Disease stress is one of the main factors causing a reduction in wheat production and threatening food security. How to distinguish similar diseases accurately and diagnose disease severity scientifically is becoming a hot topic worldwide. The objective of this study is to discriminate powdery mildew and yellow rust of winter wheat, two common fungal diseases in the Chinese wheat-growing region. In the study, a high-resolution hyperspectral imaging system(ImSpector V10E) was utilized to capture spectral and imagery information of wheat leaves infected by two diseases. The dimensionality reduction of hyperspectral images was done by using principal component analysis(PCA), and with the density slice method, the recognition accuracy for the disease area at leaf level can be 97%. On this basis, the spectral difference of two diseases was analyzed, and 12 disease-sensitive bands were selected in the light of the second principal component(PC-2) images. The bands for powdery mildew were at 519, 643, 696, 764, 795 and 813 nm, while those for yellow rust were at 494, 630, 637, 698, 755 and 805 nm. Furthermore, a support vector machine(SVM) discriminant model was established based on selected sensitive wavebands, and its accuracy reached 92%. The results revealed that the hyperspectra combined with feature extraction of high-resolution imagery could effectively achieve discrimination of powdery mildew and yellow rust at leaf level, which will provide a theoretical foundation for developing a portable recognition device.