[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-271":3,"related-271":69},{"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":28,"primary_topic":37,"secondary_topics":43,"classification":44,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},271,"全球智慧农业的模式比较与演化趋势——杨明、胡冰川《智慧农业(中英文)》2026年第3期","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1","中国社会科学院农村发展研究所杨明、胡冰川在《智慧农业(中英文)》2026年第3期发表《全球智慧农业的模式比较与演化趋势》论文。研究基于诱致性创新理论与制度比较分析视角,构建驱动主体-制度环境-技术路径-价值取向四维分析框架,识别出商业化大农场智慧农业的美国模式、绿色化集约智慧农业的欧盟模式、精细化协同智慧农业的日本模式、节水化创新智慧农业的以色列模式,以及集成化协同智慧农业的中国模式。分析发现,全球智慧农业正从单点技术引入、国别分散发展,向数据驱动、智能决策、开放协同的系统集成与趋同演化转变;全球智慧农业技术从数字化精准作业到智能化自主决策、组织从单主体推动走向开放生态共治、数据从孤岛走向跨境互操作与要素市场化、价值从单一增产走向绿色低碳与韧性导向、治理从规则碎片化走向多边协同共建的演化趋势。中国作为大国小农国情的代表性经济体,可在五维协同推进中承担技术供给者、制度试验者与规则共建者的复合角色。",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\u002FY2026\u002FV8\u002FI3\u002F1)\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): 1-12.doi: [10.12133\u002Fj.smartag.SA202605001](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202605001)\n\n• 专刊--数字技术赋能与农业经济范式转型 •[下一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22763.shtml)\n\n### 全球智慧农业的模式比较与演化趋势\n\n杨明, 胡冰川([![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:hubch@cass.org.cn))\n\n1.   中国社会科学院农村发展研究所，北京 100029，中国\n\n*   `收稿日期:`2026-04-30 `出版日期:`2026-05-30 \n*   `基金项目:`国家社会科学基金项目(23ZDA045) \n*   `作者简介:`杨 明，博士，助理研究员，研究方向为社会创业与智慧农业。E-mail：[nfs-yangming@cass.org.cn](mailto:nfs-yangming@cass.org.cn) \n*   `通信作者:`胡冰川，博士，研究员，教授，研究方向为农产品贸易与智慧农业。E-mail：[hubch@cass.org.cn](mailto:hubch@cass.org.cn) \n\n### Comparison and Evolutionary Trends of Global Smart Agriculture Models\n\nYANG Ming, HU Bingchuan([![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:hubch@cass.org.cn))\n\n1.   Rural Development Institute, Chinese Academy of Social Sciences, Beijing 100029, China\n\n*   `Received:`2026-04-30 `Online:`2026-05-30 \n*   `Foundation items:`The National Social Science Fund of China(23ZDA045) \n*   `About author:`YANG Ming, E-mail: [nfs-yangming@cass.org.cn](mailto:nfs-yangming@cass.org.cn) \n*   `Corresponding author:`HU Bingchuan, E-mail: [hubch@cass.org.cn](mailto:hubch@cass.org.cn) \n\n## [在线阅读 31](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)\n\n## [知网下载](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1)\n\n## [本地下载 59](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#1)\n\n[_![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002Fimages\u002Fknowledge\\_map1.png)_ ## 可视化 0](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002Flexeme\u002FshowArticleByLexeme.do?articleID=22803)\n\n#### [摘要\u002FAbstract](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1)\n\n**摘要：**\n\n**【目的\u002F意义】** 智慧农业是全球应对粮食安全、气候变化与劳动力老龄化挑战、培育农业新质生产力的战略选择。本文旨在系统比较全球智慧农业模式、揭示其分化机制与演化趋势，以期为中国构建自主可控且开放协同的智慧农业体系提供理论参考与实践依据。 **【进展】** 基于诱致性创新理论与制度比较分析视角，构建“驱动主体-制度环境-技术路径-价值取向”四维分析框架，识别出商业化大农场智慧农业的美国模式、绿色化集约智慧农业的欧盟模式、精细化协同智慧农业的日本模式、节水化创新智慧农业的以色列模式，以及集成化协同智慧农业的中国模式。分析发现，全球智慧农业正从单点技术引入、国别分散发展，向数据驱动、智能决策、开放协同的系统集成与趋同演化转变；然而仍面临核心技术封闭、数据治理碎片化、小农户接入鸿沟扩大与国际规则博弈加剧等挑战。全球智慧农业呈现出技术上从数字化精准作业到智能化自主决策、组织上从单主体推动走向开放生态共治、数据上从孤岛走向跨境互操作与要素市场化、价值上从单一增产走向绿色低碳与韧性导向、治理上从规则碎片化走向多边协同共建的演化趋势。 **【结论\u002F展望】** 中国作为大国小农国情的代表性经济体，可在五维协同推进中承担技术供给者、制度试验者与规则共建者的复合角色，与各国共同推动全球智慧农业走向更具包容性的共生格局。\n\n**关键词:**[智慧农业,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[数据驱动,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[智能决策,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[新质生产力,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[农业基础模型,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[开放协同,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[大国小农](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)\n\n**Abstract:**\n\n**[Significance]** Smart agriculture represents a strategic choice for addressing the global challenges of food security, climate change, and an aging agricultural labor force, and for cultivating new-quality