Abstract
The water retention performance of farmland soil is a key factor in ensuring crop growth, improving irrigation efficiency, and achieving sustainable agricultural development. In recent years, with the rapid advancement of artificial intelligence technology in the agricultural field, new opportunities have emerged for soil moisture management. Based on a systematic analysis of soil water retention characteristics and their main influencing factors, this study constructs a framework for optimizing soil water retention performance strategies using artificial intelligence, primarily focusing on three key aspects: intelligent monitoring and data mining, deep learning models, and precision irrigation. The framework is then verified through case studies with performance evaluations. By developing a real-time soil moisture monitoring system, the framework enables multi-source data collection and precise analysis; deep learning is used to model historical and real-time data, predict crop irrigation needs, and optimize irrigation control strategies; combined with typical farmland cases, field experiments are conducted to verify the effectiveness of intelligent soil improvement and management plans in enhancing soil water retention performance. This study aims to provide scientific and innovative technological support and practical guidance for farmland water resource management and sustainable agricultural development, promoting the in-depth application of artificial intelligence technology in precision agriculture.
