[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5397":3,"related-5397":84},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":83},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z","论文",25,false,66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,15,9,1,"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。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[28,29,30,31,32],"智慧农业","图像分析","香蕉","无损监测","作物营养",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,54,59],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":53},7,true,[],[51,52],"精准农业","precision agriculture",[28],{"code":55,"score":21,"supported":14,"title":56,"abstract":57,"tags":58},"B2",[],[],[30],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"I2",[],[],[29],[65,66],"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",0,"10.5281\u002Fzenodo.23469780",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":68,"oa_url":6,"card":75,"direction":81,"ingested_from":82},"W7222941290",[73],{"name":74,"orcid":9},"Anantjit Publication",{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"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","openalex","2026-10-20T23:30:22.619634Z",{"total":85,"page":21,"page_size":85,"items":86},6,[87,143,189,229,289,333],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":9,"content":9,"source_name":92,"source_url":90,"published_at":93,"category":12,"cover_url":9,"hotness":94,"is_selected":14,"score":95,"score_detail":96,"sources":101,"tags":103,"primary_topic":107,"secondary_topics":108,"classification":109,"search_phrases":122,"slug":125,"view_count":68,"doi":126,"paper":127,"created_at":142},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":97,"substance":98,"depth":18,"authority":99,"freshness":94,"relevant":21,"comment":100},16,21,14,"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。",[102],{"name":92,"url":90},[28,104,51,105,106],"农业人工智能","机器视觉","作物行检测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":110},{"candidates":111,"agricultural_evidence":48},[112,117],{"code":34,"score":113,"supported":48,"title":114,"abstract":115,"tags":116},5,[],[52],[28,51],{"code":60,"score":118,"supported":14,"title":119,"abstract":120,"tags":121},2,[],[],[104,105],[123,124],"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","10.1007\u002Fs11119-026-10473-z",{"doi":126,"openalex_id":128,"authors":129,"venue":92,"cited_by_count":68,"oa_url":9,"card":9,"direction":9,"ingested_from":82},"W7223117828",[130,133,136,139],{"name":131,"orcid":132},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":134,"orcid":135},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":137,"orcid":138},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":140,"orcid":141},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":9,"content":9,"source_name":148,"source_url":146,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":149,"score_detail":150,"sources":153,"tags":157,"primary_topic":161,"secondary_topics":162,"classification":163,"search_phrases":171,"slug":174,"view_count":68,"doi":175,"paper":176,"created_at":188},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":151,"substance":85,"depth":151,"authority":85,"freshness":20,"relevant":21,"comment":152},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。",[154,155],{"name":148,"url":146},{"name":148,"url":156},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[158,28,159,160],"数字农业","农业信息化","中非",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":164},{"candidates":165,"agricultural_evidence":48},[166],{"code":34,"score":99,"supported":48,"title":167,"abstract":169,"tags":170},[168],"digital agriculture",[168],[159,158,28],[172,173],"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":175,"openalex_id":177,"authors":178,"venue":9,"cited_by_count":68,"oa_url":146,"card":9,"direction":81,"ingested_from":82},"W7222930115",[179,181,183,186],{"name":180,"orcid":9},"Anani Ogou",{"name":182,"orcid":9},"Henry Chotangui",{"name":184,"orcid":185},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":187,"orcid":9},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":9,"source_name":148,"source_url":192,"published_at":11,"category":12,"cover_url":9,"hotness":94,"is_selected":14,"score":194,"score_detail":195,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":217,"slug":220,"view_count":68,"doi":221,"paper":222,"created_at":228},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":151,"depth":94,"authority":17,"freshness":151,"relevant":21,"comment":196},"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。",[198],{"name":148,"url":192},[28,200,51,201],"农业机器人","智能采收",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":205},{"candidates":206,"agricultural_evidence":48},[207,212],{"code":34,"score":208,"supported":48,"title":209,"abstract":210,"tags":211},13,[52],[52],[28,51],{"code":213,"score