Evaluating Quality Factors of Corn and Soybeans Using a Computer Vision System

Acomputer vision system was used to evaluate external physical damage, mold contamination, and floury-to-vitreous endosperm ratio in corn and mold contamination in soybeans. For each of these quality factors, optimal conditions for acquiring video images and processing algorithms were developed. White light in front-lighting mode with a black background for the sample was suitable for all defects except for mold contamination which required use of red light (610 nm). The image processing algorithms were suitable for defect detection in samples both individually and in groups. The average success rates for detecting broken, chipped, starch-cracked and moldy corn kernels were 100%, 83%, 88% and 84%, respectively. The success rate for detecting moldy soybeans was 80%.