In order to detect the occurrence of rice koji disease quickly and accurately, and realize the early monitoring of rice koji disease in a large area, the airborne UHD185 hyperspectrometer was used to collect multiple sets of rice canopy hyperspectral image data with the disease area, and the image data was preprocessed to establish data sets. The classification training of healthy and diseased areas was carried out, and the recognition model of support vector machine (SVM) and principal component analysis (PCA) plus artificial neural network (ANN) was established to identify diseased rice, and the accuracy of the recognition model was verified by validating the samples. The support vector machine recognition model selected false color images under two sets of feature wavelengths. The first group of wavelength combination (TZH1) was 654nm, 838nm and 898nm, and the second wavelength combination (TZH2) was 630nm, 762nm and 806nm. The total commission error/omission error of the two sets of data reached 4.24% and 5.41%, respectively. Among them, the SVM model of the S-type kernel function had the best diagnostic performance, and the overall classification accuracy could reach 95.64% and the Kappa coefficient was 0.94, which basically achieved the purpose of accurately identifying rice disease areas. The recognition model of principal component analysis plus artificial neural network used the first three principal components, and the contribution rates were 93.67%, 2.80% and 1.24%, respectively, which were used as the optimal wavelength to establish the ANN recognition model. In the classification results, the nonlinear classification was better than the linear classification, the overall classification accuracy was 96.41% and the Kappa coefficient was 0.95. The results showed that through the diagnostic results of the support vector machine in the data of the two experimental groups, it can be seen that the classification accuracy of the recognition model using the support vector machine was stable overall, and there was no obvious difference in the diagnostic effect of the four kernel functions. In terms of overall classification accuracy, the nonlinear classification in the principal component analysis plus artificial neural network recognition model was 0.77 percentage points higher than that of the S-type kernel function classification of the support vector machine recognition model. Therefore, the nonlinear classification model in principal component analysis plus artificial neural network model was more suitable for early monitoring of rice koji disease.