CLASSIFICATION OF WEED SPECIES USING COLOR TEXTURE FEATURES AND DISCRIMINANT ANALYSIS

摘要

The environmental impact of herbicide utilization has stimulated research into new methods of weed control, such as selective herbicide application on highly infested crop areas. This research utilized the Color Co-occurrence Method (CCM) to determine whether traditional statistical discriminant analysis can be used to discriminate between six different classes of groundcover. The weed species evaluated were giant foxtail, crabgrass, common lambsquarter, velvetleaf, and ivyleaf morningglory, along with a soil image data set. The between species discriminant analysis showed that the CCM texture statistics procedure was able to classify between five weed species and soil with an accuracy of 93% using hue and saturation statistics, only. A significant accomplishment of this work was the elimination of the intensity texture features from the model, which reduces computational requirements by one-third.