Journal of Agronomy and Field Innovations

Background: Water scarcity and poor irrigation scheduling remain a major limitation for sustainable production of field crops globally. Conventional irrigation scheduling methods that rely on fixed intervals or simple soil moisture threshold levels often ignore the dynamic interactions among weather variability, crop growth stages, and soil hydraulic properties. This leads to inefficient water application and lower resource-use efficiency.
Objective: The study aimed at evaluating the performance of a Deep Reinforcement Learning (DRL)-based autonomous irrigation scheduling framework that integrates real-time soil moisture sensing, weather prediction, and crop growth modeling in a closed-loop decision-support system to optimize irrigation management under varying environmental conditions.
Methods: A field experiment was conducted in a typical cereal crop under three different irrigation management strategies: (i) conventional farmer practice, (ii) sensor-threshold-based automated irrigation, and (iii) DRL-based autonomous scheduling. Treatments were repeated over several growing seasons and under contrasting climatic conditions. Soil moisture, soil and air temperature, relative humidity, solar radiation and rainfall were continuously monitored with a network of Internet of Things (IoT) sensors. These real-time data were processed by a cloud-based DRL decision engine to generate irrigation recommendations by a composite reward function built to reduce crop water stress and maximize water conservation. Appropriate statistical analyses were used to evaluate the performance of crop yield, irrigation water applied, water use efficiency, irrigation efficiency, dynamics of soil moisture, and evapotranspiration.
Results: The autonomous scheduling system using DRL greatly outperformed both farmer practice and sensor-threshold automation. Compared to conventional farmer practice, the DRL framework reduced total irrigation water application while maintaining or improving grain yield, resulting in higher water use efficiency and irrigation efficiency. Soil moisture was more consistently maintained within the optimal range for the root zone, and crop water demand was better satisfied in terms of evapotranspiration patterns during different phenological stages. Statistical analyses were used to confirm significant improvements in yield stability, water savings and resource-use efficiency under the DRL approach across multiple seasons and climate scenarios.
Conclusion: Results show that Deep Reinforcement Learning can substantially improve irrigation decision-making by integrating artificial intelligence, IoT-based sensing, forecasting the weather, and crop growth modeling into an autonomous precision irrigation framework. The proposed system provides a scalable and climate-smart solution for increasing agricultural water productivity and supporting sustainable field crop production under increasing water scarcity. The study offers conceptual and empirical evidence on the adoption of AI-driven irrigation management systems in various agro-climatic environments.