Automatic weed imaging and analysis
Agricultural techniques for weed management in crop fields involves the wide-scale spraying of herbicides. This is economically and environmentally expensive.
An increasing global population requires an increasing crop output, which requires efficient use of agricultural land.
By controlling weed growth a higher yield can be maintained. In order to reduce the amount of herbicides used, we need to identify the location and structure of weed clusters in a field.
Precision weeding using machine vision
We worked with Harper-Adams University, to detect the locations of out-of-row weed clusters from 2D image and GPS data.
Our 3D techniques enabled us to determine the structure of the weeds from surface information and identify the locations of the crucial parts of the weed.
The result was efficient, targeted weed killing techniques such as precision spraying or heat-treatment.
CMV methods for high frame-rate 3D detection of broad-leaf and grass weeds in maize crops enable precise determination of weed patch locations. These are then analysed to find the “meristem” (main growing stem) to within 1-2mm.
We conducted feasibility studies for the detection and eradication of broadleaved dock (Rumexobtusifolius) in grass crops. Broad-leaved dock can survive animal digestion, is deep-rooted and can affect the yield of desired crops.
Initial results are promising and we are interested in forming a consortium with a view to exploring this further and developing it into a fully automated robotic system.
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Projects in the Centre for Machine Vision
Current Centre for Machine Vision projects, read about the many advances we have made.
About the Centre for Machine Vision
Centre for Machine Vision is part of Bristol Robotics Laboratory, a centre of excellence in the Department of Engineering, Design and Mathematics at UWE Bristol.