productive forces in agriculture. Shaped by factors such as factor endowments, institutional traditions, and value preferences, economies have not converged upon a single optimal path, but have instead formed highly heterogeneous practical configurations across driving actors, institutional environments, technological pathways, and value orientations. Existing research remains largely confined to the parallel enumeration of country-specific experiences and lack a unified comparative framework; their mechanistic explanations seldom move beyond the single dimension of factor endowments, leaving the multidimensional, co-evolutionary dynamics inadequately understood. In view of this, the archetypal models of global smart agriculture were systematically compared, the mechanisms underlying their divergence were uncovered, and their evolutionary trends were assessed, so as to provide theoretical references and practical guidance for China to build an autonomous, controllable, and openly collaborative smart agriculture system and in advancing from technological follower to co-builder of rules. **[Progress]** Drawing on the theory of induced innovation and a comparative institutional analysis perspective, a four-dimensional analytical framework of \"driving actors-institutional environment-technological pathway-value orientation\" was constructed, and five representative economies, the United States, the European Union, Japan, Israel, and China, were selected to distill five archetypal models. The commercialized large-scale farm model exemplified by the United States is one in which the private sector leads and releases economies of scale through large-scale field operations. The green and intensive model exemplified by the European Union is one in which intensive production is steered by binding green constraints. The refined collaborative model exemplified by Japan is one in which platform-based co-governance alleviates super-aging and smallholder fragmentation. The water-saving innovative model exemplified by Israel is one in which a full-chain water-saving technology system has developed under acute water scarcity. The integrated collaborative model exemplified by China is one in which national strategy integrates multiple actors, coupling the 'large-country-with-smallholders' condition with leading digital infrastructure such as BeiDou, 5G, and AI. On this basis, four mechanisms, induced innovation, path dependence, the antitrust tradition, and rule-based contestation, were introduced to provide a process-tracing account of model divergence in terms of sequential triggering and tension-laden coupling, the underlying drivers of this divergence were traced across the three dimensions of endowment constraints, organizational systems, and value hierarchies, and thereby the distinct value sequences of the five models were identified. Five dimensions of global evolutionary trends were assessed: technology, organization, data, value, and governance. Technologically, from digital precision operations toward intelligent autonomous decision-making. Organizationally, from single-actor-driven development toward open-ecosystem co-governance. In data terms, from isolated silos toward cross-border interoperability and factor marketization. In value terms, from a singular pursuit of yield growth toward green, low-carbon, and resilience-oriented goals, and in governance, from national fragmentation toward global regulatory coopetition. **[Conclusions and Prospects]** Model divergence constitutes a stable equilibrium under the multiple coupling of factor endowments, institutional structures, and historical paths, and no universally optimal model exists; beneath this divergence lies a convergent core in which data serve as a factor of production, models as the decision-making engine, ecosystems as the organizational form, and sustainability as the value baseline. Looking ahead, global smart agriculture should evolve along the trajectory of technological inclusiveness, ecological openness, value pluralism, and governance synergy. China is well positioned to assume the composite role of technology provider, institutional experimenter, and rule co-builder, working with other nations to advance global smart agriculture toward a more inclusive and symbiotic configuration.