Construction of talent team with artificial intelligence for agriculture at universities： status， challenges， and policy

ZHAO Xiqing; LI Guoliang; WU Fei

doi:10.13300/j.cnki.hnlkxb.2026.03.015

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Construction of talent team with artificial intelligence for agriculture at universities： status， challenges， and policy

Agricultural artificial intelligence | 更新时间：2026-11-05

- Construction of talent team with artificial intelligence for agriculture at universities： status， challenges， and policy
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 173-181(2026)
- 作者机构：
  
  1.华中农业大学人力资源部，武汉 430070
  2.华中农业大学信息学院，武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.015
  CLC： S126;G64
- Received：18 December 2025，
  
  Published：25 May 2026
- 稿件说明：
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赵希庆，李国亮，吴菲.高校农业人工智能人才队伍建设：现状、挑战与政策路径［J］.华中农业大学学报，2026，45（3）：173‐181.

ZHAO Xiqing,LI Guoliang,WU Fei.Construction of talent team with artificial intelligence for agriculture at universities： status， challenges， and policy[J].Journal of Huazhong Agricultural University,2026,45(03):173-181.
赵希庆，李国亮，吴菲.高校农业人工智能人才队伍建设：现状、挑战与政策路径［J］.华中农业大学学报，2026，45（3）：173‐181. DOI： 10.13300/j.cnki.hnlkxb.2026.03.015.

ZHAO Xiqing,LI Guoliang,WU Fei.Construction of talent team with artificial intelligence for agriculture at universities： status， challenges， and policy[J].Journal of Huazhong Agricultural University,2026,45(03):173-181. DOI： 10.13300/j.cnki.hnlkxb.2026.03.015.

摘要

作为推动传统农业向现代化转型的关键技术支撑，农业人工智能的发展亟需建立一支具备多学科背景的人才队伍。本文借助 CiteSpace 文献计量软件，以 Web of Science 数据库中的5 638条文献为样本进行纵向梳理，发现农业人工智能领域的研究主要经历3个发展阶段：早期以专家系统为主导的研究时期（1990-2017年），近期大数据及智能化技术广泛应用下的快速发展时期（2018-2024年），以及目前形成完整的智能农业技术链的起步阶段（2025年至今），从中找出未来人才队伍精准引育重点领域方向。同时，本研究通过对全国26所农林类高校师资数据进行了系统收集与统计，发现当前信息与人工智能相关学科专任教师约2 700余人，其中正高职称教师435人，仅学历教育培养44万农业人工智能从业人员所需师资缺口较大。基于上述分析，本文从顶层战略画像、精准引才与体系化育才、多元分类评价激励、高能级交叉平台建设4个维度，提出了系统性的政策创新路径，以期为涉农高校破解师资瓶颈、构筑农业人工智能人才高地提供实践参考。

Abstract

The development of artificial intelligence （AI） for agriculture as a key technological support for transforming traditional agriculture into modern agriculture urgently requires the establishment of a talent team with multidisciplinary expertise.CiteSpace bibliometric software was used to vertically sort 5 638 articles publications from the Web of Science database.The results showed that the studies in the field of AI for agriculture mainly went through three stages of development including the early stage dominated by expert systems （1990—2017）， the rapid stage of development under the widespread application of big data and intelligent technologies （2018—2024）， and the initial stage of forming a complete chain of intelligent technology in agriculture （2025 onward）， from which the key areas and directions for targeted talent recruitment and training in the future were identified.The results of systematic collection and statistics of teaching staff data from 26 agricultural and forestry universities across the country showed that there are currently more than 2 700 full-time teachers in information and AI-related disciplines， of which only 435 hold senior professional titles.There is a significant shortage of teaching staff required to train 440 000 practitioners of AI for agriculture with only degree programs.A systematic path with policy innovation based on the above analysis was proposed from four dimensions including top-level strategic planning， precision recruitment paired with systematic talent training， diversified evaluation and incentive mechanisms， and the construction of high-level interdisciplinary platforms to provide theoretical basis and practical reference for agricultural and forestry universities to break through the bottleneck of teaching staff and build a highland of talents with AI for agriculture.

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references

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