[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-120":3,"related-120":55},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":48,"slug":51,"view_count":52,"doi":53,"paper":8,"created_at":54},120,"智慧农业发展综合评价体系构建、实践格局与驱动机制","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009","房洪伟等构建智慧农业发展综合评价体系,分析智慧农业实践格局与驱动机制,为科学评估智慧农业发展水平提供方法论支撑。",null,"欢迎您访问《智慧农业（中英文）》官方网站！[English](https:\u002F\u002Fwww.smartag.net.cn\u002FEN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 1](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 2](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner_mobile.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 3](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fyyzkj.png)](https:\u002F\u002Fpano.bookan.com.cn\u002F?id=60704&cid=352)\n\nToggle navigation[智慧农业](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009)\n\n*   [网站首页](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n*   [期刊介绍](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn133.shtml)\n*   [编委会](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn153.shtml)\n*   [投稿指南](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn135.shtml)\n*   [期刊服务](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn124.shtml)\n*   [下载中心](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn126.shtml)\n*   [联系我们](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn127.shtml)\n\n[Smart Agriculture](https:\u002F\u002Fwww.smartag.net.cn\u002F) ›› [2026](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026), [Vol. 8](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026) ›› [Issue (3)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fvolumn\u002Fvolumn_841.shtml): 26-39.doi: [10.12133\u002Fj.smartag.SA202510009](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202510009)\n\n• 专刊--数字技术赋能与农业经济范式转型 •[上一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22763.shtml)[下一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22558.shtml)\n\n### 智慧农业发展综合评价体系构建、实践格局与驱动机制\n\n房洪伟, 胡然然, 热孜燕·瓦卡斯([![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:1377569942@qq.com))\n\n1.   新疆农业大学经济管理学院，新疆 乌鲁木齐 830052，中国\n\n*   `收稿日期:`2025-10-11 `出版日期:`2026-05-30 \n*   `基金项目:`新疆维吾尔自治区科技特派员重点项目(2023KZ016); 新疆维吾尔自治区研究生教育创新计划项目(XJ2025G129) \n*   `作者简介:`房洪伟，硕士研究生，研究方向为农林经济。E-mail：[2109155846@qq.com](mailto:2109155846@qq.com) \n*   `通信作者:`热孜燕·瓦卡斯，博士，教授，研究方向为农林经济。E-mail：[1377569942@qq.com](mailto:1377569942@qq.com) \n\n### Construction of A Comprehensive Evaluation System, Practice Pattern, and Driving Mechanism for Smart Agriculture Development\n\nFANG Hongwei, HU Ranran, REZIYAN· Wakasi([![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:1377569942@qq.com))\n\n1.   College of Economics and Management, Xinjiang Agricultural University, Urumqi 830052, China\n\n*   `Received:`2025-10-11 `Online:`2026-05-30 \n*   `Foundation items:`Key Project of the Science and Technology Commissioner of Xinjiang Uygur Autonomous Region(2023KZ016); Project of the Postgraduate Education Innovation Plan of Xinjiang Uygur Autonomous Region(XJ2025G129) \n*   `About author:`FANG Hongwei, E-mail: [2109155846@qq.com](mailto:2109155846@qq.com) \n*   `Corresponding author:`REZIYAN·Wakasi, E-mail: [1377569942@qq.com](mailto:1377569942@qq.com) \n\n## [在线阅读 11](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)\n\n## [知网下载](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009)\n\n## [本地下载 27](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#1)\n\n[_![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Fknowledge\\_map1.png)_ ## 可视化 0](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Flexeme\u002FshowArticleByLexeme.do?articleID=22680)\n\n#### [摘要\u002FAbstract](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009)\n\n**摘要：**\n\n**【目的\u002F意义】** 发展智慧农业是构建农业新质生产力的核心路径。针对当前发展中存在的区域异质性与动力错配问题，本研究旨在构建一个融合地理与产业异质性的综合评价体系，实证分析数据要素与农业物质资本的协同驱动机制，并揭示其在省域空间关联中的作用，从而为优化农业空间布局与差异化政策制定提供理论依据。 **【方法】** 基于2015—2023年中国省级面板数据，首先采用层次分析法与熵权法相结合的组合赋权方式构建智慧农业发展综合评价指数，并进行地形与主导产业校正。