Cotton contamination detection and classification using hyperspectral fluorescence imaging

Abstract

The presence of foreign matter in ginned cotton lowers the quality and ultimately the monetary value of cotton. Previous studies have shown benefits of using ultraviolet excited fluorescence to detect certain cotton contamination that is difficult to detect using other methods. The overall goal of this study was to explore the feasibility of using hyperspectral fluorescence imaging as a complementary tool for foreign matter differentiation. The mean spectra of lint and seven types of foreign matter were extracted from the hyperspectral fluorescence images using a region-of-interest-based approach. The principal component analysis was applied to select the optimal features from a total of 113 wavelengths covering the spectral range of 425–700 nm. The linear discriminant analysis with the selected wavelengths achieved an average classification rate of 90% for all samples. Therefore, this imaging method could be used as a complementary sensing modality to current instruments that are employed for cotton quality assessment in the textile industry.