Contents - Greenmount Press

Spraying
feature…
Field testing of the prototype system was conducted on
farms in southern Queensland.
Cheryl said more research was needed to further advance the
technology so that it could be integrated with a weed classifier
system linked to the spray trigger.
If the technology was realised, she said the reduction in
herbicide usage, coupled with precise knowledge of the
species of weeds present, would enable a much larger range of
herbicides to be viable, therefore reducing the risk of herbicide
resistance developing.
Existing weed sensor imaging technology struggles to
segment leaf from weeds – a difficult task when more than one
weed species are growing together, often at different heights.
Commercial systems therefore target any green vegetation on a
soil or stubble background.
Researchers in this field have been seeking to improve
machine vision-based weed discrimination by targeting the
analyses of colour, shape and texture.
A 2008 review of weed control systems found that although
results between 65 per cent and 95 per cent accuracies can be
achieved, this can only occur in ideal conditions. The systems
were found to be unsuitable to real-world conditions where leaf
shape can be distorted by numerous factors and crops and weed
leaves often occlude each other.
Against this background, the NCEA project set out to create
a prototype machine which could identify problem weeds in
a real-world setting – this meant dealing with issues including
inconsistent light sources, interference from ground cover (i.e.
stubble) and occlusions.
Cheryl said the challenge was to develop a precision
sensing system with the “capability to extract whole leaves for
classification from a scene containing many weeds.”
The team tested two camera systems – a combined colour and
depth camera and a high resolution colour camera – for their
ability to capture effective images of weeds for analysis in realtime.
A three-metre unit was developed to house and provide
shading for the two camera systems while being towed in the
field in paddock trials on the Darling Downs. The unit was used
to collect weed images under expected operational conditions
of the machine vision system and targeted fleabane, sowthistle,
liverseed, feathertop Rhodes grass, wild sorghum and wild oats.
The results encouraged the researchers to develop a new
image analysis technique that can discriminate between grass
and broadleaf species, and between different broadleaf species.
Both active and passive methods of depth data generation were
investigated so that weed segmentation based on height could
be used as a pre-process to the more “computationally-intense”
colour-based image analysis.
“Automated analysis of colour images enabled extraction of
individual grass leaves (liverseed, wild oats, feathertop Rhodes
grass and wild sorghum) and discrimination of grasses from
broadleaf weeds (sowthistle and fleabane),” Cheryl said.
“But a greater resolution was required for the extraction of the
features of broadleaf species, than for grass species. So an active
depth sensor was found which reduced image complexity by at
least 80 per cent for images containing weeds at a distinct height
– for example, standing grass amongst low-lying broadleaves and
grasses.”
The subsequent results demonstrated that discrimination of
weed species in real-world on-farm conditions is achievable by
using combined colour and depth image analysis.
It is anticipated that a commercial unit would carry tank mixes
for grasses and broadleafs. And longer term, perhaps a variable
rate machine with different rates for different weed sizes and
growth stages.
The NCEA is now further testing its research through grants
from the Sugar Research & Development Corporation (SRDC),
Horticulture Australia Limited (HAL) and Botanical Resources
Australia (BRA), which it hopes will develop the technology from
the proof-of-concept stage towards commercialisation.
Spray App
This is a very easy system to incorporate into any operation,
because the Spray App is a very simple but valuable and
important tool to add into the cabin of a sprayer.
The bonus is you get to take the Spray App back to base every
night – so when it rains your carbon book is not stuck in the field
with all the information!
Farmers like to have something that just ‘works’ and the Spray
App may be the answer – so the ‘KISS’ method applies. What’s
more, it’s built by farmers for farmers
The Spray App works offline – so when you drive into range
the PDF, which is a legal document with date and time features,
will automatically be sent back to the office. It also has CSV
export capabilities.
You can:
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Record weather conditions as many times as required.
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It is a tool that manages spray drift.
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A tank calculator that tells the operator how many litres of
chemical is required per tank.
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It can identify all your spraying details: sprayers, operators,
nozzle codes etc.
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It is robust and reliable – you can run your business from it.
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If audited the Spray App gives you a complete account of
what’s happened in any field or paddock.
www.eziapp.com.au
26 — The Australian Cottongrower October–November 2012