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.