数智技术赋能农业高质量发展：现状、挑战及路径探索

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Smart Agriculture ›› 2026, Vol. 8 ›› Issue (3): 13-25.doi: 10.12133/j.smartag.SA202602002

• 专刊--数字技术赋能与农业经济范式转型 •上一篇下一篇

数智技术赋能农业高质量发展：现状、挑战及路径探索

何磊磊 1,3, 蒋淑吉 1, 杨军 2()

1. 1. 对外经济贸易大学国际经济贸易学院，北京 100029，中国

2. 对外经济贸易大学数字经济实验室，北京 100029，中国

3. 云南省跨境数字经济重点实验室，云南 650214，中国

• 收稿日期:2026-01-31 出版日期:2026-05-30
• 基金项目:国家自然科学基金项目(72404007)
• 作者简介:何磊磊，博士，研究方向为数字经济、农产品贸易和智慧农业。E-mail：[email protected]
• 通信作者:杨 军，博士，教授，研究方向为国际贸易、发展经济学、一般均衡模型理论与应用。E-mail：[email protected]

Digital-Intelligent Technologies Empowering High-Quality Agricultural Development: Current Status, Challenges, and Pathways

HE Leilei 1,3, JIANG Shuji 1, YANG Jun 2()

1. 1. School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China

2. Digital Economy Laboratory, University of International Business and Economics, Beijing 100029, China

3. Yunnan Key Laboratory of Cross-border Digital Economy, Yunnan 650214, China

• Received:2026-01-31 Online:2026-05-30
• Foundation items:National Natural Science Foundation of China(72404007)
• About author:HE Leilei, E-mail: [email protected]
• Corresponding author:YANG Jun, E-mail: [email protected]

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摘要/Abstract

摘要：

【目的/意义】 数智技术作为数字技术与智能技术融合的产物，是推动农业高质量发展、培育农业新质生产力的关键驱动力。本文旨在系统分析数智技术赋能农业高质量发展的内在逻辑、现实进展与主要挑战，并探索其推进路径，以期为实现农业现代化转型提供理论参考与实践依据。 【进展】 基于改造传统农业理论与农业技术扩散理论，对数智技术赋能农业高质量发展的深层逻辑进行剖析，并以此为基础，系统梳理了数智技术在生产环节、要素配置、管理服务与供应链体系中的实践应用。分析发现，当前数智技术正从局部单点应用向整体系统集成转变，持续推动农业向精准化、数据化、智能化转型。然而，仍面临核心技术瓶颈、产业生态碎片化、数据治理与安全挑战、技术推广普及困难、人才支撑体系薄弱及区域发展不平衡等问题，共同制约了数智技术在农业中的规模化与深层次应用。 【结论/展望】 推进数智技术赋能农业高质量发展需坚持系统思维，实施技术攻关、产业生态构建、数据治理、模式创新与人才培养等多路径协同。未来应聚焦关键核心技术研发与场景适配，加快构建标准化、开放化的技术生态；完善数据权属、流通与安全治理体系；创新可持续的商业模式与政策支持机制；强化多层次数字农业人才队伍建设；并注重区域差异化协同策略。通过多方联动与政策集成，推动数智技术从典型示范走向全面普及，为农业现代化注入新动能。

关键词:数智技术,智慧农业,农业转型,农业高质量发展,新质生产力,数字技术,智能技术

Abstract:

[Significance] As an integration of digital and intelligent technologies, digital-intelligent technology serves as the core engine for agricultural goals. In the current era of digitalization and intelligence, rapid technological iteration and the expansion of application scenarios reinforce each other. Therefore, leveraging digital-intelligent technology to empower agriculture is a strategic necessity to align with the digital age trends and fully realize the new connotations of smart agriculture. Specifically, driven by data as a new production factor, digital-intelligent technology is profoundly optimizing the entire agricultural production and management process. The main objective of this study is to systematically analyze the underlying operational logic of how digital-intelligent technology enables high-quality agricultural development, comprehensively review its current application progress, objectively identify the main challenges, and actively explore practical pathways for advancement. [Progress] Based on the theory of transforming traditional agriculture and the theory of agricultural technology diffusion, this paper analyzes the underlying logic of how digital-intelligent technologies empower high-quality agricultural development. Building upon this foundation, it systematically reviews the practical applications of digital-intelligent technologies in production processes, factor allocation, management services, and supply chain systems, and reveals a clear staged evolutionary feature across domains. As described below: (1) Production processes: Evolution from single-point efficiency gains to closed-loop, whole-process intelligent decision-making. (2) Factor allocation: Shift from reliance on traditional physical resources to deep value mining of data. (3) Management services: Transition from fragmented tool applications to integrated, platform-based services. (4) Supply chain systems: Evolution from a linear chain structure to a collaborative and resilient network ecosystem. Despite this progress, it still faces challenges such as core technology bottlenecks, fragmented industrial ecosystems, data governance and security issues, difficulties in technology promotion and adoption, a weak talent support system, and uneven regional development. These challenges stem not only from the limitations of the technological development stage but also from structural contradictions such as a weak industrial foundation, complex application scenarios, and inadequate systemic coordination. Together, these factors block the large-scale adoption and full value realization of digital-intelligent technology in agriculture. [Conclusions and Prospects] To promote high-quality agriculture development empowered by digital-intelligent technology, it is necessary to adhere to systematic thinking and implement a multi-path coordinated strategy. The key focuses for future work are as follows: (1) Focus on the independent RD of key core technologies and carry out adaptive innovation for specific agricultural scenarios. (2) Accelerate the construction of a standardized, open, and interconnected technology and industrial ecosystem. (3) Establish and improve a data governance system covering data ownership definition, circulation and transaction, and security protection. (4) Explore sustainable business models and precise and effective policy support mechanisms. (5) Strengthen the cultivation of a multi - tiered digital agriculture talent pool involving RD, application, and promotion. (6) Implement regionally differentiated and coordinated development strategies according to local conditions. Through multi-stakeholder collaboration among governments, industries, research institutions and market entities, as well as integrated policy support, digital-intelligent technology will be promoted to transform from "potted landscape" style isolated demonstrations to "open landscape" style large-scale popularization and deep integration. This will inject a stronger and more sustainable digital-intelligent driving force into Chinese-style agricultural modernization and high-quality agricultural development.

Key words:digital-intelligent technology,smart agriculture,agricultural transformation,high-quality agricultural development,new quality productive forces,digital technology,intelligent technology

中图分类号:

• TP399

• F323

引用本文

何磊磊, 蒋淑吉, 杨军. 数智技术赋能农业高质量发展：现状、挑战及路径探索[J]. 智慧农业(中英文), 2026, 8(3): 13-25.

HE Leilei, JIANG Shuji, YANG Jun. Digital-Intelligent Technologies Empowering High-Quality Agricultural Development: Current Status, Challenges, and Pathways[J]. Smart Agriculture, 2026, 8(3): 13-25.

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