Smart agriculture serves not only as a critical measure for driving agricultural and rural modernization, but also as a core component of new quality productive forces in agriculture. In recent years, China's smart agriculture has achieved remarkable development. However, it still faces practical challenges in policy implementation, technological application, and industrial coordination, which restricts the high-quality development of agriculture. Against this background, quantitative analysis of the central-level smart agriculture policy system was conducted from the crucial link of policy design. Grounded in practical dilemmas of smart agriculture development and top-level guiding requirements, policy logic and inherent deficiencies were clarified via scientific analytical approaches.The aim of this research is to identify the direction of policy optimization, so as to provide practical references for promoting the quality and efficiency improvement of smart agriculture and advancing the construction of a strong agricultural country.

From the perspective of policy tools, first, a three-dimensional analytical framework of "tools-topics-objectives" was constructed, which served as the core analytical logic of the overall research. A total of 240 central-level policy documents concerning smart agriculture issued from 2013 to 2025 were selected as the research sample. Systematic coding and quantitative analysis of all collected policy texts were implemented through content analysis with the assistance of NVivo14 qualitative analysis software. To further explore the latent thematic structures of China's smart agriculture policy texts, accurately identify the focal directions of policy content and their long-term evolutionary characteristics, the Latent Dirichlet Allocation (LDA) topic model was further adopted to realize automatic text topic analysis. Through the above integrated research methods and analytical pathways, core characteristics of China's smart agriculture policies were systematically summarized, and targeted optimization strategies were put forward.

First, the analysis identified four distinct evolutionary stages: the Policy Exploration Period (2013－2015), the Pilot Demonstration Period (2016－2018), the Strategic Promotion Period (2019－2023), and the Comprehensive Deepening Period (2024 to present). This progression reflected a shift from conceptual introduction and top-level design to concrete technology application, strategic scaling, and finally, systematic institutionalization. Second, in terms of policy instrument deployment, it was found that supply-side, environmental, and demand-side tools were used in a relatively balanced overall proportion. However, significant structural imbalances existed within each category. Supply-side tools were heavily skewed towards infrastructure construction and scientific research support, with relatively less emphasis on direct financial input and talent cultivation. Environmental tools were dominated by publicity and guidance, while financial support mechanisms and standard-setting were underutilized. On the demand side, policy relied heavily on pilot demonstrations, with weaker emphasis on fostering cooperation, industry-research integration, and broader social participation. Thirdly, the evolution of policy objectives exhibited an obvious phased and progressive feature, whereas its internal structure still needed optimization. On the whole, national smart agricultural policies covered five core fields, with the digital and intelligent transformation of production and operation as the primary objective. Technological innovation and data services acted as crucial supporting pillars. By contrast, two guarantee-oriented objectives, namely talent team development and institutional standard improvement, accounted for merely 10.91% and 10.13%, respectively, resulting in a prominent imbalance in the objective system. Furthermore, policy development could be divided into four sequential stages: Initial conceptual guidance and foundational planning, pilot-oriented practical implementation, systematic policy construction, and in-depth quality and efficiency upgrading, forming a clear progressive trajectory. Though digital transformation had long served as the central focus across all stages, the development of talents and institutional mechanisms had witnessed gradual yet relatively slow progress, constantly lagging behind the advancement of core policy objectives. Fourthly, the LDA topic model extracted four core policy themes: Digital transformation and intelligent upgrading, industrial integration and business model innovation, talent cultivation and entrepreneurial support, and technical equipment and intelligent application. A striking finding was the extreme concentration of policy attention, with the digital transformation and intelligent upgrading theme accounting for 57.92% of the thematic focus, significantly overshadowing the other three themes combined, the matching degree between policy themes and policy instruments needs to be improved.

While China has developed a comprehensive policy framework for smart agriculture, its effectiveness is hampered by structural imbalances in instrument use, an over-concentration on a single policy theme, and an inconsistent coordination of policy objectives. To address these challenges and foster a more robust and sustainable development path, three key optimization strategies are proposed. First, promoting the balanced deployment of policy tools by following a tripartite approach: upgrading supply-side tools, empowering environmental-side tools, and driving demand-side tools, so as to enhance synergy and complementarity among different tool types. Second, optimizing the allocation of attention to policy themes and enhance the alignment between policy instruments and policy themes, avoiding over-reliance on individual fields and under-support for key sectors. Third, advancing the coordinated optimization of policy goals, and improving the systematic mechanism led by standards, guaranteed by institutions, and supported by talent teams. Implementing these strategies will be crucial for transforming China's smart agriculture policy system from one focused on quantity and foundational building to one capable of driving high-quality, balanced, and innovative development in the years to come.

