Research on the method of predicting feeding volume of rice combine harvester base on machine vision

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%.