowledgements
Acknowledgements
Many people contributed, directly or indirectly, in shaping up my academic career and
make it possible to complete this thesis. Here is a small tribute to all of them.
First of all I would like to express my gratitude to Prof. Dr.-Ing. Peter Schulze Lammers
my academic supervisor, who gave me the opportunity to become a member of the
DFG Research Training Group GK 722 and study at the University of Bonn. I would like
to thank him for his help from the very beginning of my application onward, for his
constant encouragement and for giving me full autonomy throughout the entire time of
my studies. His many helpful suggestions, detailed comments and critical reading of
the manuscript were crucial for completing my thesis within a timeframe of 3 years. I
really appreciate the trust he had in me.
I am also grateful to Prof. Dr. Milan Martinov because our cooperation and wide
ranging discussions which were not only about the scientific topics introduced me the
funny side of the academic life. I am deeply indebted for his moral and all other types of
supports during my M. Sc studies in Novi Sad. Professor Martinov was one of the main
driving forces that I started an international academic carrier. I appreciate his
willingness to be on my examination committee.
I also take this opportunity to express my special gratitude to Prof. Dr. Mirjana Vojinovic
– Miloradov without whose support I would never have reached this point. I owe her
more than I could express here. Thanks to the academic staff of the Faculty of
Technical Sciences in Novi Sad for supporting my idea to continue my studies in
Germany.
I am grateful for the valuable financial help that I have received from Dr. Erich-
Christian Oerke, coordinator of the DFG Research Training Group 722, from the
beginning onward for development of the prototype, attending scientific conferences
and German language classes. In particular, I would like to thank him for his critical
reading of the manuscript and suggestions.
I wish to express my sincere gratitude to Prof. Dr.-Ing. habil. Jan-Welm Biermann, my
second supervisor, for allowing his precious time to read the manuscript.
I would like also to express my appreciation to Dr.-Ing. Lutz Damerow for his
stimulating support, advices and very useful discussions.

Acknowledgements
My thanks and appreciation goes to Dan for his proofreading of the work and valuable
support that I received while conducting the experiments.
I would like to acknowledge the financial support of this project by the German Science
Foundation (DFG).
I am privileged for having Oliver, Istvan, Olaf and both of my office mates Jiri and
Peter, my colleges, who have provided great company during last three years. Thanks
to Simone and Daniel for the grill parties and being great volleyball partners. Manny
thanks to Miss Frauke Beeken for assisting me to understand and exceed the
administrative and German language difficulties.
Thanks to the staff of the workshop: Mr Dreesen for helping me during the
development of the electronics, Mr Berg for the discussions and introduction of the
historical development of the sugar beet cultivating systems, useful contributions of Mr
Berchtold and Mr Petriwski at various times during the development of the physical
prototype and Mr Dürkop providing technical support during the field experiments.
Special thanks to my uncles Georg and Ladislav Lampert and their families for
continuous taking care of me, to not suffer in any shortages and let me feel that I am
not far away from my family.
Thanks to Mr. Karl-Martin Schmidt-Reindl who hosted me during my first month of
staying in Bonn.
I wish to thank my parents, Katalin and Josip Gobor, for their continuous moral support
and encouragement in all my professional endeavours. Finally, I wish to express my
gratitude and thanks to my wonderful wife Sara that she postponed her career and
provided love, understanding and patience while I ‘played in the lab with the weeder’.
I dedicate this thesis to my family

Table of contents
Table of contents
Glossary of abbreviations and symbols........................................................................ I
1 Introduction .............................................................................................................................. 1
1.1 Consumption of chemicals in agriculture............................................................................ 3
1.1.1 Impact of pesticide application on groundwater sources ............................................ 6
1.2 Organic farming .................................................................................................................. 7
1.3 Weed management............................................................................................................. 9
1.4 Organic weed management.............................................................................................. 10
1.4.1 Indirect weed control ................................................................................................. 11
1.4.2 Direct weed control.................................................................................................... 14
1.4.3 Inter-row weed control............................................................................................... 15
2 State of the art ........................................................................................................................ 19
2.1 Intra-row weed control ...................................................................................................... 19
2.1.1 Passive tools for intra-row weed control.................................................................... 19
2.1.2 Active tools for intra-row weed control ...................................................................... 21
2.2 Detection of the plants ...................................................................................................... 26
3 Definition of the problem and research objectives ............................................................ 29
3.1 Definition of the problem................................................................................................... 29
3.2 Research objectives.......................................................................................................... 31
4 Materials and methods .......................................................................................................... 33
4.1 Detection of the single plant position................................................................................ 33
4.1.1 Sensor equipment ..................................................................................................... 33
4.1.1.1 Digital colour sensor........................................................................................... 33
4.1.1.2 Digital laser sensor............................................................................................. 35
4.1.1.3 Forward position detection................................................................................. 37
4.1.1.4 Position sensor with incremental encoder ......................................................... 37
4.1.1.5 Rotary encoder position sensor ......................................................................... 38
4.1.2 Data acquisition ......................................................................................................... 39
4.1.2.1 Hardware............................................................................................................ 39
4.1.2.2 Software ............................................................................................................. 39
4.1.3 Experimental field ...................................................................................................... 40
4.1.4 Test objects ............................................................................................................... 41
4.1.5 Test of the detection system’s accuracy ................................................................... 41
4.1.6 Test of the detection system’s robustness ................................................................ 41
4.2 The use of integrated mechanism design and simulation in prototype development....... 42
4.2.1 Introduction to prototypes.......................................................................................... 42
4.2.2 Advantages of virtual prototyping .............................................................................. 43
4.2.2.1 Pro/Engineer as a software tool......................................................................... 45
4.3 Physical prototype of the hoeing tool................................................................................ 48
4.3.1 Selection of the drive for the hoeing tool................................................................... 48
4.3.1.1 Electrical servo drive .......................................................................................... 48
4.3.1.2 Power transmission............................................................................................ 51
4.3.1.3 Adjustment of the parameters of the servo drive ............................................... 52

Acknowledgements
5 Results and discussion ......................................................................................................... 57
5.1 Algorithm for detection of the plant centre position........................................................... 57
5.1.1 Evaluation of the algorithm for detection of the plant centre position........................ 61
5.1.2 Results of the accuracy test....................................................................................... 63
5.1.3 Results of the robustness test ................................................................................... 65
5.1.4 Discussion of the plant centre position detection methodology................................. 70
5.2 Virtual prototype of the rotary hoe for intra-row weeding.................................................. 71
5.2.1 Optimisation of the arm length................................................................................... 76
5.2.2 Examination of influences of the angular position θ to the hoeing trajectories ......... 77
5.2.3 Examination of influences of the ratio between the rotational speed of the hoeing tool
and the forward speed of the carrier................................................................................... 82
5.2.4 Selection of appropriate design of the hoeing tool according to the hoeing scenario84
5.2.5 Discussion of the results conducted by virtual prototyping........................................ 86
5.3 Physical prototype of the hoeing equipment..................................................................... 87
5.3.1 Determination of the maximum torque and nominal speed required......................... 87
5.3.2 Calculation and selection of the motor and transmission combination ..................... 89
5.3.2.1 External control of the rotational speed.............................................................. 90
5.3.3 Discussion of the distance between the plant detection unit and the plane in which
the hoeing tool is positioned ............................................................................................... 91
5.3.4 Algorithm for the online control of the hoeing tool’s rotational speed........................ 96
5.3.5 Test bench for evaluation of the intra-row hoeing tool .............................................. 99
5.3.6 Methodology for evaluation of the algorithm for online control of the hoeing tool... 102
5.3.7 Evaluation of the algorithm for online control of the hoeing tool by experimental
testing ............................................................................................................................... 103
6 Summary ............................................................................................................................... 121
7 Conclusions .......................................................................................................................... 125
Further work ............................................................................................................. 125
8 References ............................................................................................................................ 127
9 Appendix ............................................................................................................................... 139
List of figures............................................................................................................ 135
List of tables ............................................................................................................. 138

Glossary of abbreviations and symbols
Symbol Unit Description
Glossary of abbreviations and symbols
.NET Development environment built for the .NET Framework
A Austria
C
CAD Computer aided design
General-purpose, block structured, procedural, imperative
computer programming language
CAE Computer aided engineering
CAM Computer aided manufacturing
CPU Central processing unit (processor)
D Germany
d m Average distance between plants
Dpulley
Dsens-hoe
m
DAQ Data acquisition
DC
Diameter of the pulley assembled to the rotary encoder for
forward position detection
Distance between the plant detection unit and the plane in
which the hoeing tool is positioned
Direct current (electricity) - the flow of electric charge is
constant
DDT Dichlordiphenyltrichlorethan C14H9Cl5
dp m
E Spain
Diameter of the uncultivated area around the plant after weed
control with intra-row weeding system
ECNC European centre for nature conservation
EMPP Estimated middle point of the plant
EU European Union
I

Glossary of abbreviation and symbols
Symbol Unit Description
EUREAU
II
European union of national associations of water suppliers
and waste water services
EUROSTAT Statistical office of the European communities
F France
Fr front
G Gear ratio
GAP The concept of good agricultural practices
GPS Global positioning system
hdmin m Minimum hoeing depth
hdmax m Maximum hoeing depth
hw1 m Hoeing width which corresponds to minimum hoeing depth
hw2 Hoeing width which corresponds to maximum hoeing depth
I Italy
I/O Input - output
IUCN The World conservation union
JL kg m 2 Inertia of the load
JM kg m 2 Inertia of the motor
Lsen m Maximum detecting range of the sensor
MA Nm Acceleration torque
MD Nm Deceleration torque
MF Nm Friction torque
Mhmaxexp
Nm
Maximal torque measured on the hoeing tool’s shaft during
field experiments
Mhn Nm Nominal torque of the hoeing tool

Symbol Unit Description
Mmmax Nm Maximal torque of the servo motor
Mmn Nm Nominal torque of the servo motor
Min Nm Torque of the gearbox input shaft
Mout Nm Torque of the gearbox output shaft
Glossary of abbreviations and symbols
M-file Script file which contains Matlab commands
mi Middle
N Nitrogen
N pulse
NI National Instruments
NL Netherlands
OS Operating system
P W Phosphorus
Value acquired with incremental encoder corresponding to the
latest angular position of the hoeing tool
Pin W Power on the gearbox input shaft
Pout W Power on the gearbox output shaft
Prequred W Required power of the hoeing tool
PFM Pulse frequency modulation
PMCD Permanent magnet direct current
PWM Pulse width modulation
RLA
Rfront
Rrear
m
m
m
Distance between the hoeing tool’s rotational axis and the
joint between the forearm and upper arm of the hoeing tool
Estimated distance between the duckfoot knife’s blade
approaching the plant from the front side and the centre
position of the plant
Estimated distance between the duckfoot knife’s blade
approaching the plant from the rear side and the centre
position of the plant
III

Glossary of abbreviation and symbols
Symbol Unit Description
RUA
IV
m
Distance between the joint between the forearm and upper
arm of the hoeing tool and the cutting edge of the duckfoot
knife placed on the upper arm
RESsys ppr Resolution of the hoeing tool
Re Rear
RGB
Colour model in which red, green, and blue are combined in
various ways to reproduce other colours
RTK-GPS Real time kinematic global positioning system
∆s m Sampling distance
SL Standard deviation of the data acquired with the laser sensor
SRGB Standard deviation of the data acquired with the RGB sensor
SA Searching area
SCR Silicon controlled rectifier
SR m Sampling distance
T s
Tmin
s
∆t s Change of the time
tL
tL-1
s
s
Estimated time in which the hoeing tool arrives exactly above
the following plant centre position
Time in which one full rotation of the system can be achieved
with rated rotational speed
Absolute time value in which the plant detection unit was
positioned exactly above the centre position of the latest plant
Absolute time value in which the plant detection unit was
positioned exactly above the centre position of the latest but
one plant
TTL Transistor–transistor logic
TGSS True ground speed sensor
∆u rpm Change of the rotational speed
uhmax rpm Maximum rotational speed of the hoeing tool

Symbol Unit Description
Glossary of abbreviations and symbols
uhn rpm Nominal rotational speed of the hoeing tool
ummax rpm Maximum rotational speed of the servo motor
unew rpm Newly calculated rotational speed of the hoeing tool
uold rpm Latest rotational speed of the hoeing tool
UK United Kingdom
USB Universal serial bus
V m s -1
Vmax
m s -1
Forward speed of the hoeing tool
Maximum forward speed for the hoeing system with three
arms and hoeing strategy when one full rotation corresponds
to three cuts between every two plants in the row
VI Virtual instrument (LabVIEW)
VE Virtual environment
VP Virtual prototyping
xfr1
xfront
xmi1
xre1
xrear
yfr1
ymi1
m
m
m
m
m
m
m
Projection of the 1 section’s front duckfoot knife position to xaxis
Projection of the distance between the duckfoot knife’s blade
approaching the plant from the front side and the centre
position of the plant to x-axis
Projection of the 1 section’s middle duckfoot knife position to
x-axis
Projection of the 1 section’s rear duckfoot knife position to xaxis
Projection of the distance between the duckfoot knife’s blade
approaching the plant from the rear side and the centre
position of the plant to x-axis
Projection of the 1 section’s front duckfoot knife position to yaxis
Projection of the 1 section’s middle duckfoot knife position to
y-axis
V

Glossary of abbreviation and symbols
Symbol Unit Description
yre1
zfr1
zfront
zmi1
zre1
zrear
VI
m
m
m
m
m
Projection of the 1 section’s rear duckfoot knife position to yaxis
Projection of the 1 section’s front duckfoot knife position to zaxis
Projection of the distance between the duckfoot knife’s blade
approaching the plant from the front side and the centre
position of the plant to z-axis
Projection of the 1 section’s middle duckfoot knife position to
z-axis
Projection of the 1 section’s rear duckfoot knife position to zaxis
Projection of the distance between the duckfoot knife’s blade
approaching the plant from the rear side and the centre
position of the plant to z-axis
z(t) m Absolute position of the plant detection unit
zc(tL) m
zc(tL-1) m
Absolute coordinate of the last detected plant’s centre position
in direction of travelling
absolute coordinate of the last but one detected plant’s centre
position in direction of traveling
α rad s -2 Acceleration of the motor
∆ ° Angle between forearms implemented on an arm holder
Ε °
Angle between the axis of symmetry of the duckfoot knife and
the plane in which the arm holder is placed
Φ rad Angular position of the hoeing tool
φrecent rad Latest angular position of the hoeing tool
η Coefficient of efficiency (gearbox)
Θ °
ωin
ωnew
Angle of the hoeing arms in relation to the plane
perpendicular to the rotation axis in which the arm holder is
placed
rad s -2 Angular speed of the gearbox input shaft
rad s -2 Newly calculated rotational speed of the hoeing tool

ωold
ωout
rad s -2 Latest rotational speed of the hoeing tool
rad s -2 Angular speed of the gearbox output shaft
Glossary of abbreviations and symbols
VII

1 Introduction
Introduction
Scientifically and socially it is recognised that sustainable development,
particularly in the agricultural sector cannot be based on the intensive
application of agrochemicals. Production techniques need to be transformed
toward systems with low or no input of pesticides. However, there are opposing
requirements and conflicts between agricultural production and society
concerning pesticide application. The consumers should be supplied with high
quality and healthy food products which have an acceptable price and are
available during the whole year. Additionally, agricultural products and
foodstuffs should be produced without any negative impact on the environment.
On the other hand, farmers are interested in attaining high crop yield and profit
maximisation, meaning low production costs and high prices of their products
on the market. However, these are diametrically opposed requirements
according to the application of chemicals in agriculture. One of possible
solutions is organic farming, but average yield in these systems is lower than in
conventional farms and thus the price of the products is higher. The input of
manual labour in general, and especially for weed control, is undoubtedly higher
in organic farming, which is the major drawback of this production method
(Stonehouse et al. 1996; Clark et al. 1999; Nilsson et al. 2000).
Weeds, as one of the most significant factors in yield quality and quantity
decrease (Oerke and Steiner 1996; Schans et al 2006), demand a holistic and
interdisciplinary approach for their adequate control. It is known that weeds not
only lower the yields, but they constitute one of the most important means of
spread and survival of crop pathogens. In many cases weeds have been found
as symptomless carriers of vector-borne viruses. Considerning the worldwide
average, about 10% of losses of the total yield are caused by weeds (Agrios
1988). The greatest danger is caused by weeds which are closely allied to crop
plants and carry infections in seasons in which crops are either not grown or are
particularly susceptible (Palti 1981). Theoretically, weeds can benefit the
biodiversity by increasing the number of species and attracting wild animals, but
this aspect is not significant concerning contemporary agricultural production.
1

Introduction
The most frequently used methods of weed elimination today are chemical
treatment with herbicides and physical treatment, particularly mechanical
hoeing. Mechanical weed control is an alternative and a supplement to chemical
weed control, frequently utilised in row crops. With regard to environmental
impacts, mechanical hoeing is preferable to the application of herbicides.
The requirement for non-chemical weed control techniques increases steadily
since the last decade of the 20 th century, especially in the Western European
countries, as a consequence of the high pollution of the underground water
sources, originated by the pesticides. Another reason why non-chemical
weeding is in the limelight nowadays is increased interest in organically
produced agricultural products and foodstuffs. In the EU-Regulation 2092/91 it
is firmly stated that only non-chemical weed control can be used in organic
farming.
Concerning demands in non-chemical pest control the following
recommendations for further research and development are given (Fogelberg
2001):
2
� research on development of alternative methods of pest control
which are economically competitive with pesticides;
� research on new techniques/methods for physical weed control;
� development of self-propelled weeding robots;
� development of new pest-preventive cropping strategies and
� development of new pest-resistant crop varieties.
In row crops approximately 80% of the field can be covered by conventional
mechanical methods for inter-row weed control (Nørremark and Griepentrog
2004). Unfortunately, the weeds occurring in the remaining area between (intra-
row) and around the crop plants (close-to-crop) have a much bigger impact on
the plant development and yield. The mechanisation of the intra-row area
cultivation is a complex task and as a result, hand weeding is still the most
frequently used method of intra-row weed control.

Introduction
According to the particular requirements for mechanical weed control within the
row in row crops, further research and development should be carried out in:
� detection of the crop and weed plants, determining weed density and
species; able to provide information about the exact position of every
individual plant in real-time;
� non-chemical methods of weed control fully adaptable to different
crop sowing patterns and use of precisely guided energy delivering
systems;
� autonomous guided vehicles able to carry the weeding tool and
supply it with energy.
One of the formidable challenges for mechanical weed control research is to
improve the selectivity of tools working close to (or in) the crop row, as also to
optimise construction and adjustment possibilities of implements considering
the role of soil properties and required mechanical characteristics of the tool
itself (Kurstjens 1998). With most mechanical weeding implements available on
the market, operator skill, experience and knowledge are critical to success
(Bond et al. 2003).
1.1 Consumption of chemicals in agriculture
The use of pesticides in their various forms has existed almost as long as the
practice of agriculture. Historically, many substances, we today know to be
harmful to humans, have been used in agriculture to eliminate unwanted plants
and animals: sulphur, arsenic, lead arsenate, mercury and many others. The
discovery of the extraordinary killing effect and endurance to time and weather
conditions of DDT in the early 1940's started a new era of intensive pesticide
application. In the same time formulation and distribution of many other
chemical pesticides began suddenly. In 1944 the first selective herbicides,
composed of phenoxy acetic acids, were discovered. In the decades that
followed, more and more synthetic pesticides were created and better
application methods were implemented. In the 1950/60’s, granular herbicides
allowed easier application into the soil. According to the development, pesticide
3

Introduction
application that had previously been limited to small fields with high value crops
has been introduced into the mainstream of major field crops and became the
standard practice in the 1970's.
Realizing the negative influences of agrochemicals on the environment as a
global ecosystem, awareness and concern about their use have increased in
the past twenty years. Accordingly, the EU has taken legislative steps to limit
the future use of pesticides. In the context, particular definition of pesticides
includes pesticides, herbicides, fungicides, different growth regulators and other
substances. In 1991, the EU Directive 91/414 was introduced, providing a
common method for evaluation of the pesticides approval according to their
safety. Currently there is a list of 1,026 active ingredients which are being
thoroughly tested and hundreds have already been banned.
The Statistical Office of the European Communities - EUROSTAT, collects and
keeps many information about the use of pesticides over the past fifteen years.
It recorded that the sales of active pesticide ingredients within the EU countries
in 1992 totalled 295,173 t (DRAFT EXPLANATORY MEMORANDUM 2002).
This number rose further to 322,315 t in 1998 (DRAFT EXPLANATORY
MEMORANDUM 2002), 332,806 t in 2000, and sank to 327,280 t in 2002
(EUROSTAT 2006).
4
Active ingedient [t]
125,000
120,000
115,000
110,000
105,000
100,000
95,000
90,000
85,000
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
[Year]
Figure 1.1 Total quantity of herbicides sold in the 15 EU states in
the period from 1992 to 2001 (EUROSTAT 2006)

Introduction
The largest purchaser of pesticides in 2002 was France, with 99,635 t, followed
closely by Italy with 94,711 t. In the period 1992-2000, average annual increase
in use of pesticides was steady at about 1.6%. Between 2000 and 2002,
consumption actually fell by just under 1% each year. Among other active
substances, pesticides include herbicides, whose application can be decreased
by intensifying the mechanical weed control. The total consumption of
herbicides in 15 EU states and some member states are presented in Figure
1.1 and Figure 1.2.
Active ingredients [t]
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
F UK D E I NL A
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
[Year]
Figure 1.2 Total quantity of herbicides sold in some of the EU
member states in the period from 1990 to 2001 (F –
France, UK – United Kingdom, D – Germany, E – Spain,
I – Italy, NL – Netherlands, A – Austria) (EUROSTAT
2006)
Considering the attitude of decision makers it is obvious that application of
pesticides will be even stricter regulated in the coming years. By the year 2008
the EU plans on having finished its analysis of 1,026 active ingredients under
Directive 91/414. As a result, hundreds of pesticides currently in use will be
taken off the market and manufacturers and farmers will be forced to choose
only active ingredients appearing in Annex 1 of the Directive. Also, as of 2006
plans are underway to construct new legislation for pesticides. The European
Commission published a new proposal in July 2006 as a first step for replacing
Directive 91/414. A significant proposed change includes removing the system
of provisional nation authorisations which allow countries to self-approve a
pesticide before the EU has completed their valuation. Another change would
5

Introduction
be to go from a system of “risk” assessment – where potential hazard is
weighed in terms of likelihood of actual exposure and contamination – to a
purely “hazard” based assessment – only potential harm is evaluated, with no
consideration of actual likelihood of exposure.
6
1.1.1 Impact of pesticide application on groundwater
sources
One of the most worrisome effects of pesticide use is water contamination.
About 65% of drinking water primary sources in Europe rely on ground waters,
making the possible groundwater contamination extremely dangerous.
EUREAU, the European union of national associations of water suppliers and
waste water services, published a report in 2001 stating that the areas at
greatest risk in Europe where those containing lowland rivers, in particular in
north-western Europe in countries bordering the Netherlands such as Belgium
and Germany. Germany was among six countries listed as one of the “most
affected countries” which have between 5-10% of their water resources
regularly contaminated by pesticides over the EU established maximum of 0.1
µg/l (EUREAU 2001). The ECNC, European Centre for Nature Conservation,
confirmed this in 2003 with environmental risk assessment, stating the risk for
aquatic contamination from pesticides. The highest contamination was
monitored in northern Italy and the area around the Netherlands and northwest
Germany. The highest concentrations of herbicides were generally found during
the spraying season (Kudsk and Streibig 2003). Because of the varying types of
pesticides and methods of application in use today, pollution can occur in many
ways. These ways include leeching into underground rivers, waste disposal and
wash down of equipment, and atmospheric deposition (EUREAU 2001). The
costs of monitoring and removing pesticides from drinking water are extremely
high. For example, during the period from 1991-2000 244 mil Euro were spent
by the Dutch water industry on analysis, protection, and purification of water,
due to pesticide contamination. Nearly 130 mil Euro were the estimated annual
cost of pesticide use in Germany, taking into account monitoring costs,
production losses, and labour (Waibel and Fleischer 1998).