进一步，通过空间自相关检验与空间杜宾模型识别智慧农业发展的空间关联特征与溢出模式；运用广义矩估计模型检验数据要素的驱动作用；并通过中介效应与调节效应模型，系统验证数据要素与物质资本的协同机制及其影响路径。 **【结果和讨论】** （1）智慧农业呈现梯度发展格局，区域间增长动力与智慧化水平存在系统性错配。（2）核心驱动机制在于数据要素与农业物质资本的结构性互补，二者协同效应显著；技术资本发挥关键中介作用，且其促进作用具有非线性门槛特征。（3）空间上以竞争效应为主导：产业结构相似省份间呈现明显的“虹吸效应”；“数据-资本”协同模式具有正向空间溢出性，可形成可效仿的区域发展路径。异质性分析表明，上述机制的效果因农业功能区划与发展阶段不同而存在差异。 **【结论】** 本研究为理解智慧农业发展的要素协同机制与空间竞争逻辑提供了直接经验证据，有助于识别区域差异化驱动路径，从而为促进智慧农业均衡发展与政策精准实施提供参考。\n\n**关键词:**[智慧农业,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[数据要素,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[空间溢出,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[空间杜宾模型,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[区域异质性,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[评价体系](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)\n\n**Abstract:**\n\n**[Objective]** This study addresses regional disparities and imbalanced drivers in developing smart agriculture, a core approach to fostering new quality agricultural productivity. It aims to: (1) construct a comprehensive evaluation system incorporating geographical and industrial heterogeneity; (2) empirically analyze the synergistic drive between data elements and agricultural physical capital; and (3) reveal their role in inter-provincial spatial linkages. The significance lies in its potential to inform strategic planning and policy-making, thereby contributing to the sustainable transformation of agriculture and balanced regional development within the context of rural revitalization. **[Methods]** A quantitative spatial econometric approach was employed using panel data from 30 Chinese provinces spanning from 2015 to 2023. The research was executed in three key stages. First, a comprehensive provincial-level smart agriculture development index was constructed. This index integrated multiple dimensions and was weighted by combining the Analytic Hierarchy Process and the Entropy method, with adjustments made for terrain and leading industry heterogeneity. Second, a series of econometric models were specified. Baseline fixed-effects and generalized method of moments models were used to examine the driving role of data elements, while mediating and moderating effect models were employed to systematically verify the synergistic mechanism between data and physical capital and its pathways. Third, spatial autocorrelation tests and spatial Durbin models were employed with three spatial weight matrices—geographical contiguity, agricultural resource zoning, and agricultural economic structure similarity—to identify spatial correlation characteristics and spillover patterns. Direct and indirect effects were decomposed to precisely quantify local impacts and spatial spillovers. **[Results and Discussions]** The analysis yielded four clusters of key findings that confirmed and refined the proposed core hypotheses. 1) Gradient development and regional mismatch: Smart agriculture development exhibited a pronounced \"ladder-like\" spatial pattern. The Huang-Huai-Hai region remained the persistent leader in absolute development level. The southwestern region also maintained a relatively high level, which appeared partly \"forced\" by its challenging terrain, necessitated efficiency-seeking technology. In contrast, the Northeast and Northwest regions lagged. A critical systemic mismatch was revealed: Regions with the highest growth momentum were not necessarily those with the highest current smartization levels, indicating divergent developmental pathways. 2) The core synergistic mechanism: A significant positive interaction was found between data inputs and agricultural physical capital. Crucially, the independent coefficients of each were often found to be insignificant or even negative in spatial models, which underscored that limited or even negative marginal returns were yielded by isolated, uncoordinated investment in either domain. Significant systemic gains were unlocked precisely by their structural complementarity. Furthermore, this synergy was found to operate partially through the channel of technological capital accumulation. The mediating effect of technological capital was confirmed to be significant, and its own impact on smart agriculture output was exhibited as a nonlinear threshold characteristic. This confirmed that a critical mass of technological capital had to be accumulated before its benefits could be fully realized. 