A grain quality classification system

Abstract

Exploring the possibility of using technology for grain quality classification is necessary for the consumer market to protect consumers who are susceptible to any form of contamination that may occur in the market. Although some research has been reported on the classification of paddy seeds, no published work is found on the classification of milled rice which is the principal food in many countries in Asia. This research focused on providing a better approach for identification of rice quality by using neural network and image processing concepts. In this research, a back propagation neural network with two hidden layers has been developed for the quality classification. Thirty one texture and color features that have been extracted from rice images were used for discriminate analysis. Tests on the system for the training and test sets show accuracy in between 94% to 68% for the four grades.