Machine Learning-Based Estimation of Tractor Performance in Tillage Operations Using Soil Physical Properties

Accurate estimation of tractor performance under various soil conditions is essential for enhancing operational efficiency in precision agriculture. This study developed machine learning models to estimate tractor performance based on key soil physical properties. Three algorithms—decision tree (DT), CatBoost, and LightGBM—were employed to capture nonlinear relationships between soil parameters and tractor performance indicators. The input variables included soil moisture content, cone index, and particle composition, while the output variables were engine torque, power, slip ratio, and axle power. The models in this study were trained and validated using field data collected from eight paddy fields in Chungcheongnam-do (two in Seosan, two in Cheongyang, and four in Dangjin) and two paddy fields in Gyeonggi-do (Anseong), Republic of Korea. Results showed that models using multiple soil variables significantly outperformed those using single variables. In Model D, CatBoost demonstrated superior performance in predicting engine torque, engine power, slip ratio, and axle power, achieving R² values that were 7.0–14.2% higher than those of DT and 1.6–3.8% higher than those of LightGBM. These findings demonstrate the feasibility of using machine learning with minimal input data to estimate tractor performance, potentially reducing the reliance on extensive physical testing.