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Classification of Bulk Samples of Cereal Grains using Machine Vision

✍ Scribed by S. Majumdar; D.S. Jayas

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
209 KB
Volume
73
Category
Article
ISSN
0021-8634

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✦ Synopsis

Digital image analysis algorithms were developed to classify bulk samples of Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats, and rye using textural and colour features. The textural features of bulk samples were extracted from di!erent colours, i.e., red R, green G, or blue B, and colour band combinations

of images to determine the colour or colour band combination that gave the highest classi"cation accuracies in cereal grains. The textural features extracted from the red colour band at maximum gray-level value 32 gave the highest classi"cation accuracies in cereal grains (mean accuracy, the average of the classi"cation accuracies of the above-mentioned cereal grains, was 100% when tested on an independent data set that had 10 500 grain kernels). When the original bulk images were partitioned into sub-images and textural or colour features extracted from the sub-images were used, the classi"cation accuracies of cereal grains decreased compared to when the original bulk images were used. The mean accuracy was 100% when colour features of bulk samples were used for classi"cation of cereal grains in an independent data set.

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