In order to summarize exemplary cases of high-quality development in regional smart agriculture and contribute strategies for the sustainable advancement of the national smart agriculture cause, the spatiotemporal characteristics and key driving factors of smart farms in the Yangtze River Economic Belt were studied.

Based on data from 11 provinces (municipalities) spanning the years 2014 to 2023, a comprehensive analysis was conducted on the spatio-temporal differentiation characteristics of smart farms in the Yangtze River Economic Belt using methods such as kernel density analysis, spatial auto-correlation analysis, and standard deviation ellipse. Including the overall spatial clustering characteristics, high-value or low-value clustering phenomena, centroid characteristics, and dynamic change trends. Subsequently, the geographic detector was employed to identify the key factors driving the spatio-temporal differentiation of smart farms and to discern the interactions between different factors. The analysis was conducted across seven dimensions: special fiscal support, industry dependence, human capital, urbanization, agricultural mechanization, internet infrastructure, and technological innovation.

Firstly, in terms of temporal characteristics, the number of smart farms in the Yangtze River Economic Belt steadily increased over the past decade. The year 2016 marked a significant turning point, after which the growth rate of smart farms had accelerated noticeably. The development of the upper, middle, and lower reaches exhibited both commonalities and disparities. Specifically, the lower sub-regions got a higher overall development level of smart farms, with a fluctuating upward growth rate; the middle sub-regions were at a moderate level, showing a fluctuating upward growth rate and relatively even provincial distribution; the upper sub-regions got a low development level, with a stable and slow growth rate, and an unbalanced provincial distribution. Secondly, in terms of spatial distribution, smart farms in the Yangtze River Economic Belt exhibited a dispersed agglomeration pattern. The results of global auto-correlation indicated that smart farms in the Yangtze River Economic Belt tended to be randomly distributed. The results of local auto-correlation showed that the predominant patterns of agglomeration were H-L and L-H types, with the distribution across provinces being somewhat complex; H-H type agglomeration areas were mainly concentrated in Sichuan, Hubei, and Anhui; L-L type agglomeration areas were primarily in Yunnan and Guizhou. The standard deviation ellipse results revealed that the mean center of smart farms in the Yangtze River Economic Belt had shifted from Anqing city in Anhui province in 2014 to Jingzhou city in Hubei province in 2023, with the spatial distribution showing an overall trend of shifting southwestward and a slow expansion toward the northeast and south. Finally, in terms of key driving factors, technological innovation was the primary critical factor driving the formation of the spatio-temporal distribution pattern of smart farms in the Yangtze River Economic Belt, with a factor explanatory degree of 0.3111. Moreover, after interacting with other indicators, it continued to play a crucial role in the spatio-temporal distribution of smart farms, which aligned with the practical logic of smart farm development. Urbanization and agricultural mechanization levels were the second and third largest key factors, with factor explanatory degrees of 0.2922 and 0.2514, respectively. The key driving factors for the spatio-temporal differentiation of smart farms in the upper, middle, and lower sub-regions exhibited both commonalities and differences. Specifically, the top two key factors driver identification in the upper region were technological innovation (0.8419) and special fiscal support (0.7823). In the middle region, they were technological innovation (0.6190) and human capital (0.6001), while in the lower region, they were urbanization (0.7276) and technological innovation (0.4254). The identification of key driving factors and the detection of their interactive effects further confirmed that the spatio-temporal distribution characteristics of smart farms in the Yangtze River Economic Belt were the result of the comprehensive action of multiple factors.

The development of smart farms in the Yangtze River Economic Belt is showing a positive momentum, with both the total number of smart farms and the number of sub-regions experiencing stable growth. The development speed and level of smart farms in the sub-regions exhibit a differentiated characteristic of "lower reaches > middle reaches > upper reaches". At the same time, the overall distribution of smart farms in the Yangtze River Economic Belt is relatively balanced, with the degree of sub-regional distribution balance being "middle reaches (Hubei province, Hunan province, Jiangxi province are balanced) > lower reaches (dominated by Anhui) > upper reaches (Sichuan stands out)". The coverage of smart farm site selection continues to expand, forming a "northeastsouthwest" horizontal diffusion pattern. In addition, the spatio-temporal characteristics of smart farms in the Yangtze River Economic Belt are the result of the comprehensive action of multiple factors, with the explanatory power of factors ranked from high to low as follows: Technological innovation > urbanization > agricultural mechanization > human capital > internet infrastructure > industry dependence > special fiscal support. Moreover, the influence of each factor is further strengthened after interaction. Based on these conclusions, suggestions are proposed to promote the high-quality development of smart farms in the Yangtze River Economic Belt. This study not only provides a theoretical basis and reference for the construction of smart farms in the Yangtze River Economic Belt and other regions, but also helps to grasp the current status and future trends of smart farm development.