Image Analysis of Bulk Grain Samples Using Neural Networks

Abstract

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