抄録
Precision irrigation, as a key technology for improving water resource use efficiency and crop yield, has gradually entered a new era of intelligence and automation, driven by the rapid development of big data technologies. This paper presents a systematic review of precision irrigation technologies based on big data, focusing on key aspects such as data acquisition and processing, irrigation decision model design, and optimization algorithms. First, methods for the integration and processing of multi-source data, including meteorological data, soil moisture data, and crop growth information, are analyzed, and an efficient irrigation decision support framework is proposed by combining machine learning and deep learning techniques. Second, big data-based irrigation models and optimization methods are discussed, with particular emphasis on the soil-plant-atmosphere continuum model and its application in irrigation optimization, along with the role of multi-objective optimization algorithms in enhancing irrigation system performance. Finally, the practical impacts of precision irrigation on water conservation, crop yield improvement, economic benefits, and environmental sustainability are evaluated, and the current challenges and future development directions of the technology are analyzed. This paper aims to provide a comprehensive overview of the current status and progress of precision irrigation technologies, clarify key directions for future research, and offer theoretical foundations and technical support for the design and application of big data-driven precision irrigation systems.
