Greenhouse Irrigation Control Based on Reinforcement Learning

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.