Soil Compaction Prediction in Precision Agriculture Using Cultivator Shank Vibration and Soil Moisture Data

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data were collected from a cultivator shank using the AVDAQ system. The relationship between shank vibrations and soil compaction, as measured by a cone penetrometer, was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. These findings demonstrate the feasibility of using vibrations generated during tillage to estimate soil compaction under real-time field conditions. With further validation, this approach could be integrated into tractors to enable in-situ soil sensing, reduce tillage intensity, and support more sustainable and energy-efficient cultivation practices.