Introduction
The loss of biodiversity, in Germany in particular, is another effect of pesticide
use. A study carried out in Austria compared the population and biodiversity on
areas which had practiced organic farming for the previous 25 years and areas
that practiced conventional agriculture with pesticides. It was pointed out that
79% of animals in the IUCN Red List of Threatened Species could be found on
organic fields, while on conventional fields only 29% of animals from the same
list were present (Frieben 1990).
1.2 Organic farming
Sir Albert Howard (1873-1947) primarily initiated the organic farming movement
and with anthroposophic agricultural lectures given by Rudolf Steiner in the
early 1920’s, the main foundations of organic agriculture were laid (Steiner
1985; Barton 2001; Haneklaus et al. 2002). In 1930’s and 1940’s organic
agriculture was developed and practiced in Switzerland and Japan (Yussefi and
Willer 2002). Since then organic farming has grown and different private
associations have developed to control the demands and regulations.
In the European Union, contemporary organic farming is defined by the EU-
Regulation 2092/91 which is the minimum standard for organic farming
systems. It is important to understand that controlled production by meaning of
organic principles completely differs from the conventional farming based on the
rules of good agricultural practice (GAP). Surveying recent literature it is
possible to ascertain that land under conventional farming would have to be
decreased up to 50% to reach the level of the nitrate leaching of organic farms.
However, organic farms would realise 25% more yield at the same level of
nitrate leaching. According to the overall balance of organic farms, it is possible
to comprehend that it can reduce nutrient losses to the environment (Stolze et
al. 2000). Therefore, conversion from conventional to organic farming could
decrease the negative impact of agriculture on the environment.
From the guidelines given by EU-Regulation 2092/91 different effects on issues
of water protection can be anticipated. The synthetic pesticides and plant
growth regulators are mostly banned, pharmaceuticals for livestock are strictly
7

Introduction
limited and environmentally friendly cleaning agents need to be used in organic
farming. Because of that, input of such toxic substances, so called xenobiotics,
to aquatic ecosystems can be neglected. Also, organic farms usually have low
external N-fertiliser input, because synthetic N-fertilisers are restricted and
import of fodder and manure is limited. The use of raw phosphates instead of
highly soluble phosphates which are not allowed directly influences the lower
total P-fertiliser input. According to the limitations, it is obvious that risks in N-
and P-leaching on organic farms are generally smaller. Considering risk
assessment and outputs generated by organic farming, significant positive
influence on water protection can be realised (Paulsen et al. 2002).
In organic farming, weeds are the most significant production problem (Stopes
and Millington 1991; Beveridge and Naylor 1999; Walz 1999; Zinati 2002) and
sometimes total crop losses from weeds can occur. One research of the relative
frequency of weeds in the period of three years after conversion to organic
farming showed that the total number of weed seeds in the soil had increased
more than three times from 4,050 m -2 to 17,320 m -2 (Albrecht 2005). Similar
research in areas with different levels of fertility showed an increase in viable
weed seed numbers ranging from 54-495% at the end of one crop rotation
(Turner 2005). Hence, it is obvious that weed control in organic farming could
be designated as the most serious task which needs to be solved by means of
automation. This fact is confirmed by most research works and by farmers
directly involved in production (Yarham and Turner 1992).
While in 1985 the EU had 100,310 ha of organic farmland, that number
increased up to 1,462,349 ha in 1995, 5,904,481 in 2003 and 6,115,465 in 2005
(EUROSTAT 2007). As high as the numbers may be, the increasing trends in
organic farmland area and pesticide use have levelled off over the past five
years. The average annual growth rate of certified and policy-supported organic
and in-conversion land in the period from 1988-98 was 34%, while from 1993-
98 the annual rate had lowered to an average of 28%. However, in the period
2002-03 organic farmland grew by only 5.4% and estimates of growth for 2003-
04 are only about 3.0% (Lampkin 2003). In the same time pesticide
consumption has also stopped growing.
8

1.3 Weed management
Introduction
Weeds are the natural result of defying nature’s preferences for high species
diversity and covered ground. Theoretically, any plant growing in the wrong
place at wrong time can be considered as a weed (Parish 1990).
The total absence of weeds can be attained only with introduction of herbicides,
but the complete removal of weeds may also cause problems. In that case
insects have no alternative but to attack the crop itself and there is no suitable
cover for predators of crop pests (Altieri and Letourneau 1982).
Generally weeds can be divided into two broad categories – annuals and
perennials. Annuals germinate from seed each year, grow quickly, mature in
one growing season, flower, set seeds and die in less than 12 months.
Perennial weeds live more than one year and recover or regrow from dormant
stolons, rhizomes or tubers as well as from seed.
A soil seedbank present on the field contains a so called “memory of the land”.
It has a great influence on the future plant population and reflects the history of
soil management and cultivation, not just in the previous season but over many
years (Buhler et al. 1997). In the surface soil layer, down to plough depth, weed
seedbanks may vary in density from zero to more than one million seeds per
square meter. It is confirmed that any cultivation operation will stimulate another
flush of weeds to germinate, if huge reserves of weed seeds are present in the
soil. Sometimes when the field is “clean” with low level of weeds, where there
has been no or limited seeding in the previous season, vertical mixing or
inversion of the soil should be avoided as this will bring up un-germinated
seeds.
Many weed species could be present in the seedbank but usually some species
are predominant, comprising 70 to 90% of the total number of seeds. The
biggest impact on the seedbank comes from the plants producing seeds within
the field, although there are different mechanisms of seeds introduction. Weed
seeds remain dormant in the soil until conditions are favourable for germination.
Some annual weeds have extremely long living seeds which can survive more
9

Introduction
than 40 years before germination. The timing and method of soil management
have a great influence on the dormancy or germination of the weed seeds.
Weeds compete with crops for moisture, light, nutrients and space, and
therefore their elimination is of high importance. An understanding of the crop-
weed competition combined with the knowledge of weed characteristics and
behaviour can be critical in establishing an optimal weed management system,
or more exactly the timing of weeding operations. For most crops there exist a
critical period during which weeds must be controlled to maintain the yield.
Studies to determine the critical weeding periods under conventional growing
systems have been done for almost all crops. However, in the period
immediately after emergence, experiments showed that weeds present on the
field had little effect on the crop yield, as also after the critical period. Crop
species are tolerant to early weed competition without yield loss in certain
periods during their growth, whereas weed-free periods are required in other
development stages of the crop plants to prevent yield loss (Zimdahl 1980;
Grundy et al. 2003). Besides, weed species have distinct periods of germination
and seasonal patterns of weed emergence, which are experimentally examined
under the conventional growing system (Lampkin 1990; Naylor 2002). This
information can help to choose the optimal timing for operations such as
cultivation, sowing and weeding, according to the peak time for germination
periods of the predominant species.
10
1.4 Organic weed management
Successful organic weed control involves the combination of various operations
and cultural management methods. The right approach in organic weed
management planning has to be systematic and should start with the highest
system level and descend to the lowest. If some levels are omitted or missing,
the result will reflect on the yield directly or cause significant problems in the
next season. The ultimate aim for all organic farmers is to prevent development
of the annual weeds to the stage when they are able to produce seeds and to
restrict the dispersal and growth of perennial weeds (Taylor and Zenz 2006).

Introduction
Optimal organic systems need to include the following levels in descending
order (Merfield 2000):
� crop rotation;
� soil structure, nutrient ratio and pH value;
� crop choice;
� cultivation;
� sowing, planting and related techniques;
� crop production techniques;;
� physical (mechanical or thermal) weed control and
� hand weeding (if necessary).
There are actually two main methods of weed control: indirect weed control,
which is concentrated to improve competitive advantages of the crop over the
weeds using cultural or management techniques, and direct weed control,
where the weeds are suppressed or eliminated by physical interaction.
1.4.1 Indirect weed control
Crop rotation is an essential foundation of organic production, because the
introduction of different crop ecosystems on the same field prevents the
domination of one ecosystem. According to the different crop habits, timing of
production and cultivation requirements, the growing of different crops in
succession ensures, that no weed species can become dominant (Lockhart et
al., 1990). Oppositely, the continuous production of similar crops on the same
place will often significantly increase the population of problem weeds.
An important perspective in organic weed management is to ensure well
balanced nutrient ratio and pH value, although it is not the most important
factor. If soil nutrient ratio and pH value are suboptimal, they can become the
overriding cause of a weed problem and decrease the impact of other weeding
11

Introduction
techniques. It is known that some weeds are able to grow in soil conditions
suboptimal or even unfavourable for crops, so a soil testing is necessary before
the soil nutrient or pH can be improved or the crop selected. The relationship
between soil structure and weed development is similar to that of nutrients,
which means indirect impact. Optimal soil structure will not help the weeding,
but pure structure, compaction and cultivation pans can lead to the spread of
weeds via deep underground stems or roots, or could cause the appearance of
waterlogged soil, preferable for some weed species.
The choice of the crop should be based primarily on the soil type and climate. In
cases where more options in the choice of cultivars exist, most desirable are
species with rapid establishment, vigorous growth, prostrate – leafy types or
long straw cereals. An increase in the sowing rate could result in an
improvement of the crop’s competitiveness effect and provide compensation for
the losses incurred during mechanical weed control (Parish 1990).
Cultivation is of great importance in weed management and it should always be
properly varied depending on needs of different crop and weed populations
(Mohler and Galford 1997). Cultivation also provides many other beneficial
effects far beyond the weeds. It is important for aerating the soil, stimulating
crop root growth, conserving soil moisture and providing insulation with loose,
dry soil mulch. To choose an adequate cultivation mechanism for optimum
weed management, it is important to know the weed history of the field and to
understand their lifecycle (Buhler 1995). Different weed species require different
cultivation procedures. For example one successful method against perennials
is weakening of the weed plant by separation of the above – ground and
underground parts, which leads to exhaustion of the food reserves in the
underground part. The most significant factors of annual weeds are conditions
for their germination. Germination is dependent on the correct levels and
mixture of moisture, oxygen, carbon dioxide, temperature and in some weeds
the presence or absence of light. Combination of these conditions often causes
seeds to have distinct germination periods, so well timed cultivation can
drastically decrease the weed population. Crop seeds are usually larger and
planted deeper then most weed seeds, which provides a possibility to calculate
the cultivation depth, damaging just weed plants. A seedling is most vulnerable
12

Introduction
from the time it germinates until after the plant has fully emerged from the soil.
After the crop emerges the number of cultivations performed is usually relative
to the weed pressure and limited by growth of the crop. In a well managed
system two cultivation passes are required. The first pass is the most critical to
exterminate the annual weeds, but the second pass is often necessary to
eliminate the weeds that were stimulated to grow by the first cultivation.
By a theoretical approach cultivation can be divided into primary and secondary
cultivation, although the difference between them is often fuzzy. Usually,
primary cultivation includes all operations which refer to initial land preparation
such as subsoiling and ploughing, and also surface preparation during a fallow.
On the other hand, secondary cultivation refers to operations designed to
produce a seed bed after sufficient depth and fineness is reached by primary
cultivation. From the point of view of weeding, the key aim of secondary
cultivation is to keep the depth of cultivation within the germination depth of the
weeds, which is for most small seeds maximally 5 cm (Schans et al 2006).
Cultivation below this depth will bring up new viable seeds and should be
avoided.
There are also some techniques which encourage weed germination prior to
crop germination, i.e. the first weed elimination can be done before the crop
system is established. Two often-used methods are false seed beds and stale
seed beds (Johnson and Mullinix 1995). The false seed bed technique involves
a second pass to eliminate emerged weeds. This second pass is to be usually
done with the same technique as the one that created the initial seed bed and it
is ideal when a new seed bed is made in the same pass. A stale seed bed
involves weed elimination without soil disturbance, by thermal weeding
methods.
Another weeding technique is blind harrowing, which is a hybrid between false
and stale seed beds. A characteristic of this method is that the stale seed bed
should be created first and then the crop drilled. Just a few days before the crop
emergence a harrow, tine weeder or similar device is used to cultivate the soil
surface to kill the weeds.
13

Introduction
Sowing and planting also have an indirect impact on weed control and they
could cause problems later on, if suitable attention is not given to them. Sowing
needs to be done in a time when conditions are optimal for crop and suboptimal
for weeds (when that is feasible), to provide rapid germination and overgrowing
of weeds as fast as possible. For many crops, such as legumes and cereals, a
5% to 15% increase in the sowing rate can significantly improve their
competitiveness (Welsh et al. 2002). In row crops, adequately chosen
standardised single inter-row space for most or all of the crops on one farm can
provide considerable benefits. The loss of yield due to standardised row
spacing is more than compensated by the decrease in field operation because
the adjustment of the tools and equipment for different row spacing take a
considerable time. Sometimes, the time necessary for adjustment can take as
long as the fieldwork. Hence, the tolerance for setting up inter-row weeding
equipment in relation to the sowing equipment should be less than 1 cm, which
allows tillage in the same or opposite direction as the crop was drilled.
Crop production techniques, including irrigation and harvesting variation, can
have secondary effects that can improve or worsen weed management.
Different irrigation approaches have a considerable impact on the weed
population. Precise drip and trickle systems or buried drip tapes can result in
much lower weed germination compared to alternatives, such as over head
sprinklers.
If weeds are not eliminated during the harvesting operation, it is very important
to kill them as soon as possible after harvest, to minimise further production of
seeds. One possible solution is use of modified harvesting machines with
additional equipment able to collect the weed seeds after they had been
separated from grain and chaff (Patterson and Bufton 1986). The same
procedure can be done with a static off site seed cleaning unit, to separate crop
from weed seeds, if the harvester separates just the chaff from the seeds. This
technique can drastically decrease the weed population.
14
1.4.2 Direct weed control
Direct weed control encompasses all methods in which direct interaction
between the used tool and weeds are present. Direct methods could be roughly

Introduction
divided into chemical, physical and biological weed control. Physical weed
control itself contains mechanical, thermal and other methods based on
physical interaction.
Thermal weed control includes flame weeding, infrared radiation, steaming, use
of hot foam, freezing, direct use of heat, electrocution, microwave radiation,
irradiation, use of lasers and ultraviolet lights. Other methods usually employ
different mulching techniques with natural or artificial materials. Detailed
description of mentioned methods are available in reports and papers (Parish
1990; Fogelberg 2001; Bond et al. 2003; Kristiansen 2003).
Weeds can be mechanically eliminated by uprooting, shearing, mowing or soil
covering. Tools for these kinds of actions are specially designed, and could be
passive (non-powered) or active (powered) and are usually based on rotating
motion. Which technique will be used is defined by the crop type, weed
pressure and species present. In row crops three different areas can be
recognised: inter-row, intra-row and close to crop area (Griepentrog et al. 2003),
and according to that different tools are available for weeding the area between
the rows and area inside the row between the plants.
1.4.3 Inter-row weed control
A large number of passive or active implements are available on the market for
inter-row weed control. They include varieties of hoes, harrows, tines and brush
weeders, as well as cutting tools like mowers and strimmers. Successful
mechanical inter-row weeding equipment must fulfil the following basic
requirements (Parish 1990):
� to cut or uproot weeds, and then either completely bury them or
leave them on the soil surface for desiccation;
� to protect the crop plants;
� to control implement direction;
� to control implements penetration depth and
� to maintain or improve soil conditions.
15

Introduction
Inter-row weeding systems, their operating speeds and limitations were
reviewed by Marfield and Bond (Marfield 2002; Bond et al. 2003). Weeding
quality is highly dependant on equipment adjustment and on the row steering
accuracy. Steering can be done manually, where experience and skills are
expected from operator, or with sophisticated equipment e.g. computer vision
guidance system or DGPS.
Typical systems for inter-row weed control are toolbars pulled or pushed by a
tractor. A tractor can simply be driven along the crop row carrying a toolbar with
the weeding implement mounted at a constant horizontal spacing. The weeding
tool as a part of the system can usually be adjusted to different inter-row
distances and plant sizes. To achieve optimum weed reduction, the goal is to
work as close to the plant as possible to minimise the non-hoed intra-row area.
For most inter-row systems, the operator is just as important as the machinery.
Without precise steering and appropriate adjustment, the equipment will
damage the plant row and result in a crop loss.
Generally, inter-row weeding system can be divided into non-powered and
powered systems. The simplest non-powered systems are duckfoot-hoes with
hoeing blades either rigidly mounted to the crossbar or mounted on a spring,
forming a so called vibrating hoe. With an optimal hoeing depth of 1-2 cm,
multiple hoeing tools can be placed side by side to accommodate larger row
widths. Vibrating hoes usually cultivate the soil surface more deeply, uprooting
a larger portion of the weeds. On that way they disturb the soil more and bring
weed seeds to the surface, increasing the number of seeds which could
germinate. The rigidly mounted type effectively cuts the weeds with less
disturbance of the soil. Both types can be mounted on a parallelogram carriers
providing accommodation of the hoeing blades to different soil contours.
When a hoe is not sufficient in weed removal, powered equipment is an often-
used option. A brush weeder consists of stiff bristles mounted to a rotating disk,
which rotates with a high speed to uproot shallow weeds. The main advantage
is very minimum disturbance of the soil since the brushes do not penetrate the
ground. However, it is hard to adjust the brush width and the tractor’s speed
needs to be kept below 4 km/h. Also, they are ineffective in hard soil and can
16

Introduction
cause dust contamination on the plants. For harder soils, a rotary strip cultivator
is an option. It works on the concept of rotating cutting blades which can
cultivate very hard soils. Working depth can be 2-3 cm or more if the weeds are
very thick or large, but depths over 4 cm can cause damage on the plant’s root
system. Disadvantages are that the working width is very hard to change and
the soil must not be too wet.
One other powered cultivator option is a weed-fix. Unlike most powered
cultivators that rotate on an axis parallel to the ground, a weed-fix’s rotating axis
is perpendicular to the ground surface. A set of two or three rotating tines works
the ground and uproots the weeds. It works especially well with hard soils
because of the powerful fixed tines and allows a tractor speed of up to 8 km/h.
There are other alternatives such as pneumatic systems but they are not used
on a large scale because of very high operating costs.
One of the main problems with inter-row weeding systems is steering. Weeding
increases and decreases in effectiveness with every centimetre closer to, or
further from the row. Because most weeding systems are rigidly mounted to a
crossbar on the front of a tractor, driver skill is crucial. Even the slightest
deviation in direction can cause a movement of 3-4 cm in the toolbar position,
which added to the usual buffer space from the plant can result in a 6-8 cm
width that is not weeded. Mounted on the front of the tractor, the toolbar can be
easily seen by the driver, but is very sensitive to steering. If the toolbar is
mounted directly below or behind the driver it is much less sensitive to small,
immediate changes in steering but does not allow the driver to see the hoeing
machinery, increasing error to sometimes 8 cm per side of the crop row. The
displacement of the hoeing machinery’s centre of rotation can help to reduce
this error, as can having the centre of rotation of each individual hoe directly
behind the blade. This approach has been successfully applied on the Mutsaers
QI steering system (see Figure 1.3).
17

Introduction
18
Figure 1.3 Inter-row Mutsaers QI type 500 (1 - centre of the
rotation, 2 - individual hoeing section with separate
adjustment possibilities, 3.- carrier of the toolbar allowing
montage to the front of the tractor, 4 - row following front
sight) (Anonymous 1 2007)
More advanced technologies with optical systems show even more promise.
Image recognition through the use of cameras and sensors controls the
toolbar’s horizontal displacement using hydraulics. The Ecodan system uses
this technology and allows tractor speeds of between 6 and15 km/h. The plant
must be large enough to be detected by the camera and the camera must
distinguish between plants and soil (ECO-DAN A/S 2007; Schans et al 2006).
Another system working on the same principles, Robocrop (Anonymous 2 2007;
Schans et al 2006), has also been tested.

2 State of the art
2.1 Intra-row weed control
State of the art
Intra-row weeding is the last but very important stage of successful non-
chemical weed control. It shall be considered as a final weed elimination
procedure and not as a primary method of weed control. However, there are
many technical problems associated with intra-row weeding, which are the main
reason why even today the hand weeding is still the most frequently used
method for intra-row weeding on organic farmlands in Western European
countries. Mechanisation of the intra-row area cultivation is very complex and
there are two streams of thought for solving this problem: using passive and
active implements.
2.1.1 Passive tools for intra-row weed control
Well-known passive implements for intra-row weeding are finger weeders and
torsion weeders. Finger weeders consist of plates rotating at an acute angle
with the ground which have appendages, or fingers, extending radially from the
disk (see Figure 2.1 a). One disk operates on each side of a plant row, and the
distance from the plant can be adjusted to several centimetres apart to
interlocking with as much a 5 cm overlap. The basic idea of this weeding
method is to uproot the weeds and eject them from the crop row. Usually, finger
weeders are very effectively combined with flat hoes between the rows forming
a tool for simultaneous inter- and intra-row hoeing which can cover several crop
rows in one passage. These implements require very precise steering for row-
following, which can be done manually or automatically with vision systems
such as Ecodan and Robocrop.
A major advantage is the flexibility, as a finger weeder can work on almost any
type of crop which has sufficient distance between the rows.
19

State of the art
20
Figure 2.1 a) Finger weeder (1 - hoeing head with 14 rigid fingers
made from plastic, 2 - mounting arms allowing the
adjustment of approaching angles) (Anonymous 3 2007) b)
Torsion weeder (1 - spring tines mounted to not overlap
each other, providing low disturbance, 2 - springs allow
flexibility of the tines (Ascard 2007)
A torsion weeder is another alternative that reaches very close to the crop. Two
spring mounted tines drag along the ground parallel to the plant row, and on
their ends have a bent section that is angled toward the plant (see Figure 2.1 b).
At low tractor speeds, they overlap and are pushed apart by the plant as it
passes. As long as weeds are small, they will not survive the tines. At greater
speeds, the tines spread apart as they penetrate the soil, cultivating the ground
more but removing fewer weeds.
The main disadvantage of passive systems is that they are only suitable in a
situation when the crop is robust enough to withstand damage (e.g. from the 4 th
– 6 th true leaf stage of sugar beets) and the weeds must be relatively weak.
Timing is the most important factor when these implements are used.
Unfortunately determining the exact time and being able to weed on the field is
not always possible, because of changeable weather conditions. If the weeding
is done late, weeds are stronger and they could withstand tillage. Both methods
are effective only with small weeds, so the weeding operation which is delayed
has usually a decreased impact on weeds.

2.1.2 Active tools for intra-row weed control
State of the art
There are no commercially available powered intra-row weeders, but several
prototypes have been developed over the past 15 years. One was developed in
the late 1990’s at Halmstadt University, Sweden. It is an autonomous robot with
a vision-guided plant recognition system developed for sugar beet plants.
Standing on four wheels, it is driven by two DC-servo motors on the two rear
wheels and uses a rotating hoeing tool (1) which is lowered to weed and raised
to pass over plant by use of hydraulic cylinder (see Figure 2.2). The vision
system uses up to 19 plant characteristics to differentiate plants and weeds.
The concept and a prototype of this system are shown in Figure 2.2.
Figure 2.2 Concept and prototype of the intra-row hoeing developed
at Halmstadt University (1 - weeding tool, 2 - colour
camera for plant identification, 3 - computer) (Åstrand B.
2002)
In Denmark, a hoeing system based on geo-referenced plant maps has been
developed using the Osnabrück hoe (Griepentrog 2005). During the planting a
location of every seed is recorded and this data are used for development of a
geo-referenced seed map. The same map is a background for the control of the
hoeing tool during the weeding process. This hoeing system uses two RTK-
GPS receivers and a tilt sensor for exact orientation during the weeding.
The Osnabrück hoe is a cycloid hoe consisting of a rotating cylinder on a
vertical axis. Eight tines extend down vertically from the cylinder and are used
to work the ground (see Figure 2.3). While the tractor moves forward, the
21

State of the art
cylinder rotates and the tines, which are attached to the perimeter of the
cylinder, create cycloid patterns on the ground. In Figure 2.4, the clycloid
pattern is apparent. The novel concept is that the tines can be retracted to the
interior of the cylinder, causing the tine tip to trace a smaller cycloid that avoids
the plant. Each tine can be individually controlled in real time, avoiding plants
detected by the guidance system. The hoe is driven by a hydraulic motor and
can move vertically through the help of a custom parallelogram height control
and left to right with a hydraulic side-shift system. The major disadvantage of
this system, other than the geo-positioning, is the many moving parts, requiring
frequent, expensive maintenance.
22
Figure 2.3 Hoeing system based on geo-referenced control of the
Osnabrück hoe (1 - hydraulic motor, 2 - rotating cylinder
with eight tines, 3 - parallelogram for height control, 4 -
GPS antenna) (Griepentrog 2007)

State of the art
Figure 2.4 Clycloid trajectories of the Osnabrück hoe for a)
rotational speed is equal to the forward speed b)
rotational speed is 1.25 times higher than the forward
speed c) rotational speed is 1.5 times higher than the
forward speed (Griepentrog 2007)
The University of Wageningen in the Netherlands has also been developing an
autonomous weeding robot for sugar beets (Bakker et al. 2006). Until now they
have developed the autonomous navigation of the vehicle along the rows in the
sugar beet field. In the near future they plan to implement an intra-row weeding
system based on the previously presented mechanical device (Bontsema et al.
23

State of the art
1998). This device consists of a 30 cm diameter rotating disk that rotates on an
axis parallel to both the ground and direction of travel. Two knives, attached
with springs opposite to each other on the perimeter of the disk, can stay
retracted inside the disk area or they can fold out depending on the intensity of
the rotational speed, due to the balance between the centrifugal force and the
spring force. The disk is actuated by a hydraulic motor whose exact rotational
speed is controlled by a hydraulic controller. The controller adjusts the rotational
speed value according to the detected plant position. The detection unit
consists of a combination of three infrared transmitters and receivers placed
above each other on a gantry on different plant height levels. A weighted
summation of the signals from three sensors is combined to a single signal
which is a base for processing.
During the weeding operation the motor rotates permanently at 850 rpm with
knives extended to the working position, until the detection system does not
detect a beet plant. After a beet has been recognised the rotational speed
decreases to 700 rpm with almost immediate fold-in of the knives.
At Cranfield University in Silsoe, UK, a rotating disk weeder has been
developed (Dedousis et al. 2006). It consists of a thin rotating disc with a cut-out
sector and a bevel cut back at its circumference (see Figures 2.5 and 2.6). The
disc acts in a horizontal plane and operates at a fixed distance parallel to the
plant row. This allows the disc to work the ground in both the intra- and inter-
row areas. As the disk rotates, the weeds are cut by the sharp edge along the
perimeter. The bevelled cut-out section allows for one plant per revolution to
stay in the protected area without the contact with the disc. Assuming a plant
distance of 300 mm, the ideal disc geometry was calculated to have a 175 mm
diameter and 130° cut-out section. A bevel shape can be modified on the disk to
achieve different kinds of weed cutting. Also, the inclination angle and working
depth are adjustable depending on the plant type and soil conditions.
Preliminary tests have shown that up to 60 % of weeds within an 80 mm radius
can be eliminated, and up to 80% of weeds at greater radii. In a test in
September 2006, 77 % of weeds were judged to be removed and only one plant
out of 24 showed damage. Angular error of the disc was also quite low,
normally less than 10°. A disadvantage of this tool is that it is limited to plant
24

