Blight disease is a serious disease for corn. Therefore, there is an urgent need for a method for quickly understanding the condition of corn blight disease. In this study, UAV remote sensing is used as a new technology platform to explore the spectral response of corn canopy under the stress of blight disease, and UAV hyperspectral imaging technology is used to monitor and visualize the disease of blight disease. Therefore, this study collected data on corn growth stages (the tasseling period, the filling period, the maturity period), and used the UAV hyperspectral instrument to acquire the hyperspectral image of the canopy 500～900 nm. This research based on the original spectra and first-order differential spectral features of the acquired images, the position of the 12 sensitive spots of the blight disease was extracted. The positions of the 12 bands were: 514, 532, 553, 680, 714, 728, 756 and 818 nm, near-infrared, red, green and red edge positions. At the same time, based on the plant disease monitoring parameters proposed by the predecessors combined with the extracted sensitive band positions, 13 sets of monitoring spectral parameters for corn canopy blight disease were constructed. In this way, the sensitivity of different bands to the disease index (DI) value of blight disease was studied, and a monitoring model for monitoring corn canopy blight disease was constructed to verify the accuracy and stability of using the UAV remote sensing technology to monitor the DI value of blight disease. The results show that with the increase of the disease index, the first-order differential spectrum shows a typical “blue shift” phenomenon, and the correlation between the disease canopy DI value and the red (680～714 nm) and near-infrared (770～818 nm) reflectance and the red edge position (680～756 nm) of the first-order differential spectrum is more significantly, the correlation with the green band is low. Among the 13 groups of monitoring spectral parameters, 8 groups and the modeled canopy blight disease measured the DI value reached a very significant correlation level, R2 all reached above 0.8. Therefore, in this study, the spectral parameters of R2 with a growth period of 0.8 or higher were selected for the construction of the corn canopy blight disease monitoring model, and the correlation between the measured values of the test samples and the predicted values of the monitoring models was analyzed. The test shows that in the tasseling period, the regression slope (0.829 3) and the decision coefficient (R2=0.842 7) of the model DI-NDVI(SDλi, SDλj) are closest to 1, and the root mean square error (RMSE=4.59) and relative error (RE=12.3) are smaller, indicating that the prediction ability and accuracy of the model DI-NDVI(SDλi, SDλj) are higher than others. The results show that the corresponding models in each growth period have achieved good monitoring results, indicating that the research using UAV remote sensing has guiding significance for plant disease monitoring, and has certain reference value for the development of precision agriculture.