3) Competition-dominated spatial interactions: The spatial analysis revealed that inter-provincial dynamics were characterized primarily by competition rather than cooperation. A significant negative spatial spillover was detected specifically under the economic structure similarity matrix. This indicated that resources, talent, and investment were competed for by provinces with similar agricultural economic profiles, potentially hindering each other's growth. However, a nuanced finding was observed: while raw data or capital might be siphoned away, significant positive spatial spillovers were generated by successful provincial models of \"data-capital\" synergy. This suggested that best practices and development paradigms could be diffused, offering a pathway to transcend pure competition. 4) The effectiveness of the core drivers was found to be highly context-dependent. From a zoning perspective, the Huang-Huai-Hai region was characterized by digital-drive but was found to lack deep synergy; the Northeast was constrained by traditional path dependency; the Southwest was shown to exhibit a paradox of high knowledge spillover but low local application; and the northwest was characterized by singular, weak drivers. From a developmental stage perspective, the role of RD investment was assessed as stable, while the payoff from digital infrastructure was seen to be contingent on an \"efficiency threshold\", and its spatial spillover effect was observed to diminish as regional total factor productivity increased. **[Conclusions]** It is demonstrated that development is not merely a function of increased inputs, but is critically determined by the structural coupling of data and physical capital. This coupling is facilitated by technological capital, which acts as a nonlinear mediator. At a practical level, a one-size-fits-all approach to investment policy is argued against. For leading regions such as the Huang-Huai-Hai, policy focus should be placed on deepening existing synergies. For regions like the northeast, breaking path dependence is seen to require policies that forcefully couple new digital tools with legacy physical assets. The pervasive \"siphon effect\" is identified as necessitating national-level coordination mechanisms among structurally similar provinces to mitigate destructive competition. Ultimately, the promotion of smart agriculture is concluded to require spatially differentiated policies that strategically foster local factor synergy while managing the competitive externalities inherent in regional linkages.\n\n**Key words:**[smart agriculture,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[data factor,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[spatial spillover,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[spatial Durbin model,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[regional heterogeneity,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)[evaluation system](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)\n\n**中图分类号:**\n\n*   [F323.3](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#)\n\n#### 引用本文\n\n房洪伟, 胡然然, 热孜燕·瓦卡斯. 智慧农业发展综合评价体系构建、实践格局与驱动机制[J]. 智慧农业(中英文), 2026, 8(3): 26-39.\n\nFANG Hongwei, HU Ranran, REZIYAN· Wakasi. Construction of A Comprehensive Evaluation System, Practice Pattern, and Driving Mechanism for Smart Agriculture Development[J]. Smart Agriculture, 2026, 8(3): 26-39.