State of the art
spacings of 25 cm or above because of the current size and geometry. The disk
geometry would have to be redesigned for plant systems with intra-row distance
less than 25 cm. Also, to achieve optimal weeding the machine would either
require two passes, one on each side of the plant row, or have to include two
disks per plant row.
Figure 2.5 Illustration of the hoeing concept using the rotating disk
(Dedousis et al. 2006)
Figure 2.6 Rotating disk weeder a) the toolframe with two rotary
cultivator units without inter-row cultivation blades b) the
toolframe with one rotary cultivator without inter-row
cultivation blades mounted on the front of the tractor (1 -
rotating disc, 2 - camera for row following, 3 - toolframe,
4 - tool carrier wheel with suspension allowing cultivation
depth control (Dedousis et al. 2007)
25

State of the art
26
2.2 Detection of the plants
Generally, two concepts of the data collection can be defined in site specific
weed control:
� the weed mapping concept and
� the real-time concept.
In the weed mapping concept, the information about the weeds (location,
species, density etc.) needs to be collected before the weeding, as a separate
operation. In the real-time concept, the weed detection is carried out during the
weeding (Nordbo et al. 1994).
In the weed mapping concept data about the weeds are associated with GPS
coordinates, which refers to a possible built-in error in the accuracy of the
system. For site specific spraying systems such an error is irrelevant, but for
robotic systems which are oriented on a single plant treatment, such an error
can cause significant decrease in efficiency. The error can first occur after the
plant or weed has been detected and the combined data about the plant and
position stored to the map. During the weeding operation the system reads data
from the map and combines them with the GPS coordinates of the vehicle. That
could be a point when the error appears for the second time. The most recent
GPS systems have real time kinematic signal correction (RTK) which provides
+/– 1 inch (~2.5 cm) accuracy (Buick 2007). In the worst case, according to the
mapping concept, the error can reach twice the accuracy value, which is
approximately 5 cm. Concerning the intra-row distances in row crops, (e. g.
sugar beet 17 to 20 cm) the positioning error of the intra-row weeding
equipment can reach 33%. Also, the plants can theoretically emerge up to 2 cm
from the position where the seed was originally planted, reducing the positional
accuracy of the map even more. Because of the possibility that such an error
can occur, the weed/plant mapping concept has not been taken into
consideration in this research.
Taking into account the real-time concept, two major principles have typically
been used for plant/weed distinction (Thompson et al. 1990): either the

State of the art
detection of certain geometric differences between the crop and weed species,
or differences in spectral reflectance.
It has been confirmed that discrimination between monocots and dicots is
possible using shape feature analysis (Woebbecke et al. 1995). However it has
been stated that weed detection based on texture (leaf shape) analysis could
become fairly complicated, because of a wide spectrum of different leaf shapes
of various species (Guyer et al. 1986). Particular problems are the unfavourable
conditions which can occur on the field:
� overlapping of the leaves;
� leaf orientation in relation to the plane in which the transformation of
the 3 dimensional space to 2 dimensional image has been done;
� variation of the distance between the camera and the target plant,
causing changes in field of view and focusing and
� transformation of the leaf boundaries in images in reference to the
model due to their movement in the wind.
On the other hand, colour characteristics of plants often provide sufficient
information for distinction between different species, impose fewer restrictions
on leaf overlap, leaf orientation, camera position and camera focusing. A study
of weed detection based on the differences in the stem colour, detecting
species with stems colours ranging from reddish to purplish (El-Faki et al. 2000)
has proved the potentials of the colour approach. Low processing speed,
hardware requirements and dependence on variable conditions in the field can
be generally designated as bottlenecks of machine vision systems.
Several groups have measured the spectral reflectance of crops and weed
species to evaluate the possibility of crop/weed discrimination based on
different spectral reflectance (Franz et al. 1995; Zwiggelaar 1998). The
advantage of using spectral reflectance for plant/weed detection is the fast data
processing. However, differences in spectral characteristics are not always
large and robust enough for unique decision (Clausen et al. 2000). A review of
the use of spectral properties for weed detection and identification has been
written by Noble (Noble and Crowe 2002).
27

State of the art
The fluorescence spectroscopy as a new approach for discrimination between
crops and weeds has been recently tested. A review of the methodology and
promising results have been reported (Panneton et al. 2006).
Most of the research in weed detection has been focused on systems which can
be applied in site specific spraying. Detection of the individual plants and their
relative position has been not the primary objective of those studies. However,
new approaches like mechanical weed control and especially the mechanical
intra-row weed control have changed the target objects which need to be
recognised with machine vision. Excepting a few recent studies, systems for
recognition of individual plants are still under development at the level of
prototypes.
As mentioned before, for successful and efficient intra-row weed control
detection of the position of every crop plant is necessary. A methodical review
of individual plant recognition approaches based on spectral properties,
morphological and textural features and pattern recognition pointing out
advantages and disadvantages has been written by Åstrand (Åstrand 2005).
Particular attention is given to the implementation of the context information
(knowledge about the planting grid) in the crop/weed distinction algorithm for
increasing the classification efficiency. This overview of plant/weed detection
methodology makes evident the considerable efforts concentrated on research
in the area of single plant detection.
28

Definition of the problem and research objectives
3 Definition of the problem and research objectives
3.1 Definition of the problem
According to the fact that mechanical weeding should provide cultivation of the
entire space around the crop plants, three different areas need to be recognised
in row crops. The first is the “inter-row area”-that is, the area between rows.
Mechanical weeding of this area is more or less solved with different
commercial tools available on the market. The second is the “intra-row area”-
that is, the area between plants in one row and the third is the “close to crop
area”-that is, the area nearest to the crop plant (Nørremark and Griepentrog
2004). The definition of the inner border (diameter) of the close to crop area is
highly dependent on the crop species and growth stages, as well as from the
weeding tool which is used. To ensure, with high confidence, that a weeding
tool will not cause any negative influence on the crop, the area occupied by the
plant should be always increased with a so-called “protected” (ring-shape) area
around it. Definition of the areas of great importance for the mechanical intra-
row weed control is presented in Figure 3.1.
Distance between
successive rows
Protected area
In-row distance between plants
Figure 3.1 Areas in the row crop field
Plant area
Crop row
Crop row
29

Definition of the problem and research objectives
Although the plants were sown with constant spacing, the distance between the
plants in the row can vary and some of the expected positions can be without
plants, because of various environmental conditions. A weeding tool should
appropriately respond to all deviations from the expected plant/weed distribution
pattern, such as the missing plants (1), weeds appearing in the close-to-crop
area (2) and non-regular and varying distances between plants in the row (3)
(see Figure 3.2).
30
Figure 3.2 Deviations from the expected plant/weed distribution
pattern in field conditions
For successful and accurate intra-row weed control, the position of every single
plant has to be determined. The eventual deviation from the sowing pattern
needs to be detected and used to correct the control of the weeding tool.
Because of that, detection of the plants and the response of the system for plant
detection need to be fast enough to be applicable in combination with real time
weeding tools.
2
1
The optimal sowing distance for different crop plants is heterogeneous, thus the
weeding tool has to be flexible and adaptable to different intra-row distances.
3
3
1

3.2 Research objectives
Definition of the problem and research objectives
The main objective of this research was the development and realisation of an
autonomous intra-row weeding system based on mechanical elimination of
weeds in row crops which can be used in different plant spacing systems,
various plant intra-row distances and growth stages. The main objective can be
split into three different sub-objectives:
� Development of a hardware solution for detection of the crop plants’
centre position in real-time;
� Design of a virtual prototype of the intra-row weeding tool and
kinematical analysis of the suggested tool solution concerning hoeing
trajectories in different plant systems and
� Development of a physical prototype, based on a servo motor, which
uses the information about the crop plants’ centre position for
accurate controlling of the hoeing tool. The developed system needs
to demonstrate ability to hoe the area between two successive plants
inside the crop row in laboratory conditions in real-time.
31

4 Materials and methods
4.1 Detection of the single plant position
Materials and methods
Considering the expeditious research and development in the field of online
detection of single plant position and plant/weed distinction, it is expected that
appropriate systems will be available on the market in near future. Accordingly,
the development of a system for single plant position detection was not targeted
as the primary objective of this research. A simplified system based on the
spectral characteristics of plants combined with the context information of the
planting pattern has been constructed as an interim solution.
4.1.1 Sensor equipment
4.1.1.1 Digital colour sensor
After evaluation of several approaches for plant detection, concerning the basic
requirement, which is accurate detection of the plant centre position in real time,
a method based on non-selective detection of green-coloured objects, with an
industrial RGB fibre optic sensor connected to an RGB digital fibre optic
amplifier was chosen. The principle of colour detection in such sensors is based
on a three-colour light source transmitter and appropriate receiver. The light
emitter is located in the amplifier within which the beams of three colours are
brought into a straight line by use of mirrors.
The advantages of the three-colour light source sensors over conventional
single-colour light source sensors are:
� The received light quantity is converted into a ratio of three colours,
and the target is recognized by its colour, whereas certain colours by
single-colour illumination receive the same light quantity and they
cannot be differentiated.
� Even when the target position changes and the received light
quantity changes, the ratio of the three colours does not change
whereas by conventional systems the received light quantity changes
according to the distance between the target and the sensor head.
33

Materials and methods
34
� Since detection is based on RGB components, it is less affected by
target position and vibration.
Several sensors were tested, and the CZ – H35S optical sensor head and the
CZ – V21P digital fibre optic amplifier from Keyence Company have been
chosen as most appropriate according to the requirements. The CZ – V21P
digital fibre optic amplifier supports three different detection modes based on
the combination of colour component detection and light intensity. The control
interface built in to the amplifier allows the user to choose the optimal detection
mode and to set up a matching rate corresponding between the calibrated
target colour, as a reference, and the target colour currently being detected.
The amplifier consists of four digital physical channels which can be separately
calibrated with ad-hock autocalibration in few steps.
Depending on the intensity and position of the illumination, some parts of the
target objects can appear white as a consequence of lustre. The CZ-H35S
sensor head incorporates a polarizing filter which cancels the reflection from the
glossy section and only recognises targets by their colour components. Thus
the CZ-H35S sensor head maintains accurate detection despite changing target
conditions.
Detecting range of the CZ-H35S sensor head is between 28 mm and 52 mm,
with a spot diameter of 4.5 mm. The response time of the amplifier depending
on the chosen detection mode lies between 200 µs and 8 ms. The sensor head
and amplifier are illustrated in Figure 4.1.

Materials and methods
Figure 4.1 a) RGB fibre optic sensor CZ-H35S and RGB digital fibre
optic amplifier CZ – V21P b) RGB fibre optic sensor in
working position with illustrated optimal detection range
and spot diameter of the light source
4.1.1.2 Digital laser sensor
To increase the reliability of the plant detection, the information about the
objects appearing along the row line, their height and reflectance were tracked.
The fact, that plants as objects are higher than the level of the soil surface,
exceptions can appear only immediately after emerging when plants can have
the same size as soil clumps, induced the use of additional support information
for plant detection. High accuracy, area covering and short response time were
the main requirements which affected the sensor selection.
A hi-power definite-reflective area selection laser sensor head LV – H47 with
appropriate LV- 21AP amplifier from Keyence Company, fulfilled the
requirements and it was implemented in the plant detection unit. This type of
sensor can be used for stable detection of the objects, even when targets have
variations in shape or surface condition, which is one of the main characteristics
of the plant detection. With definite-reflective type detection, the LV-H47 can
ignore any influence of background conditions. Using a high-speed A/D
converter, the LV-21AP amplifier substantially improves upon conventional
35

Materials and methods
response speeds associated with automatic calibration type sensors. The
response time can reach 80 µs in mode for fast detection.
The amplifier supports four different detection modes: standard with normal or
increased hysteresis and differentiation method detecting upper or lower edge
of the received light change. The light sensitivity can be calibrated for two digital
channels separately. The sensor and the amplifier are illustrated in the Figure
4.2.
Height detection range of the LV-H47 sensor head is between 55 mm and 85
mm and the corresponding covered width is between 20 mm and 25 mm. As a
light source, a visible red semiconductor laser with 650 nm wavelength is
installed in this head with pulse duration of 3.5 ms.
36
Figure 4.2 a) Laser sensor head LV – H47 with appropriate LV-
21AP amplifier b) Sensor head in working position with
illustrated optimal detection range and corresponding
width of the area covered by laser
RGB sensor and the laser sensor were mounted on a joint carrier behind one
another on a 50 mm distance as shown in Figure 4.3. The sensor carrier was
mounted on a toolframe allowing accurate following of the soil surface to keep
the constant distance between the sensors and the soil surface.

Materials and methods
Figure 4.3 a) Joint carrier of plant detection sensors b) Toolframe
with wheels allowing accurate following of the soil
surface (1- laser sensor, 2 - RGB sensor, 3 - joint carrier,
4 - wheels, 5 - toolframe)
4.1.1.3 Forward position detection
Accurate detection of the forward position is necessary for adequate plant
detection and appropriate assignment of the relative coordinates to every single
plant. For the experimental work under laboratory conditions two different
methods have been used in parallel to prove the system robustness.
4.1.1.4 Position sensor with incremental encoder
This device translates linear motion into a proportional electrical signal. The
sensor consists of a calibrated measuring cable which winds onto an accurately
machined cable drum. The drum is mounted onto a shaft which is tensioned by
a coil spring which gives a specified pull-in force to maintain cable tension and
control.
The complete mechanical assembly is installed in a rugged enclosure together
with the electronic components of the sensor. The mechanical components of
the sensor drive an incremental rotary encoder with digital outputs via a
37

Materials and methods
coupling. The outputs from the sensor’s electronics can then be fed into a wide
variety of measurement and control systems. The electromechanical WS3.1
pp530 position sensor from ASM Company was used in experiments.
During the operation the free end of the measuring cable is attached to the
moving part of the machine, in our case to the sensor carrier. The position
sensor then converts the linear cable movement as it winds on and off the cable
drum into a rotary motion which is then converted into an electrical output
signal.
Resolution of this sensor is 1 pulse per mm and it has a cable length of 15,000
mm. Electrical outgoing signals are standard TTL-s.
38
4.1.1.5 Rotary encoder position sensor
The second alternative for the position measurement under laboratory
conditions was established on a standard incremental encoder mounted on a
supplementary wheel (timing belt pulley) moving together with the carrier
vehicle. The resolution of the incremental encoder and the diameter of the
pulley was chosen in accordance with the requested resolution of the forward
motion of the carrier. The idea was to find a combination providing similar
resolution as the electromechanical WS3.1 pp530 position sensor, which was 1
impulse per mm. An incremental encoder from Wachendorff electronick WDG
40A-250-ABN-G24-K2 with 250 impulses per rotation was assembled with a
26L050 pulley with diameter of 82 mm. With a simple equation it is possible to
calculate the resolution of the alternative position measurement system.
D * π 0.082 * π
resolution 250
pulley
−3
Forward resolution = = = 1.030 *10 m
encoder
(4.1)

4.1.2 Data acquisition
4.1.2.1 Hardware
Materials and methods
After conditioning, signals from sensors are acquired with a notebook PC
computer through a plug and play multifunctional USB DAQPad-6015 from
National Instruments. The combination of DAQPad-6015 and signal
conditioning unit is designed for a broad range of measurements in the field.
The DAQPad-6015 consists of sixteen 16-bit analog input channels, two analog
output channels and eight digital input/output channels and two counters. The
system is user friendly, including NI-DAQmx measurement services software for
simple applications and it is compatible with NI LabVIEW, LabWindows/CVI,
Measurement Studio for Visual Studio .NET and other USB data acquisition
devices. The maximum sampling rate of the device is up to 200,000 samples/s
for a single channel.
4.1.2.2 Software
The software solution for the data acquisition and plant position detection was
realised with LabVIEW 8 from National Instruments. LabVIEW is a platform and
development environment for a visual programming language commonly used
for data acquisition, instrument control, and industrial automation.
The visual programming language used in LabVIEW is a dataflow language
also known as G code or block diagram code. Execution is determined by the
structure of a graphical block diagram, which is actually the source code. This
language allows parallel execution on multi-processing and multi-threading
hardware. Data flow completely defines the execution sequence and it is as
well-defined as with any textually coded language such as C, Visual BASIC, etc.
Programs and subroutines are called virtual instruments (VI-s). Each VI has
three components: a block diagram, a front panel and a connector panel.
LabVIEW includes a compiler that produces native code for the CPU platform.
The graphical code is translated into executable machine code by interpreting
the syntax and by compilation. The syntax is strictly enforced during the editing
39

Materials and methods
process and compiled into the executable machine code when requested to run
as an application. Developed VI can be run under the LabVIEW environment or
as a stand-alone application. Furthermore, it is possible to create distributed
applications which communicate by a client/server scheme, and thus is easier
to implement due to the inherently parallel nature of G-code.
One benefit of LabVIEW in relation to other development environments is the
extensive support for accessing instrumentation hardware. Drivers and
abstraction layers for many different types of instruments and buses are
included or are available for inclusion. The abstraction layers offer standard
software interfaces to communicate with hardware devices.
Generally, code can be slower than equivalent compiled C code, although the
differences often lie more with program optimization than inherent execution
speed.
A benefit of the LabVIEW environment is the platform independent nature of the
G-code, which is portable between the different LabVIEW systems for different
operating systems such as Windows, Mac-OS and Linux. Code can be
uploaded onto an increasing number of targets including devices like Phar Lap
OS based LabVIEW real-time controllers, Pocket PC-s, PDA-s, FieldPoint
modules and into FPGA-s on special boards.
40
4.1.3 Experimental field
Experiments were conducted in a particularly prepared soil box containing three
different soil types, representing the most frequently appearing types in
Germany. Total length of the soil box was 9 metres and it was divided into three
equal parts. The first partition was filled with a sandy soil containing 56.9%
sand, 34.5% silt and 8.6% clay. The second partition was filled with a silty soil
containing 10.7% sand, 71.8% silt and 17.5% clay. The last partition was filled
with a clay soil containing16.1% sand, 52.6% silt and 45.6% clay. The
experimental field was 2 metres wide providing a possibility to drive the carrier
over the soil surface through several crop rows positioned parallel to each
other.

4.1.4 Test objects
Materials and methods
For sensors and detection algorithm testing, three different artificial objects
simulating different plant development stages have been used. Green coloured
wood sticks with 5-mm and 10-mm diameter were used to simulate the early
development stage of the plants. The simulation of the plants in 4- to 6-leaf
stage has been done using artificial plants with diameter range between 30 and
50 mm. Test objects palette is shown in Figure 4.4.
Figure 4.4 Test objects used in experiments for detection of the plant
centre position
4.1.5 Test of the detection system’s accuracy
Two experimental rows of 30 green coloured wood sticks, with 5-mm and 10-
mm diameter respectively, were used to test the robustness of the detection
system. The detection of the rows was repeated 10 times for two different
scenarios in which sampling distance were set to 2 mm and 5 mm. It
corresponds to the detection of 300 objects for each scenario. The conditions
(sensor adjustment and input features) were the same for each series of
experiments.
4.1.6 Test of the detection system’s robustness
The intensity of the illumination in field conditions influences the quality of the
plant detection. To check the robustness of the developed system for detection
of the plant centre position experiments on an experimental row of 30 artificial
41

Materials and methods
plants were repeated 20 times in darkness, by daylight and with intensive
artificial illumination respectively.
42
4.2 The use of integrated mechanism design and
simulation in prototype development
The path from an idea to a prototype can be significantly shortened by the use
of integrated mechanism design and simulations. With the intensified growth of
processing power and software capabilities the concept of virtual prototyping
(VP) has become an appropriate alternative to conventional physical
prototypes.
4.2.1 Introduction to prototypes
The design process is nowadays shifting rapidly toward the use of advanced
three-dimensional modelling techniques and a relatively new technology called
virtual prototyping. This technique involves the creation of a comprehensive
three-dimensional model capable of interacting with a virtual environment that
allows for the testing of every functional aspect of a mechanism. Any
mechanism can be simulated to interact directly with the same elements it will
eventually encounter in its real-world environment.
Up until very recently, just the last five to ten years, all prototype testing was
conducted on physical prototypes that had to be manually built and tested.
Although procedures for physical prototypes development have been perfected
and are today quite efficient, several major drawbacks still exist, mainly
because of financial, material, labour, and time demands required to complete
the cycle. For any new prototyping process to be useful, it will have to continue
to provide all the feedback of physical prototyping, while at the same time
reducing or eliminating its drawbacks. Ideally, it will also provide additional
usefulness not currently available in physical prototyping. Virtual prototyping
meets all of these requirements and, as it is still a new technology, promises to
continuously improve in the coming years.
With the exponential growth of software and hardware capabilities at the
beginning of the 21 st century, the concept of a virtual prototype has only recently

Materials and methods
become a possibility. The question now is whether it is more beneficial to
continue to rely on and seek to further improve physical prototyping or to switch
over to a virtual prototyping centred testing process. Virtual prototyping offers
many significant advantages over physical prototyping that makes it worthy of
consideration and use.
4.2.2 Advantages of virtual prototyping
The advantages of virtual prototyping have caused it to become widely used in
the academic and industrial sectors. The most important advantages include
cost savings in labour and materials, nearly immediate feedback from prototype
testing, increased possibilities for design variations and shortened time from a
concept to a market ready product.
Many designs are unique and they entail a lot of trials to eliminate errors in
order to achieve the first prototype ready for manufacturing. Time demanding
development results in higher costs and delays in prototype production. The
time delays are often of vital concern, because the progress in design is often
halted awaiting the results of prototype testing. Often the data required from a
prototype test is essential to the next design decision.
Usually trained lab technicians, and not the designer, carry out the testing. This
means, that the designer needs to take a lot of time to explain and describe
what he exactly hopes to gain out of the test. The understanding of the
experiment is potentially subject to error, and it is sometimes the case that the
designer does not get all data that he would have liked from a prototype test
and the experiment needs to be repeated. Also, motion experiments with a
conventional physical prototype can take a lot of time and often it is hard to
conduct explicit measurements.
On the other hand use of virtual prototypes reduces the build-test-analyze
process from weeks or even months, when all the delays are factored, to terms
of minutes and hours. A simulation-ready model is simply an assembly of all the
parts that have been designed, so a virtual model can be built just by defining
the relations between parts and their motion constrains. The power of virtual
43

Materials and methods
prototyping is even more evident in the testing and analysis phase. With a
virtual prototype, upon completion of the simulation, all the calculated data can
be accessed and processed immediately. The raw data can be directly
analysed inside the software or easily imported into another numerical
application for further analysis. The scope of the data includes almost
everything that exists under real operating conditions: forces, deflections,
displacements, stresses, etc.
Another advantage of virtual prototyping is the flexibility it offers in prototype
design. In traditional prototyping, many different ideas are brainstormed,
eventually resulting in several scaled mock-ups. The number of prototypes
produced is usually limited because of cost, material availability, and
manufacturing and storage space limitations. Once a prototype is produced, any
changes in the design need to be implemented manually. Unless a major
overhaul of the prototype is acceptable, only small changes are possible,
because large design changes are impractical. With virtual prototyping there is
no limitation caused by costs or space requirements to build the prototype, so
any number of design variations are possible. A part change or modification is
automatically updated, maintaining the project without necessity for corrections
in all versions of the prototype. Additionally, a current version can be easily
reverted back to an earlier design stage, because all the changes made during
the design process have been stored as a back-up.
Most of the outcomes of physical prototyping can be achieved in virtual
prototyping. All the dimensional interactions between parts can be examined.
By being able to look at the virtual model from any angle, any issue involving
line-of-sight or part interferences can be checked and different kinds of
kinematical interactions can be tested.
It should be noted that virtual prototyping does not by definition include a
comprehensive virtual reality environment, but any virtual prototype can
certainly be extended and experienced in virtual reality if the proper equipment
is available (Wang 2002). A virtual environment (VE) allows a user to
completely interact within the same environment as the product, often with the
use of large-scale flat-wall displays, goggles, or CAVE immersive room
44

Materials and methods
environments (Waurzyniak 2000). If necessary, virtual prototypes can be
transferred across different platforms for further design modifications, when
some special influences are of crucial importance (e.g. environment is not solid
but fluid etc.).
A final possibility with the virtual prototyping approach is the creation of a mock–
up. In contrast to a prototype, which is usually functional and represents the
latest version of a design, a mock–up is normally set up early in the design
process when design ideas have not yet been solidified (Holub 2007). It is often
either for a purely functional purpose with no aesthetics taken into account, or
the opposite, where only the external appearance is created and no functionality
is made available. The advantage is that a mock–up can be created quickly to
give an early preview of a design, before it has been developed very far. Virtual
prototyping allows very quick design of a mock–up, as a simulation assembly
with very little functionality, including the prototype within its environment. This
assembly can then be used to show what a model will look like without having
to waste time defining the motion constrains required for movement. In this way,
the functionality, speed and usefulness of a mock–up development are easily
duplicated.
4.2.2.1 Pro/Engineer as a software tool
In the past, even when parts and models were drawn in three-dimensions using
software such as AutoCAD, performing any sort of kinematical or dynamic test
required a copy of the original to be completely redrawn and redefined in
another software application. This was because the above mentioned
simulation capabilities were simply not present in drawing software packages.
However, several software packages have recently begun to offer all in one
solutions. For example PTC’s Pro/ENGINEER (Pro/E), has the built in
capabilities of Pro/MECHANICA, which is a linear Finite Element Analysis
package, since the Wildfire 2.0 release. This integration allows utilisation of the
same software tool for both the design and simulation, making possible building,
testing, and analysing of a virtual prototype in parallel. With full CAD/CAM/CAE
45

