Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging

Authors:
Chandra B. Singh;Digvir S. Jayas;Jitendra Paliwal;Noel D. G. White
Affiliations:
Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6;Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6;Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6;Agriculture and Agri-Food Canada, Cereal Research Centre, Winnipeg, MB, Canada R3T 2M9
Venue:
Computers and Electronics in Agriculture
Year:
2010

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Abstract

Healthy wheat kernels and wheat kernels damaged by the feeding of the insects: rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum) were scanned using a near-infrared (NIR) hyperspecrtal imaging system (700-1100nm wavelength range) and a colour imaging system. Dimensionality of hyperspectral data was reduced and statistical and histogram features were extracted from NIR images of significant wavelengths and given as input to three statistical discriminant classifiers (linear, quadratic, and Mahalanobis) and a back propagation neural network (BPNN) classifier. A total of 230 features (colour, textural, and morphological) were extracted from the colour images and the most contributing features were selected and used as input to the statistical and BPNN classifiers. The quadratic discriminant analysis (QDA) classifier gave the highest accuracy and correctly identified 96.4% healthy and 91.0-100.0% insect-damaged wheat kernels using the top 10 features from 230 colour image features combined with hyperspectral image features.