Crop management, including nitrogen (N) fertilization and irrigation management, has a significant impact on the crop yield, economic profit, and the environment. Although management guidelines exist, it is challenging to find the optimal management practices given a specific planting environment and a crop. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system which optimizes the N fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require all state information from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a limited amount of state information that can be readily obtained in the real world (denoted as partial observation) by mimicking the actions of the previously RL-trained policies under full observation. We conduct experiments on a case study using maize in Florida and compare trained policies with a maize management guideline in simulations. Our trained policies under both full and partial observations achieve better outcomes, resulting in a higher profit or a similar profit with a smaller environmental impact. Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.
翻译:包括氮(N)肥沃和灌溉管理在内的作物管理对作物产量、经济利润和环境有重大影响。虽然有管理准则,但找到适合种植环境和作物的最佳管理做法仍具有挑战性。以前的工作使用了强化学习(RL)和作物模拟器来解决问题,但经过培训的政策绩效有限,或者无法在现实世界部署。在本文中,我们提出了一个智能作物管理系统,通过RL、模仿学习(IL)和利用农业技术转让决策支持系统(DSSAT)进行作物模拟,同时优化N肥沃和灌溉。我们首先利用深度的RL,特别是深Q-网络,来培训需要模拟器提供所有国家信息的管理政策,作为观察(作为全面观察)。然后我们援引IL来培训管理政策,只要在现实世界可以轻易获得的有限的国家信息(被看成是部分观察),通过全面观察对以前RL培训的政策的行动进行模拟。我们首先利用深层次的RL,然后在佛罗里达进行案例研究,然后用经过培训的更高水平的政策来进行更精确地模拟,然后用我们经过培训的玉米管理的结果进行更精确的模拟。</s>