Materials and methods
capabilities, Pro/E is the world's most commercially adopted 3D product design
solution (Anonymous 4 2007).
The combination of powerful mechanical and mathematical tools built into
Pro/Engineer software application helps designers to understand, evaluate, and
optimise the complex motion behaviour of developed assemblies against
functional performance design targets. There is a possibility to rapidly evaluate
multiple design alternatives early in the design process, as well as to test and
refine the digital prototype until the optimal system performance is achieved.
Another benefit is the use of multiple simulation scenarios, which can be
evaluated simultaneously.
Pro/MECHANICA is capable of several different kinds of analysis, including
structural, thermal, fatigue and kinematical. By using the Pro/MECHANICA’s
simple structural analysis interface, there is a possibility to perform standard
analysis types, including linear static, modal, buckling, contact, and steady state
thermal. Pro/MECHANICA also contains an interesting feature named Global
Sensitivity Study (ProCAD Engineering 2004). This feature assigns a range of
values to a particular dimension rather than a fixed value. Successive test
iterations can then be run, each time providing data that Pro/E can compare to
previous results, resulting in a fast and efficient geometry optimisation.
Additional more advanced tools are also available within the same package.
There is a fatigue advisor option that can predict the damage that cyclical
forces, even if relatively small in magnitude, can have on a design when
repeated over long periods of time. The fatigue advisor is capable of checking
and optimising for design durability and predicting the lifespan of a design
(Anonymous 4 2007).
The Mechanism/Pro built in kinematical toolbox allows analysis of the
interaction of moving joints even when a model is either too crowded or simply
too complex for visual inspection. If two parts are found to interfere with each
other, the kinematical analysis within Pro/E can inform the user which of
geometrical parameters need to be changed and exactly what the optimal
46

Materials and methods
dimensions need to be. This would be extremely difficult and would require
multiple trial-and-error attempts in a real-life prototype.
Another thing that makes simulations with Pro/E extremely realistic is the ability
to integrate a prototype’s environment into a simulation, something what is not
typically available in standard design software. Even the ability to virtually
operate and manipulate a prototype on demand can be of limited use if its
interaction with its environment cannot be seen. Whether a mechanism
operates in an environment that includes the soil, a plant, or other copies of
itself, all of these elements can be added to the simulation and made to interact
with the prototype. Also, if the additional elements also require movement,
additional motors can easily be defined to make that possible.
Not only is it possible to view the simulations on the screen in real-time as they
are running, but there is also a possibility to capture images and videos. Zoom
and view angle rotations are adjustable during the simulation, providing ability to
capture exactly the desired part or motion.
47

Materials and methods
48
4.3 Physical prototype of the hoeing tool
4.3.1 Selection of the drive for the hoeing tool
Preferably, a hydro engine system would be chosen for controlling the hoeing
tool as a tractor implement. Although the hydraulic system can fulfil the
requirements, for the high accuracy and dynamics expected from the intra-row
weeding tool a hi-pressure servo-hydraulic solution will be necessary. Such a
solution would significantly increase the costs of the experiments and after
consideration of the required parameters an electrical servo drive able to
provide same performance like a hydro system was choosen.
4.3.1.1 Electrical servo drive
A servo motor, or servo drive, is a system which combines a motor with a
controller and positioning device to precisely regulate the speed or position of a
load. Such a system allows controlling of the load, concerning different
parameters such as speed, torque or position or direction. Its basic
components are an interface panel which sends a command signal, a
positioning controller which receives the signal, a servo control amplifier which
provides power to the servo motor, and a feedback device which relays
information about position or velocity of the motor and load.
The servo can be controlled through semiconductors, such as silicon controller
rectifiers (SCR). Used alone, an SCR is effective but does not provide compete
control (Baldor Electric company 2007). Power from the SCR comes in discrete
pulses, thus instant acceleration and maintaining of the steady state are
feasible, but when the motor must be slowed down the SCR can only be turned
off. In such a case there is no direct control of the motor and the deceleration of
the load is affected by inertial force. With additional electronics a transport delay
in the load response can be introduced to provide more uniform speed changes,
but this often introduces too much system inertia and is not suitable for
applications requiring rapid modification of the speed. Transistors can be used
to create a more linear power response which is delivered constantly rather
than in discrete pulses like the SCR alone. Other techniques exist as well,
including pulse width modulation (PWM) and pulse frequency modulation

Materials and methods
(PFM). As the names imply, either the width of constant frequency pulse signals
or the frequency of constant width pulse signals can tell the motor to apply more
or less power at any given time.
The actual motor combined with a servo drive can theoretically be of any type.
Earlier, direct current (DC) motors were generally combined with a servo drive
because the only type of control for large currents was through SCRs. However,
alternating current (AC) motors, the most commonly used motors in industrial
applications, become used more often as transistors became capable of
controlling large currents and switching the large currents at high frequencies. If
a motor is intended to be used as a servo motor it needs to be able to operate
at range of speeds without overheating, to operate at zero speed and retain
sufficient torque to hold a load in position and to operate at very low speed for
long periods of time without overheating.
In stepper motors, a reasonable alternative to servo motors, the drive receives
low-level signals from the indexer or control system and converts them into
electrical (step) pulses to run the motor. A stepper drive is a typical example of
an open loop system. Generally, stepper motors are limited to small torque and
speed requirements.
The permanent magnet DC (PMDC) motor is the best and predominantly used
motor for start-stop, servo device applications. The permanent magnets
produce a constant field flux at all speeds and therefore a linear speed torque
curve. Other advantages are high starting torque, small frame, light weight, and
linear and predictable behaviour.
A unique aspect of a servo motor is its feedback control, closed loop function.
Motors controlled in open loop, comprising most simple motors including
stepper motors, operate under the assumption that motion has taken place
once the control signal has been sent. In case the motor’s move is disabled, for
example due to jamming, the controlling unit of the open loop system does not
receive information about the problem and cannot make any correction. Closed
loop systems contain a feedback device that can detect whether the desired
effect on the load has taken place, and subsequently alert the control device to
49

Materials and methods
change its input to the motor to cause the desired motion. If the servo motor in
speed-control mode is trying to achieve a speed of 1000 rpm but is only running
at 900 rpm, the feedback control will detect the problem and inform the
positioning controller to provide more power until the detected speed reaches
1000 rpm (Baldor Electric company 2007). Closed loop systems are required for
applications whose motion profile is complex, when high resolution and
accuracy are necessary, when the rotation speed ranges from very slow to very
high and when high torques are demanded in small package sizes.
The actual feedback device is the last important part of a servo device.
Feedback devices included analog and digital tachometers and resolvers. An
analog tachometer is essentially a small generator, usually hard-wired to the
output shaft of the motor and using the resulting rotational speed to output a
variable voltage which varies linearly with speed. While they are cheap and
simple, analog tachometers always introduce some AC-type ripple into the
output signal because they are not ideal devices and subject to design and
manufacturing tolerances. Digital tachometers operate either optically with
photoelectric detection or with contact through a brush assembly. Two types
exist: absolute encoders which assign a discrete address to each position
through 360° and incremental encoders which simply emit electrical pulses at
defined intervals which must then be counted to obtain position and distance
(Baldor Electric company 2007).
A concept of a servo system is illustrated in the Figure 4.5.
50
External
controller
+
Servo
amplifier
Servo
motor
Feedback loop
Encoder
Figure 4.5 Concept of a servo system in a closed loop
Controlled
system (load)

Materials and methods
A command signal created by the software or issued from the user interface
enters as a low level power signal into the servo drive. In the servo drive, this
signal is compared with the feedback value from the encoder to calculate the
reference signal. The reference signal needs to be multiplied with the loop gain
to generate appropriately amplified outgoing signal for motor control; higher
voltage level for higher speed and higher current level for higher torque value.
4.3.1.2 Power transmission
All the motors appropriate for the intra-row weeding system including the
selected one, run at speeds higher than is required. To reduce the speed and
increase the torque the output shaft of the motor was attached to a
transmission.
The rotational speed of the hoeing tool is G times slower than the input speed of
the servo motor shaft.
If the input shaft to an ideal gearbox is turning with a speed ωin and torque Min,
then the output turns with a speed ωout and torque Mout can be calculated as
M = G * M and (4.2)
out in
ω
in ω out = (4.3)
G
where G is the gear ratio, and usually G > 1.
In case of ideal gearbox output power Pout stays equal to input power Pin
ωin
Pout = Mout * ωout = G * Min * = Min * ωin = Pin
(4.4)
G
Due to the power lost caused by friction between moving parts of the gearbox
efficiency of the transmission can not reach 100%. Gearboxes have an
associated coefficient of efficiency η < 1, and the output power is smaller than
the input power
51

Materials and methods
P = η * P
(4.5)
52
out in
After imputing Equation (4.5) and transforming the Equation (4.4) the output
torque becomes the following form
M = η * G * M
(4.6)
out in
By gearboxes attached to each other the final coefficient is a multiplier of the
individual efficiency coefficients.
The backlash is an important characteristic of a gearbox and can be defined as
the possible angular displacement of the gearbox’s output shaft while the input
shaft remains stationary. Simple gears can have a few degrees of backslash,
precision gears much less and special gear systems like harmonic drive gears
nearly zero. The main characteristics of a gearbox, such as maximum allowed
torque at the output shaft and maximum allowed speed at the input shaft, are
given in their specification.
4.3.1.3 Adjustment of the parameters of the servo drive
The chosen servo drive allows 4 types of control:
� speed control (controls servomotor speed by means of an analog
voltage speed reference),
� position control (controls the position of the servomotor by means of
a pulse train position reference),
� torque control (controls the servomotor’s output torque by means of
an analog voltage torque reference) and
� contact input speed control (three operating speeds can be set in the
drive and activated by switching).

Materials and methods
The drive contains a mathematical model of the whole system based on the
mechanical inertia ratio of the system. The inertia ratio parameter is given as a
percentage of the ratio between the inertia of the load and motor inertia. This
parameter need to be properly set to optimise the speed gain.
In the traditional approach, inertia ratio needs to be calculated, using the
dimensions and densities of each component in the load, before the selection of
the servomotor as a decisive parameter for its sizing. The total inertia of the
system is a sum of the coupling, the screw and the load inertias. However,
specifications of all elements are often unreliable or unavailable and the
complexity of the system can be a limiting factor for accurate calculation of the
load inertia. The servo drive has an online auto-tuning algorithm but external
factors can often limit its accuracy. A simple technique based on graphical
analysis of the speed and torque values in the starting sequence, before the
system reaches the steady state, can be applied. In this method the same
equations are used as for motor sizing, but in reverse. The graphically
determined torque and speed characteristics can be used to calculate the
inertia of the load. The equation for accelerating torque calculation (Equation
4.7) can be transformed to solve it for JL (Equation 4.8)
M = ( J + J ) * α
(4.7)
A M L
MA
JL = − JM
(4.8)
α
where MA is the torque it takes to accelerate, JM is the inertia of the motor, JL is
the inertia of the load and α is the actual acceleration of the motor.
Typical change of the torque intensity which can cause a trapezoidal response
of the speed (acceleration, constant speed and deceleration) is illustrated on
the diagram in Figure 4.6.
53

Materials and methods
54
Figure 4.6 Typical change of the torque intensity for trapezoidal
response of the speed (MA – acceleration torque, MF –
friction torque, MD – deceleration torque, ∆u – change of
the rotational speed, ∆t – change in time)
It is obvious that the acceleration torque can be calculated as a subtraction
between the maximal torque value applied for acceleration and the friction
torque. A possibility to graphically interpret the both torque values, the rotational
speed change and change in the time (picking up any two points along the
speed profile) of the accelerating system based on the online measurement
allows calculation of the load inertia using Equation (4.8). The actual
acceleration of the motor α can be calculated as a quotient of the rotational
speed change and change in the time, while the inertia of the motor JM is a
characteristic provided by the manufacturer.
SigmaWin is the accompanying software of the servo drive which beside other
acquisitioning possibilities provides tracing of the torque and speed. Tracing
graphs of the acceleration to 750 rpm with torque limitation to 100 % of the
nominal value are presented in Figure 4.7 and Figure 4.8. Figure 4.8 contains
the zoom of the acceleration, making possible the measurement of the change
in time while the system accelerate from 100 to 600 rpm. Vertical marker
cursors can be adjusted to the points where the speed reaches the mentioned
values and the software automatically returns the time passed between these
two points.

Materials and methods
Another tool provided by Yaskawa allows the calculation of the inertia ratio
parameter based on the theoretical approach introduced in this chapter. For the
system rotating with 30 rpm (motor rotates with 750 rpm) and limitation of the
torque peak to 100 % of the nominal value, the inertia ratio parameter was 4.
This parameter was adjusted using the SigmaWin software to provide optimal
response of the system to the speed change during the hoeing.
Reference torque [%]
Reference torque [%]
160
140
120
100
80
60
40
20
0
-20
Rotational speed
Reference torque
-40
0 50 100 150 200 250 300 350 400 450 500 -200
Time [ms]
Figure 4.7 Experimental determination of hoeing tool’s inertia ration
160
140
120
100
80
60
40
20
0
-20
Rotational speed
Reference torque
15.88 ms
500 rpm
-40
300 305 310 315 320 325 330 335 -200
Time [ms]
Figure 4.8 Experimental determination of hoeing tool’s inertia ration
with zoomed area of interest
800
700
600
500
400
300
200
100
0
-100
800
700
600
500
400
300
200
100
0
-100
Rotational speed of the motor [rpm]
Rotational speed of the motor [rpm]
55

5 Results and discussion
Results and discussion
The development process of the intra-row weeding tool can be divided in three
almost independent research phases: detection of the plant centre position,
development of the virtual prototype and development of the physical prototype.
Considering this particular characteristics the results are reported separately for
the each development phase separately.
5.1 Algorithm for detection of the plant centre position
The algorithm for the detection of the plant centre position combines the context
information of the planting pattern data with the spectral and typical geometrical
characteristics of the crop plant. Before a detection can be started, features like:
� average distance between the plants,
� sampling distance,
� size of the searching area,
� minimum size (diameter) of the plant which will be identified as a
crop plant and
� maximal error inside the plant area
need to be given as inputs in the algorithm. The sampling distance is
changeable and it can be set from 1 to 50 mm depending on the plant intra-row
distance and the plants average development stage. A size of the area in which
a plant is expected, the minimum size of the plant and maximal error inside the
plant area are directly dependent on the plant habit and growth stage. If one
plant is represented with an array of TRUE and FALSE values, the maximal
error inside the plant area will be the number of allowed FALSE values. An
example of the plant definition is presented in Figure 5.1.
57

Results and discussion
58
Figure 5.1 Interpretation of different plants with arrays of TRUE and
FALSE signals (Lsen – maximum detecting range of the
sensor)

Results and discussion
Before the activation of the system, the vertical positions of the RGB fibre optic
and laser sensors need to be adjusted in order to cover as many plants as
possible with their detecting range. RGB sensor reacquires an additional
calibration of the target colour typical for the crop plant and its growing stage. If
the plants’ colour palette includes wider spectral range, the sensitivity of the
RGB sensor to certain colour can be increased. After the adjustment has been
done the system needs to be positioned to the initial position which is actually
the first plant in one row. At this point the system is ready for the plant centre
position detection. After the start, the algorithm waits until the linear position
reaches the first/next value equal to the multiplier of the sampling distance
which is a triggering signal for the execution of the next step. If the linear
position is located inside the searching area it is necessary to calculate the
indexing counter value which is used for building an array with the date
containing information about the position and sensor values (TRUE/FALSE).
This subroutine repeats until the index reaches the value of the array length
equal to the quotient of searching area size and sampling distance. In that
moment building of the array is completed and the middle position of the data
pattern can be calculated. Middle position calculator returns the linear position
value corresponding to the middle position of the plant and the time stamp when
the plant detection sensor has acquired this value. The flow chart of the
algorithm for the plant centre position detection is illustrated in Figure 5.2.
59

Results and discussion
60
Figure 5.2 Algorithm of the plant centre position detection

Results and discussion
After the measurement is completed the software generates a report-file,
containing general information such as: date and time, sampling distance,
distance between plants, additional information about the measurement,
distance from the soil surface, and settings for both sensors. The data set is a
two dimensional array in which every row contains the value sampled with the
position sensors, the values sampled with the RGB and laser sensor, time
stamp, and centre position of the last detected plant. The time stamp is used for
calculation of the carrier’s average forward speed in every sampling loop.
5.1.1 Evaluation of the algorithm for detection of the
plant centre position
For detection of the plant centre position according to the algorithm described in
chapter 5.1, VI-s were written. The calculation mechanism of the pattern’s
centre position, as one of the key functions built-in the algorithm for detection of
the plant centre position, needs to be described. After the array building was
completed the data acquired from the RGB and laser sensor are extracted and
transformed to a 1-dimensional row vector. The search_1D_array function
searches for the first element which has a TRUE value, and returns its index. In
one loop starting from the obtained index a nested subroutine checks the array
until the number of FALSE values does not reach the maximal error inside plant
area or the last element of the array is reached. In case the length of counted
TRUE values and allowed FALSE values remain smaller than the imputed
minimum plant size feature, the checking subroutine starts again from the next
detected TRUE value. When the plant size corresponds to the expectation, it is
bigger than the minimum plant size feature, another nested subroutine
calculates the arithmetic mean of the last and first index subtract. The VI returns
the linear position and time stamp corresponding to the calculated arithmetic
mean element. Sometimes, a plant is smaller than the minimum plant size
feature or it is missing because of suboptimal conditions. When that happens
the VI returns the expected centre position of the plant which is a simple sum of
the previous centre position and average distance between the plants.
Statistically, this position will be the most probable, and the displacement of the
following plant will be low enough to stay inside the searching area. The block
diagram of the VI is illustrated on the Figure 5.3.
61

Results and discussion
62
Figure 5.3 Block diagram of the VI for detection of the plant centre
position

5.1.2 Results of the accuracy test
Results and discussion
The accuracy tests conducted on the row with 5 mm wood sticks proved the
Nyquist–Shannon sampling theorem (Jerri 1977) that the sampling distance
during the detection needs to be at least two times lower than the size of the
objects. A number of numeric calculations and I/O operations with a sampling
distance lower than 5 mm can overrun the allowed time limits predicted for VI
execution and cause delays in execution of the main hoeing algorithm in real-
time. Because of that, the size of the object which can be detected with the
developed system is limited to 10 mm.
The accuracy tests conducted on the row with 10 mm wood sticks showed that
both of the sensors can be used for fairly accurate detection of the object. All
the wood sticks in the experimental row were detected with the RGB sensor as
objects sized between 5 mm and 20 mm when sampling distance was 5 mm
and as objects sized between 2 mm and 20 mm when sampling distance was 2
mm. The dispersions of the detected objects size generated by RGB and laser
sensor for experiments with 2 mm and 5 mm sampling distance are presented
in Figure 5.4.
Obviously, the size of the objects detected with RGB sensor frequently exceeds
the size of the measured objects. As an argument why such an error appears is
the shape and size of the fibre optic sensor’s light emitter. The spot diameter of
the light emitter is around 5 mm and the sensor can detect the object when
approximately one half of the emitted light returns to the receiver. It can happen
two times, first when the sensor approaches the object from the front side and
second time when the sensor leaves the object on the back side. According to
this explanation the number of impulses which corresponds to one object can
be higher up to 2 impulses per object for sampling distance 5 mm and up to 5
impulses per object for sampling distance 2 mm, depending on the RGB
sensors position when the sampling executes.
As mentioned before, with a sampling distance 2 mm an overrun of the allowed
time limits predicted for VI execution can appear and that is the reason why
some of the wood sticks were detected as 2 mm, 4 mm and 6 mm sized
63

Results and discussion
objects. According to this observation all following experiments were done with
a sampling rate set to 5 mm.
Distribution [%]
Distribution [%]
64
60
50
40
30
20
10
0
40
35
30
25
20
15
10
5
0
5 10 15 20 25
Size of the detected object [mm]
2 4 6 8 10 12 14 16 18 20 22
Size of the detected object [mm]
80
RGB Laser
70
RGB
Distribution [%]
Distribution [%]
60
50
40
30
20
10
0
60
50
40
30
20
10
0 5 10 15 20
Size of the detected object [mm]
0
0 2 4 6 8 10 12 14
Size of the detected object [mm]
Figure 5.4 Dispersion of the impulses generated for every detected
object (10 mm wood sticks) a) with RGB sensor
sampling distance 5 mm b) with laser sensor sampling
distance 5 mm c) with RGB sensor sampling distance
2mm d) with laser sensor sampling distance 2 mm
Laser

5.1.3 Results of the robustness test
Results and discussion
The robustness test showed that there is no significant difference between the
estimated size of the plants in the darkness, by daylight and artificial illumination
neither for the detection with RGB sensor nor for the detection with laser
sensor. Table 5.1 and Table 5.2 contain the mean values of estimated plant
sizes from experiments, conducted without change in sensor setting and
repeated 10 times for every variation of the illumination intensity and number of
allowed FALSE values in the plant pattern. The estimated size can vary from
the measured diameter of the plant, but the estimated size stays uniform
regardless the illumination intensity change. Tables containing the mean values
of the estimated plant sizes for every variation of the illumination intensity and
number of allowed FALSE values in the plant pattern with calculated standard
deviation of the estimated values for both of sensors are given in appendix (see
Table 9.1, Table 9.3, Table 9.5, Table 9.7, Table 9.9 and Table 9.11).
More obvious confirmation of the detection system applicability can be observed
by analysing the estimated centre positions of the plants. Table 5.3 and Table
5.4 contain the mean values of estimated plant centre positions from
experiments, conducted without change in sensor setting and repeated 10 times
for every variation of the illumination intensity and number of allowed FALSE
values in the plant pattern. Tables containing the mean values of the estimated
centre positions for every variation of the illumination intensity and number of
allowed FALSE values in the plant pattern with calculated standard deviation of
the estimated values for both of sensors are given in appendix (see Table 9.2,
Table 9.4, Table 9.6, Table 9.8, Table 9.10 and Table 9.12).
The maximum deviation of the estimated centre positions from the plant
measured centre positions, detected by RGB sensor, was 31 mm, whereby
50% of the samples were inside the interval 0 to 5 mm and 90% of the samples
were inside the interval 0 to 16,9 mm. For the laser sensor, the maximum
deviation of the estimated centre positions from the plant measured centre
positions was 25 mm, whereby 50% of the samples were inside the interval 0 to
3 mm and 90% of the samples were inside the interval 0 to 6.9 mm.
65

Results and discussion
Plant
position
66
Table 5.1 Size of the plants estimated by RGB sensor with 1 and 2
allowed FALSE values in the plant pattern in the darkness,
by daylight and intensive artificial illumination
Plant size
[mm]
0 48
1 allowed FALSE values in the
plant pattern
artificial
darkness daylight
light
Estimated size of the plant [mm]
2 allowed FALSE values in the
plant pattern
artificial
darkness daylight
light
1 44 35 31 39 40 37 44
2 37 35 34 30 40 39 35
3 40 32 32 30 37 37 35
4 40 32 30 27 37 35 32
5 42 18 18 19 23 23 24
6 32 30 30 30 35 35 35
7 45 18 18 17 23 23 22
8 45 49 50 50 54 55 55
9 45 48 46 46 53 51 51
10 46 39 36 32 44 41 37
11 35 31 31 30 36 36 35
12 42 40 41 44 45 46 49
13 33 40 39 27 45 44 34
14 38 36 36 36 41 41 41
15 35 36 29 28 41 37 35
16 42 32 32 37 37 37 42
17 40 26 27 34 42 42 45
18 34 23 23 29 35 36 37
19 40 43 42 43 48 47 48
20 48 48 48 49 53 53 54
21 38 34 35 36 39 40 41
22 30 15 14 12 34 32 33
23 40 38 37 39 43 42 44
24 28 23 23 22 28 28 27
25 35 35 33 33 40 38 38
26 35 31 33 32 31 34 32
27 40 17 19 44 36 35 48
28 28 24 29 29 31 34 34
29 28 22 23 21 26 28 26

Plant
position
Results and discussion
Table 5.2 Size of the plants estimated by laser sensor with 1 and 2
allowed FALSE values in the plant pattern in the darkness,
by daylight and intensive artificial illumination
Plant size
[mm]
0 48
1alloved FALSE values in the plant
pattern
artificial
darkness daylight
light
Estimated size of the plant [mm]
2alloved FALSE values in the plant
pattern
artificial
darkness daylight
light
1 44 64 66 66 69 71 71
2 37 61 61 62 66 66 67
3 40 53 52 52 58 57 57
4 40 44 45 44 49 50 49
5 42 63 65 64 68 73 72
6 32 48 50 50 53 55 55
7 45 54 58 58 59 63 63
8 45 51 53 53 56 58 58
9 45 34 36 41 39 41 46
10 46 52 51 51 57 56 56
11 35 52 51 50 57 56 55
12 42 53 55 55 58 60 60
13 33 41 34 40 47 45 46
14 38 18 24 28 67 69 68
15 35 45 48 47 50 53 55
16 42 33 34 33 38 39 38
17 40 39 37 37 44 42 42
18 34 42 43 41 47 48 46
19 40 47 47 48 50 52 52
20 48 36 42 44 46 47 49
21 38 58 56 59 61 60 63
22 30 37 39 41 38 41 44
23 40 17 17 16 22 22 21
24 28 31 34 35 36 39 40
25 35 24 28 32 30 33 37
26 35 26 26 27 27 27 27
27 40 44 41 31 49 47 42
28 28 15 25 25 22 30 30
29 28 17 17 16 22 22 21
67

