植物学报

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人工智能驱动下智慧种植发展建设研究

陶婷1, 2, 张薇1, 2, 曾威1, 2, 4, 郑聪聪2, 高树琴2, 张晓博2, 蒋祥泰1, 2, 李裕如1, 2, 常传昊1, 2, 邵灵玉1, 3*, 张玉成1, 2*   

  1. 1北京国科廪科技有限公司, 雄安 071899; 2中国科学院计算技术研究所, 北京 100089; 3中国农业科学院农业资源与农业区划研究所, 北京 100081; 4湖南农业大学生物科学技术学院, 长沙 410128


  • 收稿日期:2025-09-29 修回日期:2025-11-21 出版日期:2025-12-09 发布日期:2025-12-09
  • 通讯作者: 邵灵玉, 张玉成
  • 基金资助:

    中国科学院战略性先导科技专项(No.XDA0450203)

Research on the Development and Construction of AI-Driven Smart Farming

Ting Tao1, 2, Wei Zhang 1, 2, Wei Zeng 1, 2, 4, Congcong Zheng 2, Shuqin Gao 2, Xiaobo Zhang2, Xiangtai Jiang1, 2, Yuru Li1, 2, Chuanhao Chang1, 2, Lingyu Shao1, 3*, Yucheng Zhang1, 2
  

  1. 1Beijing Guo Ke Lin Technology Co., Ltd., Xiong'an, Hebei 071899, China; 2Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100089, China; 3Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 4College of Bioscience and biotechnology, Hunan Agricultural University, Changsha 410128, China


  • Received:2025-09-29 Revised:2025-11-21 Online:2025-12-09 Published:2025-12-09
  • Contact: Lingyu Shao, Yucheng Zhang

摘要: 智慧农业作为农业现代化发展的核心方向, 正推动农业生产由经验驱动向数据驱动,由人工操作向智能化、自动化转型。该文系统梳理了人工智能在智慧农业中的应用及智慧种植发展现状、趋势与挑战。明晰“感知-决策-执行-管理”闭环架构, 以伏羲农场为案例阐述其在智慧种植全流程中的作用与价值。感知层依托多源传感器、成像技术与物联网实现作物与环境的精准监测; 决策层结合数据驱动算法与作物生长模型, 实现施肥、灌溉与病虫害防控的智能预测与优化; 执行层通过无人农机、无人机与机器人完成高精度无人化作业; 管理层则借助云边协同与数字孪生, 实现农田运行的可视化与可持续管理。当前智慧农业发展迅猛, 但在数据同化标准上仍有所欠缺, 装备智能化与适应性还有待提高, 随着海量数据获取其数据安全与隐私保护也是一大难点。智慧种植的核心目标是实现增产、提质、降本与绿色发展, 并进一步推动农业多源感知融合、智能决策闭环、自主化作业和低碳耕作。研究结果不仅为智慧农业理论体系完善提供参考, 也为农业物联网、人工智能和数字农业的工程实践提供了技术支撑, 对于推动高效、智能和可持续农业具有重要意义。

关键词: 智慧农业, 人工智能, 感知-决策-执行, 智慧种植, 智能感知, 伏羲农场

Abstract: Smart agriculture, as a core direction of modern agricultural development, is driving the transformation of agricultural production from experience-driven to data-driven, and from manual operations to intelligent and automated systems. This paper systematically reviews the applications of artificial intelligence in smart agriculture and the current status, trends, and challenges of smart cultivation, constructing a “sensing-decision-execution-management” closed-loop framework to elucidate its role and value across the entire agricultural process. The sensing layer leverages multi-source sensors, imaging technologies, and the Internet of Things to achieve precise monitoring of crops and environmental conditions; the decision layer integrates data-driven algorithms and crop growth models to enable intelligent prediction and optimization of fertilization, irrigation, and pest and disease management; the execution layer employs unmanned agricultural machinery, drones, and robots to perform high-precision autonomous operations; and the management layer utilizes cloud-edge collaboration and digital twins to realize visualized and sustainable farm management. Although smart agriculture is developing rapidly, challenges remain in data assimilation and standardization, and the level of equipment intelligence and environmental adaptability still needs improvement. Moreover, with the massive acquisition and utilization of agricultural data, issues of data security and privacy protection have become increasingly prominent, posing significant challenges to the sustainable development of smart agriculture. The paper highlights that the core goal of smart cultivation is to increase yield, improve quality, reduce costs, and promote green development, while further advancing multi-source sensing integration, intelligent decision-making loops, autonomous operations, and low-carbon farming. The findings provide a reference for the theoretical framework of smart agriculture and offer technical support for the practical implementation of agricultural IoT, AI, and digital agriculture, playing a significant role in promoting efficient, intelligent, and sustainable agricultural production.

Key words: Smart agriculture, artificial intelligence, perception-decision-execution, smart cultivation, intelligent sensing, Fuxi fganmaoarm