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2026 , Vol. 70 >Issue 11: 3 - 14

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.11.001

INVITED ARTICLE

Agricultural Scientific Research Services Driven by Artificial Intelligence: Mode, Framework, and Strategies

  • Zhao Ruixue ,
  • Ning Lianju ,
  • Gao Qifang ,
  • Yang Xiao
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  • 1 Agricultural Information Institute of CAAS, Beijing 100081;
    2 Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Beijing 100081;
    3 School of Economics and Management, Beijing University of Posts & Telecommunications, Beijing 100876
Cao Gaohui, Professor, PhD, Doctoral Supervisor; Dong Huanqing, Doctoral Candidate, Corresponding Author, E-mail: donghuanqing13@163.com.

Received date: 2025-07-14

  Revised date: 2025-09-23

  Online published: 2026-05-22

Supported by

This work is supported by Scientific and Technological Innovation 2030-Major project titled “R&D and Application Demonstration of Agricultural Intelligent Knowledge Service Platform” (Grant No. 2021ZD0113705).

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Abstract

[Purpose/Significance] In the context of the Fifth Research Paradigm, artificial intelligence(AI) technology is deeply integrated with scientific research scenarios. Focusing on the demand for the intelligent transformation of agricultural research services under the new paradigm, and based on the evolutionary trends of research paradigms and the domain uniqueness of agricultural research, this study aims to construct a theoretical framework and practical pathway for the intelligent development of agricultural research. [Method/Process] First, by combining agricultural research practices with a literature review approach, this paper distinguishes the core differences between traditional agricultural research and AI-driven models, and extracts the characteristic dimensions of the new agricultural research paradigm. Subsequently, it systematically deconstructs an AI-enabled agricultural research service mode from five dimensions: service positioning, target users, service content, operational mechanisms, and supporting elements. Finally, drawing on systems theory, an intelligent service platform with a four-layer architecture(data, model, application, interaction) is proposed, with clearly defined functions and technical logic for each layer. [Result/Conclusion] This study proposes an AI-driven agricultural research service framework with the aim of fostering a corresponding service ecosystem. Furthermore, practical strategies for the service framework are elaborated from the dimensions of institutional support, scenario innovation, model transformation, and talent cultivation, to provide, a theoretical basis and practical guidance for the intelligent upgrading of agricultural research.

Key words： generative artificial intelligence; agricultural scientific research; scientist service model; scientific research ecology

Cite this article

Zhao Ruixue , Ning Lianju , Gao Qifang , Yang Xiao . Agricultural Scientific Research Services Driven by Artificial Intelligence: Mode, Framework, and Strategies[J]. Library and Information Service, 2026 , 70(11) : 3 -14 . DOI: 10.13266/j.issn.0252-3116.2026.11.001

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