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%.

翻译：农业部门目前在水资源保护和作物产量优化方面面临重大挑战，主要源于淡水资源的稀缺性问题。传统灌溉调度方法往往难以满足大规模灌溉系统的需求。为解决这一问题，本文提出一种预测性灌溉调度器，该调度器利用机器学习的三种范式优化灌溉方案。所提出的调度器采用k-means聚类方法，基于土壤水力参数和地形信息将农田划分为不同的灌溉管理区。此外，采用长短期记忆网络为每个管理区建立动态模型，实现土壤湿度动态的准确预测。该调度器被构建为混合整数模型预测控制问题，旨在最大化水分吸收的同时最小化总用水量和灌溉成本。为应对混合整数优化挑战，采用近端策略优化算法训练负责制定每日灌溉决策的强化学习智能体。为评估所提调度器的性能，选取加拿大莱斯布里奇市一块26.4公顷的农田作为案例研究对象，涵盖2015和2022两个生长季。结果表明，与传统灌溉调度方法相比，所提调度器在两个生长季中均在水资源利用效率和作物产量提升方面表现出优越性。值得注意的是，所提调度器实现了6.4%至22.8%的节水效果，同时产量提升2.3%至4.3%。