ENHANCING PRECISION AGRICULTURE THROUGH MACHINE LEARNINGBASED GPR SOIL MOISTURE ANALYSIS

Abstract

Precision agriculture plays a crucial role in improving crop productivity and resource management in large-scale farming. Accurate assessment of soil moisture, particularly in the root zone, is essential for efficient irrigation planning and sustainable agricultural practices. Traditional soil moisture monitoring methods often rely on manual sampling or basic sensor-based techniques, which may provide limited spatial coverage and require significant labor and time. This study proposes an advanced approach that integrates Ground Penetrating Radar (GPR) with machine learning techniques to enhance soil moisture analysis in precision agriculture. GPR technology enables nondestructive and large-scale subsurface soil analysis by capturing electromagnetic signal reflections from different soil layers. However, interpreting GPR signals can be challenging due to noise, soil heterogeneity, and environmental factors. To address these challenges, machine learning algorithms are employed to process and analyze GPR signal data, enabling accurate estimation of root zone soil moisture levels. The proposed system includes data preprocessing, feature extraction from GPR signals, and predictive modeling using machine learning methods such as Random Forest, Support Vector Machine, or Neural Networks. These models learn complex relationships between GPR signal characteristics and soil moisture conditions.The integration of machine learning with GPR technology improves the accuracy, efficiency, and scalability of soil moisture assessment in large agricultural fields. The proposed approach supports data-driven irrigation management, optimized water usage, and improved crop health monitoring, contributing to sustainable and intelligent precision agriculture systems.