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Fusarium head blight is one of the main infection diseases in wheat, and the infection of wheat has serious impact on food safety. In order to explore the rapid and nondestructive detection of wheat scab, the identification of wheat scab was carried out using spectral analysis and image processing in hyperspectral imaging technology. Standard normal variable transform (SNV) and multiple scatter correction (MSC) methods were used for spectral data pretreatment, and continuous projection algorithm (CARS) and the positive adaptive weighted (SPA) algorithm were used to select wavelength. The results showed that the determination coefficients ( R 2 ) of MSC-SPA and SNV-SPA were 0.901 9 and 0.900 6, respectively, the root mean square errors were 0.223 8 and 0.223 2, respectively, and the numbers of selected wavelength were 7 and 5, respectively. Support vector machine (SVM) and BP neural network algorithms were used for modeling. The results showed that the accuracy of the four models were above 90%. The accuracy rates of MSC-SPA-SVM and SNV-SPA-SVM were 97.08% and 94.17% for model calibration set, respectively, and those for the model validation set were 98.33% and 97.50%, respectively, which were better than those for model calibration set. According to image information analysis of disease wheat in hyperspectral image, the principal component analysis method was applied, and the best wavelength image was chosen at 627.698 nm according to the weight coefficient. Image processing method was used for preprocessing, feature extraction, etc. The morphological parameters and texture feature parameters of the best wavelength image were extracted respectively, and the optimal parameters of the model were selected according to the correlation analysis of the feature parameters. Ten-fold cross-validation method was adopted to establish linear discriminant analysis, support vector machine and BP neural network identification models. The results showed that the recognition accuracy of the three identification algorithms were all above 90%, which indicated that the proposed method were feasible and effective.