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

Authors
Journal
Computers and Electronics in Agriculture
0168-1699
Publisher
Elsevier
Publication Date
Volume
73
Issue
2
Identifiers
DOI: 10.1016/j.compag.2010.06.001
Keywords
  • Grain Quality
  • Nir
  • Hyperspectral Imaging
  • Machine Vision
Disciplines
  • Computer Science
  • Physics

Abstract

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 (700–1100 nm 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.

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