Journal Article

Research on Performance Evaluation Method of Rice Thresher Based on Neural Network

Guo, Weiling, Da, Qiang, Zhang, Xiaolei, Huang, Yanfei, Li, Dexin, He, Dongyu, He, Gengchao

2022

Overview

Because the threshing device of a combine harvester determines the harvesting level and threshing separation performance of a combine harvester, the analysis and study of the threshing device of a combine harvester is key to improving its performance. Based on the threshing device of a half-feed combine harvester, the simulation model of a discrete element threshing device is established in this paper. With the threshing drum rotation speed, feed volume, and concave sieve vibration frequency as the variable factors, the BP neural network model and linear regression equation model established for the loss rate and impurity content for two kinds of threshing performance indicators, respectively, and through the discrete element threshing performance test, two kinds of methods of threshing performance prediction are analyzed. The results show that the neural network and linear regression can be used for the threshing performance indicators, however, the BP neural network prediction effect has a better prediction precision, better reliability, and the trained neural network can be used in the general case of the threshing performance indicators. This provides a new idea for improving the threshing performance of a combine harvester.

• Publisher: MDPI AG
• Subject: Agricultural machinery / Analysis / Back propagation networks / Business metrics / Combine harvesters / Discrete element method / Farm equipment / Harvesting / Indicators / Methods / Network reliability / neural network / Neural networks / Performance evaluation / Performance prediction / Performance tests / prediction / Regression analysis / Regression models / rice thresher / Simulation / Simulation models / threshing performance