Modeling and control theory applied to precision agriculture irrigation systems have been essential to reduce water consumption while growing healthy crops. Specifically, implementing closed-loop control irrigation based on soil moisture measurements is an effective approach for obtaining water savings in this resource-intensive activity. To enhance this strategy, the work presented in this paper proposed a new set of water management strategies for the case in which multiple irrigation areas share a single water supply source and compared them with heuristic approaches commonly used by farmers in practice. The proposed water allocation algorithms are based on techniques used in real-time computing, such as dynamic priority and feedback scheduling. Therefore, the multi-area irrigation system is presented as a resource allocation problem with availability constraints, where water consumption represents the main optimization parameter. The obtained results show that the data-driven water allocation strategies preserve the water savings for closed-loop control systems and avoid crop water stress due to the limited access to irrigation water.

Precision irrigation provides a sustainable approach to enhancing water efficiency while maintaining crop productivity. This study evaluates a reinforcement learning approach, using the advantage actor–critic algorithm, for closed-loop irrigation control in a greenhouse environment. The reinforcement learning control is designed to regulate soil moisture near the maximum allowable depletion threshold, minimizing water use without compromising plant health. Its performance is compared against two common strategies: an on–off closed-loop controller and a time-based open-loop controller. The results show that the proposed controller consistently reduces irrigation water consumption relative to both benchmarks, while adapting effectively to environmental variability and the crop’s increasing water demand during growth. These findings highlight the potential of reinforcement learning to achieve a more efficient balance between water conservation and crop health in controlled agricultural systems.