AI-Based Soil Moisture Monitoring Using Sensor Networks and Machine Learning

Abstract

The efficient monitoring of soil moisture is essential for optimizing irrigation practices, improving crop productivity, and ensuring sustainable water resource management in agriculture. This study presents an artificial intelligence-based soil moisture monitoring system that integrates sensor networks with machine learning techniques to enable accurate and real-time assessment of soil water dynamics. The proposed methodology utilizes multi-source data collected from distributed soil moisture sensors, along with environmental parameters such as temperature, humidity, rainfall, and soil characteristics. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning models, including Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, are employed to capture complex nonlinear relationships between environmental variables and soil moisture variations. The system is designed with real-time data acquisition and adaptive learning capabilities, enabling continuous monitoring and dynamic analysis under changing field conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse soil types and agro-climatic environments. Experimental results demonstrate that the proposed approach achieves high monitoring accuracy and significantly improves soil moisture assessment compared to conventional manual and empirical methods. Additionally, the system provides actionable insights for optimized irrigation scheduling, reducing water wastage and enhancing crop yield. The findings highlight the effectiveness of integrating artificial intelligence with sensor-based monitoring for precision agriculture applications. The study concludes that AI-based soil moisture monitoring systems offer a scalable, efficient, and data-driven solution for improving water management, enhancing agricultural productivity, and promoting sustainable farming practices.