":21,"supported":14,"title":214,"abstract":215,"tags":216},"F6",[],[],[200],[218,219],"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":221,"openalex_id":223,"authors":224,"venue":9,"cited_by_count":68,"oa_url":192,"card":9,"direction":227,"ingested_from":82},"W7222971426",[225],{"name":226,"orcid":9},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":9,"source_name":235,"source_url":232,"published_at":11,"category":12,"cover_url":9,"hotness":94,"is_selected":14,"score":236,"score_detail":237,"sources":241,"tags":243,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":258,"slug":261,"view_count":68,"doi":262,"paper":263,"created_at":288},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":238,"depth":239,"authority":99,"freshness":151,"relevant":21,"comment":240},22,19,"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。",[242],{"name":235,"url":232},[28,244,245,246,247],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":251},{"candidates":252,"agricultural_evidence":48},[253],{"code":34,"score":47,"supported":48,"title":254,"abstract":255,"tags":257},[],[28,256],"smart agriculture",[28],[259,260],"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":262,"openalex_id":264,"authors":265,"venue":235,"cited_by_count":68,"oa_url":9,"card":283,"direction":227,"ingested_from":82},"W7222917387",[266,268,271,273,275,278,280],{"name":267,"orcid":9},"Hengwei Xiong",{"name":269,"orcid":270},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":272,"orcid":9},"Weijie Li",{"name":274,"orcid":9},"Kangwen Shi",{"name":276,"orcid":277},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":279,"orcid":9},"Wenpeng Fan",{"name":281,"orcid":282},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":284,"method":285,"finding":286,"direction":227,"opportunity":287},"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":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":10,"source_url":292,"published_at":11,"category":12,"cover_url":9,"hotness":94,"is_selected":14,"score":95,"score_detail":295,"sources":297,"tags":299,"primary_topic":302,"secondary_topics":303,"classification":304,"search_phrases":317,"slug":320,"view_count":68,"doi":321,"paper":322,"created_at":332},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":238,"depth":18,"authority":17,"freshness":20,"relevant":21,"comment":296},"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。",[298],{"name":10,"url":292},[28,104,300,51,301],"数字孪生","植物表型",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":305},{"candidates":306,"agricultural_evidence":48},[307,313],{"code":34,"score":308,"supported":48,"title":309,"abstract":311,"tags":312},20,[310],"digital twin",[51,300,52],[300,28,51],{"code":60,"score":21,"supported":14,"title":314,"abstract":315,"tags":316},[],[],[104],[318,319],"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":321,"openalex_id":323,"authors":324,"venue":10,"cited_by_count":68,"oa_url":292,"card":327,"direction":79,"ingested_from":82},"W7222885652",[325],{"name":326,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":328,"method":329,"finding":330,"direction":79,"opportunity":331},"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",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":9,"content":9,"source_name":338,"source_url":9,"published_at":339,"category":12,"cover_url":9,"hotness":94,"is_selected":14,"score":340,"score_detail":341,"sources":344,"tags":346,"primary_topic":351,"secondary_topics":352,"classification":353,"search_phrases":365,"slug":368,"view_count":21,"doi":9,"paper":369,"created_at":377},5298,"姚霞等《江苏高教》:新质生产力背景下新农科建设的实践探索——以智慧农业交叉学科为例","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F172563.html","《江苏高教》2026年第08期刊载南京农业大学姚霞、曹卫星、刘小军、刘蕾蕾、朱艳等论文,阐述新质生产力背景下建设智慧农业交叉学科的必要性,解析其组建模式及\"四大融合\"(学科、专业、课程、平台)的实践路径,剖析我国智慧农业交叉学科发展现状,并提出未来发展对策。","江苏高教(2026,08)","2026-10-13T00:00:00Z",75,{"impact":18,"substance":308,"depth":342,"authority":99,"freshness":85,"relevant":21,"comment":343},17,"核心期刊论文，系统阐述智慧农业交叉学科建设路径，对农业信息化人才培养有参考价值，但属学术探讨、时效一般，可入精选备选。",[345],{"name":338,"url":336},[28,347,348,349,350],"新质生产力","农业教育","新农科","交叉学科",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":354},{"candidates":355,"agricultural_evidence":48},[356,360],{"code":34,"score":17,"supported":48,"title":357,"abstract":358,"tags":359},[28],[28],[28],{"code":361,"score":21,"supported":14,"title":362,"abstract":363,"tags":364},"J2",[],[],[347],[366,367],"南京农业大学 智慧农业 交叉学科","姚霞 新农科 江苏高教","南京农业大学智慧农业交叉学科-5298",{"doi":9,"openalex_id":9,"authors":370,"venue":9,"cited_by_count":68,"oa_url":9,"card":371,"direction":81,"ingested_from":376},[],{"tldr":372,"method":373,"finding":374,"direction":81,"opportunity":375},"以智慧农业交叉学科为例，探讨新质生产力背景下新农科建设的实践路径。","案例分析与现状剖析，提出学科、专业、课程、平台“四大融合”路径。","智慧农业交叉学科需通过“四大融合”组建，并针对现状提出发展对策。","可延伸研究智慧农业交叉学科融合成效的量化评价与人才培养质量追踪机制。","agent","2026-10-19T00:07:48.366123Z"]