CLASSIFICATION OF SWEET ONIONS BASED ON INTERNAL DEFECTS USING IMAGE PROCESSING AND NEURAL NETWORK TECHNIQUES

Abstract

Maintaining product quality is the key to success in the fresh fruit and vegetable market. Quality assessment withcomputer vision techniques is possible; however, two basic issues need to be addressed before an automatic sorting systemcan be developed: (1) which image features best correlate with the product quality, and (2) which classifier should be usedfor optimal classification. To address these issues, sweet onions were linescanned for internal defects using xray imaging.Spatial and transform features were evaluated for their contributions to product classification based on internal defects. TheBayesian method was used for selecting the salient features. Spatial edge features combined with selected discrete cosinetransform (DCT) coefficients proved to be good indicators of internal defects. A neural classifier performed better than theBayesian classifier for sorting onions into two classes (good or defective) by achieving an overall accuracy of 90%. Lossesand false positives were limited to 6% and 10%, respectively. The accuracy, losses, and false positives for the Bayesianclassifier were 80%, 16%, and 17%, respectively.