Abstract: Rice blast is one of the most serious rice diseases and significantly impacts rice yields. In recent years, it is a hotspot to use hyperspectral imaging technology for the non-destructive identification of rice blast. However, nutrient deficiency in rice(such as nitrogen, potassium, etc.) will probably result in the chlorosis similar to rice blast. Therefore, to differentiate between them is very important for field management. In this study, field trials of rice blast and nitrogen stress were carried out in Fangzheng, Harbin, and 2 rice varieties with weak resistance were involved. From 8 to 10 in July, 2015, 4 types of rice leaves from both 2 varieties, including 60 in group of health, 60 in group of nitrogen deficiency, 60 in group of mild infection and 60 in group of severe infection, were collected and their hyperspectral images were captured with the HeadWall hyperspectral imaging system, and then the average reflectance spectrum of interest region of different leaves were acquired using the environment for visualizing images. In order to explore 4 types of spectral characteristics, the average spectrum of each type sample data, which was smoothed with polynomial convolution smoothing(Savitzky-Golay, SG), were calculated as a spectral curve of each category. Significant differences were found at the following three positions: the range around 560 nm in the reflection peak of green wavelength region; the range from 620 nm to 670 nm in red wavelength region; and particularly remarkable in the range around 760 nm in high reflectance of the near infrared region. The models of rice leaf blast recognition were established by taking advantage of a partial least squares-discriminate analysis method(PLS-DA) and the principle component analysis plus support vector machine(PCA-SVM), and using three different data pretreatment methods to preprocess original reflectance spectrum data, i.e., SG, standard normal variate transformation(SNV) and multiplicative scatter correction(MSC). The models were tested with the cross-validation strategy. The key of PLS-DA model is to select appropriate number of factors, 20 of which were determined by repeated testing, and the PLS-DA models were established by Fisher method. The prediction effects of the three models with preprocessing spectrum were all greater than 96.3%, and better than the original reflectance spectra. The PLS-DA model with SNV pretreatment got the best discrimination results and the prediction accuracy rate was 100%. The PCA-SVM models were constructed by taking the first 15 principal components as inputs and by adopting radial basis function as kernel function with the penalty coefficient was 10000 and kernel radius was 0.00599. The prediction accuracy of the three models with preprocessing spectrum were all equal to or greater than 95%, also better than the original reflectance spectra, and the discrimination results of PCA-SVM model with MSC pretreatment was 97.5%. This study provides a new idea and method for the nondestructive detection and identification of rice leaf blast, and also lays a foundation for a wide range of remote sensing and monitoring for rice blast.