\n\n#### 使用本文\n\n**[[收藏文章](javascript:;)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#) \u002F[推荐](javascript:void(null))**\n**导出引用管理器**[EndNote](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=EndNote&id=22680)|[Ris](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=22680)|[BibTeX](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=22680)\n\n**链接本文:**[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009)\n\n[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F26](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F26)\n\n#### [图\u002F表 12](javascript:;)\n\n[![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14749)\n\n**表1**\n\n智慧农业核心变量数据来源与处理（2015—2023）\n\n| 变量类别 | 变量名称 | 主要数据来源 | 处理与计算说明 |\n| :---: | :---: | :---: | :---: |\n| 被解释变量 | 智慧农业应用水平 | 综合各年鉴指标计算 | 基于AHP-熵权组合赋权法构建，经地形与主导产业校正，详见2.1节 |\n| 核心解释变量 | 数据要素（移动基站密度） | 《中国高技术产业统计年鉴》 | 取对数处理 |\n| 农业物质资本（农业投资强度） | 《中国农业机械工业年鉴》 | 以农林牧渔业固定资产投资额与总产值比值衡量，并经价格平减 |\n| 技术资本（RD投入强度） | EPS数据库 | 以RD内部经费支出与主营业务收入比值衡量 |\n| 控制变量 | 人均农业产值、受灾比例等 | 《中国人口与就业统计年鉴》EPS数据库 | 具体定义见1.2.3节，连续变量均进行标准化 |\n| 工具变量 | 历史邮局数量交互项 | 《中国电子信息产业统计年鉴》 | 1984年地级市邮局数与上一年全国信息技术服务收入的交互项，取对数 |\n| 地形因素 | 地形修正系数 | 《中华人民共和国地貌图集（1∶100万）》 | 通过量化各省主导地形的农业适宜性（1-10分），并经过非线性映射（α=0.7）将其转换为0.6至1.0的加权值，用于修正种植业相关的智慧农业指标 |\n| 主导产业权重 | 各省“农林牧渔产值占比” | 《中国统计年鉴》 | 各省农业内部各产业（种植业、林业、畜牧业、渔业）的产值占总产值的比重 |\n\n 表1 \n\n[![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14750)\n\n**表2**\n\n智慧农业变量描述性统计（2015—2023）\n\n| 变量 | 观测值 | 均值 | 标准差 | 最小值 | 最大值 |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| 智慧农业应用水平 | 270 | 30.838 | 22.304 | 3.141 | 100.000 |\n| 技术资本 | 270 | 0.074 | 0.965 | -1.558 | 2.468 |\n| 数据要素 | 270 | 0.309 | 0.749 | -0.921 | 3.343 |\n| 农业物质资本 | 270 | 8.084 | 9.030 | 1.353 | 10.937 |\n| 数据要素×农业物质资本 | 270 | 0.020 | 1.043 | -0.390 | 6.155 |\n| 受灾比例 | 270 | 5.460 | 1.714 | -1.050 | 8.349 |\n| 人均农业产值 | 270 | 0.160 | 0.149 | 0.003 | 0.799 |\n| 农业土地流转率 | 270 | 0.376 | 0.167 | 0.041 | 0.922 |\n| 受教育年限 | 270 | 0.145 | 0.750 | -2.290 | 2.637 |\n| 农林水事务支持度 | 270 | 0.000 | 0.000 | 0.000 | 0.001 |\n| 工具变量 | 270 | 14.424 | 0.573 | 13.291 | 16.714 |\n\n 表2 \n\n[![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14751)\n\n**表3**\n\n智慧农业发展评价指标体系\n\n| 一级指标 | 指标阐释 | 数据来源 |\n| :---: | :---: | :---: |\n| 生产加工数字化 | 智能农机作业面积占比 | 《中国农业机械工业年鉴》 |\n| 单位面积无人机作业强度 | 《中国农业机械工业年鉴》 |\n| 智能灌溉面积占比 | EPS数据库 |\n| 智能化播种作业占比 | 《中国林业和草原统计年鉴》 |\n| 智能化养殖场占比 | 《中国畜牧兽医年鉴》 |\n| 林业智能监测覆盖率 | 《中国林业和草原统计年鉴》 |\n| 智能化渔船占比 | 《中国渔业统计年鉴》 |\n| 流通营销数字化 | 电商企业占比 | 《中国高技术产业统计年鉴》 |\n| 村级物流网点覆盖率 | 《中国高技术产业统计年鉴》 |\n| 农产品网络零售额占比 | EPS数据库 |\n| 数字技术服务业 | 智慧服务组织数量密度 | 《中国统计年鉴》 |\n| 高新技术企业密度 | 《中国高技术产业统计年鉴》 |\n| 农业科研创新数字化 | 发明专利占比 | 国家知识产权局 |\n| 数字农业研发投入强度 | 《中国统计年鉴》 |\n| 数字农业研发人员占比 | 《中国人口与就业统计年鉴》 |\n\n 表3 \n\n[![Image 10](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-26\u002Fthumbnail\u002FFFE56FD7-8638-44f2-9E94-97017DDCBAEF-F001.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#figureClass14752)\n\n**图1**\n\n智慧农业发展的理论分析框架\n\n![Image 11](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-26\u002FFFE56FD7-8638-44f2-9E94-97017DDCBAEF-F001.jpg)\n\n 图1 \n\n[![Image 12](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14753)\n\n**表4**\n\n智慧农业发展的空间计量模型选择检验结果\n\n| 空间权重矩阵 | LR统计量 | P值 | 模型选择 |\n| :---: | :---: | :---: | :---: |\n| 农业分区权重 | 15.090 | 0.002 | SDM |\n| 经济权重 | 42.430 | 0.001 | SDM |\n| 地理权重 | 2.630 | 0.452 | SAR |\n\n 表4 \n\n[![Image 13](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-26\u002Fthumbnail\u002FFFE56FD7-8638-44f2-9E94-97017DDCBAEF-F002.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#figureClass14754)\n\n**图2**\n\n区域智慧农业发展指数与增速表\n\n![Image 14](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-26\u002FFFE56FD7-8638-44f2-9E94-97017DDCBAEF-F002.jpg)\n\n 图2 \n\n[![Image 15](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14755)\n\n**表5**\n\n省级智慧农业结构趋同动态研究的变异系数与莫兰指数\n\n| 年份 | 全国结构指数CV | 空间邻接平均差异 | 经济组内平均差异 | 莫兰指数 | _Z_ 值 |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| 2015 | 0.809 | 0.100 | 0.124 | 0.161* | 1.638 |\n| 2016 | 0.858 | 0.112 | 0.122 | 0.159* | 1.585 |\n| 2017 | 0.762 | 0.113 | 0.139 | 0.113 | 1.211 |\n| 2018 | 0.816 | 0.128 | 0.165 | 0.204** | 2.015 |\n| 2019 | 0.741 | 0.093 | 0.133 | 0.334*** | 3.031 |\n| 2020 | 0.555 | 0.07 | 0.105 | 0.286*** | 2.632 |\n| 2021 | 0.681 | 0.107 | 0.155 | 0.308*** | 2.846 |\n| 2022 | 0.740 | 0.114 | 0.159 | 0.295*** | 2.720 |\n| 2023 | 0.707 | 0.122 | 0.170 | 0.251** | 1.963 |\n\n 表5 \n\n[![Image 16](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-26\u002Fthumbnail\u002FFFE56FD7-8638-44f2-9E94-97017DDCBAEF-F003.