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

Citation:  Transactions of the ASAE.  VOL. 43(6): 1669-1675 . (doi: 10.13031/2013.3107) @2000
Authors:   S. Majumdar, D. S. Jayas
Keywords:   Machine vision, Digital image processing, Pattern recognition, Morphology, Automated grain grading, Discrimination, Cereal grain classification, Wheat, Barley, Oats, Rye

An algorithm was developed based on morphological features to classify individual kernels of Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats, and rye. Twenty-three morphological features were used for the discriminant analysis. Grains from 15 growing regions (300 kernels per growing region) across Western Canada were used as the training data set and from another five growing regions as the test data set. The classification accuracies of individual kernels using the 10 most significant features in the morphology model were 98.9, 93.7, 96.8, 99.9, and 81.6%, respectively for CWRS wheat, CWAD wheat, barley, oats, and rye when tested on an independent data set (i.e., the test data set where the total number of kernels used was 10 500; for CWRS wheat, 300 kernels each were selected for three grades). When the model was tested on the training data set (total number of kernels used was 31 500), the classification accuracies of CWRS wheat, CWAD wheat, barley, oats, and rye were 98.9, 91.6, 97.9, 100.0, and 91.6%, respectively.

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