Optimizing Water Use in Maize Irrigation with Reinforcement Learning

Muhammad Alkaff, Abdullah Basuhail and Yuslena Sari ()
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Muhammad Alkaff: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Abdullah Basuhail: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Yuslena Sari: Department of Information Technology, Universitas Lambung Mangkurat, Banjarmasin 70123, Indonesia

Mathematics, 2025, vol. 13, issue 4, 1-21

Abstract: As global populations grow and environmental constraints intensify, improving agricultural water management is essential for sustainable food production. Traditional irrigation methods often lack adaptability, leading to inefficient water use. Reinforcement learning (RL) offers a promising solution for developing dynamic irrigation strategies that balance productivity and resource conservation. However, agricultural RL tasks are characterized by sparse actions—irrigation only when necessary—and delayed rewards realized at the end of the growing season. This study integrates RL with AquaCrop-OSPy simulations in the Gymnasium framework to develop adaptive irrigation policies for maize. We introduce a reward mechanism that penalizes incremental water usage while rewarding end-of-season yields, encouraging resource-efficient decisions. Using the Proximal Policy Optimization (PPO) algorithm, our RL-driven approach outperforms fixed-threshold irrigation strategies, reducing water use by 29% and increasing profitability by 9%. It achieves a water use efficiency of 76.76 kg/ha/mm, a 40% improvement over optimized soil moisture threshold methods. These findings highlight RL’s potential to address the challenges of sparse actions and delayed rewards in agricultural management, delivering significant environmental and economic benefits.

Keywords: reinforcement learning; sparse actions; delayed rewards; proximal policy optimization; maize; water use efficiency; irrigation strategies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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