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#figureClass14756)\n\n**图3**\n\n中国省域智慧农业指数空间分布格局及演变 a. 2019年智慧农业发展水平空间分布格局 b. 2023年智慧农业发展水平空间分布格局 注：该图基于自然资源部标准地图服务网站下载的审图号为GS（2024）0650号的标准地图制作，底图边界无修改；数据来源：AHP+熵权法综合指数评估，灰色区域表示数据缺失。\n\n![Image 17](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-26\u002FFFE56FD7-8638-44f2-9E94-97017DDCBAEF-F003.jpg)\n\n 图3 \n\n[![Image 18](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14757)\n\n**表6**\n\n数据要素与农业物质资本协同驱动智慧农业发展的实证检验结果\n\n| 变量 | OLS | 中介路径 | 直接效应 | 调节效应 | GMM | TOPSIS | 传感器企业 |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| （1） | （2） | （3） | （4） | （5） | （6） | （7） |\n| 数据要素 | 0.577*** | 0.017*** | 0.450** | 0.629*** | 0.300*** | 3.865 1*** | 74.907*** |\n| （0.193） | （0.005） | （0.202） | （0.194） | （0.109） | （1.106） | （17.486） |\n| 农业物质资本 |  |  |  | 1.153* | 3.494*** | 0.001 1*** | 96.078* |\n|  |  |  | （0.571） | （0.864） | （0.000） | （54.038） |\n| 数据要素×农业物质资本 |  |  |  | 1.763*** | 2.358** | 7.705 6*** | -70.306 |\n|  |  |  | （0.710） | （1.045） | （2.386） | （41.580） |\n| 技术资本 |  |  | 7.442** |  |  |  |  |\n|  |  | （3.538） |  |  |  |  |\n| 滞后项 |  |  |  |  | 0.731*** |  |  |\n|  |  |  |  | （0.137） |  |  |\n| 常数项 | 12.173 | -0.260 | 14.110* | 12.813* | 5.595* | -10.087 | -1 247.619* |\n| （7.537） | （0.255） | （8.009） | （7.336） | （3.131） | （7.200） | （657.436） |\n| AR（1）p值 |  |  |  |  | 0.014 |  |  |\n| AR（2）p值 |  |  |  |  | 0.671 |  |  |\n| Hansen检验p值 |  |  |  |  | 0.424 |  |  |\n|  |  |  |  | 0.957 |  |  |\n| 控制年份 | 是 | 是 | 是 | 是 | 是 | 是 | 是 |\n| 控制省份 | 是 | 是 | 是 | 是 | 是 | 是 | 是 |\n| _R_ 2 | 0.657 | 0.555 | 0.670 | 0.668 |  | 0.576 | 0.746 |\n| 观测值 | 270 | 270 | 270 | 270 | 270 | 270 | 270 |\n\n 表6 \n\n[![Image 19](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14758)\n\n**表7**\n\n数据要素与物质资本协同效应的空间计量分析结果\n\n| 变量 | 农业分区权重 | 经济权重 | 地理权重 |\n| :---: | :---: | :---: | :---: |\n| 数据要素 | -8.800*** | -3.997* | -8.230*** |\n| （2.235） | （2.400） | （2.292） |\n| 农业物质资本 | -0.001** | -0.002*** | -0.001* |\n| （0.001） | （0.001） | （0.000） |\n| 数据要素×农业物质资本 | 9.879*** | 18.314*** | 8.269** |\n| （4.054） | （5.737） | （3.867） |\n| 技术资本 | 12.299*** | 10.857*** | 11.112*** |\n| （1.986） | （1.957） | （2.014） |\n| 常数项 | 42.594*** | 202.173*** | 49.546*** |\n| （7.384） | （48.680） | （7.439） |\n| Rho | 0.025 | -0.326* | 0.044 |\n| （0.065） | （0.313） | （0.083） |\n| LR | 13.66 | 26.41 | 1.21 |\n| P值 | 0.001 0 | 0.000 0 | 0.545 1 |\n| 控制年份 | 是 | 是 | 是 |\n| 控制省份 | 是 | 是 | 是 |\n| _R_ 2 | 0.490 | 0.474 | 0.483 |\n| 观测值 | 240 | 240 | 240 |\n\n 表7 \n\n[![Image 20](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202510009#inline_content14759)\n\n**表8**\n\n智慧农业发展的空间溢出效应分解结果\n\n| 变量 | 农业分区权重 | 经济权重 |\n| :---: | :---: | :---: |\n| 直接效应 | 间接效应 | 总效应 | 直接效应 | 间接效应 | 总效应 |\n| 数据要素 | -8.832*** | 6.225*** | -2.607 | -3.638*** | -38.691*** | -42.329*** |\n| （2.158） | （2.005） | （2.864） | （2.381） | （13.505） | （13.387） |\n| 农业物质资本 | -0.001** | 0.001 | -0.000 | -0.002*** | -0.02** | -0.026*** |\n| （0.000） | （0.001） | （0.001） | （0.001） | （0.013） | （0.013） |\n| 数据要素×农业物质资本 | 9.395*** | -6.419 | 2.976 | 15.689*** | 181.581** | 197.271** |\n| （3.795） | （5.274） | （5.168） | （5.305） | （91.371） | （95.212） |\n| RD投入强度 | 12.396*** | 0.302 | 12.699*** | 10.995*** | -2.201 | 8.794*** |\n| （2.043） | （0.800） | （2.238） | （2.031） | （2.853） | （3.015） ","智慧农业(中英文) 2026, 8(3): 26-39","2026-07-26T02:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,2,1,"学术论文，构建了智慧农业综合评价体系，方法新颖，数据详实，但发表于2026-05-30，时效性较低。",[24],{"name":10,"url":6},[26,27,28],"智慧农业","综合评价","驱动机制",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":47},12,true,[26],[26],[26],[49,50],"智慧农业 综合评价 驱动机制","智慧农业 