RGB image and monochromatic image of hyperspectral image for identification of apple fungi infection

Abstract

Detection of apple fungi infection is significant to provide the customized prevention and control strategies and ensure food safety. In this study, an identification method of infection of Botrytis cinerea and Rhizopus stolonifera was developed using the RGB images and monochromatic images (MIs) of effective wavelengths (EWs) of hyperspectral imaging. RGB images converted by CIE 1931 colour matching functions, and MIs of EWs were screened by random frog from hyperspectral images. U-Net combining data splicing strategy was adopted to segment the region of rot (ROR). Network features of RGB images and MIs of EWs of ROR were extracted by VGG16 and adopted to develop the classification models of fungi infection by using SVM, RF and KNN. The fused features of two-type images obtained the better classification, outperforming the other one-type image, and the optimal accuracy in prediction set of 99.25% was gotten from the SVM model. The proposed method provides the accurate detection of apple fungi infection and is beneficial to improve the quality of apple fruit.