Chandra B. Singh

TL;DR: In this article, the potential of near-infrared hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated, where 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 hyperspectra imaging system.

TL;DR: In this article, the authors used a back propagation neural network (BPNN) classifier to identify 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).

TL;DR: In this paper, the potential of near-infrared hyperspectral imaging to detect fungal infection in wheat was investigated, and two-class and four-class classification models were developed by applying k-means clustering and discriminant (linear, quadratic, and Mahalanobis) analyses.

TL;DR: In this paper, the potential of soft X-ray imaging to detect fungal infection in wheat was investigated and a total of 34 image features (maximum, minimum, mean, median, variance, standard deviation, and 28 grey-level co-occurrence matrix (GLCM) features) were extracted and given as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis) and back-propagation neural network (BPNN) classifier.

TL;DR: In this paper, a procedure was developed to differentiate these contaminants from wheat using near-infrared (NIR) hyperspectral imaging and three experiments were conducted to identify the best combinations of spectral pre-processing technique and statistical classifier to classify contaminants represented by seven foreign material types (barley, canola, maize, flaxseed, oats, rye, and soybean); six dockage types (broken wheat kernels, buckwheat, chaff, wheat spikelets, stones, and wild oats); and two animal excreta types (deer and rabbit dro