  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 principled 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 by a conventional rule common
in the region. The discovered decision rule prescribed daily irrigation amounts
that uniformly improved on the conventional rule for all the testing years, and
the largest improvement reached 17% in 2018. The framework is general and
applicable to a wide range of cropping systems with realistic optimisation
problems.
