In the present study, near-infrared reflectance spectroscopy (NIRS) technology was applied to implement early diagnosis of two kinds of wheat rusts, i.e. wheat stripe rust and wheat leaf rust, by detecting wheat leaves as disease symptom has not appeared. The wheat leaves were divided into five categories including healthy leaves, leaves in the incubation period infected with P. striiformis f. sp. tritici, leaves showing symptom infected with P. striiformis f. sp. tritici, leaves in the incubation period infected with P. recondita f. sp. tritici and leaves showing symptom infected with P. recondita f. sp. tritici. Near infrared spectra of 150 wheat leaves were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using distinguished partial least squares (DPLS). For building the model, second-order derivative method was regarded as the best preprocessing method of the spectra and the spectral region 4 000~8 000 cm-1 was regarded as the optimal spectral region. Using the model with different training sets and testing sets, the average identification rate of the training sets was 96.56% and the average identification rate of the testing sets was 91.85%. The results proved the model’s stability. The optimal identification rates were obtained while the ratio of training set to testing set was 2∶1 and the number of principal components was 10. The identification rate of the training set was 97.00% and the identification rate of the testing set was 96.00%. The results indicated that the identification method based on the NIRS technology developed in this study is feasible for early diagnosis of wheat stripe rust and wheat leaf rust.