Algorithms were written to extract a total of 230 features (51 morphological, 123 colour, and 56 textural) from the high-resolution images of kernels of five grain types [barley, Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat, oats, and rye] and five broad categories of dockage constituents [broken wheat kernels, chaff, buckwheat, wheat spikelets (one to three wheat kernels inside husk), and canola (rapeseed with low erucic acid content in the oil and low glucosinolate content in the meal)]. Different feature models, viz. morphological, colour, texture, and a combination of the three, were tested for their classification performances using a neural network classifier. Kernels and dockage particles with well-defined characteristics (e.g. CWRS wheat, buckwheat, and canola) showed near-perfect classification whereas particles with irregular and undefined features (e.g. chaff and wheat spikelets) were classified with accuracies of around 90%. The similarities in shape and size of some of the particles of chaff and wheat spikelets with the kernels of barley and oats affected the classification accuracies of the latter, adversely. (C) 2003 Silsoe Research Institute. All rights reserved. Published by Elsevier Science Ltd.