\n\n**Key words:**[smart agriculture,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[data-driven,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[intelligent decision-making,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[new quality productive forces,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[agricultural foundation models,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[open collaboration,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)[large country with smallholder farming](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)\n\n**中图分类号:**\n\n*   [S126](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)\n\n*   [F323](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#)\n\n#### 引用本文\n\n杨明, 胡冰川. 全球智慧农业的模式比较与演化趋势[J]. 智慧农业(中英文), 2026, 8(3): 1-12.\n\nYANG Ming, HU Bingchuan. Comparison and Evolutionary Trends of Global Smart Agriculture Models[J]. Smart Agriculture, 2026, 8(3): 1-12.\n\n#### 使用本文\n\n**[[收藏文章](javascript:;)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#) \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=22803)|[Ris](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=22803)|[BibTeX](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=22803)\n\n**链接本文:**[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202605001](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202605001)\n\n[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1)\n\n#### [图\u002F表 5](javascript:;)\n\n[![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#inline_content14657)\n\n**表1**\n\n全球智慧农业典型模式比较\n\n| 比较维度 | 美国 | 欧盟 | 日本 | 以色列 | 中国 |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| 模式类型 | 商业化大农场智慧农业 | 绿色化集约智慧农业 | 精细化协同智慧农业 | 节水化创新智慧农业 | 集成化协同智慧农业 |\n| 驱动主体 | 风险资本+装备厂商+农化巨头 | 超国家规制机构+农户协会 | 国家研究机构+农协+私营企业 | 国家研发体系+军工+风投 | 国家战略+龙头企业+科研院所 |\n| 制度供给重心 | 立法授权+连接性补贴+研发资助 | CAP+绿色硬约束+数据空间立法 | 基本计划+开放API指南+协议会共治 | 国家研发资助+创新政策+水资源立法 | 国家行动计划+政策密度迭代+示范项目 |\n| 技术路径 | 大农场+自动导航+变量喷雾+数据平台 | 智能温室+变量施肥+绿色合规 | 小尺度+共享平台+机器替人 | 滴灌+再生水+精准节水+AI水肥 | 北斗+5G+植保无人机+集成大模型 |\n| 代表平台\u002F项目 | Operations Center、Climate FieldView | Common European Agricultural Data Space、SmartAgriHubs、DataConnect | WAGRI、SIP、Smart Food Chain | Netafim、Volcani Center、GrowingIL | 伏羲农场、国家智慧农业大数据平台 |\n| 价值取向 | 商业回报+规模经济 | 绿色低碳+生物多样性+多功能 | 替人增效+老龄化适应 | 资源效率+技术输出 | 粮食安全+新质生产力 |\n\n 表1 \n\n[![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002Fthumbnail\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F001.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#figureClass14658)\n\n**图1**\n\n全球智慧农业演化阶段 注：数据来源于USDA ERS《数字时代的精准农业》报告（2023年）、欧盟智慧农业枢纽（SmartAgriHubs）项目年度评估报告（2019—2024年）、日本农林水产省《食物·农业·农村基本计划》（2024—2025年）及日本农业数据协同平台协议会成员数据、欧盟农业数据空间蓝图（CEADS Blueprint, 2024年）。\n\n![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F001.jpg)\n\n 图1 \n\n[![Image 10](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002Fthumbnail\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F002.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#figureClass14659)\n\n**图2**\n\n日本WAGRI平台演化曲线 注：数据来源于日本农林水产省（Ministry of Agriculture, Forestry and Fisheries, MAFF）、NARO官方公告、亚太地区粮食与肥料技术中心（Food and Fertilizer Technology Center, FFTC）技术报告（2019）、WAGRI协议会成员页（截至2026年1月5日协议会总成员数530社）。\n\n![Image 11](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F002.jpg)\n\n 图2 \n\n[![Image 12](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002Fthumbnail\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F003.