Detection of internal wheat seed infestation by Rhyzopertha dominica using X-ray imaging

Abstract

Standardization for grain grades has been established in most countries to maintain the quality of a crop until it reaches consumers. Different methods have been investigated for their potential to detect insect infestations in grain destined for domestic and export markets. The potential of detecting infestations caused by Rhyzopertha dominica in wheat kernels using a real-time soft X-ray method was determined in this study. Artificially infested wheat kernels were incubated at 30°C and 70% relative humidity and X-rayed sequentially for larval, pupal, and adult stages of R. dominica. Algorithms were used to extract histogram features, textural features, and histogram and shape moments from the X-ray images of wheat kernels. A backpropagation neural network (BPNN) and statistical classifiers were used to identify uninfested and infested kernels using the 57 extracted features. The BPNN correctly identified all uninfested and infested kernels and more than 99% of kernels infested by R. dominica larvae. The classification accuracies determined by the BPNN were higher using all 57 features than when using the histogram and textural features separately. The BPNN performed better than the parametric and non-parametric classifiers in the identification of uninfested and infested kernels by different stages of R. dominica.