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