AN INTEGRATED GPR AND MACHINE LEARNING APPROACH FOR PRECISION SOIL MOISTURE MONITORING

Abstract

Precision agriculture has emerged as a critical approach for optimizing resource utilization and improving crop productivity in large-scale farming systems. Among the key factors influencing crop health and yield, root-zone soil moisture plays a vital role in determining irrigation efficiency and plant growth. Traditional soil moisture monitoring techniques, such as gravimetric sampling and sensor-based methods, often lack scalability and fail to provide continuous spatial coverage in mega farms. Ground Penetrating Radar (GPR) has gained attention as a non-invasive geophysical technique capable of estimating subsurface soil properties, including moisture content. However, interpreting GPR signals is complex due to noise, heterogeneous soil conditions, and nonlinear relationships between signal characteristics and moisture levels. This paper proposes a machine learning-enhanced framework for analyzing GPR data to accurately assess root-zone soil moisture in large agricultural fields. The framework integrates signal preprocessing, feature extraction, and advanced learning models, including support vector machines, random forests, and deep neural networks, to map GPR reflections to moisture levels. Spatial and temporal patterns are incorporated to improve prediction accuracy and adaptability to varying environmental conditions. Experimental results demonstrate that the proposed approach significantly improves estimation accuracy compared to traditional GPR analysis methods, reducing error rates and enhancing spatial resolution. The system is scalable and capable of real-time deployment using mobile GPR systems integrated with farm management platforms. This research contributes to the development of intelligent, data-driven irrigation strategies, enabling farmers to optimize water usage, reduce environmental impact, and increase crop yield in mega farming environments.