[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1874":3,"related-1874":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":66,"created_at":76},1874,"高群教授团队：中国智慧农业政策演进特征与优化策略——基于工具-主题-目标三维框架（《智慧农业(中英文)》2026年第3期）","https:\u002F\u002Fcj.sina.com.cn\u002Farticle\u002Fnorm_detail?url=https%3A%2F%2Ffinance.sina.com.cn%2Fwm%2F2026-09-03%2Fdoc-iniqqfvs1923424.shtml","该研究基于政策工具理论，构建工具-主题-目标三维分析框架，以2013—2025年240份智慧农业中央政策文本为研究对象，运用潜在狄利克雷分配主题模型及内容分析法；中国智慧农业政策时序演进趋势显著，分为政策探索期(2013—2015年)、试点示范期(2016—2018年)、战略推进期(2019—2023年)、全面深化期(2024年至今)4个阶段。DOI: 10.12133\u002Fj.smartag.SA202603016。",null,"《智慧农业(中英文)》2026, 8(3): 253-269","2026-09-03T00:00:00Z","论文",10,false,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},25,22,18,14,5,1,"基于三维框架系统分析中国智慧农业政策演进，数据详实，对政策制定有参考价值。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","政策工具","政策演进","主题模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"J1","policy-planning","政策制度与发展规划","\u002Ftopic\u002Fpolicy\u002Fpolicy-planning","J",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,55],{"code":31,"score":50,"supported":51,"title":52,"abstract":53,"tags":54},12,true,[26],[26],[26],{"code":38,"score":56,"supported":51,"title":57,"abstract":59,"tags":60},11,[58],"农业政策",[58],[],[62,63],"主题模型 政策工具 政策演进 智慧农业","主题模型 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253-269）","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1071531957_122014422","南昌大学高群、罗银芳基于政策工具理论构建\"工具-主题-目标\"三维分析框架，以 2013—2025 年 240 份智慧农业中央政策文本为研究对象，运用 LDA 主题模型及内容分析法揭示智慧农业政策演进特征。结果显示：中国智慧农业政策时序演进趋势显著，分为政策探索期（2013—2015）、试点示范期（2016—2018）、战略推进期（2019—2023）、全面深化期（2024 至今）4 个阶段；综合运用了供给型、环境型、需求型 3 类政策工具但内部工具构成存在显著失衡；政策注意力高度集中于数字转型与智慧升级主题，政策主题与政策工具的适配度有待提升。","智慧农业(中英文)期刊","2026-09-01T00:00:00Z",82,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":65,"relevant":21,"comment":89},"基于三维框架系统分析2013-2025年智慧农业政策演进，数据详实，对政策制定有参考价值。",[91],{"name":85,"url":83},[26,27,93,94],"政策分析","演进阶段",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[97],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":99},{"candidates":100,"agricultural_evidence":51},[101,105],{"code":31,"score":50,"supported":51,"title":102,"abstract":103,"tags":104},[26],[26],[26],{"code":38,"score":56,"supported":51,"title":106,"abstract":107,"tags":108},[58],[58],[],[110,111],"政策分析 政策工具 智慧农业 演进阶段","政策分析 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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":130,"substance":131,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":132},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。",[134],{"name":126,"url":124},[26,136,137,138,139],"农业人工智能","精准农业","机器视觉","作物行检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":143},{"candidates":144,"agricultural_evidence":51},[145,150],{"code":31,"score":20,"supported":51,"title":146,"abstract":147,"tags":149},[],[148],"precision agriculture",[26,137],{"code":151,"score":152,"supported":13,"title":153,"abstract":154,"tags":155},"I2",2,[],[],[136,138],[157,158],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 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López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":171,"orcid":172},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":174,"orcid":175},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":8,"source_name":184,"source_url":181,"published_at":185,"category":11,"cover_url":8,"hotness":16,"is_selected":13,"score":186,"score_detail":187,"sources":191,"tags":195,"primary_topic":200,"secondary_topics":201,"classification":202,"search_phrases":219,"slug":222,"view_count":68,"doi":223,"paper":224,"created_at":235},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":50,"substance":18,"depth":188,"authority":50,"freshness":189,"relevant":21,"comment":190},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。",[192,193],{"name":184,"url":181},{"name":184,"url":194},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[26,196,197,198,199],"图像分析","香蕉","无损监测","作物营养",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":203},{"candidates":204,"agricultural_evidence":51},[205,210,215],{"code":31,"score":206,"supported":51,"title":207,"abstract":208,"tags":209},7,[],[137,148],[26],{"code":211,"score":21,"supported":13,"title":212,"abstract":213,"tags":214},"B2",[],[],[197],{"code":151,"score":21,"supported":13,"title":216,"abstract":217,"tags":218},[],[],[196],[220,221],"香蕉叶片 营养浓度 图像分析","香蕉 无损监测 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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":244,"substance":78,"depth":244,"authority":78,"freshness":189,"relevant":21,"comment":245},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。",[247,248],{"name":241,"url":239},{"name":241,"url":249},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[251,26,252,253],"数字农业","农业信息化","中非",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":257},{"candidates":258,"agricultural_evidence":51},[259],{"code":31,"score":19,"supported":51,"title":260,"abstract":262,"tags":263},[261],"digital agriculture",[261],[252,251,26],[265,266],"中非 数字农业","农业信息化 数字农业 智慧农业 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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":244,"depth":12,"authority":50,"freshness":244,"relevant":21,"comment":289},"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。",[291],{"name":241,"url":285},[26,293,137,294],"农业机器人","智能采收",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":298},{"candidates":299,"agricultural_evidence":51},[300,305],{"code":31,"score":301,"supported":51,"title":302,"abstract":303,"tags":304},13,[148],[148],[26,137],{"code":306,"score":21,"supported":13,"title":307,"abstract":308,"tags":309},"F6",[],[],[293],[311,312],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 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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":331,"authority":19,"freshness":244,"relevant":21,"comment":332},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。",[334],{"name":328,"url":325},[26,336,337,338,339],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":343},{"candidates":344,"agricultural_evidence":51},[345],{"code":31,"score":206,"supported":51,"title":346,"abstract":347,"tags":349},[],[26,348],"smart agriculture",[26],[351,352],"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":354,"openalex_id":356,"authors":357,"venue":328,"cited_by_count":68,"oa_url":8,"card":375,"direction":320,"ingested_from":176},"W7222917387",[358,360,363,365,367,370,372],{"name":359,"orcid":8},"Hengwei Xiong",{"name":361,"orcid":362},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":364,"orcid":8},"Weijie Li",{"name":366,"orcid":8},"Kangwen Shi",{"name":368,"orcid":369},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":371,"orcid":8},"Wenpeng Fan",{"name":373,"orcid":374},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":376,"method":377,"finding":378,"direction":320,"opportunity":379},"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"]