A computer vision system for rice kernel quality evaluation

A computer vision system is developed for evaluating the quality of rice kernels. To finding the quality, which is determined by percentage of broken rice and percentage of adulterate rice, rice kernels are placed randomly on the plate in one layer. Some of kernels are touching one another. Touching kernel features consist of two forms: point and line touching. Therefore image analysis algorithms are developed to extract touching features. Fuzzy logic is used to organize and classify the class of each kernel by utilizing area, perimeter, and circularity and shape compactness as criterions. Concept of translucency is applied for viewing the adulterate of rice. The different rice varieties show different levels of intensity in image. By setting light intensity to 4500-4600 Lux, the results clearly show of shade difference for the different kind of rice. The overall results of image analysis for finding the percentage of broken rice and percentage of adulterate of rice give 92% in accuracy.