Development and Experimentation of a Universal Array-Style Grain Flow Sensor for Multiple Crops Based on Random Forest Flow Regression Model

To develop a grain flow sensor for combine auger grain outlets, a combine auger elevator was evaluated as the research object. A multi-point distributed array-style differential grain flow sensor for rice and wheat has been developed and tested on three field crops, rice, wheat, and rapeseed. The open system flow test bench was designed to compare the effects of differential processing in the time and frequency domains, as well as different filtering methods on the pre-processing of the collected raw sensor signals. Moreover, a random forest algorithm-based flow regression model was constructed for rice, wheat, and rapeseed based on the comparison of the flow signals of different grains. A weighted multiple linear regression model was constructed as the control group, and both bench and field tests were conducted. The results show that the sensor designed in this study can meet the needs of on-line grain flow monitoring. Meanwhile, the field monitoring errors for rice, wheat, and rapeseed based on the random forest flow regression model were −6.42~8.23%, −7.21~5.71%, and −4.19~4.78%, respectively, significantly better than the control group. The universal array-style grain flow sensor developed in this study provides significant practical value for the promotion and development of precision agriculture.