Abstract: Rape biomass is one of the most important influencing factors for feed quantity, and operation quality, efficiency, and timely acquisition of feed quantity are of great significance to improve agricultural management. This paper aims to study the influencing factors and distribution law of rape biomass in the harvest period and establishes a prediction model on rape biomass. Firstly, the digital images of rape were taken by Unmanned Aerial Vehicle, and the actual biomass information was measured manually. 32 characteristic parameters related to rape biomass were extracted, and 10 characteristic parameters related to rape biomass were selected according to the significance test results and correlation analysis. With 10 selected features as input set and rape biomass as output, 3 prediction models of rape biomass based on random forest（RF）, principal component analysis（PCA）, and support vector machine（SVM） were established, respectively. Then, 3 established prediction models were trained using training set data, and parameters for the 3 prediction models were obtained. Finally, the 3 models were used to estimate rape biomass. The root mean square error（RMSE）, relative error（RE） and determination coefficient（R~2） of three models were 0.24 kg/m~2, 0.04%-22.23%, 0.87, 0.36 kg/m~2, 0.92%-21.14%, 0.71, and 0.28 kg/m~2, 0.28%-4.17%, 0.84, respectively. Compared to the estimation results of the 3 models, the RMSE estimation model based on RF was less than the models based on PCA and SVM; R~2 was the highest, and its RE was the smallest. Therefore, it is a better method to estimate rape biomass in the combined harvest period. The method of estimating rape biomass based on the UAV digital image proposed in this paper could provide reference and basis for intelligent prediction of feeding quantity in combined rape harvest operation.