Influence of weed maturity levels on species classification using machine vision

Abstract The environmental effect of weed control systems has stimulated research into new practices for weed control, such as selective herbicide application methods on weed-infested crop areas. This research used the color co-occurrence method (CCM) texture analysis to determine the effects of plant maturity on the accuracy of weed species classification of digitized images. Two different experimental combinations of weed species and maturity level were examined. The weed species evaluated were ivyleaf morningglory, giant foxtail, large crabgrass, and velvetleaf, with soil image sets added to each experiment. One study examined classification accuracies for two weed species at three maturity levels, and the second study examined four weed species at two maturity levels. For each species-maturity level combination, 40 digital images were collected from a manually seeded outdoor plant bed. Digitized images were transformed from the red–green–blue (RGB) color format into hue–saturation–intensity (HSI) format to generate CCM texture feature data. Stepwise variable reduction procedures were used to select texture variables with the greatest discriminant capacity. Then discriminant analysis was used to determine the classification accuracy for the two different experiments. When using HSI texture statistics, discriminant analysis correctly classified weed species within and across maturity levels with an accuracy above 97% for both experimental groups. These image processing algorithms demonstrate potential use for weed scouting, weed infestation mapping, and weed control applications using site-specific farming technology. Nomenclature: Ivyleaf morningglory, Ipomoea hederacea (L.) Jacq. IPOHE; giant foxtail, Setaria faberi Hermm. SETFA; large crabgrass, Digitaria sanguinalis (L.) Scop. DIGSA; velvetleaf, Abutilon theophrasti Medik. ABUTH.