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：作物管理（包括氮肥施用和灌溉管理）对产量、经济效益及环境具有显著影响。尽管存在管理指南，但在特定种植环境和作物条件下，寻找最优管理实践仍具挑战性。前期研究采用强化学习与作物模拟器解决该问题，但训练出的策略要么性能有限，要么无法部署于现实场景。本文提出一种智能作物管理系统，通过强化学习、模仿学习及基于农业技术转让决策支持系统的作物模拟，同步优化氮肥施用与灌溉。我们首先采用深度强化学习（特别是深度Q网络）训练需要模拟器全部状态信息作为观测的管理策略（记为全观测），继而通过模仿学习，以先前全观测下训练的强化学习策略动作为引导，训练仅需少量即可从现实世界获取的状态信息的管理策略（记为部分观测）。以佛罗里达州玉米为例开展实验，将训练策略与玉米管理指南进行模拟对比。结果表明，全观测与部分观测条件下的训练策略均取得更优效果：实现更高利润，或在维持相近利润的同时降低环境影响。此外，部分观测管理策略直接使用可获取的信息，具备现实部署可行性。