A multi-parameter control method for maize threshing based on machine learning algorithm optimisation

Abstract

A complex field environment and differences in maize density cause feed rate fluctuations in the combine. Traditional threshing methods do not adjust multiple operating parameters based on the feed rate, resulting in low threshing performance and high power consumption. A multi-parameter control method for maize threshing based on the feed rate was proposed to overcome these problems. Random forest, support vector machine, and multiple linear regression machine learning algorithms were used to estimate the threshing performance indices for different feed rates. The algorithm (random forest) was used to construct a control model for the rotor speed, threshing gap, and top cover guide vane angle. The performance of the control system was compared with a threshing system with constant parameters to verify the control model's accuracy. The results demonstrated that the threshing unit with the control system outperformed the one without. The broken grain rate and the power consumption were 2.18% and 36.22% lower, respectively, whereas the unthreshed grain rate was not significantly different. Random forest was more suitable than the support vector machine and multiple linear regression models for establishing a multi-parameter control model for maize threshing. The adjustment of multiple threshing parameters significantly improved the operating performance for different feed rates.