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#figureClass14660)\n\n**图3**\n\n全球农业食品科技投资规模演化 注：数据来源于 AgFunder Global AgriFoodTech Investment Report 2019—2025。\n\n![Image 13](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F003.jpg)\n\n 图3 \n\n[![Image 14](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002Fthumbnail\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F004.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1#figureClass14661)\n\n**图4**\n\n全球智慧农业“五维共演”趋势框架\n\n![Image 15](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-1\u002FC3B92C94-3694-40fa-8E3E-C7791FDC8214-F004.jpg)\n\n 图4 \n\n[](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F1)\n#### [参考文献 51](javascript:;)\n\n[1]张绮雯, 林青宁, 毛世平. 国际视角下中国智慧农业发展的路径探寻[J]. 世界农业, 2022(8): 17-26.\nZHANG Q W, LIN Q N, MAO S P. 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(2026-03-27) [2026-05-13].\n[36]大疆农业. 农业无人机行业白皮书(2023)[R\u002FOL]. 深圳: 深圳市大疆创新科技有限公司, 2024[2026-05-14].\n[37]张玉成, 张晓博, 高树琴, 等. \"伏羲农场\": 智慧农业技术集成创新的实践探索与思考[J]. 中国科学院院刊, 2025, 40(2): 301-309.\nZHANG Y C, ZHANG X B, GAO S Q, et al. The Fuxi Farm: Practice and reflection on integrated innovation of smart agriculture technology[J]. Bulletin of Chinese Academy of Sciences, 2025, 40(2): 301-309.\n[38]HAYAMI Y, RUTTAN V W. Factor prices and technical change in agricultural development: The United States and Japan, 1880-1960[J]. Journal of Political Economy, 1970, 78(5): 1115-1141.\n[39]DAVID P A. Clio and the Economics of QWERTY[J]. The American Economic Review, 1985, 75(2): 332-337.\n[40]ARTHUR W B. Competing technologies, increasing returns, and lock-In by historical events[J]. The Economic Journal, 1989, 99(394): 116-131.\n[41]BRADFORD A. The Brussels effect: How the European Union rules the world[M]. Oxford: Oxford University Press, 2020.\n[42]曾寅初, 计薇. 欧盟共同农业政策2023—2027年改革的动向及其启示[J]. 农业经济问题, 2023(6): 43-57.\nZENG Y C, JI W. Common agricultural policy 2023—2027 reform in European Union: Trends and enlightenment[J]. Issues in Agricultural Economy, 2023(6): 43-57.\n[43]全国人民代表大会常务委员会. 中华人民共和国数据安全法[Z\u002FOL]. (2021-06-10)[2026-06-14].\n[44]United States Department of Agriculture, National Agricultural Statistics Service. 2017 Census of Agriculture Data Now Available[EB\u002FOL]. (2019-04-11)[2026-04-26].\n[45]袁祥州, 黄恩临. 欧盟智慧农业发展经验及其借鉴[J]. 世界农业, 2022, (5): 27-36.\nYUAN X Z, HUANG E L. Experience and Reference of Smart Agriculture Development in European Union[J]. World Agriculture, 2022, (5): 27-36.\n[46]易小燕, 吴勇, 尹昌斌, 等. 以色列水土资源高效利用经验对","智慧农业(中英文) 2026, 8(3): 1-12","2026-08-05T00:00:00Z","论文",25,false,87,{"impact":13,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,15,7,1,"核心期刊论文，系统比较全球智慧农业模式，提出五维演化趋势，对政策与学术研究有较高参考价值。",[24,25],{"name":10,"url":6},{"name":26,"url":27},"智慧农业(中英文) 2026, 8(3): 1-12 \u002F 中国社会科学院 2026-08-05","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7670449363466846766",[29,30,31,32,33,34,35,36],"智慧农业","模式比较","演化趋势","美国模式","欧盟模式","日本模式","以色列模式","中国模式",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,56],{"code":38,"score":51,"supported":52,"title":53,"abstract":54,"tags":55},12,true,[29],[29],[29],{"code":57,"score":58,"supported":52,"title":59,"abstract":60,"tags":62},"G",3,[],[61],"农村发展",[],[64,65],"以色列模式 中国模式 日本模式 智慧农业","以色列模式 中国模式","以色列模式中国模式日本模式智慧农业-271",14,"2026-08-07T00:18:31.323614Z",{"total":70,"page":21,"page_size":70,"items":71},6,[72,132,189,235,275,334],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":8,"source_name":77,"source_url":75,"published_at":78,"category":12,"cover_url":8,"hotness":79,"is_selected":14,"score":80,"score_detail":81,"sources":85,"tags":87,"primary_topic":92,"secondary_topics":93,"classification":94,"search_phrases":109,"slug":112,"view_count":113,"doi":114,"paper":115,"created_at":131},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",10,79,{"impact":82,"substance":83,"depth":18,"authority":67,"freshness":79,"relevant":21,"comment":84},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。",[86],{"name":77,"url":75},[29,88,89,90,91],"农业人工智能","精准农业","机器视觉","作物行检测",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":95},{"candidates":96,"agricultural_evidence":52},[97,103],{"code":38,"score":98,"supported":52,"title":99,"abstract":100,"tags":102},5,[],[101],"precision