Results and discussion
Plant
position
68
Table 5.3 Centre position of the plants estimated by RGB sensor with
1 and 2 allowed FALSE values in the plant pattern in the
darkness, by daylight and intensive artificial illumination
Distance to
the next
[mm]
Estimated centre position of the plant [mm]
1alloved FALSE values in the plant
pattern
artificial
darkness daylight
light
2alloved FALSE values in the plant
pattern
artificial
darkness daylight
light
0 195 193 191 193 196 194 196
1 395 (200) 395 395 398 398 397 401
2 600 (205) 601 601 601 604 603 603
3 805 (205) 796 795 795 799 798 798
4 1000 (195) 1002 1001 1001 1005 1005 1005
5 1180 (180) 1172 1171 1171 1177 1176 1176
6 1395 (215) 1389 1387 1387 1391 1391 1391
7 1610 (215) 1598 1597 1597 1602 1602 1601
8 1800 (190) 1801 1800 1800 1804 1802 1801
9 2005 (205) 1996 1997 1996 2000 1998 1999
10 2205 (200) 2200 2200 2200 2205 2204 2205
11 2410 (205) 2406 2405 2405 2409 2407 2406
12 2600 (190) 2603 2601 2591 2605 2604 2595
13 2780 (180) 2804 2803 2802 2805 2804 2803
14 3005 (225) 3009 2997 3003 3010 3001 3008
15 3215 (210) 3210 3209 3208 3213 3212 3211
16 3425 (210) 3403 3404 3407 3411 3411 3412
17 3620 (195) 3606 3605 3608 3615 3613 3613
18 3820 (200) 3814 3813 3813 3817 3816 3816
19 4015 (195) 4014 4012 4012 4016 4016 4015
20 4215 (200) 4220 4219 4220 4221 4220 4220
21 4435 (220) 4419 4418 4417 4428 4427 4429
22 4630 (195) 4613 4612 4611 4616 4614 4615
23 4820 (190) 4818 4816 4816 4820 4818 4818
24 5015 (195) 5019 5018 5018 5021 5020 5020
25 5235 (220) 5230 5231 5230 5230 5232 5230
26 5415 (180) 5401 5403 5421 5410 5411 5422
27 5630 (215) 5617 5624 5623 5599 5628 5627
28 5830 (200) 5827 5825 5825 5830 5827 5829

Plant
position
Results and discussion
Table 5.4 Centre position of the plants estimated by laser sensor with
1 and 2 allowed FALSE values in the plant pattern in the
darkness, by daylight and intensive artificial illumination
Distance to
the next
[mm]
Estimated centre position of the plant [mm]
1alloved FALSE values in the
plant pattern
artificial
darkness daylight
light
2alloved FALSE values in the
plant pattern
artificial
darkness daylight
light
0 195 195 194 195 196 196 195
1 395 (200) 394 394 393 398 397 397
2 600 (205) 603 602 602 605 606 606
3 805 (205) 806 805 805 807 806 807
4 1000 (195) 1000 1001 1001 1002 1004 1004
5 1180 (180) 1181 1181 1181 1185 1185 1185
6 1395 (215) 1399 1396 1395 1402 1399 1398
7 1610 (215) 1610 1608 1607 1612 1609 1609
8 1800 (190) 1801 1801 1803 1804 1803 1806
9 2005 (205) 2007 2006 2006 2009 2008 2010
10 2205 (200) 2209 2207 2206 2212 2211 2211
11 2410 (205) 2415 2414 2415 2417 2414 2415
12 2600 (190) 2606 2603 2599 2610 2609 2601
13 2780 (180) 2770 2771 2771 2794 2793 2792
14 3005 (225) 3011 3004 3005 3012 3008 3010
15 3215 (210) 3217 3216 3216 3220 3218 3219
16 3425 (210) 3425 3423 3425 3429 3427 3428
17 3620 (195) 3621 3621 3620 3625 3623 3624
18 3820 (200) 3824 3822 3823 3826 3825 3825
19 4015 (195) 4016 4018 4019 4022 4021 4021
20 4215 (200) 4218 4216 4216 4220 4218 4219
21 4435 (220) 4434 4437 4432 4435 4438 4434
22 4630 (195) 4631 4631 4631 4634 4632 4631
23 4820 (190) 4822 4821 4821 4826 4822 4821
24 5015 (195) 5019 5018 5020 5021 5020 5022
25 5235 (220) 5239 5239 5238 5239 5239 5238
26 5415 (180) 5416 5416 5418 5417 5418 5425
27 5630 (215) 5605 5633 5633 5607 5634 5635
28 5830 (200) 5833 5831 5831 5835 5833 5831
69

Results and discussion
70
5.1.4 Discussion of the plant centre position detection
methodology
As mentioned before, the presented methodology for detection of the plant
centre position is an interim solution based on a simplified system using
spectral and typical geometrical characteristics of plants combined with the
context information of the planting pattern.
The main shortage of the presented system is that sensors require ad-hoc
parameter adjustment before the measurement to provide good plant detection.
For broad usage of a system, parameter adjustments need to be replaced by a
more systematic method.
The sensors utilised during the tests are intended for industrial detection. It
needs to be confirmed that intensive application in hard field conditions will not
cause negative influence on their stable work. The digital RGB fibre optic
sensor CZ-H35S uses a light emitter with a spot diameter of 4.5 mm. For
reliable detection the detection system needs to be improved to cover an area
at least as wide as the laser sensor. This could be done by parallel
implementation of several sensors but in that case the solution will be more
expensive.
Although the system has shortages the experimental results showed that the
combination of the RGB sensor and laser sensor can be used for accurate
detection of the plant centre position independently from illumination conditions.

5.2 Virtual prototype of the rotary hoe for intra-row
weeding
Results and discussion
The design of uncommon systems such as the rotary hoeing tool entails a lot of
trials and contains many errors, which need to be fixed before the first real
prototype can be built.
The optimisation of the hoeing tool design was done with Pro/Mechanca, which
allows simulation of the trajectories, accelerations, velocities and forces acting
with the prototype. A variety of objects can be assembled together to resemble
more closely the real conditions, including springs, motors, friction, and gravity.
All the input variables such as speed and initial position can be altered at any
time, providing testing and analysis flexibility. Constraints can also be placed on
the prototype’s range of motion to more closely emulate a physical prototype.
During a simulation, the motion behaviour of the parts of the prototype can be
activated at different positions in the time domain, because their movement
corresponds to a particular motion controller known as motor. Motors can be
independently programmed to start and end at any point in the simulation, so if
only certain parts of the assembly are of interest for a certain simulation, all the
other parts can be simply turned off and left idle.
Taking into account demands and constrains, a virtual prototype of the rotary
hoeing tool for intra-row weed control was designed. Several concepts were
brainstormed and the idea of the system emulating the manual hoeing motions
under the soil surface was chosen. The hoeing tool consists of an arm carrier
and three or more integrated arms rotating around a horizontal axis above the
crop row. The axis is attached to the motor shaft whose rotational speed is
calculated and tuned according to the forward speed of the carrier vehicle, the
intra-row distance between plants and the observed position of the arms. The
working height of the whole assembly is adjustable in accordance to keep the
hoeing depth within optimal limits, which should be between 10 mm and 30 mm.
The concept of the rotary hoeing tool for intra-row weeding is presented in
Figure 5.5.
71

Results and discussion
72
Figure 5.5 Rotary intra-row hoeing concept (1 – hoeing tool, 2-
system for the hoeing depth adjustment, 3 – system for
the plant detection, 4 – forward speed measurement
unit, 5 – camera system for row following)
The path from the idea to the final virtual prototype was very similar to the usual
design process. The main difference was an additional step in the design when
the virtual prototype was placed into a virtual environment and rigorously tested.
After a preliminary concept had been sketched out on paper, the component
parts of the hoeing tool, carrier and simulation environment were designed. All
parts of the final assembly were drawn individually, from wheels to rods and to
even connecting pins and bolts. Defining dimensions of all parts required
particular care to provide proper fit for all elements into the assembly. This
consideration is exactly the same as would be taken into account when building
a physical prototype.
After all the parts had been individually drawn, they were assembled together.
Rather than having a single master assembly that contains every part, it was
more manageable to devide the entire simulation model into smaller

Results and discussion
subassemblies. As main subassemblies the soil with the plants (environment),
the carrier vehicle, the plant detection system, the positioning system and the
rotary hoeing tool were specified.
During the assembly process, parts were connected to each other using the
same constrains as in conventional prototyping and all connections were
defined exactly like they would be on the real system. All parameters which
have not been varied during the simulation process have been locked in, or
constrained, to avoid their move during the simulation. An example of this is the
placement of the plants on the soil surface. Since an individual plant does not
need to move during the simulation, all six degrees of freedom have been
constrained. This can be accomplished, for example, by constraining all three of
its coordinate planes. If a parameters value varies during the simulation, such
as the rotation of the hoeing tool, it has been left unconstrained. Its movement
parameters have been defined by simulation motors using the mechanism
design tool.
One of the main characteristics of the developed hoeing system is modular
design. The base is a disk-shaped arm carrier on whose rim different number of
arm holders can be attached. Typical number of arms is 2, 3 or 4. Arm holders
have 3 design variants with 1, 2 or 3 holders forming forearms (see Figure 5.6).
Figure 5.6 Design variants of the arm holder
73

Results and discussion
Connection between the forearm and upper arm has two degrees of freedom,
realised through a slider and a hinge joint, so-called pin-in-slot joint (see Figure
5.7).
74
Figure 5.7 Exploded view of a one-arm hoeing tool assembly with
indicated joints
Finally, to the end of the upper arm, a tool for soil cultivation can be connected.
As basic cultivation tools small duckfoot knives are anticipated, but several
other tools (tines or torsion weeders) can also be easily implemented on the
hoeing tool. Variants of the hoeing tool consisting of 4, 6 and 12 arms are
illustrated in the Figure 5.8).
Figure 5.8 Design variants of the hoeing tool assembly

Results and discussion
To demonstrate the ability of the hoeing system for adaptation to different
hoeing scenarios, a virtual mechanism of the hoeing tool consisting of 3
sections with a 3-arm hoeing subsystems (fr=front, mi=middle and re=rear) was
constructed, like it is shown in Figure 5.9.
R LA
y
x
θ δ δ
R UA
z
x
Section 3
fr3
mi3
re3
Figure 5.9 Kinematics and design of the rotary hoe
y
z
φ
Section 2
Section 1
120°
According to the plant intra-row distance, growth stage, soil surface cultivation
quality and inter-row weeding method, optimal hoeing width can be calculated
and adjusted by changing the lengths of the arms RLA+RUAcosθ and their
angular position θ in relation to the plane perpendicular to the rotation axis in
which the arm holder is placed (Figure 5.9). Thus, small duckfoot knives (cutting
tools) placed on the ends of the arms have sufficient degrees of freedom, which
allow the selection of the optimal trajectories in the intra-row and close to crop
area.
It needs to be emphasised that construction constrains RLA, δ, and number of
hoeing arms have been fixed for the set of simulations presented, but they
could be also subjects of optimisation.
For testing and simulation purposes a virtual sugar beet field was created with a
400 mm distance between the rows and a 200 mm intra-row distance between
the plants.
75

Results and discussion
76
5.2.1 Optimisation of the arm length
The maximum and minimum arm lengths was calculated considering the
required hoeing depth d and the necessary confidence that duckfoot knives
need to achieve a cut under the soil surface cultivating the specified hoeing
width. It was estimated that an arm length in the range of 350 - 550 mm,
measured from the rotational axis to the cutting edge of the duckfoot knife,
offers enough freedom for positioning the trajectory between plants and
provides a hoeing width that is wide enough to cultivate the whole area which
cannot be reached with inter-row equipment.
Depending on the soil surface cultivation quality and roughness, a minimum
hoeing depth hdmin needs to be defined, providing the optimal impact and
necessary hoeing width on the root system of the weeds with a high level of
confidence (see Figure 5.10). On the other hand a maximum hoeing depth
hdmax needs to be defined because it influences the soil resistance to hoeing
and on that way it also affects the torque on the shaft of the motor.
hdmin = hdmax – surface roughness
Figure 5.10 Trajectory of the duckfoot knife under the soil surface
with minimum and maximum hoeing depth

Results and discussion
The Pythagorean theorem (Equation 5.1 and 5.2), was used for calculating a
hoeing widths hw1 and hw2. A few characteristic values of the hoeing width are
given in Table 5.5.
2
⎛ hw1⎞
⎜ ⎟ = R - ( R - ( hdmax - hdmin))
⎝ 2 ⎠
2
2 2
⎛ hw2
⎞
⎜ ⎟ = R - ( R - hdmax)
⎝ 2 ⎠
2 2
Table 5.5 Calculation of hoeing width in dependence on the arm
length and hoeing depth
For hdmin=15 mm
Arm length [mm]
350 450 550
hw2 (hdmax=20 mm) 233 265 294
hw1 (surface roughness =5 mm) 118 134 148
hw2 (hdmax =25 mm) 260 296 328
hw1(surface roughness =10 mm) 166 189 209
hw2 (hdmax =30 mm) 284 323 358
hw1(surface roughness =15 mm) 203 230 255
5.2.2 Examination of influences of the angular position θ
to the hoeing trajectories
(5.1)
(5.2)
Kinematical behaviour of the hoe’s virtual prototype was simulated in order to
optimise the hoeing process and trajectories of duckfoot knives under the soil
surface in the intra-row area. The forward speed of the carrier, the plant growth
stage and the arms length and angular position of the duckfoot knives have
been varied.
For better understanding of the mechanism, kinematical equations of the points
presenting the cutting edge of each duckfoot knife in one section are given.
77

Results and discussion
xmi1 = ( RLA + RUA cos θmi )sinϕ
(5.3)
ymi 1 = ( RLA + RUA cos θmi )cos ϕ + ( RLA + RUA cos θmi
− hdmi
)
(5.4)
z = z + R sinθ
(5.5)
mi1 UA mi
xfr 1 = ( RLA + RUA cos θfr )sin( ϕ + δ )
(5.6)
yfr 1 = ( RLA + RUA cos θfr )cos( ϕ + δ ) + ( RLA + RUA cos θfr
− hdfr
)
(5.7)
z = z + R sinθ
(5.8)
fr1 UA fr
xre1 = ( RLA + RUA cos θre )sin( ϕ − δ )
(5.9)
yre1 = ( RLA + RUA cos θre )cos( ϕ − δ ) + ( RLA + RUA cos θre
− hdre
)
(5.10)
z = z + R sinθ
(5.11)
re1 UA re
The angular motion of the mechanism can be defined depending on the forward
position of the carrier z – z0, intra-row distance between plants d and number of
cuts between two successive plants in the row. As the most valuable simulation
presented in this research, a solution with 3 cuts between two successive plants
had been chosen. Kinematical equation of the three-cut hoeing method defining
the exact angular position of the rotary hoeing tool depending on the forward
position z is given in Equation (5.12).
0
ϕ ϕ 0
(5.12)
78
2 ( z − z )
= +
3 d
π
Using such an approach for the calculation of the angular position, it is obvious
that any change in intra-row distance between plants can generate only a
stretching or shrinking of the helix in relation to the z-axis (see Figure 5.11).
This fact indicates the adaptability of the rotary hoe to different plant spacing
systems and variable intra-row distances.

Horizontal position (x) [mm]
Vertical position (y) [mm]
400
200
0
-200
-400
50
0
-50
mi1
re1
+
fr2
mi2
re2
Results and discussion
400 500 600 700 800 900 1000 1100
Travelled distance (z) [mm]
re1 fr2 mi2 re2
fr3 mi3 re3 fr1
400 500 600 700 800 900 1000 1100
Travelled distance (z) [mm]
+
mi3
+ + +
Figure 5.11 Hoeing trajectories of the hoeing tool with nine arms in a
field with 300 mm intra-row distance between plants
(arm length 440 mm, angular position of all arms
adjusted to 0°, � - position of the plant)
The trajectories of the duckfoot knives under the soil surface for equal length of
the arms can be optimised by adjustment of the angular position of the arms θ.
The main advantage of this kind of fine-tuning is that a small angular change
provides the possibility of controlling the distance between consecutive cuts of
each section. Also, the adjustment of the angular position of the arms θ affects
the penetration order of the front, middle and rear duckfoot knives of each
section, forwards or backwards, in relation to the z-axis. An example of the
forward-backward rearrangement of the trajectories by changing the angular
position of the front and rear duckfoot knives in each section on the rotary hoe
with nine arms is illustrated in Figures 5.12 and 5.13. The segments of the
trajectories under the soil surface are highlighted on both graphs. The expected
crop positions are x=200 mm, 400 mm, 600 mm; y=0 and they are marked with
“+” on the graphics.
fr3
re3
+
fr1
79

Results and discussion
With the invariable setting of the arm lengths and with an angular adjustment of
the front and rear duckfoot knives in all sections (Figure 5.13), the equal intra-
row area covering can be realised, like with the system without angular
adjustment (Figure 5.12). Another very important aspect of the forward-
backward rearrangement is the relative position of the arm, working in the
close-to-crop area. In the first case with the system without angular adjustment
(Figure 5.12) arms are always perpendicular to the soil surface. In the
rearranged system, arms cultivating the close-to-crop area always have an
obtuse angle in relation to the soil surface, which means that they are shifted
from the plant. That allows cultivation of plants with bigger leaves as well as
cultivation closer to the plant without causing damage on the leaf system
(Figure 5.13).
80
Horizontal position (x) [mm]
Vertical position (y) [mm]
600
400
200
0
-200
-400
100 200 300 400 500 600 700
Travelled distance (z) [mm]
50
0
re3
fr1
fr2
+ mi1 + mi2 +
re1
+ +
-50
100
re3
200
fr1 mi1
300
re1
400
fr2 mi2
500
re2
600
fr3 mi3
700
Travelled distance (z) [mm]
Figure 5.12 Hoeing trajectories of the hoeing tool with nine arms in a
field with 200 mm intra-row distance between plants
(arm length 520 mm, angular position of all arms
adjusted to 0°, � - position of the plant)
re2
+
fr3
mi3

Horizontal position (x) [mm]
Vertical position (y) [mm]
600
400
200
0
-200
-400
Results and discussion
100 200 300 400 500 600 700
Travelled distance (z) [mm]
50
0
mi3
re3
+
fr3
re1
mi1
fr1
+
+ + +
-50
mi3
100 200
re1 mi1
300
fr1
400
re2 mi2
500
fr2
600
mi3
700
Travelled distance (z) [mm]
Figure 5.13 Hoeing trajectories of the hoeing tool with nine arms in a
field with 200 mm intra-row distance between plants
(arm length 520 mm, angular position of all arms
adjusted to fr= 17°, mi= 0°, re= –17°, � - position of the
plant)
Another nine-arm hoeing scenario with shorter arm lengths (440 mm) and
angular position θ adjusted to 17°, 0°, –17° is introduced in Figure 5.14. The
segments of the trajectories under the soil surface are highlighted and the
expected crop positions are x=200 mm, 400 mm, 600 mm; y=0. In this case
arms are again shifted from the plant by the forward-backward rearrangement
and the distances between consecutive cuts of every section are much closer to
one another. Thus, the protected area around the crop plant is much bigger in
relation to the hoeing scenario described in Figures 5.12 and 5.13. With this
adjustment variation the applicability of the rotary hoe to different crop systems
and growth stages can be easily demonstrated.
re2
mi2
fr2
+
mi3
81

Results and discussion
82
Horizontal position (x) [mm]
Vertical position (y) [mm]
400
200
0
-200
-400
100 200 300 400 500 600 700
Travelled distance (z) [mm]
50
0
mi3
re3
fr3
re1
re2
+ mi1 + mi2 +
fr1
+ +
-50
mi3
100 200
re1 mi1
300
fr1
400
re2 mi2
500
fr2
600
mi3
700
Travelled distance (z) [mm]
Figure 5.14 Hoeing trajectories of the hoeing tool with nine arms in a
field with 200 mm intra-row distance between plants
(arm length 440 mm, angular position of all arms
adjusted to fr= 17°, mi= 0°, re= –17°, � - position of the
plant)
5.2.3 Examination of influences of the ratio between the
rotational speed of the hoeing tool and the forward
speed of the carrier
One very important aspect of the optimisation is defining of the angular speed of
the hoeing tool. The preliminary idea was designing of the rotary hoeing tool
with 3 arms, with a 120° degree angular shift between each. In that case a full
rotation of the hoeing tool is necessary to achieve three cuts between every two
following plants. The necessary number of cuts between two plants depends on
the shape and size of the duckfoot knives and the average intra-row distance
between plants. For successful extermination or injuring of the weeds, an
overlapping of the areas covered by successive cuts is desired. The size and
shape of the duckfoot knives were designed based on the assumption that it will
fr2
+
mi3

Results and discussion
be mostly used for hoeing in plant system with average intra-row distance
around 200 mm and three cuts weeding strategy between every two following
plants. Shape and dimensions of one duckfoot knife are given in Figure 5.15.
Figure 5.15 The shape and dimensions of one duckfoot knife
The angle ε between the axis of symmetry of the duckfoot knife and the plane in
which the arm holder is placed plays an important role concerning the
interaction between the soil and the cutting edge of the duckfoot knife. The
cutting edge needs to be correctly oriented to generate as little friction as
possible. The mentioned angle is directly proportional to the ratio between the
angular speed of the hoeing tool and the forward speed of the carrier, as also to
the number of arms implemented. It is obvious that several parameters have
influence on the design of the tool directly responsible for the weed elimination,
so it is impossible to define a universal solution which will be optimal for
different plant systems in the same time. An appropriate combination of cutting
tool, number of cuts between two following plants and necessary rotational
speed can be suggested, only by using methods for multiparameter optimisation
and exact information about the crops, weeds and soil conditions. The objective
of this study was not to develop a database with suggestions for different
condition combinations, but to design a tool which can be easily adapted to a
range of different conditions.
83

Results and discussion
84
5.2.4 Selection of appropriate design of the hoeing tool
according to the hoeing scenario
A very important aspect is selection of the appropriate number of hoeing arms
depending on the number of cuts between two following plants. An example for
broad adaptability of the developed concept are trajectories of the duckfoot
knives when the ratio between the angular speed of the hoeing tool with 3 arms
and the forward speed of the carrier is adjusted to provide two cuts between
every two following plants (see Figure 5.16). Like before, segments of the
trajectories under the soil surface are highlighted and the expected crop
positions are x=200 mm, 400 mm, 600 mm; y=0. Trajectories are marked with
numbers from 1 to 3 corresponding to duckfoot knives.
Horizontal position (x) [mm]
Vertical position (y) [mm]
400
200
0
-200
-400
100 200 300 400 500 600 700
Travelled distance (z) [mm]
50
0
1
+ + +
+
2 3 1
2
3
+ +
-50
100
1
200
2
300
3
400
1
500
2
600
3
700
Travelled distance (z) [mm]
Figure 5.16 Hoeing trajectories of the hoeing tool with three arms
providing two cuts between following plants in a field with
200 mm intra-row distance between plants (arm length
440 mm, angular position of arms adjusted to:
duckfoot1= 0°, duckfoot2= 0°, duckfoot3= 0°, � -
position of the plant)

Results and discussion
With a hoeing tool with odd number of arms it is possible to achieve even
number of cuts between every two following plants, but in that case hoeing
pattern cannot be optimised by angular adjustment and the distance between
cuts stays constant. The reason why such a limitation exist is the fact that a
duckfoot knife alternately hoes every following plant first from the front and after
from the back, or vice versa. Hence, for full adaptation of the hoeing tool to the
hoeing strategy with odd number of cuts, the number of arms needs to be odd
also. A simulation of a hoeing scenario with a hoeing tool with 4 implemented
arms demonstrates the ability of angular adjustment when two cuts between
every two following plants are required. Again, segments of the trajectories
under the soil surface are highlighted and the expected crop positions are
x=200 mm, 400 mm, 600 mm; y=0. Trajectories are marked with numbers from
1 to 4 corresponding to duckfoot knives. Results of this simulation are
presented in Figure 5.17.
Horizontal position (x) [mm]
Vertical position (y) [mm]
400
200
0
-200
-400
100 200 300 400 500 600 700
Travelled distance (z) [mm]
50
0
1 3
1
+ 4 2
4
+
+ + +
1
-50
100 200
4 3
300 400
2 1
500 600
4
700
Travelled distance (z) [mm]
Figure 5.17 Hoeing trajectories of the hoeing tool with four arms
providing two cuts between following plants in a field with
200 mm intra-row distance between plants (arm length
440 mm, angular position of arms adjusted to:
duckfoot1= 20°, duckfoot2= -20°, duckfoot3= 20°,
duckfoot4= -20° , � - position of the plant)
+
85

Results and discussion
86
5.2.5 Discussion of the results conducted by virtual
prototyping
Virtual prototyping of course has some disadvantages as well. As with any
computer simulation, there are real-world elements that cannot be exactly
reproduced in a virtual form. While a simulation may show a mechanism to
function perfectly, in reality a rather small change in the soil roughness may
cause problems.
Until now, virtual prototyping technology has not completely rendered physical
testing obsolete. However, the role of physical prototypes has now shifted to
that of validating the virtual model. After the virtual prototyping process has
yielded a design that on the computer appears robust and meets all the design
objectives, a physical model can be built to validate the data from the computer
model to ensure the design is indeed applicable in real life.
However, because most of the problems have been discovered and worked out
in the virtual prototyping process, the physical prototype is likely to be nearly
ready for manufacture with very few errors.
All of these advantages resulted in a drastically reduced time frame, from the
idea to the introduction of the first prototype of the intra-row weeding tool. The
reduction of costs and time needed for prototype development was one of the
major benefits of virtual prototyping.

