Performance Prediction of Web Wrapping Mechanism for Straw Baler Based on Data-Driven Approach

The importance of intelligent agricultural machinery in the development of modern agriculture cannot be ignored. This article first constructs a system modeling and data analysis framework, collects key operating parameters through sensors, and performs data preprocessing and feature engineering. Subsequently, this article selects the Random Forest (RF) algorithm to construct a preliminary performance prediction model, and evaluates and optimizes the model through methods such as cross validation. In addition, this article also explores the optimization of control strategies, achieving real-time monitoring and adaptive control based on predicted results. The experimental results show that the random forest model exhibits significant advantages in all aspects, with a prediction accuracy of up to 99.8%, a coefficient of determination above 0.95, and significantly better prediction efficiency than other models. In addition, the control strategy optimization experiment further confirmed the practical application value of the data-driven prediction model in improving the performance of the baler winding mechanism.