大数据AI算法驱动灌溉与耕耘新模式

A New Model of Irrigation and Cultivation Driven by Big Data and AI Algorithms

摘要: 面对全球水资源短缺、农业劳动力不足与粮食安全挑战，传统农业正加速智能化转型。本文梳理大数据与AI算法在农业灌溉、耕耘领域的研究现状，指出当前存在数据融合困难、模型泛化性弱、灌溉与耕耘环节割裂等问题。研究构建“感知–分析–决策–执行”四层一体化智能农业框架，并以北方冬小麦种植区开展实证。结果表明，该模式可节水15%~20%、节肥10%~15%、增产8%~10%，作业效率提升3倍以上，为智慧农业规模化应用提供理论与实践参考。

Abstract: Faced with global water shortage, agricultural labor shortage and food security challenges, traditional agriculture is accelerating its intelligent transformation. This paper reviews the research status of big data and AI algorithms in agricultural irrigation and cultivation, and points out the existing problems including difficult data fusion, insufficient model generalization, and separation of irrigation and cultivation. A four-layer integrated intelligent agriculture framework of “perception-analysis-decision-execution” is constructed and verified in a winter wheat planting area in North China. The results show that the model can save 15%~20% water, 10%~15% fertilizer, increase yield by 8%~10% and improve operation efficiency by more than 3 times, providing theoretical and practical references for the large-scale application of smart agriculture.