CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: IV. COMBINED MORPHOLOGY, COLOR, AND TEXTURE MODELS

Abstract

Classification models by combining two or three feature sets (morphological, color, textural) were developedto classify individual kernels of Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD)wheat, barley, oats, and rye. The mean accuracies (the average of the classification accuracies of the above mentionedcereal grains) of 98.6 and 99.3% were achieved when the morphology-texture model with the 15 most significant featureswas used to test on an independent data set (total number of kernels used was 10,500) and on the training data set (totalnumber of kernels used was 31,500), respectively. When the morphology-color model (with the 15 most significantfeatures) was tested on the independent data set and on the training data set, the mean accuracies were 99.4 and 99.6%,respectively. Similarly, using the texture-color model (with the 15 most significant features) the mean accuracies were98.4 and 98.0% for the independent and the training data sets, respectively. The highest classification accuracies wereachieved when the morphology-texture-color model was used. The mean accuracies using the 20 most significant featuresin the morphology-texture-color model were 99.7 and 99.8% when tested on the independent and the training data sets,respectively. The differences in mean accuracies were not significant when the models were tested with independent andtraining data sets.