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In order to quickly, nondestructively and efficiently monitor the diseases and insect pests of red mite in chestnut trees, the hyperspectral images and RGB images of each leaf were collected from locally infected leaves, unevenly infected leaves, recovered infected leaves and infected leaves with different degrees of infection by using UHD185 hyperspectral camera and digital camera. The RGB images were used as reference to select the regions of interest (ROI) of each leaf. The spectral curves of ROI were extracted from the hyperspectral images, and six spectral features and characteristic wavelengths of green peak, red valley, low position, red edge, high position and high shoulder of the spectral curves were extracted by differential operation. A large number of measured data were used to analyze the leaf-level variation of the spectral characteristics and characteristic wavelengths of chestnut leaves with the damage degree of red mite pests and diseases, so as to obtain the best spectral characteristics for identifyingred mite pests. After that, the hyperspectral image of the experimental area was obtained by using the UHD185 camera carried by the unmanned aerial vehicle (UAV). The results showed that the best spectral characteristics of monitoring the harm degree of Chinese chestnut red mite were low position and red edge, and the coefficient of determination between red mite and disease and pest exceeded 0.6. The blue shift of characteristic wavelength could be found in mild red mite pest by using these two characteristics, which proved that the red mite pest could be found in 14～21d before the large-scale occurrence of red mite in chestnut tree by UAV hyperspectral remote sensing. The research result can provide a scientific basis for the timely management of diseases and insect pests.