Machine Learning-Based Prediction of Soil Bulk Density Using Soil Penetration Resistance and Moisture

Soil bulk density (BD) is a critical indicator for evaluating soil physical properties and compaction levels. However, the traditional core method is labor-intensive, time-consuming, and destructive. Given the significant positive correlation between BD and soil penetration resistance (PR), and the crucial influence of gravimetric water content (GW), this study investigated the potential of using PR and GW data to predict BD. We integrated datasets from three representative study sites in China and Brazil, covering diverse soil texture types (sandy loam, clay loam, and clay), and employed two traditional empirical models (Multiple Linear Regression (MLR) and Multiple Nonlinear Regression (MNLR)) and three advanced machine learning (ML) models (Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB)) for prediction. The results demonstrated that the ML models significantly outperformed the traditional empirical models in prediction accuracy. On the independent validation set, the RF model exhibited the highest predictive performance, achieving a coefficient of determination (R2) as high as 0.932 and a root mean square error (RMSE) of only 0.074 g cm−3. Feature importance analysis indicated that GW was the most influential factor for predicting BD, showing a negative correlation. Furthermore, a critical accelerating point in the nonlinear positive relationship between BD and PR was identified at GW = 0.15 g g−1. Our findings confirm that ML approaches, especially RF and SVM models, offer an efficient and high-accuracy alternative for the rapid, routine estimation of soil BD, providing a crucial quantitative basis for managing agricultural soil compaction, while recognizing their limitations in transferability to environments with different soil characteristics or limited data availability.