5.3 Physical prototype of the hoeing equipment
Results and discussion
After the comprehensive analysis of the results observed with the virtual
prototype the first physical prototype of the hoeing tool has been built. The
selection of the appropriate driving engine for accurate rotational speed and
position control of the hoeing tool required a determination of the average and
maximum torque on the axis, during the weeding process in field conditions.
5.3.1 Determination of the maximum torque and nominal
speed required
To confirm that the designed construction can withstand the maximum load and
to observe the torque value, experiments in the field conditions were conducted.
The determination of the maximum torque was of crucial significance in order to
choose a motor with adequate power for the hoeing tool. In the experiment the
rotary hoeing tool with three implemented arms was pulled by a tractor and
plugged to the cardan shaft of the tractor over a gear with 10:1 transmission
ratio. The aim of the experiments was to acquire information about the forward
speed, angular position of the hoeing tool and required torque for hoeing. The
forward speed can be calculated if the travelled distance and the time passed
are known. Travelled distance in field conditions can be estimated with several
methods. Methods which are based on the contact with the soil surface need to
be avoided if high accuracy is required, because of the slipping effect which can
occur in hard field conditions. One accepted non-contact method for estimation
of the travelled distance is with systems based on the Doppler shift effect.
Depending on the adjustment, such a system can provide up to 125 impulses
per meter of travelled distance, which satisfies accuracy demands in the field
experiments. For simple on-line acquisition of the tractor’s forward position an
RDS true ground speed sensor (TGSS) was chosen. The angular position of the
hoeing was acquired with an incremental encoder attached to the hoeing tool’s
shaft. This information was used for calculation of the rotational speed of the
hoeing tool.
87

Results and discussion
Conventional method for measurement of a rotary torque is use of a strain
gauge based sensors. In these types of sensors strain gauges are coupled with
brushes and slip rings to transform the torque signal into output voltage. The
output voltage depends on a resistance change due to deformation in strain
gauges that are bonded to the torque sensor structure. The magnitude of the
resistance change is proportional to the deformation of the torque sensor and
therefore the applied torque. A rotary torque sensor manufactured by
Walterscheid with detection range 0 and 250 daNm was attached between the
cardan shaft and the gear for measuring the torque.
All sensors were connected to the DAQPad-6015 through the signal
conditioning unit. A simple VI generating a report file was written for data
acquisition. The report file contains basic information about the measurement
such as: date and time of the experiment, arm lengths, hoeing depth and a data
array with forward speed of the tractor, rotational speed of the hoe and torque
on the axis. The results of one experimental hoeing are illustrated in the Figure
5.18.
Rot speed [RPM]
Fwd speed [km/h]
Torque [Nm]
88
100
60
20
20
10
0
500
400
300
200
100
0
0 5 10 15 20 25 30
0 5 10 15 20 25 30
0 5 10 15 20 25
Time [s]
Figure 5.18 Results of the field experiment providing insight into the
size of the torque required for undisturbed hoeing with
hoeing tool with three arms in extreme conditions
30

Results and discussion
During the experiments it was observed that the soil type, previous tillage,
roughness of the soil surface, soil humidity, forward speed of the carrier,
rotational speed of the hoeing tool, number of arms, as also the hoeing depth
have significant influence on the torque. There was no possibility to draw
general conclusion because of a high number of varying parameters. Hence, a
case with worst condition was chosen to ensure that the resulting prototype will
be able to work in all conditions. For rotational speed of uhmax=100 rpm, forward
speed around 2m/s, arm lengths of 550 mm and average hoeing depth 5 cm the
torque value does not exceeded the value of Mhmaxexp= 600 Nm. According to
this worst case experiment for the selection of the motor 350 Nm as a nominal
value was taken.
The impact of the rotational speed to the level of damage caused to the weeds
was observed, as an additional important fact, during the field experiments. It
was observed that for arm lengths of 550 mm with implemented duckfoot
knives, a rotational speed of 30 rpm provides enough energy to cut the weeds.
Higher rotational speed over 80 rpm can originate throwing of the soil particles
from the row towards the neighbouring row. For another type of direct weeding
implement different from the duckfoot knives, higher speed could be applied
because of the reduced contact between the tool and the soil surface. Taking
into consideration all the above mentioned facts for a nominal value of the
rotational speed uhn=60 rpm were used in calculations.
5.3.2 Calculation and selection of the motor and
transmission combination
To make an appropriate selection of the motor/transmission combination it is
necessary to know the required power, speed range and the operating point of
the torque for the system in typical working conditions. Usually the smallest,
lightest and most inexpensive motor that meets the requirements will be
chosen. As the motor is intended to be implemented on a first prototype of the
weeding tool applicable in laboratory condition, the type of the power supply
required by motor was not a limiting factor.
89

Results and discussion
Since the first prototype will be used for intensive experimental testing, the
motor was oversized. Both of the two parameters relevant for calculation of the
required power were increased with safety factor equal to 6 to avoid overloading
or damaging of the system during the testing.
P = 6 * M * u = 6 * 600 *1 = 3600W
(5.13)
90
required hmaxexp hn
The intra-row weeding system requires a closed loop control of the rotational
speed because the on-line calculation of the optimal speed takes into account
the carrier’s forward speed, observed distance between two successive crop
plants and position of the hoeing arms. One of the main advantages of servo
motors is the ability to keep a constant torque at both very low and high engine
speeds which is in case of the intra-row weeding tool of crucial importance.
According to the calculation of the required power of the hoeing system, an AC-
servo motor from Yaskawa type SGMGH-44DCA6F with Pmn= 4.4 kW, with
maximum rotational speed ummax= 1500 rpm, nominal torque Mmn= 28.4 Nm,
maximum torque Mmmax= 71.1 Nm, and appropriate servo drive from Yaskawa
type SGDH-50DE were chosen. The servo system includes a 17-bit encoder as
feedback device. The motor was combined with a planetary gearing type
TP110S-MF2 with gear ratio G= 25 and nominal torque on the gearbox output
shaft Mhn= 710 Nm and corresponds to the torque of the hoeing tool.
In that way the prototype was able to provide continuous and highly accurate
speed change in a range from 0 to 60 rpm with available torque up to Mhn=
710 Nm in the whole range of speeds.
5.3.2.1 External control of the rotational speed
The advantages of the servo system implemented into the intra-row hoe’s
prototype can be taken with the servo drive in the speed control mode, by an
analog signal created in external controlling unit. The combination of DAQPad-
6015 and signal conditioning unit used for plant detection was also used for
speed control as an external controller. Controlled inputs to the servo drive were
rotation speed, as analog signal, and servo ON as digital signal. Acquired

Results and discussion
outputs from the servo drive were data from the incremental encoder and alarm
signals.
The motor’s rated (maximum) speed is 1,500 rpm and the equivalent rated
speed of the hoe is
u
hmax
ummax
1500
= = = 60 rpm = 1 rps
(5.14)
G 25
To define the adequate voltage/speed ratio the speed reference input gain
Pn300 in the servo drive needed to be adjusted. The parameter Pn300 has a
setting range between 150 and 3000 and corresponds to the input voltage V-
REF with the ratio 100 to 1. E.g. if the Pn300 is adjusted to 600, the rated speed
will be achieved with V-REF equal to 6 V.
The servo ON signal is required as a main signal for starting the servo motor. In
the case where this signal is “low” the servo motor cannot be controlled with
external signals.
To receive appropriate information about the position of the motor and
accordingly the angular position of the hoeing tool, in relation to the “zero”
position, the encoder dividing ratio was set to 24 pulses per rotation. Hence the
gear ratio is G=25, 600 pulses correspond to one full rotation of the hoeing tool
and this represents the resolution of the hoeing tool system RESsys.
5.3.3 Discussion of the distance between the plant
detection unit and the plane in which the hoeing
tool is positioned
The rotational speed of the hoeing tool is directly proportional to the forward
speed of the carrier. For travelled distances which are short, speed can be
approximated with a constant value as shown in Equation (5.15).
∂s ∆s
V = ≅
∂t ∆ t
(5.15)
91

Results and discussion
To avoid continuous changing of the calculated speed value, caused by
exaggerated accuracy, it is necessary to increase the sampling distance ∆s. On
the other hand, a too big sampling distance ∆s would cause decrease of the
dynamics and accuracy of the hoe’s angular positioning, directly implying plant
damaging.
The distance between the plant detection unit and the plane in which the hoeing
tool is positioned is limited by the construction of the prototype and the software
solution responsible for the online speed adjustment. For the best performance,
positioning software requires initialisation of the system before the hoeing
starts. The initial position of the hoeing tool’s angle is achieved when the
inductive position transducer mount on the arm number 3 reaches the upper
vertical position corresponding to the angle of zero degrees and corresponds to
time t0 (see Figure 5.19 a).
92
Figure 5.19 Definition of the desired angular position of the hoeing
tool with three arms a) when it is in the start position and
b) when it is placed exactly above a crop plant
One full rotation of the hoeing tool with three arms corresponds to the travelling
distance equal to the average distance between plants d. In such a kinematical
assumption the rotation needs to be started, respectively corrected, in the
moment when the forward position of the plane analogous to the centre plane of
the hoeing tool A reaches the 1/3 of the distance between two successive
plants.

Results and discussion
According to the listed requirements, one correction of the rotation speed on the
distance equal to the average distance of the sowing pattern is scheduled in the
controlling algorithm.
An important question is at which point the correction of the rotation speed
should take place in the time schedule. Considering the time consumption of
different VI-s during the real time detection of the plant position it is obvious that
the speed correction VI shall find place in the timetable when the processor has
no tasks related to the plant detection. That means that the speed correction
needs to be executed in the period when the plant detection unit travels
between two searching areas (see Figure 5.20).
SA SA
EMPP EMPP
d
Figure 5.20 Time schedule of the VI-s execution during the real time
detection of the plant position (SA - searching area,
EMPP - estimated middle point of the plant, d -
estimated distance between plants, - creation of the
data array, - estimation of the middle point)
t
93

Results and discussion
As the size of the searching area depends on the average plant size in the field,
theoretically it could be almost as big as the distance between plants. Hence,
the optimal point for the execution will be the moment, when the plant detection
unit reaches the middle point between two searching areas. A value
approximately equal to double of the average intra-row distance between plants
can be defined as a necessary distance between the plant detection unit and
the plane in which the hoe is positioned. Such an approach is determined by the
software solution because it requires the position of two following plants for
calculation of the forward speed (see Figure 5.21).
94
Figure 5.21 Definition of the required distance between the plant
detection unit and the plane in which the hoeing tool is
placed

Results and discussion
Based on indicated requirements the optimal distance can be identified. For the
average intra-row distance between plants of 200 mm optimal distance can be
calculate as:
⎛ 2 1 ⎞ 13
Dsens −hoe
= ⎜ + 1+
⎟d
= d
⎝ 3 2 ⎠ 6
for d= 200 mm
(5.16)
D − = 433,33 mm ≈ 435 mm
(5.17)
sens hoe
This value is optimised for the hoeing tool with three arms, three cuts between
two successive plants, average intra-row distance about 200 mm and initial
position of the system as shown in Figure 5.19.
Using the same approach presented in this chapter, there is a possibility to
calculate the optimal distance between the detection unit and the plane in which
the hoeing tool is placed, for other hoeing strategies with different number of
arms, increased or decreased number of cuts between successive plants and
alternative intra-row distances between plants.
As mentioned, the software solution requires centre positions of two following
plants for calculation of the forward speed. Using such a method of speed
acquisition, the necessity for implementation of an additional hardware system
for speed measurement was avoided. Of course, application of an additional
hardware would simplify the software, because calculation of the forward speed
would not be necessary, and it would allow shortening of the distance between
the detection unit and the plan in which the hoeing tool is placed for one
average intra-row distance.
To avoid additional investments into speed measuring hardware, whose costs
grow with the required accuracy, especially in low speed ranges, the alternative
of speed measurement with software was chosen.
95

Results and discussion
96
5.3.4 Algorithm for the online control of the hoeing tool’s
rotational speed
The idea built into the algorithm for online control of the hoeing tool’s rotational
speed is based on the theoretical approach presented in the previous chapter.
Before the rotational speed unew can be calculated, it is necessary to know the
relative distance between the hoeing tool and the plant which need to be hoed,
the latest forward speed V, latest angular position of the hoeing tool and latest
rotational speed uold. With assumption that the forward speed will not change on
the next section of travelled distance equal to the average intra-row distance
between plants d, the forward speed can be calculated using the following
equitation:
zc( tL) − zc( tL−1)
V =
t − t
L L−1
(5.18)
where zc(tL) is the absolute coordinate of the last detected plant’s centre
position in direction of traveling, zc(tL-1) is the absolute coordinate of the last but
one detected plant’s centre position and the tL and tL-1 are the time stamps
when the detection unit was above the centre position of the last and last but
one plant successively.
Knowing the distance and the forward speed it is possible to estimate the time
in which the hoeing tool will arrive to the position exactly above the plant centre
position.
T
z ( t ) − z( t) − D
V
c L−1 sens−hoe = (5.19)
where z(t) corresponds to the absolute position of the detection unit for plant
detection.
At that moment the angular position of the hoeing tool φrecent needs to be
acquired. Because of non uniform distances between plants it can happen that
one very fast rotating sequence can be followed by one very slow sequence or
vice versa. It means that the system needs to accelerate or decelerate
depending on the recent angular position of the hoeing tool. As the angular

Results and discussion
position of the tool is acquired with an incremental encoder the most recent
position of the system can be calculated as the reminder value after dividing the
value acquired from encoder N with the number of pulses corresponding to one
rotation RESsys. To make a decision of whether the hoeing tool needs to be
accelerated or decelerated a simple rule was applied: in cases where the latest
position is between 0° and 180°, the system needs to be decelerated and
between 180° and 360° it needs to be accelerated as defined in Equation 5.20.
⎧ ⎛ N ⎞ R ⎛ sys N ⎞
⎪rest ⎜ ⎟ for ≥ rest ⎜ ⎟ ≥ 0
⎪
⎜ RES ⎟
sys 2 ⎜ RES ⎟
⎝ ⎠ ⎝ sys ⎠
ϕ recent = ⎨
(5.20)
⎪ ⎛ N ⎞ ⎛ N ⎞ RESsys
⎪rest ⎜ − R sys for Rsys > rest >
⎜
⎟ ⎜ ⎟
RES ⎟ ⎜
sys RES ⎟
⎩ ⎝ ⎠ ⎝ sys ⎠ 2
where rest is defined as:
x / y = q * y + rest
(5.21)
The required rotation speed value ωnew can be estimated based on the recent
angular position and the remaining time until the hoeing tool arrives to the
position exactly above the next plant centre position, as follows:
ωnew
ϕ − ϕ
T
desired recent
= (5.22)
The required rotational speed need to be transformed into voltage and sent to
the servo drive as an external control signal. If there is only a small difference
between unew and uold it is better to leave the servo system in the steady state,
achieved after the previous adjustment, than to change the input voltage and
upset the system from the steady state. A size of the difference between unew
and uold which will be considered as small can be defined in the VI and it
depends on the size of the protected area, number of arms, number of cuts
between successive plants and angular adjustment of the arms.
The flow chart of the algorithm for the hoeing tool’s rotational speed control is
illustrated in Figure 5.22.
97

Results and discussion
98
V =
zc( tL-1 ) – zc( tL
)
t L-1 – tL
ω new
ω old
Figure 5.22 Algorithm for the online control of the hoeing tool’s
rotational speed

5.3.5 Test bench for evaluation of the intra-row hoeing
tool
Results and discussion
After the engine for the hoeing tool was chosen and the controlling algorithm
was elaborated, a test bench which allows evaluation of the prototype for intra-
row weed control was built. A movable carrier for the servo system with
attached hoeing tool was constructed over the soil box described in Chapter
4.1.3. The carrier was designed with requirement to allow adjustment of the
hoeing system vertically and horizontally. Vertical adjustment of the carrier
allows testing of different arm lengths and hoeing depths, while horizontal
adjustment makes possible cultivation of several parallel plant rows. The design
of the carrier is presented in the Figure 5.23.
Figure 5.23 Carrier vehicle of the hoeing tool (1 – carrier of the servo
motor allowing vertical V and horizontal H adjustment of
the hoeing tool in relation to the soil box)
The carrier vehicle was attached with steel ropes (8) through a driving pulley (6)
to an electro motor (5) providing its forward-backward positioning over the soil
box (1) (see Figure 5.24). The transmission between the motor and the pulley
allowed continuous change of the speed and thus the forward speed of the
carrier was adjustable in range 0.1 – 0.4 m/s. The maximal forward speed for
the hoeing system with three arms and hoeing strategy when one full rotation
99

Results and discussion
corresponds to three cuts between every two plants in the row can be
calculated using the following equitation:
V
max
100
d
= (5.23)
T
min
where d is the average intra-row distance between plants and Tmin the time in
which one full rotation of the system can be achieved with rated rotational
speed.
u = 60 rpm = 1 rps → T = 1 s
(5.24)
rated min
In Table 5.6 maximum forward speeds of the carrier for several intra-row
distances are given.
Table 5.6 Maximum forward speeds of the carrier for several intrarow
distances
d [mm] 50 100 200 300 400 500
Vmax [m/s] 0,05 0,1 0,2 0,3 0,4 0,5
Vmax [km/h] 0,18 0,36 0,72 1,08 1,44 1,8

Results and discussion
Figure 5.24 Scheme of the hoeing tool’s prototype placed on the soil
box (1 – soil box; 2 – carrier of the hoeing tool; 3 –
hoeing tool; 4 – small carrier with the system for plant
detection; 5 – electromotor allowing forward-backward
motion of the hoeing tool along the soil box; 6 – driving
pulley; 7 – guiding jockey pulley; 8 – steel ropes)
101

Results and discussion
102
5.3.6 Methodology for evaluation of the algorithm for
online control of the hoeing tool
For the controlling of the hoeing tool’s rotational speed, a VI was developed
based on the algorithm presented in the previous chapter and implemented
together with the VI-s for plant centre position detection in a final software
solution.
After the hoeing, the controlling VI generates a report file containing information
about TRUE/FALSE values measured with sensors for plant detection,
estimated forward speed, position of the last recognised plant, calculated
rotation speed and angular position of the hoeing tool for every sampling step.
For graphical analysis of the acquired data an M-file in Matlab was written. This
function provide automatic graphical interpretation of the data from the report
file, creating three graphs: the first contains the forward speed of the carrier and
the rotational speed of the hoeing tool; the second contains angular positions of
the hoeing tool and estimated centre positions of detected plants; and the third
contains the positions at which plants have been detected. In the second graph
positions under the soil surface are particularly pointed out for each duckfoot
knife.
Quantitative evaluation of the hoeing accuracy can be done when distances
between the hoeing tool blades under the soil surface and nearest plant middle
position are known. The analysis can be classified in two groups: the duckfoot
knife trajectories approaching the plants from the front side and duckfoot knife
trajectories approaching the plants from the rear side. In the hoeing strategy
with three cuts between plants, as described in chapter 5.3.3, duckfoot knife
number 3 always approaches the plant from the front and duckfoot knife
number 1 from the rear. The distance between the hoeing tool blade and the
centre position of the plant can be calculated as:
R = z + x (5.25)
2 2
front front front
R = z + x (5.26)
2 2
rear rear rear

Results and discussion
where zfront is the projection of the distance between the blade of the duckfoot
knife number 3 and estimated centre position of the plant to z-axis, xfront is the
projection of the distance between the blade of the duckfoot knife number 3 and
estimated centre position of the plant to x-axis, zrear is the projection of the
distance between the blade of the duckfoot knife number 1 and estimated
centre position of the plant to z-axis, xrear is the projection of the distance
between the blade of the duckfoot knife number 1 and estimated centre position
of the plant to x-axis (see Figure 5.25).
x
x front
z front
R front
z c
R rear
Figure 5.25 Position of duckfoot knife trajectories approaching the
crop plant with projection of their distances from the
plant centre position to coordinate axis
The x component can be estimated as the modified projection of the arm length
to the x-axis as described in Chapter 5.2.2. The correction is necessary
because the motion equations introduced in Chapter 5.2.2 were based on a
simplified kinematical model of the hoeing tool. To avoid complex calculation of
the blade’s exact position, the modifying parameters which allow estimation of
the blade’s exact position were determined by experimental research. Finally,
another M-file was written to summarise the distribution of distances between
the blades and plants.
z rear
x rear
5.3.7 Evaluation of the algorithm for online control of the
hoeing tool by experimental testing
Experimental testing of the physical prototype was done in order to verify the
robustness of the system and controlling VI-s. The first test was the test of the
z
103

Results and discussion
hoeing accuracy of the system with nearly continuous forward speed. Average
distance between plants was 200 mm, arm length 495 mm, maximum hoeing
depth 18 mm, plant searching area size in the detection VI was set to 50 mm,
minimum size of the recognised object which will be considered as a plant 15
mm and 1 allowed FALSE sample inside the plant area. The graphical
interpretation of the results of one typical test is shown in the Figure 5.26.
Forward speed [m/s]
104
Angular position [deg]
0.048
0.04
0.032
0.024
0.016
0.008
360
270
180
Forward speed
Rotation speed
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0
90
Detected plants
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Forward position [mm]
Figure 5.26 Report graph attained after the experiment with constant
speed (| Estimated middle position of the plant; +
Angular position of the hoeing tool; � Detected plant; �
Duckfoot2 under the soil surface; � Duckfoot3 under the
soil surface; � Duckfoot1 under the soil surface)
0.03
0.025
0.02
0.015
0.01
0.005
Rotation speed [rps]

Results and discussion
In the second graph angular positions of the hoeing tool in relation to the
forward position of the system, with highlighted position of the duckfoot knife
trajectories under the soil surface are given. According to the theoretical
approach of the rotational speed controlling, the first cut needs to be done with
the duckfoot knife number 2 (marked with �) and its optimal placement is the
middle position between two plants. The second cut needs to be done with the
duckfoot knife number 3 (marked with �) and its optimal placement is the front
side of the plant (marked with |) to which it approaches. Finally, the third cut
needs to be done with the duckfoot knife number 1 (marked with �) and its
optimal placement is the rear side of the plant. The angular position of the
hoeing tool is marked with + and corresponds to the position of the plan in
which the centres of cutting edges lie, when duckfoot knives are adjusted to 0°.
In the experiments the positions of the duckfoot knives were shifted from the 0°
position with intention to provide bigger protected area. Duckfoot knife 3 was
shifted backwards by 25 mm and duckfoot knife 1 was shifted forwards by 25
mm. This modification is visible on the graph.
Analysing the graph it can be concluded that significant deviation from the
desired hoeing strategy was not present and 3 cuts were performed between
every two plants. The third graph contains data about the positions on which the
sensor for plant detection has generated a TRUE signal (marked with �). The
estimated value of the plant centre position was calculated and highlighted in
graph 2 (marked with |). Graph 3 shows that on all the expected plant positions
sensor equipment generated several signals which provided accurate detection
of all the plants in the test field.
More accurate estimation of the hoeing quality in the areas near to crop plant
was done by transformation of the hoeing trajectories to a relative distance from
the origin of the coordinate system, which corresponds to the plant centre
position (marked with �). The graph a) in Figure 5.27 shows the distribution of
the sampled points under the soil surface transformed to a relative distance
from the origin for duckfoot knife number 3 (marked with �) and duckfoot knife
number 1 (marked with �). It is obvious that the cuts have expressive trend
around the plant centre position. The cuts made with duckfoot knife number 1
look like shifted from the plant. It is caused intentionally to avoid contact
105

Results and discussion
between the arm (duckfoot holder) and foliage system because of asymmetric
shape of the duckfoot knives (see Figure 5.25). The size of the uncultivated
area, which corresponds to the protected area around the plant, can be
described with a circle which diameter is equal to the distance between centre
position of the plant and nearest position of the blade which was detected
during the measurement. In this experiment the nearest detected position of the
blade was 27 mm distanced from the plant centre position, hence an area
around the plant with diameter at least big as dp = 54 mm was left uncultivated.
As the size of the test plants was between 35 and 50 mm, no plant was
endangered by the weeding process. The graph b) shows the mean distances
between duckfoot knife number 3 (marked with �) and duckfoot knife number 1
(marked with �) for every longitudinal position in which they appear, in relation
to the plant centre position (marked with �). For every longitudinal position
beside the mean values, the ranges between the minimum and maximum are
pointed out.
x [mm]
106
100
50
0
-50
-100
+
dp=54 mm
-100 -50 0
z relative [mm]
50 100
Distance from the plant centre position [mm]
140
120
100
80
60
40
20
0
+
-80 -60 -40 -20 0 20 40 60 80
z relative [mm]
Figure 5.27 Estimation of the hoeing quality attained during the
experiment with constant speed a) distribution of the cuts
under the soil surface transformed to a relative distance
from the plant centre position (�) for duckfoot number 3
(�) and duckfoot number 1 (�); b) mean distances of
duckfoot number 3 (�) and duckfoot number 1 (�) in
relation to the plant centre position (�)

Results and discussion
As a final check the obtained data set can be limited to the intra-row area,
taking into consideration that inter-row systems leave an approximately 100 mm
wide non-cultivated strip around the crop row. The estimation of the hoeing
quality using the same methodology as described, only limited to the 100 mm
transversal area around the plant centre position is shown in Figure 5.28.
x [mm]
80
60
40
20
0
-20
-40
-60
-80
+
dp=54 mm
-60 -40 -20 0 20 40 60
z relative [mm]
Distance from the plant centre position [mm]
80
70
60
50
40
30
20
10
0
+
-60 -40 -20 0 20 40 60
z relative [mm]
Figure 5.28 Estimation of the hoeing quality attained during the
experiment with constant speed limited to the 100 mm
transversal area around the plant centre position: a)
distribution of the cuts under the soil surface transformed
to a relative distance from the plant centre position (�)
for duckfoot number 3 (�) and duckfoot number 1 (�);
b) mean distances of duckfoot number 3 (�) and
duckfoot number 1 (�) in relation to the plant centre
position (�)
Analysing the graph b) from Figure 5.28 it can be observed that all the cuts
made by duckfoot knife 3 are roughly distanced in the range from 25 to 65 mm
and the cuts made by duckfoot knife 1 in the range from 55 to 80 mm from the
plant centre position. The distribution of the total number of cuts based on their
107

Results and discussion
distance from the plant centre position is shown on graph a) in Figure 5.29 for
duckfoot knife 3 and on graph b) in Figure 5.29 for duckfoot knife 1.
Distribution [%]
25
20
15
10
108
5
0
20 30 40 50 60 70
Distance from the plant centre position [mm]
25
Duckfoot 3 Duckfoot 1
Distribution [%]
20
15
10
5
0
50 55 60 65 70 75 80 85
Distance from the plant centre position [mm]
Figure 5.29 Distribution of the total number of cuts based on their
distance from the plant centre position for the experiment
with constant speed
The distribution graphs confirm the expected high accuracy of the hoeing
system. For duckfoot knife 3 around 85% of the cuts were normally distributed
inside the area distanced from the plant centre position between 30 and 55 mm.
For duckfoot knife 1 results are even better, all the cuts were ranged between
55 and 80 mm with distribution looks like normal.
To certify the conclusions, drawn out from the hoeing experiment presented
above, the experiment was performed 8 more times under same conditions as
the first one and analysed. These 9 experiments correspond to an approximate
row length of 47 m and 235 plants. The conditions which were kept constant
during this series of experiment were: constant forward speed V=const., hoeing
dept hdmax= 15 mm, arm length 495 mm and average distance between plants
200 mm. It needs to be emphasised that the distance between plants was not
exactly 200 mm, but randomly disposed from 170 to 230 mm. In Figure 5.30
cumulative results for the experiments with constant speed are presented.

x [mm]
100
80
60
40
20
0
-20
-40
-60
-80
-100
+
dp=50 mm
-80 -60 -40 -20 0 20 40 60 80
z relative [mm]
Distance from the plant centre position [mm]
90
80
70
60
50
40
30
20
10
0
Results and discussion
-80 -60 -40 -20 0 20 40 60 80
z relative [mm]
Figure 5.30 Estimation of the hoeing quality attained during a series
of experiments with constant speed, limited to the 100
mm transversal area around the plant centre position: a)
distribution of the cuts under the soil surface transformed
to a relative distance from the plant centre position (�)
for duckfoot number 3 (�) and duckfoot number 1 (�);
b) mean distances of duckfoot number 3 (�) and
duckfoot number 1 (�) in relation to the plant centre
position (�)
Again, cuts have expressive trend around the plant centre position, keeping a
protected area with a diameter at least equal to dp= 50 mm. For duckfoot knife
3 cuts are ranged from 25 to 80 mm, while for duckfoot knife 1 they are ranged
between 40 and 85 mm. Distribution graphs of the cuts are given in Figure 5.31.
+
109

Results and discussion
Distribution [%]
30
25
20
15
10
110
5
0
20 30 40 50 60 70 80
Distance from the plant centre position [mm]
40
Duckfoot 3 Duckfoot 1
35
Distribution [%]
30
25
20
15
10
5
0
49 56 63 70 77 84 91 98 105
Distance from the plant centre position [mm]
Figure 5.31 Distribution of the total number of cuts based on their
distance from the plant centre position for a series of
experiments with constant speed
For duckfoot knife 3, around 91 % of the cuts were normally distributed inside
the area distanced from the plant centre position between 30 and 55 mm, while
90 % of the cuts done by duckfoot knife 1 were ranged between 60 and 90 mm
with normal distribution.
Another important aspect of the evaluation was to observe the behaviour of the
system and the distribution of the cuts when the forward speed accelerates or
decelerates. Experiments were done with conditions similar to previous
experiments: hoeing dept hdmax=15 mm, arm length 495 mm and average
distance between plants 200 mm. Only the forward speed was changed during
the forward motion of the system. Graphical results of two experiments with
acceleration and deceleration are shown in Figure 5.32 and Figure 5.35.

