DETERMINING VITREOUSNESS OF DURUM WHEAT USING TRANSMITTED AND REFLECTED IMAGES

摘要

Digital imaging technology has found many applications in the grain industry. In this study, images of durum wheat kernels acquired under three illumination conditions (reflected, side-transmitted, and transmitted) were used to develop artificial neural network models to classify durum wheat kernels by their vitreousness. The results showed that the models trained using transmitted images provided the best classification for the nonvitreousness class (100% for non-vitreous kernels and 92.6% for mottled kernels). Results of the study also indicated that using transmitted illumination may greatly reduce the hardware and software requirements for the inspection system, while providing faster and more accurate results for inspection of vitreousness of durum wheat