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Determination of durum wheat vitreousness using transmissive and reflective images

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  033138,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.15016) @2003
Authors:   Ning Wang, Naiqian Zhang, Floyd Dowell, Tom Pearson
Keywords:   Grading, Inspection, Automation, Machine Vision, Color

Digital imaging technology has found many applications in grain industry.In this study, images of durum wheat kernels acquired under three illumination conditions - reflective, side-transmissive, and transmissive - were used to develop artificial neural network (ANN) models to classify durum wheat kernels by their vitreousness. The results showed that the models trained using transmissive 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 transmissive 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.

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