agriculture",[29,89],{"code":104,"score":105,"supported":14,"title":106,"abstract":107,"tags":108},"I2",2,[],[],[88,90],[110,111],"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":114,"openalex_id":116,"authors":117,"venue":77,"cited_by_count":113,"oa_url":8,"card":8,"direction":8,"ingested_from":130},"W7223117828",[118,121,124,127],{"name":119,"orcid":120},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":122,"orcid":123},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":125,"orcid":126},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":128,"orcid":129},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":8,"source_name":138,"source_url":135,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":140,"score_detail":141,"sources":144,"tags":148,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":171,"slug":174,"view_count":113,"doi":175,"paper":176,"created_at":188},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",66,{"impact":51,"substance":18,"depth":19,"authority":51,"freshness":142,"relevant":21,"comment":143},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。",[145,146],{"name":138,"url":135},{"name":138,"url":147},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[29,149,150,151,152],"图像分析","香蕉","无损监测","作物营养",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":156},{"candidates":157,"agricultural_evidence":52},[158,162,167],{"code":38,"score":20,"supported":52,"title":159,"abstract":160,"tags":161},[],[89,101],[29],{"code":163,"score":21,"supported":14,"title":164,"abstract":165,"tags":166},"B2",[],[],[150],{"code":104,"score":21,"supported":14,"title":168,"abstract":169,"tags":170},[],[],[149],[172,173],"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":175,"openalex_id":177,"authors":178,"venue":138,"cited_by_count":113,"oa_url":135,"card":181,"direction":187,"ingested_from":130},"W7222941290",[179],{"name":180,"orcid":8},"Anantjit Publication",{"tldr":182,"method":183,"finding":184,"direction":185,"opportunity":186},"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":190,"title":191,"url":192,"summary":193,"summary_zh":8,"content":8,"source_name":194,"source_url":192,"published_at":139,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":199,"tags":203,"primary_topic":207,"secondary_topics":208,"classification":209,"search_phrases":217,"slug":220,"view_count":113,"doi":221,"paper":222,"created_at":234},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":197,"substance":70,"depth":197,"authority":70,"freshness":142,"relevant":21,"comment":198},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。",[200,201],{"name":194,"url":192},{"name":194,"url":202},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[204,29,205,206],"数字农业","农业信息化","中非",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":210},{"candidates":211,"agricultural_evidence":52},[212],{"code":38,"score":67,"supported":52,"title":213,"abstract":215,"tags":216},[214],"digital agriculture",[214],[205,204,29],[218,219],"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":221,"openalex_id":223,"authors":224,"venue":8,"cited_by_count":113,"oa_url":192,"card":8,"direction":187,"ingested_from":130},"W7222930115",[225,227,229,232],{"name":226,"orcid":8},"Anani Ogou",{"name":228,"orcid":8},"Henry