Abstract
Against the backdrop of intensified global climate change, increasing water scarcity, and growing demands for high-quality agricultural development, data-driven precision irrigation technology is increasingly emerging as a key pathway for promoting sustainable agricultural transformation. This paper systematically reviews five major research frontiers in the field: plot-scale irrigation decision modeling based on multi-source remote sensing data, deep perception and dynamic response systems for crop water stress, field microenvironment regulation platforms supported by the Internet of Things and edge computing, time-series water demand prediction modeling for intelligent scheduling, and cyber-physical irrigation systems driven by digital twins. Centered on these frontiers, the paper explores critical technical challenges such as multi-source heterogeneous data fusion mechanisms, strategies for improving model generalization and interpretability, optimization of edge–cloud collaborative computing architectures, and intelligent scheduling methods for multi-objective irrigation strategies. This paper aims to provide a systematic reference for the broad application and intelligent evolution of precision irrigation technologies.
