Deep reinforcement learning has considerable potential to improve irrigation
scheduling in many cropping systems by applying adaptive amounts of water based
on various measurements over time. The goal is to discover an intelligent
decision rule that processes information available to growers and prescribes
sensible irrigation amounts for the time steps considered. Due to the technical
novelty, however, the research on the technique remains sparse and impractical.
To accelerate the progress, the paper proposes a general framework and
actionable procedure that allow researchers to formulate their own optimisation
problems and implement solution algorithms based on deep reinforcement
learning. The effectiveness of the framework was demonstrated using a case
study of irrigated wheat grown in a productive region of Australia where
profits were maximised. Specifically, the decision rule takes nine state
variable inputs: crop phenological stage, leaf area index, extractable soil
water for each of the five top layers, cumulative rainfall and cumulative
irrigation. It returns a probabilistic prescription over five candidate
irrigation amounts (0, 10, 20, 30 and 40 mm) every day. The production system
was simulated at Goondiwindi using the APSIM-Wheat crop model. After training
in the learning environment using 1981--2010 weather data, the learned decision
rule was tested individually for each year of 2011--2020. The results were
compared against the benchmark profits obtained using irrigation schedules
optimised individually for each of the considered years. The discovered
decision rule prescribed daily irrigation amounts that achieved more than 96%
of the benchmark profits. The framework is general and applicable to a wide
range of cropping systems with realistic optimisation problems.

该研究采用深度强化学习技术，提出了一种有效的灌溉方法，旨在为农民提高粮食产量和效益。对于考虑的每天，决策规则基于九个状态变量输入，返回每天五个不同灌溉量的概率性建议，经过测试证明该决策规则可以在大多数情况下同时实现最高的灌溉效益和经济效益。