Classification of Fungal Infected Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Support Vector Machine

Abstract

A classification algorithm was developed to differentiate individual fungal infected (Aspergillus niger, Aspergillus glaucus, and Penicillium spp.) and healthy wheat kernels. A near-infrared reflectance hyperspectral imaging system captured hyperspectral images at 20 wavelengths spaced evenly between 1000 nm and 1600 nm. Four statistical features (mean, variance, skewness, and kurtosis) were extracted from the hyperspectral image data of single kernels at each wavelength. The statistical features at all wavelength levels composed the pattern vector of a single kernel. The dimensionality of pattern vectors was reduced by principal component analysis. A multi-class support vector machine with kernel of radial basis function was used for classification. Using the statistical features, the wheat kernels infected by Aspergillus niger, Aspergillus glaucus, and Penicillium spp. and healthy wheat kernels were classified with accuracies of 92.9%, 87.2%, 99.3%, and 100%, respectively. Almost perfect classification was obtained under the infected vs. healthy model. There was 10.0% misclassification between Aspergillus niger and Aspergillus glaucus infected wheat samples.