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The feeding volume parameter of combine harvester greatly affects harvesting efficiency. Therefore, this paper proposes
a feeding volume prediction method. This method uses the camera installed at a certain height above the front of combine
harvester to capture the images of crops during operation. Then, the ARM processor unit does image processing and BP
neural network inference. Here, the image processing includes color space conversion, histogram equalization, filtering
operation and other operations. And its output is the pixel value of rice panicle layer. As for the neural network, the pixel
value, rice stubble height, moisture content and grass to grain ratio are used as the input parameters, and the output of
network is feeding volume. The result of neural network shows the decisive factor of predictive model up to 0.95, which
is able to predict the feeding volume properly. At last, according to the result of experiments, this method predicts feeding
volume well, and the relative error of the predicted average feeding amount is 10%.
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