Automatic weed imaging and analysis


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.

Figure 1: Typical view of a maize field, showing two crop-rows interspersed with both grasses and broad-leaf weeds







By controlling weed growth a higher yield can be maintained; however in order to reduce the amount of herbicides used, it is important to identify the location and structure of weed clusters growing in a field.

Use of machine vision for implementing precision weeding

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.

Figure 2: 3D reconstruction resulting from a 4-source photometric stereo scan of an artificially planted weed bed.


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.

Figure 3: Testing of an early prototype of the in-field imaging system.

men and heat detecting box







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.

Figure 4: Initial results from a feasibility study looking at dock-leaf detection in grass crops

weeds in digital







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.

Figure 5: A typical maize field, captured while in late “establishment” and early “vegetative” growth stages








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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