Sustainable irrigation is a critical component of modern agriculture due to increasing water scarcity, climate variability, and the need for precision resource management. Traditional irrigation systems, often based on fixed schedules or coarse environmental data, frequently lead to over-irrigation, under-irrigation, and inefficient water use. To address these limitations, this work introduces an AI-powered irrigation advisory framework that combines microclimate simulation, machine learning models, and real-time field-level sensing to generate accurate and adaptive water-use recommendations. The proposed system models localized microclimate parameters, including soil moisture, evapotranspiration, humidity flux, and temperature gradients, to provide more accurate short-term water demand estimates than traditional farm-level predictions. Machine learning algorithms continuously optimize the system, forecast crop-specific water needs, and dynamically identify patterns. To ensure robustness across diverse farming scenarios, the framework incorporates adaptive calibration mechanisms that adjust recommendations based on changing crop phenology and environmental conditions. We describe the implementation of this software-driven decision-support tool and its validation using both simulated and real-world agricultural datasets. Results demonstrate improved prediction reliability, a reduction in irrigation waste, and enhanced water-use efficiency compared to conventional scheduling methods. The proposed AI-powered sustainable irrigation advisor illustrates how microclimate-aware systems can advance next-generation smart agriculture, supporting productivity, environmental sustainability, and water conservation.
Solunke et al. (2026) studied this question.