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The potential of soft X-ray imaging to detect fungal infection in wheat was investigated. Healthy wheat kernels and kernels infected with the common storage fungi namely Aspergillus niger, A. glaucus group, and Penicillium spp. were scanned using a soft X-ray imaging system and algorithms were developed to extract the image features and for classification. 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. A two-class Mahalanobis discriminant classifier classified 92.2–98.9% fungal-infected wheat kernels. Linear discriminant classifier gave better results than other statistical (quadratic and Mahalanobis) and neural network classifiers in identifying healthy kernels with more than 82% classification accuracy. In most of the cases, the statistical classifiers gave better classification accuracies and lower false positive errors than the BPNN classifier. [Copyright &y& Elsevier]

Algorithms were written to extract a total of 230 features (51 morphological, 123 colour, and 56 textural) from the high-resolution images of kernels of five grain types [barley, Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat, oats, and rye] and five broad categories of dockage constituents [broken wheat kernels, chaff, buckwheat, wheat spikelets (one to three wheat kernels inside husk), and canola (rapeseed with low erucic acid content in the oil and low glucosinolate content in the meal)]. Different feature models, viz. morphological, colour, texture, and a combination of the three, were tested for their classification performances using a neural network classifier. Kernels and dockage particles with well-defined characteristics (e.g. CWRS wheat, buckwheat, and canola) showed near-perfect classification whereas particles with irregular and undefined features (e.g. chaff and wheat spikelets) were classified with accuracies of around 90%. The similarities in shape and size of some of the particles of chaff and wheat spikelets with the kernels of barley and oats affected the classification accuracies of the latter, adversely. [Copyright &y& Elsevier]