Prediction of Cleaning Loss of Rice Wheat Combine Harvester Based on Dynamic Bayesian

Abstract

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate.