Integrating Recurrent Neural Networks and Deep Q-Networks for Precision Irrigation Control

Abstract

Agricultural irrigation accounts for a substantial portion of global water consumption, necessitating intelligent irrigation strategies. Conventional systems, such as fixed irrigation or evapotranspiration (ET)-based control, often exhibit inefficiencies due to reliance on static measurements and empirical farmer knowledge, lacking adaptive decision-making capabilities. To address this challenge, this paper presents an IoT-based framework integrating Recurrent Neural Networks (RNNs) for weather prediction and Deep Q-Networks (DQNs) for adaptive irrigation decisions. The RNN achieves high-precision forecasts for solar radiation, rainfall, and reference evapotranspiration (ETo) with determination coefficients ($R^{2}$) of 0.819,0.915, and 0.892, respectively, surpassing the other two neural network models in our tests. Simultaneously, the DQN agent learns irrigation policies that reduce water consumption by $\mathbf{1 6. 6 - 2 5. 3 \%}$ compared to conventional methods while maintaining post-irrigation soil moisture above the critical threshold ($V_{\text {mad }}=0.50$) in testbed experiments. The proposed RNN-DQN architecture demonstrates significant improvements in water-use efficiency and plant health maintenance, offering a robust solution for smart agriculture in water-limited, data-scarce arid regions.