抄録
Agricultural irrigation is transitioning from technology-oriented management to digitally enabled, governance-driven systems capable of addressing increasing water scarcity, climate uncertainty, and sustainability challenges. This review systematically synthesizes recent advances in intelligent irrigation technologies, including multi-source sensing, digital twins, and data-driven decision support, while evaluating their capabilities and application boundaries in improving water allocation, operational efficiency, and adaptive management. To integrate fragmented research, a four-layer analytical framework comprising data acquisition, process understanding, decision optimization, and governance coordination is proposed, providing a system-level perspective that links digital technologies with irrigation governance. The review further identifies critical scientific challenges, including limited model generalization, the trade-off between algorithm robustness and engineering applicability, insufficient cross-scale water allocation and decision coordination, and weak integration of institutional arrangements with digital technologies. These challenges reveal a fundamental paradigm shift from technology-centered optimization toward reliable, adaptive, and governance-oriented irrigation systems. Future research priorities are highlighted in trustworthy data infrastructures, physics-informed artificial intelligence, cross-scale collaborative optimization, and digitally enabled governance frameworks. By conceptualizing agricultural irrigation as an integrated socio–technical–ecological system, this review establishes a unified analytical framework that connects technological innovation, governance coordination, and system resilience, providing a theoretical foundation for the next generation of intelligent and sustainable irrigation systems.
