CSKNN: Cost-sensitive K-Nearest Neighbor using hyperspectral imaging for identification of wheat varieties

Abstract

Hyperspectral imaging techniques are widely used for rapid, efficient, and non-destructive identification of wheat varieties. However, the interference of noise in hyperspectral images and the underutilization of spatial information by most methods are two challenging issues in identifying wheat varieties. In this paper, we present a new approach called Cost-sensitive K-Nearest Neighbor using Hyperspectral imaging (CSKNN) to address these issues. First, we fuse 128 bands acquired by hyperspectral imaging equipment to obtain hyperspectral images of wheat grains, and employ a central regionalization strategy to extract the region of interest. We then use a smoothing denoising strategy to remove noise from the hyperspectral images and improve the saliency of the object grains. Furthermore, we consider the characteristics of different bands and use linear discriminant analysis to compress features, reducing intra-class differences and increasing inter-class differences. Finally, we propose a Cost-sensitive KNN for training and testing of wheat varieties. Our experiments on different strains and varieties of wheat datasets in the same region show that our CSKNN achieves high classification accuracies of 98.09% and 97.45%, outperforming state-of-the-art methods.