抄録
Smart irrigation systems integrating the Internet of Things (IoT), advanced sensing technologies, and intelligent decision-making have become a key pathway toward precision water management and sustainable agriculture. This review synthesizes recent advances in multi-source sensing, sensor networks, and low-power communication, edge–cloud collaborative computing, intelligent decision-making models, and automated irrigation control. The strengths and limitations of representative sensing technologies, communication architectures, and decision-support approaches are critically compared in terms of sensing accuracy, reliability, energy efficiency, and application suitability. Current evidence shows that, despite significant progress in precise irrigation and real-time regulation, major challenges persist in heterogeneous data fusion, model generalization and interpretability, system robustness, operational costs, and large-scale deployment. Regional differences in climate, soil properties, and crop requirements further complicate system optimization and transferability. Future development should focus on multi-source information integration, hybrid mechanistic–data-driven modeling, edge intelligence, digital twins, and system-level optimization for water conservation and sustainable agricultural production. This review provides an integrated framework for understanding the evolution of smart irrigation technologies and highlights critical pathways toward scalable, intelligent, and resource-efficient irrigation systems.
