Integrating Machine Learning and IoT for Precision Irrigation: A Comprehensive Survey and Framework

The increasing global population and growing food requirements have placed immense pressure on freshwater availability, making efficient irrigation management crucial. Traditional irrigation approaches that rely on fixed schedules or farmers’ intuition often waste water and reduce yields. With recent progress in Machine Learning (ML) and Internet of Things (IoT) technologies, it is now possible to build intelligent irrigation systems that monitor real-time environmental and soil parameters to provide accurate water management recommendations. Unlike older systems that work on preset thresholds, this model uses continuous sensor feedback to learn and refine irrigation decisions dynamically. The proposed framework includes edge computing and farmer dashboards to ensure low latency and reliability, even in rural settings. It also focuses on explainability, helping farmers understand which factors most influence each irrigation suggestion. This paper provides an extensive review of papers (2020–2024) involving ML and IoT-based models for soil moisture prediction, crop selection, and nutrient management. Findings show that such frameworks can reduce water usage by up to 30% and improve yield consistency. The paper further presents a unified model integrating soil, nutrient, and environmental sensing into one predictive irrigation system and discusses challenges like limited datasets, sensor calibration issues, and scalability. Future directions include applying AIoT, edge computing, and interpretable ML models for transparent and sustainable precision agriculture.