Chotangui",{"name":230,"orcid":231},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":233,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":8,"content":8,"source_name":194,"source_url":238,"published_at":139,"category":12,"cover_url":8,"hotness":79,"is_selected":14,"score":240,"score_detail":241,"sources":243,"tags":245,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":263,"slug":266,"view_count":113,"doi":267,"paper":268,"created_at":274},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":18,"substance":197,"depth":79,"authority":51,"freshness":197,"relevant":21,"comment":242},"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。",[244],{"name":194,"url":238},[29,246,89,247],"农业机器人","智能采收",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":251},{"candidates":252,"agricultural_evidence":52},[253,258],{"code":38,"score":254,"supported":52,"title":255,"abstract":256,"tags":257},13,[101],[101],[29,89],{"code":259,"score":21,"supported":14,"title":260,"abstract":261,"tags":262},"F6",[],[],[246],[264,265],"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":267,"openalex_id":269,"authors":270,"venue":8,"cited_by_count":113,"oa_url":238,"card":8,"direction":273,"ingested_from":130},"W7222971426",[271],{"name":272,"orcid":8},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":8,"source_name":281,"source_url":278,"published_at":139,"category":12,"cover_url":8,"hotness":79,"is_selected":14,"score":282,"score_detail":283,"sources":286,"tags":288,"primary_topic":293,"secondary_topics":294,"classification":295,"search_phrases":303,"slug":306,"view_count":113,"doi":307,"paper":308,"created_at":333},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":18,"substance":17,"depth":284,"authority":67,"freshness":197,"relevant":21,"comment":285},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。",[287],{"name":281,"url":278},[29,289,290,291,292],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":296},{"candidates":297,"agricultural_evidence":52},[298],{"code":38,"score":20,"supported":52,"title":299,"abstract":300,"tags":302},[],[29,301],"smart agriculture",[29],[304,305],"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":307,"openalex_id":309,"authors":310,"venue":281,"cited_by_count":113,"oa_url":8,"card":328,"direction":273,"ingested_from":130},"W7222917387",[311,313,316,318,320,323,325],{"name":312,"orcid":8},"Hengwei Xiong",{"name":314,"orcid":315},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":317,"orcid":8},"Weijie Li",{"name":319,"orcid":8},"Kangwen Shi",{"name":321,"orcid":322},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":324,"orcid":8},"Wenpeng Fan",{"name":326,"orcid":327},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":329,"method":330,"finding":331,"direction":273,"opportunity":332},"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":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":8,"source_name":138,"source_url":337,"published_at":139,"category":12,"cover_url":8,"hotness":79,"is_selected":14,"score":80,"score_detail":340,"sources":342,"tags":344,"primary_topic":347,"secondary_topics":348,"classification":349,"search_phrases":362,"slug":365,"view_count":113,"doi":366,"paper":367,"created_at":377},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":18,"substance":17,"depth":18,"authority":51,"freshness":142,"relevant":21,"comment":341},"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。",[343],{"name":138,"url":337},[29,88,345,89,346],"数字孪生","植物表型",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":350},{"candidates":351,"agricultural_evidence":52},[352,358],{"code":38,"score":353,"supported":52,"title":354,"abstract":356,"tags":357},20,[355],"digital twin",[89,345,101],[345,29,89],{"code":104,"score":21,"supported":14,"title":359,"abstract":360,"tags":361},[],[],[88],[363,364],"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":366,"openalex_id":368,"authors":369,"venue":138,"cited_by_count":113,"oa_url":337,"card":372,"direction":185,"ingested_from":130},"W7222885652",[370],{"name":371,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":373,"method":374,"finding":375,"direction":185,"opportunity":376},"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"]