Quantitative prediction of disease degree of rice panicle and neck blast is essential on accurate prevention and control measures. The study of field canopy scale can provide a theoretical basis for hyperspectral sensors. In this paper, the rice which was damaged by panicle and neck blast was regarded as the research object, and hyperspectral canopy reflectance was acquired by SVC HR768i spectral radiometer at two different periods during the filling stage. The percentage of rice plants diseased represented disease degree index. The canopy spectral data were preprocessed by nine-point smoothing and resampled at 1 nm intervals. Vegetation indexes were calculated and hyperspectral characteristic parameters were extracted by continuum removal (CR) and first derivative reflectance. Were totally analyzed between each period, the response ability of different spectral transformation, vegetation index and hyperspectral characteristic parameters to disease degree through correlation analysis, and prediction models of disease degree were established through the random forest (RF) based on vegetation index and hyperspectral characteristic parameters, respectively. The two single-period prediction models were compared to select the common input to generate a disease degree prediction model which mixed data in two periods. The results demonstrated that: (1) Canopy hyperspectral reflectance processed by continuum removal (CR) method could effectively enhance the spectral information which isclosed related to the disease degree. The sensitive bands were the near-infrared region (960～1 050 nm) and (1 150～1 280 nm), and the correlation coefficient was above 0.80. (2) In the correlation analysis between hyperspectral characteristic parameters and the disease degree, the correlation coefficient of absorption valley parameters extracted by CR was higher than other parameters, and that of area (A3, A4), depth (DP3, DP4) and slope (SL4, SR4) in the absorption valley V3(910～1 100 nm) and V4(1 100～1 300 nm) was above 0.74. (3) The absorption valley parameters which played a role as the model input showed the best result in the mixed data of two periods and that of every single period. In addition, the prediction accuracy reached a peak at the later filling stage, with R2=0.91 and RMSE=0.02 in the validation set. (4) The prediction accuracy of the mixed data of two periods was between that of two single-period, with R2=0.85, and RMSE=0.03 in the validation set. The results revealed the spectral response mechanism of rice panicle and neck blast at different periods during the filling stage and it was practical to predict disease degree by combining absorption valley parameters extracted by CR with the random forest model, which can be used to rapidly, accurately and nondestructively predictthe disease degree of rice panicle and neck blast and provided a theoretical basis for precise application of pesticides. Beyond that, it also provided some technical reference for aviation and aerospace remote sensing monitoring in the future.