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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
Page 1 and 2: Institut für Landtechnik der Rhein
Page 3 and 4: Development of a novel mechatronic
Page 5 and 6: Kurzfassung Entwicklung eines neuar
Page 7 and 8: Acknowledgements Acknowledgements M
Page 9 and 10: Table of contents Table of contents
Page 11 and 12: Glossary of abbreviations and symbo
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Page 20 and 21: Introduction The most frequently us
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Page 24 and 25: Introduction be to go from a system
Page 26 and 27: Introduction limited and environmen
Page 28 and 29: Introduction than 40 years before g
Page 30 and 31: Introduction techniques. It is know
Page 32 and 33: Introduction Sowing and planting al
Page 34 and 35: Introduction Inter-row weeding syst
Page 36 and 37: Introduction 18 Figure 1.3 Inter-ro
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Page 48 and 49: Definition of the problem and resea
Page 51 and 52: 4 Materials and methods 4.1 Detecti
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Page 57 and 58: 4.1.2 Data acquisition 4.1.2.1 Hard
Page 59 and 60: 4.1.4 Test objects Materials and me
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Results and discussion 76 5.2.1 Opt
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Results and discussion xmi1 = ( RLA
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Results and discussion With the inv
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Results and discussion 82 Horizonta
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Results and discussion 84 5.2.4 Sel
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Results and discussion 86 5.2.5 Dis
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Results and discussion Conventional
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Results and discussion Since the fi
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Results and discussion To avoid con
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Results and discussion As the size
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Results and discussion 96 5.3.4 Alg
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Results and discussion 98 V = zc( t
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Results and discussion corresponds
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Results and discussion 102 5.3.6 Me
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Results and discussion hoeing accur
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Results and discussion between the
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Results and discussion distance fro
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Results and discussion Distribution
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Results and discussion cuts are ran
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Results and discussion Forward spee
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Results and discussion x [mm] Distr
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Results and discussion The experime
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6 Summary Summary Neither a mechani
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Summary The third part of the work
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7 Conclusions Conclusions The prese
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8 References References Agrios, G.
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References in California's Sacramen
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References Johnson, W C and Mullini
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References Schans, D. v. d. et al.
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List of figures List of figures Fig
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List of figures Figure 5.24 Scheme
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Plant position 9 Appendix Table 9.1
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Plant position Table 9.3 Detection
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Personal data Education About the a
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