Forward speed [m/s]
Angular position [deg]
0.12
0.1
0.08
0.06
0.04
0.02
360
270
180
Results and discussion
Forward speed
Rotation speed
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 0
90
Detected plants
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Forward position [mm]
Figure 5.32 Report graph attained after the experiment with
acceleration and deceleration of the forward speed (|
Estimated middle position of the plant; + Angular position
of the hoeing tool; � Detected plant; � Duckfoot2 under
the soil surface; � Duckfoot3 under the soil surface; �
Duckfoot1 under the soil surface)
Cumulative results of the experiments with acceleration and deceleration of the
forward speed are shown in Figure 5.33. The cuts have a trend around the plant
centre position more fuzzy than in the experiment with constant forward speed
and the uncultivated area is this time smaller dp= 32 mm. For duckfoot knife 3
0.06
0.05
0.04
0.03
0.02
0.01
Rotation speed [rps]
111

Results and discussion
cuts are ranged from 16 to 70 mm, while the cuts done by duckfoot knife 1 were
ranged between 40 and 105 mm with normal distribution.
x [mm]
-100
-60 -40 -20 0 20 40 60 80 100
z relative [mm]
112
100
80
60
40
20
0
-20
-40
-60
-80
+
dp=32 mm
Distance from the plant centre position [mm]
100
90
80
70
60
50
40
30
20
10
0
+
-100 -80 -60 -40 -20 0 20 40 60 80 100
z relative [mm]
Figure 5.33 Estimation of the hoeing quality attained after the
experiment with acceleration and deceleration of the
forward speed, limited to the 100 mm transversal area
around the plant centre position: a) distribution of the
cuts under the soil surface transformed to a relative
distance from the plant centre position (�) for duckfoot
number 3 (�) and duckfoot number 1 (�); b) mean
distances of duckfoot number 3 (�) and duckfoot number
1 (�) in relation to the plant centre position (�)
For duckfoot knife 3, around 97 % of the cuts were normally distributed inside
the area distanced from the plant centre position between 15 and 60 mm, while
around 90 % of the cuts done by duckfoot knife 1 were ranged between 50 and
90 mm with normal distribution as shown in Figure 5.34.

Distribution [%]
30
25
20
15
10
5
Duckfoot 3
0
0 20 40 60 80
Distance from the plant centre position [mm]
Distribution [%]
30
25
20
15
10
5
Results and discussion
Duckfoot 1
0
20 40 60 80 100 120
Distance from the plant centre position [mm]
Figure 5.34 Distribution of the total number of cuts based on their
distance from the plant centre position for a series of
experiments with the experiment with acceleration and
deceleration of the forward speed
Another result attained from an experiment with intensive acceleration of the
forward speed is shown in Figure 5.35. The aim of this experiment was to prove
if the system could be stabilised after a significant change of the speed. In
graph 2 (Figure 5.35) response of the system with delay can be observed for
the first time. The cuts were shifted toward the forward direction endangering
the protected area, what is visible on the highlighted part of the trajectory.
113

Results and discussion
Forward speed [m/s]
114
Angular position [deg]
0.12
0.1
0.08
0.06
0.04
0.02
360
270
180
Forward speed
Rotation speed
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 0
90
Detected plants
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Forward position [mm]
Figure 5.35 Report graph attained after the experiment with intensive
acceleration of the forward speed (| Estimated middle
position of the plant; + Angular position of the hoeing
tool; � Detected plant; � Duckfoot2 under the soil
surface; � Duckfoot3 under the soil surface; �
Duckfoot1 under the soil surface)
Analysing the results it is obvious that three crop plants ( plant 4, plant 5 and
plant 6) were cut and two (plant 7 and plant 8) were damaged because duckfoot
knife 3 was inside the protected area or even passed the plant from the front
side (see Figure 5.36).
0.06
0.05
0.04
0.03
0.02
0.01
Rotation speed [rps]

x [mm]
80
60
40
20
0
-20
-40
-60
-80
+
-50 0
z relative [mm]
50 100
Distance from the plant centre position [mm]
120
100
80
60
40
20
0
Results and discussion
-50 0 50
z relative [mm]
100
Figure 5.36 Estimation of the hoeing quality attained after the
experiment with intensive acceleration of the forward
speed, limited to the 100 mm transversal area around
the plant centre position: a) distribution of the cuts under
the soil surface transformed to a relative distance from
the plant centre position (�) for duckfoot number 3 (�)
and duckfoot number 1 (�); b) mean distances of
duckfoot number 3 (�) and duckfoot number 1 (�) in
relation to the plant centre position (�)
After removing the data acquired in the range of 450 to 1650 mm from the data
set, it is possible to analyse the quality of the hoeing on the remaining track. It
allows checking of the system behaviour after hasty change of the speed. The
graphs containing filtered data are presented in Figures 5.37 and 5.38.
Obviously, the system had successfully overcome the difficulties caused by the
highly dynamical speed change and immediately after the decrease of the
acceleration it was able to stabilise the rotational speed and correctly position
the cuts around the following plants. The smallest undisturbed area with a
diameter equal to dp= 46 mm, and normally distributed cuts done by both of the
duckfoot knives can indicate the stable work of the hoeing system.
+
115

Results and discussion
x [mm]
Distribution [%]
80
60
40
20
-20
-40
-60
-80
116
0
-60 -40 -20 0 20 40 60 80 90
z relative [mm]
20
15
10
5
+
dp=46 mm
Distance from the plant centre position [mm]
90
80
70
60
50
40
30
20
10
0
+
-60 -40 -20 0 20 40 60 80 90
z relative [mm]
Figure 5.37 Estimation of the hoeing quality for the filtered data set
acquired after intensive change of the forward speed,
limited to the 100 mm transversal area around the plant
centre position: a) distribution of the cuts under the soil
surface transformed to a relative distance from the plant
centre position (�) for duckfoot number 3 (�) and
duckfoot number 1 (�); b) mean distances of duckfoot
number 3 (�) and duckfoot number 1 (�) in relation to
the plant centre position (�)
Duckfoot 3
0
20 30 40 50 60 70
Distance from the plant centre position [mm]
Distribution [%]
35
30
25
20
15
10
5
Duckfoot 1
0
20 40 60 80 100
Distance from the plant centre position [mm]
Figure 5.38 Distribution of the total number of cuts based on their
distance from the plant centre position for filtered data
set acquired after intensive change of the forward speed

Results and discussion
Finally, experiments with intra-row distances changed to d= 400 mm were
conducted to prove applicability of the hoeing system to different crop plant
species. The distance d= 400 mm was chosen because this distance required
the smallest transformation of the experimental field from previous configuration
when distance between plants was d= 200 mm. However, the hoeing system
can be adapted to any inter-row distance between plants, using the same
adjustment methodology as it is shown in the Figure 5.39.
Forward speed [m/s]
Angular position [deg]
0.1
0.05
360
270
180
Forward speed
Rotation speed
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0
90
Detected plants
0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Forward position [mm]
Figure 5.39 Report graph attained after the experiment with changing
forward speed on the crop system with 400 mm average
distance between plants (| Estimated middle position of
the plant; + Angular position of the hoeing tool; �
Detected plant; � Duckfoot2 under the soil surface; �
Duckfoot3 under the soil surface; � Duckfoot1 under the
soil surface)
0.03
0.015
Rotation speed [rps]
117

Results and discussion
The experiments on the test field with 400 mm average distance between plants
were performed 3 times under the following conditions: hoeing dept hdmax=15
mm, arm length 495 mm and constant forward speed. These 3 experiments
correspond to an approximate row length of 15 m and 42 plants.
x [mm]
118
100
50
0
-50
-100
+
dp=120 mm
-100 -50 0
z relative [mm]
50 100
Distance from the plant centre position [mm]
120
100
80
60
40
20
0
+
-100 -50 0
z relative [mm]
50 100
Figure 5.40 Estimation of the hoeing quality attained during a series
of experiments on the field with 400 mm average intrarow
distance between plants, limited to the 100 mm
transversal area around the plant centre position: a)
distribution of the cuts under the soil surface transformed
to a relative distance from the plant centre position (�)
for duckfoot number 3 (�) and duckfoot number 1 (�);
b) mean distances of duckfoot number 3 (�) and
duckfoot number 1 (�) in relation to the plant centre
position (�)
This series of results shows that cuts have expressive trend around the plant
centre position, with a bigger uncultivated area with diameter dp= 120 mm (see
Figure 5.40). It was assumed that plants in the field with bigger intra-row
distance would also have a bigger size. For duckfoot knife 3 cuts are ranged
from 60 to 105 mm, while for duckfoot knife 1 they are ranged between 85 and

Results and discussion
120 mm. However, by adjusting the angular position of duckfoot knives
approaching the plant from the front and rear side any size of the protected area
can be achieved. Distribution graphs of the cuts are given in Figure 5.41. In this
case, as before, cuts done by duckfoot knife 3 and duckfoot knife 1 are normally
distributed.
Distribution [%]
40
35
30
25
20
15
10
5
0
Duckfoot 3
60 70 80 90 100
Distance from the plant centre position [mm]
Distribution [%]
25
20
15
10
5
Duckfoot 1
0
80 90 100 110 120
Distance from the plant centre position [mm]
Figure 5.41 Distribution of the total number of cuts based on their
distance from the plant centre position for the hoeing the
field with 400 mm average intra-row distance between
plants
119

6 Summary
Summary
Neither a mechanical nor a physical system for effective intra-row weed control
in row crops has been commercialised until today. Online detection of single
plant position and plant/weed distinction are the bottlenecks of intra-row
weeding systems, but considering the rapidly growing research and
development in this field, it is expected that appropriate systems will soon be
available. In the meantime, construction and adjustment possibilities of
implements which take into account the role of soil properties and mechanics
need to be optimised toward universal intra-row weeding tools, which can be
used in different plant spacing systems, different plant intra-row distances and
growth stages.
In this work development of a universal intra-row weeding tool is presented from
the idea to the first prototype.
Accurate intra-row weed control requires either detection of the position of every
crop plant or detection of every weed. In this work a simplified methodology and
system for plant position detection based on the spectral characteristics of crop
plants combined with the context information of the planting pattern was
developed and tested.
The experimental results showed that the combination of the RGB sensor and
laser sensor can be used for accurate detection of the plant centre position
independently from illumination conditions. In the conducted experiments the
maximum deviation of the estimated centre positions from the plant measured
centre positions, detected by RGB sensor, was 31 mm, whereby 50% of the
samples were inside the interval 0 to 5 mm and 90% of the samples were inside
the interval 0 to 16,9 mm. For the laser sensor, the maximum deviation of the
estimated centre positions from the plant measured centre positions was 25
mm, whereby 50% of the samples were inside the interval 0 to 3 mm and 90%
of the samples were inside the interval 0 to 6.9 mm.
The main shortage of the presented system is that sensors require ad-hoc
parameter adjustment before the measurement to provide good plant detection.
121

Summary
For broad usage of the system, parameter adjustments need to be replaced by
a more systematic method.
The second part of the work deals with the development of a virtual prototype of
a system for intra-row weeding, emulating the manual hoeing motions under the
soil surface. The hoeing tool consists of an arm holder and three or more
integrated arms rotating around the horizontal axis above the crop row. The
hoeing tool is attached to the motor shaft and the working height of the whole
assembly is adjustable in order to provide optimal hoeing depth, which should
be between 20 and 30 mm. There is a possibility to change the arms’ length
and their angular position in relation to the surface perpendicular to the rotation
axis in which the arm holder is placed. Depending on the duckfoot knives’
shape and size the necessary number of cuts between two plants could be set,
controlling the rotational speed of the hoe.
The virtual prototype of the hoe was designed in Pro/engineer®. Kinematical
behaviour of the developed prototype in different hoeing strategies was
simulated and tested. The hoeing trajectories of the design variants consisting
of 3, 4 or 9 arms were simulated in hoeing strategies with 2 and 3 cuts between
every two plants. The optimal length of the arms was estimated and the hoeing
depth/width ratio was discussed according to the roughness of the soil surface.
The influence of the arms’ angular adjustment to the hoeing trajectories was
examined for different arm lengths and selection of the appropriate design
variant according to the hoeing strategy was discussed. The aim of the
comprehensive analysis of the weeding tool’s kinematical behaviour was to
verify that the newly designed hoeing system can be adapted to different intra-
row distances and growth stages of the crop plants.
Most of the kinematical problems were discovered and worked out in the virtual
prototyping process, providing easier development of the physical prototype
with very few errors. Finally, one of the major benefits of virtual prototyping was
reduction of the costs and time needed for development of the physical
prototype.
122

Summary
The third part of the work was development and testing of the physical
prototype realised using a servo motor in laboratory conditions. The servo motor
was sized according to the experimental measurement of the torque in field
conditions. The motor was selected in combination with transmission gear to
cover the full range of speeds and torques which were expected in laboratory
experiments. The experimental research was done in a soil box with different
soil types. Forward-backward moving, vertical and horizontal positioning of the
hoeing tool above the soil box was realised through a carrier with a separate
electro motor.
The servo system was operated in a mode with direct software control providing
rotational speed adjustment according to the forward speed of the carrier, intra-
row distance between successive crop plants and the observed angular position
of the arms. The controlling algorithm and software solution were developed in
the Labview® environment. The main task of the controlling software was
permanent calculation, checking and change of the recent rotational speed of
the hoeing tool in real time. The software solution used an expanded version of
the software previously developed for detection of the plants’ centre position.
For well-timed execution of different subfunctions of the software in the time
schedule, the optimal distance between the plant detection unit and the plane in
which the hoeing tool is positioned was discussed and a methodology for its
calculation was proposed. The speed controlling algorithm was thoroughly
explained. Finally, the controlling algorithm, stability and robustness of the
prototype were comprehensively evaluated by experimental testing. Tests were
done with continuous and changeable forward speed on the crops rows with
200 mm and 400 mm intra-row distances.
Tests have proved that depending on the angular adjustment of the duckfoot
knives an uncultivated area big enough to avoid damaging of the plants can be
left around the plants during the intra-row weeding with the developed system.
The system is able to autonomously adapt the rotational speed of the hoeing
tool in case of non-intensive forward speed change. After intensive forward
speed change, depending on the intensity level, several plants could be
123

Summary
damaged while the system stabilises its work, but stable state can be reached
immediately after the stabilisation period.
After parameters adjustment in the controlling software system was successfully
adapted for intra-row hoeing of the row with different sowing pattern.
124

7 Conclusions
Conclusions
The presented concept of the intra-row hoeing system can fulfil the
requirements; it has sufficient degrees of freedom to allow full adaptation to
different crop species, different plant intra-row distances and plant growth
stages. In combination with an inter-row hoe or installed on an autonomous
vehicle, the developed robotic system could be a solution for accurate and rapid
mechanical weed control.
Concept for detection of the plants centre position developed in this research
needs to be concerned as an interim solution for plant detection. However,
results achieved during experiments in laboratory conditions certify about
stabile and accurate detection of the plant centre position in real-time
independently from illumination conditions.
Tests and simulations carried out with the virtual prototype have increasingly
facilitated the design process and significantly shortened the path from the idea
to the prototype. Furthermore, the virtual prototype of the intra-row hoeing tool
provides an expeditious and accurate procedure for determination of the optimal
hoeing scenario if the conditions on the field, like the intra-row plant distance,
average growth stage of the plants and the size of the duckfoot knives, are
known.
Finally, experimental tests conducted with the hoeing tool’s physical prototype
proved the hypothesis that the hoeing system based on servo motor in
combination with the detection of the plant centre position can provide accurate
intra-row hoeing controlled in real-time.
Further work
The presented methodology for detection of the single plant centre position is
as mentioned before only an interim solution. For development of this solution
to a higher level, applicable in field conditions, it requires a comprehensive
revision. The main shortage is that the implemented sensors require ad-hoc
parameter adjustment according to the field conditions before the
measurement, to provide desired plant detection quality. For broad usage of a
125

Conclusions
system, parameter adjustments need to be replaced by a more systematic
method.
The sensors utilised during the tests are intended for industrial detection.
However, it needs to be confirmed that intensive application in hard on the field
conditions will not cause any negative influence on their robust work. The digital
RGB fibre optic sensor CZ-H35S uses a light emitter with a spot diameter of 4.5
mm. Parallel implementation of several sensors covering an area at least as
wide as the laser sensor is required for reliable detection.
The developed prototype for intra-row weed control could be used in future for
accurate measurement of the torque. The potentials for optimisation of the
torque are highly influenced by the size and shape of the duckfoot knives. Their
behaviour in different conditions needs to be investigated. Change of the soil
moisture content, change of the hoeing speed, change of the forward speed,
change of the hoeing depth are only several aspects which can deliver new
substantial knowledge very important for further development of the mechanical
intra-row weed control.
A possibility for improvement of the controlling algorithm will be substitution of
the currently used P with PI, PID or another more sophisticated one, like a fuzzy
or neural network based algorithm.
Finally, testing of the intra-row weeding system combined with an inter-row
system in field condition will be of great importance.
126

8 References
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134

List of figures
List of figures
Figure 1.1 Total quantity of herbicides sold in the 15 EU states in the period from
1992 to 2001 ......................................................................................... 4
Figure 1.2 Total quantity of herbicides sold in some of the EU member states in the
period from 1990 to 2001 ...................................................................... 5
Figure 1.3 Inter_row Mutsaers QI type 500 ...........................................................18
Figure 2.1 a) Finger weeder b) Torsion weeder ....................................................20
Figure 2.2 Concept and prototype of the intra-row hoeing developed at Halmstadt
University ............................................................................................21
Figure 2.3 Hoeing system based on geo-referenced control of the Osnabrück hoe
.............................................................................................................22
Figure 2.4 Clycloid trajectories of the Osnabrück hoe for a) rotational speed is
equal to the forward speed b) rotational speed is 1.25 times higher than
the forward speed c) rotational speed is 1.5 times higher than the
forward speed .....................................................................................23
Figure 2.5 Illustration of the hoeing concept using the rotating disk ......................25
Figure 2.6 Rotating disk weeder a) the toolframe with two rotary cultivator units
without inter-row cultivation blades b) the toolframe with one rotary
cultivator without inter-row cultivation blades mounted on the front of the
tractor...................................................................................................25
Figure 3.1 Areas in the row crop field....................................................................29
Figure 3.2 Deviations from the expected plant/weed distribution pattern in field
conditions.............................................................................................30
Figure 4.1 a) RGB fibre optic sensor CZ-H35S and RGB digital fibre optic amplifier
CZ – V21P b) RGB fibre optic sensor in working position with illustrated
optimal detection range and spot diameter of the light source..............35
Figure 4.2 a) Laser sensor head LV – H47 with appropriate LV- 21AP amplifier b)
Sensor head in working position with illustrated optimal detection range
and corresponding width of the area covered by laser..........................36
Figure 4.3 a) Joint carrier of plant detection sensors b) Toolframe with wheels
allowing accurate following of the soil surface ......................................37
Figure 4.4 Test objects used in experiments for detection of the plant centre
position ................................................................................................41
Figure 4.5 Concept of a servo system in a closed loop .........................................50
Figure 4.6 Typical change of the torque intensity for trapezoidal response of the
speed .....................................................................................................
.............................................................................................................54
Figure 4.7 Experimental determination of hoeing tool’s inertia ration.....................55
Figure 4.8 Experimental determination of hoeing tool’s inertia ration with zoomed
area of interest .....................................................................................55
Figure 5.1 Interpretation of different plants with arrays of TRUE and FALSE signals
................................................................................................................
.............................................................................................................58
Figure 5.2 Algorithm of the plant centre position detection ....................................60
135

List of figures
Figure 5.3 Block diagram of the VI for detection of the plant centre position......... 62
Figure 5.4 Dispersion of the impulses generated for every detected object (10 mm
wood sticks) a) with RGB sensor sampling distance 5 mm b) with laser
sensor sampling distance 5 mm c) with RGB sensor sampling distance
2mm d) with laser sensor sampling distance 2 mm ............................. 64
Figure 5.5 Rotary intra-row hoeing concept ......................................................... 72
Figure 5.6 Design variants of the arm holder........................................................ 73
Figure 5.7 Exploded view of a one-arm hoeing tool assembly with indicated joints
............................................................................................................ 74
Figure 5.8 Design variants of the hoeing tool assembly........................................ 74
Figure 5.9 Kinematics and design of the rotary hoe.............................................. 75
Figure 5.10 Trajectory of the duckfoot knife under the soil surface with minimum and
maximum hoeing depth ....................................................................... 76
Figure 5.11 Hoeing trajectories of the hoeing tool with nine arms in a field with 300
mm intra-row distance between plants (arm length 440 mm, angular
position of all arms adjusted to 0°, � - position of the plant)................ 79
Figure 5.12 Hoeing trajectories of the hoeing tool with nine arms in a field with 200
mm intra-row distance between plants (arm length 520 mm, angular
position of all arms adjusted to 0°, � - position of the plant)................ 80
Figure 5.13 Hoeing trajectories of the hoeing tool with nine arms in a field with 200
mm intra-row distance between plants (arm length 520 mm, angular
position of all arms adjusted to fr=17°, mi=0°, re= –17°, � - position of
the plant)............................................................................................. 81
Figure 5.14 Hoeing trajectories of the hoeing tool with nine arms in a field with 200
mm intra-row distance between plants (arm length 440 mm, angular
position of all arms adjusted to fr=17°, mi=0°, re= –17°, � - position of
the plant)............................................................................................. 82
Figure 5.15 The shape and dimensions of one duckfoot knife................................ 83
Figure 5.16 Hoeing trajectories of the hoeing tool with three arms providing two cuts
between following plants in a field with 200 mm intra-row distance
between plants (arm length 440 mm, angular position of all arms
adjusted to duckfoot1=17°, duckfoot2=0°, duckfoot3= –17°, � - position
of the plant) ......................................................................................... 84
Figure 5.17 Hoeing trajectories of the hoeing tool with four arms providing two cuts
between following plants in a field with 200 mm intra-row distance
between plants (arm length 440 mm, angular position of arms adjusted
to: duckfoot1= 20°, duckfoot2= -20°, duckfoot3= 20°, duckfoot4= -20° ,
� - position of the plant)...................................................................... 85
Figure 5.18 Results of the field experiment providing insight into the size of the
torque required for undisturbed hoeing with hoeing tool with three arms
in extreme conditions .......................................................................... 88
Figure 5.19 Definition of the desired angular position of the hoeing tool with three
arms a) when it is in the start position and b) when it is placed exactly
above a crop plant............................................................................... 92
Figure 5.20 Time schedule of the VI-s execution during the real time detection of the
plant position....................................................................................... 93
Figure 5.21 Definition of the required distance between the plant detection unit and
the plane in which the hoeing tool is placed ........................................ 94
Figure 5.22 Algorithm for the online control of the hoeing tool’s rotational speed ... 98
Figure 5.23 Carrier vehicle of the hoeing tool......................................................... 99
136

