Defective kernel detection using a linear colour CCD

Abstract

This study was aimed at detecting defective wheat (Triticum durum Desf) with a machine vision system of linear colour charge-coupled device. One thousand one hundred and sixty-nine images were captured for sound kernels, 710 for black germ kernels and 627 for broken kernels. A software package was developed to extract various morphological, colour and texture features from the images captured. Then the experimental data were subjected to multivariate analysis. Principal component analysis was employed to differentiate samples from different categories. Partial least square discriminant analysis and venetian blinds cross-validation were used to develop classification models. The best detection accuracies of samples were 92·7, 88·0 and 89·6% for black germ kernels, broken kernels and sound kernels. The results have proved that it is feasible and effective to employ partial least square discriminant analysis for feature selection and defective kernel detection.