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Quantification of Variations in Machine-Vision-Computed Morphological Features of Cereal Grains

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

Citation:  Paper number  023131,  2002 ASAE Annual Meeting . (doi: 10.13031/2013.10462) @2002
Authors:   J. Paliwal, N.S. Visen, D.S. Jayas, N.D.G. White

Six morphological features (area, perimeter, maximum radius, minimum radius, major axis length, and minor axis length) were extracted from high-resolution images of five different cereal grains (barley, Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat, oats, and rye). The variability in these features that can occur due to the kernel orientation, growing region, and images acquired from line- and area-scan cameras, was calculated. The variation in features was mainly dependent on the maturity levels of kernels because samples coming from different growing regions showed comparable variability to those coming from one growing region. The variability in features due to different image acquisition devices was statistically insignificant. Keywords. Digital image analysis, cereal grains, feature extraction.

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