Agricultural techniques for management of weeds in crop fields often involve the wide-scale spraying of herbicides, which is expensive both economically and environmentally. In addition, an increasing global population requires an increasing crop output, which in turn requires more efficient use of existing agricultural land.

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By controlling weed growth a higher yield can be maintained.

A collaborative project, involving UWE and Harper-Adams University, aimed 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 to identify the locations of the crucial parts of the weed allowing the use of 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.

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We have recently been conducting feasibility studies for the detection and eradication of broadleaved dock (Rumexobtusifolius) in grass crops. Broad-leaved dock is a serious issue as it can survive animal digestion, is deep-rooted and some relevant herbicides 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.