IoT-driven soil moisture analytics integrating sensor networks and predictive models for efficient irrigation scheduling and water resource management

Abstract

Increasing irrigation demand under declining freshwater availability requires agricultural systems that can determine when, where, and how much water should be applied using continuously updated field conditions. This study develops an IoT-driven soil moisture analytics framework that integrates distributed sensor networks, temporal data processing, and predictive models for irrigation scheduling and water resource management. Soil moisture sensors deployed across root-zone depths and management zones generate time-stamped measurements alongside soil temperature, air temperature, relative humidity, rainfall, and evapotranspiration variables. Sensor observations are transmitted through IoT gateways and transformed into irrigation-relevant features, including moisture depletion rate, root-zone water deficit, antecedent moisture, drying trends, and threshold exceedance duration. Time-series and machine-learning models predict short-term soil moisture dynamics and estimate impending irrigation requirements before critical depletion occurs. Predicted moisture trajectories are combined with crop-specific allowable depletion thresholds and weather conditions to determine irrigation timing and required water volumes. Model evaluation incorporates MAE, RMSE, prediction deviation, irrigation water applied, water-use efficiency, and avoided over-irrigation. The resulting closed-loop system converts continuous field measurements into predictive irrigation decisions, enabling zone-specific water application, reducing unnecessary irrigation, preventing crop water stress, and improving allocation of limited agricultural water resources.