综合评价","智慧农业综合评价驱动机制-120",11,"10.12133\u002Fj.smartag.sa202510009","2026-07-31T00:00:56.196529Z",{"total":56,"page":21,"page_size":56,"items":57},6,[58,116,176,222,262,321],{"id":59,"title":60,"url":61,"summary":62,"summary_zh":8,"content":8,"source_name":63,"source_url":61,"published_at":64,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":65,"score_detail":66,"sources":70,"tags":72,"primary_topic":77,"secondary_topics":78,"classification":79,"search_phrases":93,"slug":96,"view_count":97,"doi":98,"paper":99,"created_at":115},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":67,"substance":68,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":69},16,21,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[71],{"name":63,"url":61},[26,73,74,75,76],"农业人工智能","精准农业","机器视觉","作物行检测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":80},{"candidates":81,"agricultural_evidence":44},[82,88],{"code":30,"score":83,"supported":44,"title":84,"abstract":85,"tags":87},5,[],[86],"precision agriculture",[26,74],{"code":89,"score":20,"supported":14,"title":90,"abstract":91,"tags":92},"I2",[],[],[73,75],[94,95],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539",0,"10.1007\u002Fs11119-026-10473-z",{"doi":98,"openalex_id":100,"authors":101,"venue":63,"cited_by_count":97,"oa_url":8,"card":8,"direction":8,"ingested_from":114},"W7223117828",[102,105,108,111],{"name":103,"orcid":104},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":106,"orcid":107},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":109,"orcid":110},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":112,"orcid":113},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":8,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":8,"hotness":124,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":134,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":158,"slug":161,"view_count":97,"doi":162,"paper":163,"created_at":175},5397,"The Relationship Between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23469780","Relationship between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study explores the relationship between nutrient concentrations and the optical characteristics of banana leaves using quantitative image analysis. The study focuses on measurable leaf features, including colour intensity, RGB values, and other image-derived parameters, to identify patterns associated with plant nutrient status. Digital images of banana leaves can provide valuable information about variations in leaf appearance that may reflect differences in nutrient availability. By analysing these characteristics alongside nutrient concentration measurements, the study aims to establish quantitative relationships that can support plant nutrient assessment. This approach may contribute to the development of cost-effective, non-destructive, and image-based techniques for monitoring banana plant health. The findings can be useful for precision agriculture, crop management, and future research on digital image processing for agricultural applications.","香蕉叶片养分浓度与可测量光学特征之间的关系：一项基于定量图像的研究探讨了利用定量图像分析研究香蕉叶片养分浓度与光学特征之间的关系。该研究聚焦于可测量的叶片特征，包括颜色强度、RGB值及其他图像衍生参数，以识别与植物养分状况相关的模式。香蕉叶片的数字图像可提供关于叶片外观变化的有价值信息，这些变化可能反映养分有效性的差异。通过将这些特征与养分浓度测量结果一并分析，该研究旨在建立可支持植物养分评估的定量关系。该方法可能有助于开发低成本、非破坏性且基于图像的香蕉植株健康监测技术。研究结果可用于精准农业、作物管理以及未来农业应用数字图像处理的研究。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",25,66,{"impact":43,"substance":17,"depth":127,"authority":43,"freshness":128,"relevant":21,"comment":129},15,9,"该研究基于图像定量分析香蕉叶片营养状况，方法新颖且面向无损监测，对精准农业有参考价值，但属单作物探索性研究，影响范围有限。",[131,132],{"name":122,"url":119},{"name":122,"url":133},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[26,135,136,137,138],"图像分析","香蕉","无损监测","作物营养",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":142},{"candidates":143,"agricultural_evidence":44},[144,149,154],{"code":30,"score":145,"supported":44,"title":146,"abstract":147,"tags":148},7,[],[74,86],[26],{"code":150,"score":21,"supported":14,"title":151,"abstract":152,"tags":153},"B2",[],[],[136],{"code":89,"score":21,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[135],[159,160],"香蕉叶片 营养浓度 图像分析","香蕉 