Weed and crop discrimination using image analysis and artificial intelligence methods

Development of a visual method of discriminating between crop seedlings and weeds is an important and necessary step towards the automation of non-chemical weed control systems in agriculture, and towards the reduction in chemical use through spot spraying. Two methods were applied to recognise carrot (Daucus carota L.) seedlings from those of ryegrass (Lolium perenne) and Fat Hen (Chenopodium album) using digital imaging. The first method involved the use of a simple morphological characteristic measurement of leaf shape (perimeter2/area), which had varying effectiveness (between 52 and 74%) in discriminating between the two types of plant, with the variation dependent on plant size. The second involved a selforganising neural network more biologically plausible than many commonly used NN methods. While the latter did not give results as good as those required for commercial purposes, it showed that a neural network-based methodology exists which allows the system to learn and discriminate between species to an accuracy exceeding 75% without predefined plant descriptions being necessary.