<![CDATA[Efficient water management has become increasingly important in modern agriculture due to growing increasing demand for finite water supplies and the necessity of promoting sustainable farming practices. Traditional time-based irrigation approaches often result in inefficient water use and limited adaptability to dynamic environmental conditions. This study presents the design and preliminary validation of an Internet of Things (IoT)- and Machine Learning (ML)-based smart irrigation framework at Technology Readiness Level (TRL) 3. The proposed framework integrates real-time sensor measurements, external weather information, and a Random Forest–based forecasting algorithm to determine crop water demand support adaptive irrigation scheduling. Experimental and simulation-based evaluations demonstrated that the Random Forest model achieved satisfactory predictive performance, with an RMSE of 0.19 L/m² and an MAE of 0.16 L/m². Furthermore, the proposed framework showed the potential to reduce irrigation water consumption by approximately 30% compared with conventional fixed-schedule irrigation while maintaining adequate water availability for crop growth. The integration of multi-source environmental data and predictive analytics enabled more accurate irrigation decisions, contributing to improved water-use efficiency and reduced irrigation-related operational costs. These findings highlight integrating connected sensing systems with machine learning techniques can facilitate evidence-based irrigation management while promoting long-term agricultural sustainability.]]>