无损监测 精准农业","香蕉叶片营养浓度图像分析-5397","10.5281\u002Fzenodo.23469780",{"doi":162,"openalex_id":164,"authors":165,"venue":122,"cited_by_count":97,"oa_url":119,"card":168,"direction":174,"ingested_from":114},"W7222941290",[166],{"name":167,"orcid":8},"Anantjit Publication",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"用定量图像分析探究香蕉叶片养分浓度与可测光学特征的关系。","采集香蕉叶片数字图像，提取颜色强度、RGB值等参数，与养分浓度做定量关联分析。","叶片图像光学特征与养分浓度存在可量化的关联，可支撑无损养分评估。","农业遥感与作物表型","可扩展到多品种、多生育期与田间光照校正，并构建图像-养分反演模型与便携诊断工具。","数字乡村与农业信息化","2026-10-20T23:30:22.619634Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":8,"content":8,"source_name":181,"source_url":179,"published_at":123,"category":12,"cover_url":8,"hotness":124,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":190,"primary_topic":194,"secondary_topics":195,"classification":196,"search_phrases":204,"slug":207,"view_count":97,"doi":208,"paper":209,"created_at":221},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex",37,{"impact":184,"substance":56,"depth":184,"authority":56,"freshness":128,"relevant":21,"comment":185},8,"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[187,188],{"name":181,"url":179},{"name":181,"url":189},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[191,26,192,193],"数字农业","农业信息化","中非",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":197},{"candidates":198,"agricultural_evidence":44},[199],{"code":30,"score":19,"supported":44,"title":200,"abstract":202,"tags":203},[201],"digital agriculture",[201],[192,191,26],[205,206],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":208,"openalex_id":210,"authors":211,"venue":8,"cited_by_count":97,"oa_url":179,"card":8,"direction":174,"ingested_from":114},"W7222930115",[212,214,216,219],{"name":213,"orcid":8},"Anani Ogou",{"name":215,"orcid":8},"Henry Chotangui",{"name":217,"orcid":218},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":220,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":8,"content":8,"source_name":181,"source_url":225,"published_at":123,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":236,"classification":237,"search_phrases":250,"slug":253,"view_count":97,"doi":254,"paper":255,"created_at":261},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex",56,{"impact":17,"substance":184,"depth":13,"authority":43,"freshness":184,"relevant":21,"comment":229},"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[231],{"name":181,"url":225},[26,233,74,234],"农业机器人","智能采收",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":238},{"candidates":239,"agricultural_evidence":44},[240,245],{"code":30,"score":241,"supported":44,"title":242,"abstract":243,"tags":244},13,[86],[86],[26,74],{"code":246,"score":21,"supported":14,"title":247,"abstract":248,"tags":249},"F6",[],[],[233],[251,252],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":254,"openalex_id":256,"authors":257,"venue":8,"cited_by_count":97,"oa_url":225,"card":8,"direction":260,"ingested_from":114},"W7222971426",[258],{"name":259,"orcid":8},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":8,"source_name":268,"source_url":265,"published_at":123,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":273,"tags":275,"primary_topic":280,"secondary_topics":281,"classification":282,"search_phrases":290,"slug":293,"view_count":97,"doi":294,"paper":295,"created_at":320},5375,"Triboelectric Nanogenerator Based on All‐Cellulose Triboelectric Layer for Droplet Energy Harvesting and Self‐Powered Agriculture Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsmll.76354","Cellulose-based triboelectric nanogenerators (TENGs) attract increasing attention; however, developing high-performance all-cellulose-based triboelectric layer TENGs remains challenging. In this study, a regenerated cellulose nanofiber film (R-CNF) is used as a substrate, and an aminosilane intermediate layer is introduced. By utilizing the silanol groups on the silane chains as compatible reactive sites for fluorination grafting, and simultaneously enhancing the bonding strength between modified layers through