关键词: 智慧农业, 农业绿色转型, 赋能逻辑, 赋能障碍, 对策建议, 算法, 设备

Abstract:

[Objective] The adoption of smart agricultural technologies has significantly accelerated the development of modern agriculture, emerging as a pivotal driver for green transition within the agricultural sector. By enabling a fundamental shift in agricultural production paradigms, these innovations not only enhance desirable agricultural outputs but also effectively reduce undesirable environmental outputs. This dual effect substantially contributes to the improvement of Agricultural Green Total Factor Productivity (AGTFP). [Progress] This study undertakes a systematic investigation into the pivotal role of smart agriculture technology innovation in driving the green transition within the agricultural sector. The analysis is meticulously structured across three sequential stages: Firstly, it elucidates the intrinsic mechanisms through which technological advancements serve as enablers for this green transition, detailing how innovations in agriculture technology translate into environmental sustainability gains. Secondly, it identifies and categorizes the existing implementation barriers that hinder the widespread adoption of smart agriculture solutions, providing insights into the challenges faced by stakeholders at various levels. Smart agricultural technologies advancement facilitate a paradigm shift, moving from isolated, fragmented data systems to cohesive, interconnected ecological systems that support sustainable growth. The core technological logic underpinning this transition operates through a synergistic "data-algorithm-equipment-ecology" evolutionary trajectory: 1.Data Infrastructure Construction: Establishing comprehensive, multi-source heterogeneous agricultural data acquisition systems that collect, process, and analyze data from diverse sources, forming the foundational data assets necessary for the development and deployment of smart agriculture solutions. 2.Algorithmic Decision-Making: Developing sophisticated AI-driven resource allocation models that leverage big data analytics to enable precision management, optimizing resource use, and enhancing productivity through intelligent decision-making systems. 3.Equipment Modernization: Creating technical closed loops characterized by a virtuous cycle of "equipment upgrading - efficiency enhancement - cost reduction - environmental benefits" through the implementation of automated production management and intelligent harvesting systems that minimize human intervention and maximize output. 4.Ecological Optimization: Building circular systems for waste valorization, carbon footprint tracking, and ecological monitoring that facilitate the recycling of agricultural by-products, reduce greenhouse gas emissions, and promote the restoration and preservation of agricultural ecosystems, ultimately achieving self-regulating, resilient agricultural environments. However, the implementation process faces three categories of barriers: 1.Technological Barriers: High infrastructure costs, inefficient technology commercialization, insufficient digital literacy among farmers, and inadequate full-process automation/intelligence levels constrain transformation depth. 2.Market Barriers: Significant mismatches between supply-side capabilities and demand-side requirements in technology applicability. 3.Institutional Barriers: Firstly, the lack of policy coordination is evident in the "prioritizing construction over operation"approach, leading to many agricultural big data platforms failing to effectively implement green technologies post-completion due to inadequate policies supporting sustained operations and practical applications. Secondly, structural deficiencies in the standard system, particularly the absence of unified classification standards for agricultural information resources, create technical barriers to cross-departmental and cross-regional data sharing. [Conclusions and Prospects] To effectively advance smart agriculture technology in driving the agricultural green transition and systematically address identified barriers, this study proposes the following policy recommendations: 1.To effectively overcome the persistent "last mile" barrier that impedes the widespread adoption of technological innovations among smallholder farmers, it is imperative to actively promote multi-stakeholder participation across the agricultural sector. This involves fostering collaboration among governments, private enterprises, non-profit organizations, and local communities. Additionally, establishing a comprehensive technical service network is crucial, one that seamlessly integrates government guidance to ensure policy alignment, market operations to drive efficiency and sustainability, and social engagement to enhance community buy-in and knowledge dissemination. 2.To significantly boost the efficiency of green resource allocation within the agricultural sector, it is imperative to harness the power of market-oriented reforms. These reforms should serve as the cornerstone for forging a novel supply-demand-driven model that is both dynamic and responsive. By integrating intelligent algorithms and advanced data analytics, this coordinated approach will facilitate the precise matching of essential resources such as land, capital, and cutting-edge green technologies. Such precision will enable a paradigm shift from traditional, scale-oriented production methods to more flexible, demand-driven customization tailored to specific market needs. Ultimately, this transformation will cultivate sustainable endogenous growth momentum, providing a robust foundation for the agricultural sector's green transition and ensuring its long-term viability and competitiveness. 3.Governments ought to take the lead in driving systemic and comprehensive reform of existing policy formulation frameworks, ensuring that agricultural development strategies are aligned with long-term sustainability goals. Additionally, they should strengthen technological support systems for agricultural resource zoning, leveraging advanced tools such as big data analytics and geographic information systems to optimize land use and resource allocation. By doing so, governments can foster a new universal smart agriculture development paradigm that integrates digital innovation with ecological principles. These targeted measures will not only enhance agricultural productivity but also accumulate valuable experience for comprehensively advancing the agricultural green transition and promoting rural ecological revitalization in a systematic manner.

Key words: Smart agriculture, green transformation in agriculture, technology-enabled empowerment Logic, empowerment impediments, implementation pathways, algorithm, equipment