List of figures
Figure 5.24 Scheme of the hoeing tool’s prototype placed on the soil box ...........101
Figure 5.25 Position of duckfoot knife trajectories approaching the crop plant with
projection of their distances from the plant centre position to coordinate
axis ....................................................................................................103
Figure 5.26 Report graph attained after the experiment with constant speed .......104
Figure 5.27 Estimation of the hoeing quality attained during the experiment with
constant speed...................................................................................106
Figure 5.28 Estimation of the hoeing quality attained during the experiment with
constant speed limited to the 100 mm transversal area around the plant
centre position ...................................................................................107
Figure 5.29 Distribution of the total number of cuts based on their distance from the
plant centre position for the experiment with constant speed..............108
Figure 5.30 Estimation of the hoeing quality attained during a series of experiments
with constant speed, limited to the 100 mm transversal area around the
plant centre position ...........................................................................109
Figure 5.31 Distribution of the total number of cuts based on their distance from the
plant centre position for a series of experiments with constant speed 110
Figure 5.32 Report graph attained after the experiment with acceleration and
deceleration of the forward speed .....................................................111
Figure 5.33 Estimation of the hoeing quality attained after the experiment with
acceleration and deceleration of the forward speed, limited to the 100
mm transversal area around the plant centre position ........................112
Figure 5.34 Distribution of the total number of cuts based on their distance from the
plant centre position for a series of experiments with the experiment with
acceleration and deceleration of the forward speed ...........................113
Figure 5.35 Report graph attained after the experiment with intensive acceleration of
the forward speed .............................................................................114
Figure 5.36 Estimation of the hoeing quality attained after the experiment with
intensive acceleration of the forward speed, limited to the 100 mm
transversal area around the plant centre position...............................115
Figure 5.37 Estimation of the hoeing quality for filtered data set acquired after
intensive change of the forward speed, limited to the 100 mm
transversal area around the plant centre position...............................116
Figure 5.38 Distribution of the total number of cuts based on their distance from the
plant centre position for filtered data set acquired after intensive change
of the forward speed ..........................................................................116
Figure 5.39 Report graph attained after the experiment with changing forward speed
on the crop system with 400 mm average distance between plants ..117
Figure 5.40 Estimation of the hoeing quality attained during a series of experiments
on the field with 400 mm average intra-row distance between plants,
limited to the 100 mm transversal area around the plant centre position
...........................................................................................................118
Figure 5.41 Distribution of the total number of cuts based on their distance from the
plant centre position for the hoeing the field with 400 mm average intrarow
distance between plants ..............................................................119
137

Appendix
138
List of tables
Table 5.1 Size of the plants estimated by RGB sensor with 1 and 2 allowed FALSE
values in the plant pattern in the darkness, by daylight and intensive
artificial illumination ................................................................................ 66
Table 5.2 Size of the plants estimated by laser sensor with 1 and 2 allowed FALSE
values in the plant pattern in the darkness, by daylight and intensive
artificial illumination ................................................................................ 67
Table 5.3 Centre position of the plants estimated by RGB sensor with 1 and 2
allowed FALSE values in the plant pattern in the darkness, by daylight and
intensive artificial illumination ................................................................. 68
Table 5.4 Centre position of the plants estimated by laser sensor with 1 and 2
allowed FALSE values in the plant pattern in the darkness, by daylight and
intensive artificial illumination ................................................................. 69
Table 5.5 Calculation of hoeing width in dependence on the arm length and hoeing
depth...................................................................................................... 77
Table 5.6 Maximum forward speeds of the carrier for several intra-row distances 100
Table 9.1 Detection of the plants size in the darkness (1 FALSE value allowed inside
the plant pattern).................................................................................. 139
Table 9.2 Detection of the plants centre position in the darkness (1 FALSE value
allowed inside the plant pattern)........................................................... 140
Table 9.3 Detection of the plants size by daylight (1 FALSE value allowed inside the
plant pattern)........................................................................................ 141
Table 9.4 Detection of the plants centre position by daylight (1 FALSE value allowed
inside the plant pattern)........................................................................ 142
Table 9.5 Detection of the plants size by artificial illumination (1 FALSE value
allowed inside the plant pattern)........................................................... 143
Table 9.6 Detection of the plants centre position by artificial illumination (1 FALSE
value allowed inside the plant pattern) ................................................. 144
Table 9.7 Detection of the plants size in the darkness (2 FALSE value allowed inside
the plant pattern).................................................................................. 145
Table 9.8 Detection of the plants centre position in the darkness (2 FALSE value
allowed inside the plant pattern)........................................................... 146
Table 9.9 Detection of the plants size by daylight (2 FALSE value allowed inside the
plant pattern)........................................................................................ 147
Table 9.10 Detection of the plants centre position by daylight (2 FALSE value allowed
inside the plant pattern)........................................................................ 148
Table 9.11 Detection of the plants size by artificial illumination (2 FALSE value
allowed inside the plant pattern)........................................................... 149
Table 9.12 Detection of the plants centre position by artificial illumination (2 FALSE
value allowed inside the plant pattern) ................................................. 150

Plant
position
9 Appendix
Table 9.1 Detection of the plants size in the darkness (1 FALSE value
allowed inside the plant pattern)
Size [mm]
0 48
Estimated size
RGB [mm]
SRGB
Estimated size
L [mm]
Appendix
1 44 35 8.930 64 2.681
2 37 35 4.323 61 2.000
3 40 32 2.861 53 2.449
4 40 32 2.291 44 2.784
5 42 18 2.947 63 3.708
6 32 30 0.000 48 2.861
7 45 18 3.674 54 5.268
8 45 49 2.165 51 3.742
9 45 48 2.449 34 4.548
10 46 39 1.785 52 3.269
11 35 31 1.785 52 2.487
12 42 40 3.345 53 2.487
13 33 40 4.153 41 6.874
14 38 36 1.785 18 9.811
15 35 36 2.179 45 1.920
16 42 32 2.449 33 2.500
17 40 26 10.232 39 2.550
18 34 23 9.782 42 2.385
19 40 43 2.500 47 5.025
20 48 48 2.487 36 9.434
21 38 34 1.785 58 2.861
22 30 15 5.000 37 3.345
23 40 38 2.449 17 2.487
24 28 23 2.958 31 2.179
25 35 35 3.873 24 4.146
26 35 31 2.550 26 3.631
27 40 17 6.180 44 3.391
28 28 24 7.224 15 6.418
29 28 22 3.905 17 2.784
SL
139

Appendix
Plant
position
140
Table 9.2 Detection of the plants centre position in the darkness (1
FALSE value allowed inside the plant pattern)
Distance to
the next [mm]
Estimated
centre RGB
[mm]
SRGB
Estimated
centre L [mm]
0 195 193 4.323 195 2.681
1 395 (200) 395 3.162 394 2.000
2 600 (205) 601 2.550 603 2.449
3 805 (205) 796 3.841 806 2.784
4 1000 (195) 1002 2.385 1000 3.708
5 1180 (180) 1172 2.385 1181 2.861
6 1395 (215) 1389 2.291 1399 5.268
7 1610 (215) 1598 2.449 1610 3.742
8 1800 (190) 1801 3.112 1801 4.548
9 2005 (205) 1996 2.385 2007 3.269
10 2205 (200) 2200 2.947 2209 2.487
11 2410 (205) 2406 2.385 2415 2.487
12 2600 (190) 2603 3.354 2606 6.874
13 2780 (180) 2804 3.391 2770 9.811
14 3005 (225) 3009 2.784 3011 1.920
15 3215 (210) 3210 2.947 3217 2.500
16 3425 (210) 3403 6.782 3425 2.550
17 3620 (195) 3606 7.562 3621 2.385
18 3820 (200) 3814 3.000 3824 5.025
19 4015 (195) 4014 3.202 4016 9.434
20 4215 (200) 4220 2.947 4218 2.861
21 4435 (220) 4419 3.961 4434 3.345
22 4630 (195) 4613 3.354 4631 2.487
23 4820 (190) 4818 3.700 4822 2.179
24 5015 (195) 5019 2.784 5019 4.146
25 5235 (220) 5230 1.090 5239 3.631
26 5415 (180) 5401 4.975 5416 3.391
27 5630 (215) 5617 4.365 5605 7.071
28 5830 (200) 5827 3.674 5833 2.784
SL

Plant
position
Table 9.3 Detection of the plants size by daylight (1 FALSE value
allowed inside the plant pattern)
Size [mm]
0 48
Estimated size
RGB [mm]
SRGB
Estimated size
L [mm]
Appendix
1 44 31 10.440 66 3.000
2 37 34 4.899 61 2.550
3 40 32 3.674 52 2.385
4 40 30 3.536 45 1.500
5 42 18 2.385 65 1.090
6 32 30 1.090 50 1.920
7 45 18 4.023 58 4.023
8 45 50 1.500 53 2.915
9 45 46 2.165 36 3.391
10 46 36 2.550 51 3.832
11 35 31 2.165 51 1.500
12 42 41 4.062 55 1.090
13 33 39 6.442 34 9.984
14 38 36 2.681 24 19.564
15 35 29 7.176 48 2.947
16 42 32 2.487 34 2.165
17 40 27 11.303 37 3.631
18 34 23 10.770 43 2.487
19 40 42 2.449 47 3.345
20 48 48 2.385 42 2.291
21 38 35 1.500 56 3.961
22 30 14 6.572 39 4.815
23 40 37 3.700 17 2.291
24 28 23 2.385 34 2.000
25 35 33 4.000 28 4.330
26 35 33 2.947 26 4.437
27 40 19 11.023 41 7.628
28 28 29 2.000 25 1.500
29 28 23 2.947 17 2.947
SL
141

Appendix
Plant
position
142
Table 9.4 Detection of the plants centre position by daylight (1 FALSE
value allowed inside the plant pattern)
Distance to
the next [mm]
Estimated
centre RGB
[mm]
SRGB
Estimated
centre L [mm]
0 195 191 4.437 194 2.165
1 395 (200) 395 3.700 394 2.165
2 600 (205) 601 2.385 602 2.487
3 805 (205) 795 1.920 805 1.920
4 1000 (195) 1001 2.681 1001 2.179
5 1180 (180) 1171 2.550 1181 1.500
6 1395 (215) 1387 2.487 1396 2.385
7 1610 (215) 1597 2.915 1608 2.487
8 1800 (190) 1800 1.920 1801 3.122
9 2005 (205) 1997 2.385 2006 2.385
10 2205 (200) 2200 1.920 2207 2.385
11 2410 (205) 2405 1.581 2414 2.000
12 2600 (190) 2601 2.861 2603 5.339
13 2780 (180) 2803 2.500 2771 8.307
14 3005 (225) 2997 5.761 3004 4.359
15 3215 (210) 3209 1.785 3216 2.165
16 3425 (210) 3404 5.890 3423 3.345
17 3620 (195) 3605 6.500 3621 2.179
18 3820 (200) 3813 2.487 3822 2.915
19 4015 (195) 4012 2.487 4018 2.500
20 4215 (200) 4219 2.000 4216 3.112
21 4435 (220) 4418 5.356 4437 4.848
22 4630 (195) 4612 2.784 4631 2.385
23 4820 (190) 4816 2.681 4821 2.179
24 5015 (195) 5018 2.861 5018 2.861
25 5235 (220) 5231 2.681 5239 4.969
26 5415 (180) 5403 9.287 5416 3.269
27 5630 (215) 5624 2.784 5633 2.449
28 5830 (200) 5825 1.581 5831 2.385
SL

Plant
position
Table 9.5 Detection of the plants size by artificial illumination (1
FALSE value allowed inside the plant pattern)
Size [mm]
0 48
Estimated size
RGB [mm]
SRGB
Estimated size
L [mm]
Appendix
1 44 39 4.500 66 1.785
2 37 30 5.477 62 2.784
3 40 30 3.873 52 2.291
4 40 27 2.487 44 2.165
5 42 19 2.000 64 2.385
6 32 30 1.920 50 1.090
7 45 17 3.700 58 2.487
8 45 50 1.090 53 4.023
9 45 46 2.000 41 2.861
10 46 32 2.947 51 2.861
11 35 30 1.090 50 0.000
12 42 44 2.000 55 1.090
13 33 27 7.141 40 8.515
14 38 36 2.681 28 21.360
15 35 28 5.099 47 10.756
16 42 37 3.571 33 2.500
17 40 34 10.677 37 3.317
18 34 29 8.408 41 2.000
19 40 43 2.487 48 2.500
20 48 49 2.291 44 3.391
21 38 36 1.500 59 2.681
22 30 12 5.568 41 9.121
23 40 39 1.785 16 1.785
24 28 22 2.487 35 1.090
25 35 33 4.603 32 3.571
26 35 32 2.915 27 2.947
27 40 44 3.961 31 9.695
28 28 29 3.571 25 3.345
29 28 21 4.265 16 2.550
SL
143

Appendix
Plant
position
144
Table 9.6 Detection of the plants centre position by artificial
illumination (1 FALSE value allowed inside the plant
pattern)
Distance to
the next [mm]
Estimated
centre RGB
[mm]
SRGB
Estimated
centre L [mm]
0 195 193 2.487 195 2.179
1 395 (200) 398 4.031 393 2.861
2 600 (205) 601 2.681 602 2.449
3 805 (205) 795 1.920 805 1.090
4 1000 (195) 1001 2.165 1001 2.000
5 1180 (180) 1171 2.165 1181 1.500
6 1395 (215) 1387 2.487 1395 1.090
7 1610 (215) 1597 2.291 1607 2.385
8 1800 (190) 1800 1.090 1803 2.500
9 2005 (205) 1996 2.000 2006 2.165
10 2205 (200) 2200 1.090 2206 2.165
11 2410 (205) 2405 1.090 2415 1.920
12 2600 (190) 2591 12.577 2599 14.799
13 2780 (180) 2802 2.784 2771 9.601
14 3005 (225) 3003 12.390 3005 6.103
15 3215 (210) 3208 2.947 3216 2.165
16 3425 (210) 3407 6.000 3425 3.700
17 3620 (195) 3608 5.761 3620 1.090
18 3820 (200) 3813 2.861 3823 2.915
19 4015 (195) 4012 2.385 4019 2.681
20 4215 (200) 4220 2.179 4216 1.500
21 4435 (220) 4417 3.631 4432 8.860
22 4630 (195) 4611 2.000 4631 1.500
23 4820 (190) 4816 1.500 4821 1.500
24 5015 (195) 5018 3.345 5020 1.581
25 5235 (220) 5230 1.090 5238 2.500
26 5415 (180) 5421 2.861 5418 2.915
27 5630 (215) 5623 3.345 5633 2.861
28 5830 (200) 5825 2.487 5831 1.785
SL

Plant
position
Table 9.7 Detection of the plants size in the darkness (2 FALSE value
allowed inside the plant pattern)
Size [mm]
0 48
Estimated size
RGB [mm]
SRGB
Estimated size
L [mm]
Appendix
1 44 40 8.367 69 2.681
2 37 40 4.323 66 2.000
3 40 37 2.861 58 2.449
4 40 37 2.291 49 2.784
5 42 23 2.947 68 4.023
6 32 35 0.000 53 2.861
7 45 23 3.674 59 5.268
8 45 54 2.165 56 3.742
9 45 53 2.449 39 4.548
10 46 44 1.785 57 3.269
11 35 36 1.785 57 2.487
12 42 45 3.345 58 2.487
13 33 45 4.153 47 3.674
14 38 41 1.785 67 2.915
15 35 41 2.179 50 1.920
16 42 37 2.449 38 2.500
17 40 42 3.631 44 2.784
18 34 35 1.090 47 2.385
19 40 48 2.500 50 4.743
20 48 53 2.487 46 2.000
21 38 39 1.785 61 3.491
22 30 34 7.000 38 3.674
23 40 43 2.449 22 2.487
24 28 28 2.958 36 2.179
25 35 40 3.873 30 3.700
26 35 31 2.550 27 3.202
27 40 36 2.179 49 3.391
28 28 31 4.543 22 4.157
29 28 26 3.491 22 2.784
SL
145

Appendix
Plant
position
146
Table 9.8 Detection of the plants centre position in the darkness (2
FALSE value allowed inside the plant pattern)
Distance to
the next [mm]
Estimated
centre RGB
[mm]
SRGB
Estimated
centre L [mm]
0 195 196 8.367 196 3.491
1 395 (200) 398 4.323 398 2.861
2 600 (205) 604 2.861 605 3.345
3 805 (205) 799 2.291 807 2.385
4 1000 (195) 1005 2.947 1002 3.961
5 1180 (180) 1177 0.000 1185 3.536
6 1395 (215) 1391 3.674 1402 4.603
7 1610 (215) 1602 2.165 1612 2.487
8 1800 (190) 1804 2.449 1804 2.385
9 2005 (205) 2000 1.785 2009 3.112
10 2205 (200) 2205 1.785 2212 2.291
11 2410 (205) 2409 3.345 2417 2.915
12 2600 (190) 2605 4.153 2610 3.345
13 2780 (180) 2805 1.785 2794 3.391
14 3005 (225) 3010 2.179 3012 2.449
15 3215 (210) 3213 2.449 3220 3.122
16 3425 (210) 3411 3.631 3429 2.000
17 3620 (195) 3615 1.090 3625 3.122
18 3820 (200) 3817 2.500 3826 1.785
19 4015 (195) 4016 2.487 4022 2.915
20 4215 (200) 4221 1.785 4220 2.947
21 4435 (220) 4428 7.000 4435 3.500
22 4630 (195) 4616 2.449 4634 3.202
23 4820 (190) 4820 2.958 4826 3.491
24 5015 (195) 5021 3.873 5021 3.491
25 5235 (220) 5230 2.550 5239 2.000
26 5415 (180) 5410 2.179 5417 2.291
27 5630 (215) 5599 4.543 5607 7.857
28 5830 (200) 5830 3.491 5835 6.124
SL

Plant
position
Table 9.9 Detection of the plants size by daylight (2 FALSE value
allowed inside the plant pattern)
Size [mm]
0 48
Estimated size
RGB [mm]
SRGB
Estimated size
L [mm]
Appendix
1 44 37 9.274 71 3.000
2 37 39 4.899 66 2.550
3 40 37 3.674 57 2.385
4 40 35 3.536 50 1.500
5 42 23 2.385 73 2.958
6 32 35 1.090 55 1.920
7 45 23 4.023 63 4.023
8 45 55 1.500 58 2.915
9 45 51 2.165 41 3.112
10 46 41 2.550 56 3.832
11 35 36 2.165 56 1.500
12 42 46 4.062 60 1.090
13 33 44 5.974 45 4.717
14 38 41 2.681 69 3.832
15 35 37 5.268 53 2.947
16 42 37 2.487 39 2.165
17 40 42 4.770 42 3.631
18 34 36 2.000 48 2.487
19 40 47 2.449 52 3.269
20 48 53 2.385 47 2.291
21 38 40 1.500 60 3.536
22 30 32 10.050 41 4.146
23 40 42 3.700 22 2.291
24 28 28 2.385 39 2.000
25 35 38 4.000 33 4.023
26 35 34 3.391 27 2.947
27 40 35 6.708 47 4.583
28 28 34 2.000 30 1.500
29 28 28 2.947 22 2.947
SL
147

Appendix
Plant
position
148
Table 9.10 Detection of the plants centre position by daylight (2 FALSE
value allowed inside the plant pattern)
Distance to
the next [mm]
Estimated
centre RGB
[mm]
SRGB
Estimated
centre L [mm]
0 195 194 4.265 196 2.385
1 395 (200) 397 3.631 397 2.487
2 600 (205) 603 2.861 606 2.179
3 805 (205) 798 2.487 806 2.385
4 1000 (195) 1005 2.179 1004 3.905
5 1180 (180) 1176 2.385 1185 1.090
6 1395 (215) 1391 3.000 1399 2.784
7 1610 (215) 1602 2.291 1609 2.861
8 1800 (190) 1802 2.291 1803 2.449
9 2005 (205) 1998 3.269 2008 2.958
10 2205 (200) 2204 2.000 2211 2.165
11 2410 (205) 2407 2.784 2414 2.385
12 2600 (190) 2604 2.291 2609 3.269
13 2780 (180) 2804 2.385 2793 2.947
14 3005 (225) 3001 5.449 3008 3.708
15 3215 (210) 3212 2.449 3218 2.500
16 3425 (210) 3411 3.112 3427 3.700
17 3620 (195) 3613 2.500 3623 2.487
18 3820 (200) 3816 2.385 3825 1.090
19 4015 (195) 4016 2.179 4021 2.000
20 4215 (200) 4220 2.179 4218 2.487
21 4435 (220) 4427 4.848 4438 5.099
22 4630 (195) 4614 2.861 4632 2.487
23 4820 (190) 4818 2.449 4822 2.291
24 5015 (195) 5020 2.947 5020 2.487
25 5235 (220) 5232 3.202 5239 4.899
26 5415 (180) 5411 6.869 5418 4.330
27 5630 (215) 5628 2.500 5634 3.742
28 5830 (200) 5827 2.915 5833 2.500
SL

Plant
position
Table 9.11 Detection of the plants size by artificial illumination (2
FALSE value allowed inside the plant pattern)
Size [mm]
0 48
Estimated size
RGB [mm]
SRGB
Estimated size
L [mm]
Appendix
1 44 44 4.500 71 1.785
2 37 35 5.477 67 2.784
3 40 35 3.873 57 2.291
4 40 32 2.487 49 2.165
5 42 24 2.000 72 3.961
6 32 35 1.920 55 1.090
7 45 22 3.700 63 2.487
8 45 55 1.090 58 4.023
9 45 51 2.000 46 2.861
10 46 37 2.947 56 2.861
11 35 35 1.090 55 0.000
12 42 49 2.000 60 1.090
13 33 34 5.890 46 7.949
14 38 41 2.681 68 4.265
15 35 35 3.873 55 3.122
16 42 42 3.571 38 2.500
17 40 45 4.153 42 3.269
18 34 37 2.487 46 2.000
19 40 48 2.487 52 2.915
20 48 54 2.291 49 3.112
21 38 41 1.500 63 2.385
22 30 33 7.822 44 5.449
23 40 44 1.785 21 1.785
24 28 27 2.487 40 1.090
25 35 38 4.603 37 3.571
26 35 32 2.915 27 2.947
27 40 48 3.708 42 3.700
28 28 34 3.571 30 2.947
29 28 26 4.265 21 2.550
SL
149

Appendix
Plant
position
150
Table 9.12 Detection of the plants centre position by artificial
illumination (2 FALSE value allowed inside the plant
pattern)
Distance to
the next [mm]
Estimated
centre RGB
[mm]
SRGB
Estimated
centre L [mm]
0 195 196 3.112 195 1.090
1 395 (200) 401 5.068 397 2.784
2 600 (205) 603 2.861 606 1.500
3 805 (205) 798 2.958 807 2.291
4 1000 (195) 1005 1.090 1004 2.681
5 1180 (180) 1176 1.500 1185 1.090
6 1395 (215) 1391 2.000 1398 2.915
7 1610 (215) 1601 2.165 1609 2.000
8 1800 (190) 1801 2.165 1806 1.785
9 2005 (205) 1999 2.385 2010 2.179
10 2205 (200) 2205 1.581 2211 2.165
11 2410 (205) 2406 2.165 2415 1.581
12 2600 (190) 2595 11.832 2601 14.515
13 2780 (180) 2803 2.861 2792 2.449
14 3005 (225) 3008 12.400 3010 4.867
15 3215 (210) 3211 1.785 3219 2.681
16 3425 (210) 3412 2.915 3428 2.861
17 3620 (195) 3613 2.958 3624 2.385
18 3820 (200) 3816 1.500 3825 1.090
19 4015 (195) 4015 1.090 4021 2.000
20 4215 (200) 4220 1.581 4219 2.291
21 4435 (220) 4429 3.905 4434 5.148
22 4630 (195) 4615 1.090 4631 2.165
23 4820 (190) 4818 2.861 4821 1.785
24 5015 (195) 5020 3.536 5022 2.915
25 5235 (220) 5230 1.090 5238 2.500
26 5415 (180) 5422 2.291 5425 2.179
27 5630 (215) 5627 4.867 5635 3.122
28 5830 (200) 5829 3.571 5831 2.165
SL

Personal data
Education
About the author
Name: Zoltan Gobor
Date of Birth:: 05. 06. 1975.
Place of Birth: Apatin, Serbia
Citizenship: Serbian
Marital status: married
About the author
1994-2000: Dipl.-Ing. mechanical engineer, specialisation in automation
technologies, University of Novi Sad, Faculty of Technical Sciences
(ten semesters), Novi Sad (Serbia)
2000-2004: Master of Sciences, University of Novi Sad, Association of Centers for
Interdisciplinary and Multidisciplinary Studies and Developmental
Research - ACIMSI, University Center for Environmental Engineering,
specialization in water quality control, Novi Sad (Serbia)
2004-2007: Ph.D. Student and member of the DFG Research Training Group
(Graduiertenkolleg) 722 at the University of Bonn, Institute of
Agricultural Engineering, Bonn (Germany)
Professional development
2001-2004: Researcher at the University Center for Environmental Engineering,
Association of Centers for Interdisciplinary and Multidisciplinary
Studies and Developmental Research - ACIMSI, University of Novi
Sad (Serbia)
2004-2006: Teaching Assistant at the University of Novi Sad (Serbia), Faculty of
Technical Sciences, Department of Environmental Engineering
since 10. 2007: Employed in Bosch Rexroth AG, Lohr am Main (Germany), on the
position of coordination and realisation of the hardware development
and participant of the Junior Engineer Program
Acknowledgment
With the part of the research work presented in this thesis the author
was placed in TOP–10 of FERCHAU Engineering awards Innovation
Prise 2006
151