Color Classifier for Symptomatic Soybean Seeds Using Image Processing

ABSTRACT Symptoms associated with fungal damage, viral diseases, and immature soybean (Glycine max) seeds were characterized using image processing techniques. A Red, Green, Blue (RGB) color feature-based multivariate decision model discriminated between asymptomatic and symptomatic seeds for inspection and grading. The color analysis showed distinct color differences between the asymptomatic and symptomatic seeds. A model comprising six color features including averages, minimums, and variances for RGB pixel values was developed for describing the seed symptoms. The color analysis showed that color alone did not adequately describe some of the differences among symptoms. Overall classification accuracy of 88% was achieved using a linear discriminant function with unequal priors for asymptomatic and symptomatic seeds with highest probability of occurrence. Individual classification accuracies were asymptomatic 97%, Alternaria spp. 30%, Cercospora spp. 83%, Fusarium spp. 62%, green immature seeds 91%, Phom...