Abstract

This paper presents a novel Ground Penetrating Radar (GPR)-based framework for precisely estimating rootzone soil moisture, a key parameter in precision agriculture. The approach is structured as follows: First, we generate a synthetic dataset using gprMax, carefully calibrated against realworld data to reflect actual soil conditions. Feature engineering techniques are then employed to extract meaningful features from the GPR signals, followed by a rigorous selection process to identify the most effective machine learning (ML) model for soil moisture prediction. The proposed framework is crucial for optimizing irrigation practices and water resource management, directly contributing to more sustainable agriculture. Finally, we validate our model by integrating synthetic data with real GPR data collected at the SoilX Lab at Worcester Polytechnic Institute (WPI), enhancing prediction accuracy and generalization capability. By ensuring our model's applicability to diverse agricultural environments, we aim to support efficient farming practices that lead to better crop yields and food security.