  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.
