Optimizing Irrigation Scheduling Using Deep Reinforcement Learning

task of irrigation scheduling involves sequentially establishing both the timing and quantity of irrigation to be administered to the field throughout the course of the growing season. This task can be conceptualized as a Markov decision process. Reinforcement learning (RL), a machine learning approach that leverages rewards acquired through interactions with the environment to steer behavior and progressively develops a strategy to maximize cumulative rewards, is well-suited for managing sequential decision-making processes such as irrigation scheduling. Deep RL, a combination of RL with deep learning techniques, has the potential to offer novel solutions to intricate cognitive decision-making challenges in intricate states. In this research, a deep RL-based irrigation scheduling approach will be presented to enhance the optimization of economic return in irrigation applications. This method involves computing the irrigation quantity for each step while taking evapotranspiration (ET), soil moisture, future precipitation probability, and the current crop growth stage into consideration. The simulation results showed a significant improvement in economic return, 5.7% and 17.3% for a wet season and a dry season, respectively, while water-saving effect is similar to conventional threshold-based methods.