Chinese Bulletin of Botany >

2026 , Vol. 61 >Issue 4: 549 - 714

DOI: https://doi.org/10.11983/CBB25172

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

  • Ting Tao 1, 2 ,
  • Wei Zhang 1, 2 ,
  • Wei Zeng 1, 2, 3 ,
  • Congcong Zheng 2 ,
  • Shuqin Gao 2 ,
  • Xiaobo Zhang 2 ,
  • Xiangtai Jiang 1, 2 ,
  • Yuru Li 1, 2 ,
  • Chuanhao Chang 1, 2 ,
  • Lingyu Shao , 1, 4, * ,
  • Yucheng Zhang , 1, 2, *
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  • 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 date: 2025-09-29

  Accepted date: 2025-12-06

  Online published: 2025-12-09

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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

Cite this article

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[J]. Chinese Bulletin of Botany, 2026 , 61(4) : 549 -714 . DOI: 10.11983/CBB25172

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