Early Classification and Detection of Kiwifruit Soft Rot Based on Hyperspectral Image Band Fusion

Kiwifruit soft rot is the most serious fungal disease in the kiwifruit postharvest storage and sales process. It has a long incubation period, and it is difficult to classify it by manual screening when it does not show obvious symptoms in the early stage of infection. Therefore, hyperspectral imaging technology (470 similar to 900 nm) was used to study the early detection and identification of kiwifruit soft rot. In the experiment, 295 hyperspectral images of healthy kiwifruit and early and late kiwifruit infected with soft rot were collected, and the samples were divided into training set and test set samples according to 7: 3 by Kennard stone algorithm. Firstly, the region of interest of the samples was selected, and then the average spectrum of the region was taken as the original spectral curve of the sample. Principal component analysis (PCA), successive projections algorithm (SPA) and competitive adaptive reweighting sampling algorithm (CARS) were used to extract spectral features from original spectral curves. Secondly, non subsampled contourlet transform (NSCT) was used for band fusion of the 8 feature bands in the SPA solution process to obtain the fusion image, and then gray level co-occurrence matrix method (GLCM) was used to extract the texture features of the fusion image. Finally, the spectral features and texture features were fused, and the nearest neighbor algorithm (KNN), random forest (RF) and support vector machine (SVM) classification models were established respectively for the early classification and detection of kiwifruit soft rot. In addition, this paper also compares with the texture features extracted from principal component images or feature bands in other literatures. The main innovation of this paper is using NSCT to fuse the feature band images and then extract the texture features, which not only reduces the feature dimension and feature redundancy, but also integrates the complementary information of different band images to improve the classification accuracy. The experimental results show that SVM is the most suitable classifier for this study, and the classification results using spectral features or texture features alone are not satisfactory. However, the classification accuracy can reach 92.05% after the fusion of the two features. Most of the early samples of kiwifruit soft rot have been correctly identified, which indicated that the fusion of the two features obtained the different information of spectrum and image in hyperspectral images. It embodies the "spatial spectral unity" of hyperspectral images. In this study, a rapid and accurate non-destructive test was carried out on kiwifruit at the early stage of soft rot, which could provide some reference and guiding significance for the quality classification of kiwifruit after harvest.