Joseph Balderas / Mathematics / Faculty Mentor: Li Wang
Crop management is essential for optimizing yield and minimizing a field’s environmental impact, yet it remains challenging due to the complex stochastic processes involved. Recently, researchers have turned to reinforcement learning (RL), a promising tool for developing adaptive crop management policies. RL models aim to optimize long-term rewards by continuously interacting with the environment, making them well-suited for tackling the uncertainties in crop management. In the gym-DSSAT environment, one of the most widely used simulators for crop management, proximal policy optimization (PPO) and deep Q-networks (DQN) have shown promising results. However, these methods have not yet been systematically evaluated under identical conditions. In this study, we evaluated PPO and DQN across three different RL tasks using consistent default parameters, identical reward functions, and the same environment settings. Our results indicate that PPO outperforms DQN in fertilization and irrigation tasks, while DQN excels in the mixed management task.
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