人工智能驱动下智慧种植发展建设研究

doi:10.11983/CBB25172

植物学报 ›› 2026, Vol. 61 ›› Issue (4): 549-714.DOI: 10.11983/CBB25172

• 特邀综述 • 下一篇

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^,³, 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^,⁴^,^*(), 张玉成¹^,²^,^*()

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 湖南农业大学生物科学技术学院 , 长沙 410128
⁴ 中国农业科学院农业资源与农业区划研究所 , 北京 100081

收稿日期:2025-09-29 接受日期:2025-12-06 出版日期:2026-07-10 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(XDA0450203)

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

Ting Tao¹^,², Wei Zhang¹^,², Wei Zeng¹^,²^,³, Congcong Zheng², Shuqin Gao², Xiaobo Zhang², Xiangtai Jiang¹^,², Yuru Li¹^,², Chuanhao Chang¹^,², Lingyu Shao¹^,⁴^,^*(), Yucheng Zhang¹^,²^,^*()

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

Received:2025-09-29 Accepted:2025-12-06 Online:2026-07-10 Published:2025-12-09
Contact: Lingyu Shao, Yucheng Zhang

摘要/Abstract

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。构建了“感知-决策-执行-管理”闭环架构体系, 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; 决策层整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技术, 实现农田运行的可视化与可持续管理。尽管当前智慧农业发展迅猛, 但在数据同化标准上仍有欠缺, 装备智能化与适应性还有待提高, 海量数据的数据安全与隐私保护问题日益突出。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技术支撑, 对于推动高效、智能和可持续农业发展具有重要意义。

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 (AI) 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 (IoT) 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, sensing-decision-execution, smart cultivation, intelligent sensing, Fuxi Farm

陶婷, 张薇, 曾威, 郑聪聪, 高树琴, 张晓博, 蒋祥泰, 李裕如, 常传昊, 邵灵玉, 张玉成. 人工智能驱动下智慧种植发展建设研究. 植物学报, 2026, 61(4): 549-714.

Ting Tao, Wei Zhang, Wei Zeng, Congcong Zheng, Shuqin Gao, Xiaobo Zhang, Xiangtai Jiang, Yuru Li, Chuanhao Chang, Lingyu Shao, Yucheng Zhang. Research on the Development and Construction of AI-driven Smart Farming. Chinese Bulletin of Botany, 2026, 61(4): 549-714.

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https://www.chinbullbotany.com/CN/Y2026/V61/I4/549

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人工智能驱动下智慧种植发展建设研究

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

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