  The agricultural sector currently faces significant challenges in water
resource conservation and crop yield optimization, primarily due to concerns
over freshwater scarcity. Traditional irrigation scheduling methods often prove
inadequate in meeting the needs of large-scale irrigation systems. To address
this issue, this paper proposes a predictive irrigation scheduler that
leverages the three paradigms of machine learning to optimize irrigation
schedules. The proposed scheduler employs the k-means clustering approach to
divide the field into distinct irrigation management zones based on soil
hydraulic parameters and topology information. Furthermore, a long short-term
memory network is employed to develop dynamic models for each management zone,
enabling accurate predictions of soil moisture dynamics. Formulated as a
mixed-integer model predictive control problem, the scheduler aims to maximize
water uptake while minimizing overall water consumption and irrigation costs.
To tackle the mixed-integer optimization challenge, the proximal policy
optimization algorithm is utilized to train a reinforcement learning agent
responsible for making daily irrigation decisions. To evaluate the performance
of the proposed scheduler, a 26.4-hectare field in Lethbridge, Canada, was
chosen as a case study for the 2015 and 2022 growing seasons. The results
demonstrate the superiority of the proposed scheduler compared to a traditional
irrigation scheduling method in terms of water use efficiency and crop yield
improvement for both growing seasons. Notably, the proposed scheduler achieved
water savings ranging from 6.4% to 22.8%, along with yield increases ranging
from 2.3% to 4.3%.