N-H···F-C hydrogen-bond interactions between amino and fluorine groups, a fluorinated regenerated cellulose nanofiber film (F-R-CNF) is successfully prepared. The degradation rate of F-R-CNF within 13 days is approximately 0.96%. When assembled into a TENG with an ethyl cellulose nanofiber film(ECNF), the output voltage of the resulting TENG is ten times higher than that of the R-CNF-based TENGs, while maintaining 85% of its output performance at 80% relative humidity. Furthermore, a droplet energy harvesting TENG (DE-TENG) is constructed to harvest energy from falling water droplets, demonstrating the potential of the DE-TENG to power agricultural micro-sensors. This study provides a feasible strategy for designing high-performance all-cellulose-based triboelectric layer triboelectric nanogenerators and reveals the great potential of F-R-CNF for self-powered devices and smart agriculture applications.","纤维素基摩擦纳米发电机（TENGs）日益受到关注，然而开发高性能全纤维素基摩擦电层TENG仍面临挑战。本研究以再生纤维素纳米纤维膜（R-CNF）为基底，引入氨基硅烷中间层，利用硅烷链上的硅醇基团作为氟化接枝的相容反应位点，同时通过氨基与含氟基团之间的N-H···F-C氢键相互作用增强改性层间的结合强度，成功制备了氟化再生纤维素纳米纤维膜（F-R-CNF）。F-R-CNF在13天内的降解率约为0.96%。将其与乙基纤维素纳米纤维膜（ECNF）组装成TENG后，所得TENG的输出电压比基于R-CNF的TENG高十倍，同时在80%相对湿度下仍保持85%的输出性能。此外，构建了液滴能量收集TENG（DE-TENG）以收集下落水滴的能量，展示了DE-TENG为农业微传感器供电的潜力。本研究为设计高性能全纤维素基摩擦电层摩擦纳米发电机提供了可行策略，并揭示了F-R-CNF在自供电器件和智慧农业应用中的巨大潜力。","Small",81,{"impact":17,"substance":18,"depth":271,"authority":19,"freshness":184,"relevant":21,"comment":272},19,"全纤维素摩擦纳米发电机实现液滴能量收集并驱动农业微传感器，方法新颖、数据扎实，属智慧农业自供电传感的实质进展，值得入选。",[274],{"name":268,"url":265},[26,276,277,278,279],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":283},{"candidates":284,"agricultural_evidence":44},[285],{"code":30,"score":145,"supported":44,"title":286,"abstract":287,"tags":289},[],[26,288],"smart agriculture",[26],[291,292],"全纤维素 摩擦纳米发电机 液滴能量收集","F-R-CNF 自供电 农业监测","全纤维素摩擦纳米发电机液滴能量收集-5375","10.1002\u002Fsmll.76354",{"doi":294,"openalex_id":296,"authors":297,"venue":268,"cited_by_count":97,"oa_url":8,"card":315,"direction":260,"ingested_from":114},"W7222917387",[298,300,303,305,307,310,312],{"name":299,"orcid":8},"Hengwei Xiong",{"name":301,"orcid":302},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":304,"orcid":8},"Weijie Li",{"name":306,"orcid":8},"Kangwen Shi",{"name":308,"orcid":309},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":311,"orcid":8},"Wenpeng Fan",{"name":313,"orcid":314},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":316,"method":317,"finding":318,"direction":260,"opportunity":319},"制备全纤维素摩擦电层TENG，收集水滴能量为农业微传感器供电。","再生纤维素纳米纤维膜经氨基硅烷中间层氟化接枝，与乙基纤维素膜组装TENG。","输出达R-CNF基TENG的10倍，80%湿度下保持85%性能，13天降解约0.96%。","可探索全纤维素TENG在田间高湿环境下的长期稳定性及与无线传感节点的集成供电。","2026-10-20T23:30:08.252085Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":8,"source_name":122,"source_url":324,"published_at":123,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":65,"score_detail":327,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":335,"classification":336,"search_phrases":349,"slug":352,"view_count":97,"doi":353,"paper":354,"created_at":364},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。",{"impact":17,"substance":18,"depth":17,"authority":43,"freshness":128,"relevant":21,"comment":328},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[330],{"name":122,"url":324},[26,73,332,74,333],"数字孪生","植物表型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":337},{"candidates":338,"agricultural_evidence":44},[339,345],{"code":30,"score":340,"supported":44,"title":341,"abstract":343,"tags":344},20,[342],"digital twin",[74,332,86],[332,26,74],{"code":89,"score":21,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[73],[350,351],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":353,"openalex_id":355,"authors":356,"venue":122,"cited_by_count":97,"oa_url":324,"card":359,"direction":172,"ingested_from":114},"W7222885652",[357],{"name":358,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":360,"method":361,"finding":362,"direction":172,"opportunity":363},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z"]