Color classifier for symptomatic soybean seeds using image processing

Symptoms associated with fungal damage, viral diseases, and immature soybea n (Glycine max) seeds were characterized using image processing techniques. A Red, Green, Blue (RGB) color feature-based multivariate decision model d iscriminated between asymptomatic and symptomatic seeds for inspection and grading. The color analysis showed distinct color differences between the a symptomatic and symptomatic seeds. A model comprising six color features in cluding averages, minimums, and variances for RGB pixel values was develope d for describing the seed symptoms. The color analysis showed that color al one did not adequately describe some of the differences among symptoms. Ove rall classification accuracy of 88% was achieved using a linear discriminan t function with unequal priors for asymptomatic and symptomatic seeds with highest probability of occurrence. Individual classification accuracies wer e asymptomatic 97%, Alternaria spp. 30%, Cercospora spp. 83%, Fusarium spp. 62%, green immature seeds 91%, Phomopsis spp. 45%, soybean mosaic potyviru s (black) 81%, and soybean mosaic potyvirus (brown) 87%. The classifier per formance was independent of the year the seed was sampled. The study was su ccessful in developing a color classifier and a knowledge domain based on c olor for future development of intelligent automated grain grading systems.