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Image Analysis of Bulk Grain Samples Using Neural Networks

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

Citation:  Paper number  033055,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.15002) @2003
Authors:   Neeraj Singh Visen, Jitendra Paliwal, Digvir Jayas, N.D.G. White
Keywords:   Image processing, Neural Networks, cereal grains

Algorithms were developed to acquire and process color images of bulk grain samples of five grain types, namely barley, oats, rye, wheat, and durum wheat. The images were acquired using a video camera and were digitized using a frame grabber board. The images were stored on a personal computer from where they were accessed by an image processing program which extracted over 150 color and textural features. A neural-network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to a back propagation neural network for training purposes. The trained network was then used to identify the unknown grain types. Results showed a classification accuracy of over 90% for all grain types.

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