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Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging

Journal of Stored Products Research
DOI: 10.1016/j.jspr.2012.12.005
  • Hyperspectral Imaging
  • Mung Bean
  • Callosobruchus Maculatus
  • Non-Parametric Classifiers
  • Physics


Abstract Mung bean (Vigna radiata (L.) R. Wilczek) is one of the major pulse crops grown in India. Cowpea weevil (Callosobruchus maculates F.) is the major insect that causes qualitative and quantitative losses of mung bean kernels during storage. There is an increasing demand from grain buyers and consumers toward zero-tolerance to contamination by insects in grains and grain products. Uninfested mung bean kernels and kernels infested with different stages of C. maculatus were imaged using a near-infrared (NIR) hyperspectral imaging system within the wavelength region of 1000–1600 nm at 10 nm intervals. The wavelengths corresponding to the highest principal components (PC) factor loadings (1100, 1290 and 1450 nm) were considered to be significant. Six statistical features (maximum, minimum, mean, median, standard deviation, and variance) and ten histogram features from images at the significant wavelengths were extracted and given as input to non-parametric statistical classifiers. Average classification accuracies of more than 85% and 82% were obtained using statistical classifiers for identifying uninfested and infested mung bean kernels, respectively. Mung beans kernels with pupal and adult stages of infestation had higher classification accuracies than the egg and larval stages of infestation using both the classifiers.

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