Journal
JOURNAL OF STORED PRODUCTS RESEARCH
Volume 45, Issue 3, Pages 151-158Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jspr.2008.12.002
Keywords
Hyperspectral imaging; Near-infrared; Insect damage; Grain quality
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Funding
- Natural Sciences and Engineering Research Council of Canada
- Canada Foundation for Innovation, and the Canada Research Chairs Program
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Insect damage in wheat adversely affects its quality and is considered one of the most important degrading factors in Canada. The potential of near-infrared (NIR) hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated. Healthy wheat kernels and wheat kernels visibly damaged by Sitophilus oryzae, Rhyzopertha dominica, Cryptolestes ferrugineus, and Tribolium castaneum were scanned in the 1000-1600 nm wavelength range using an NIR hyperspectral imaging system. Dimensionality of the acquired hyperspectral data was reduced using multivariate image analysis. Six statistical image features (maximum, minimum, mean, median, standard deviation, and variance) and 10 histogram features were extracted from images at 1101.69 and 1305.05 nm and given as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis) for classification. Linear discriminant analysis and quadratic discriminant analysis classifiers correctly classified 85-100% healthy and insect-damaged wheat kernels. (c) 2009 Elsevier Ltd. All rights reserved.
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