Powdery mildew and stripe rust are two of the most prevalent and destructive wheat diseases causing severe decreases in wheat yield in China. It is necessary to quantitatively identify different diseases for spraying specific fungicides. In this study, a line-scanning hyperspectral imaging system (ImSpector V10E) was utilized to capture spectral and imagery information of wheat leaves infected by powdery mildew, stripe rust and normal leaves. Based on 320 hyperspectral images, strong spectral reflectivity responses were discovered at the bands of 550～680 nm in the wheat leaves infected with powdery mildew and stripe rust after the savitzky-golay （SG） smoothing method. To reduce the dimensionality of the spectral matrix, 3, 6 and 30 variables were extracted as sensitive wavelengths from full spectra for different diseases using X-loadings of principal component analysis (PCA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), respectively. Least squares support vector machine (LS-SVM) and extreme leaning machine (ELM) were applied to build identification models using full spectra and sensitive wavelengths extracted by X-loadings of PCA,SPA and CARS to distinguish powdery mildew, stripe rust and normal leaves. The accuracy rates of all the models in the calibration set and test set were above 94.58%. Among these models, the ELM classification model combined with X-loadings of PCA had the best performance, with accurate identification rates of 99.18% on the calibration set and 100% on the test set. Moreover, this model was simple in structure with only three variables (560, 680 and 758 nm). Meanwhile, the microstructure of three kinds of wheat leaves were also studied. Although the infection mechanisms of these two diseases were slightly different, they both destroyed the mesophyll cells, reduced chlorophyll content and photosynthesis markedly. The string of changes leaded to weakened light absorption but increased reflectivity in the visible light band. Thus, the results indicated the potential for the rapid and non-destructive detection of wheat diseases by hyperspectral imaging, which could help to develop online multispectral detection system for different kinds of plant diseases.