Application of Hyperspectral Imaging Technology in Classification of Tobacco Leaves and Impurities

In this paper, a classification method of tobacco leaves and impurities is presented, which is based on hyperspectral imaging technology. By combining savitzky-golay smoothing filter, multiplicative scatter correction and random forest classifier, the problem of the classification of tobacco leaves and impurities is overcome. The classification accuracy of the sample was characterized by the overall classification accuracy (OA) and Kappa coefficient, and the OA is 99% and the Kappa coefficient is equal to 0.987. The experimental results indicate that the random forest is an excellent hyperspectral data classifier, and the classification method of tobacco leaves and impurities based on hyperspectral imaging technology can accurately distinguish tobacco leaves and impurities which are in tobacco leaves.