[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4545":3,"related-4545":71},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":65,"slug":68,"view_count":69,"doi":8,"paper":8,"created_at":70},4545,"上海市农科院智慧农业科研团队在国际主流期刊Computers and Electronics in Agriculture发表论文——多资源约束下猪场长期行为分析云边协同系统","https:\u002F\u002Fwww.saas.sh.cn\u002Fxx\u002Fxwzx\u002Fttxw\u002Fcontent_54686","上海市农业科学院农业科技信息研究所\u002F智慧农业研究中心科研团队在Computers and Electronics in Agriculture（中科院一区，影响因子8.9）发表题为\"Research on a Cloud-Edge Collaborative System for Long-Term Behavior Analysis in Intensive Pig Farms Under Multi-Resource Constraints\"的研究论文。团队针对边云协同框架下猪只行为分析系统多重资源约束下\"持续采集—稳定上传—及时推理\"难兼顾的难题，在国际上率先提出适度降低视频分辨率、帧率并采用队列上传的融合策略。评估显示，相对基线方案单位视频源CPU开销降至约1\u002F3、视频数据生成大小降至约1\u002F11.6、平均传输码率降至约1\u002F39.7；云端窗口吞吐量提升约9倍。第一作者郭孝坤。",null,"近日，院农业科技信息研究所\u002F智慧农业研究中心科研团队在农业工程领域国际主流期刊 Computers and Electronics in Agriculture（中科院一区IF=8.9）上发表了题为“Research on a Cloud-Edge Collaborative System for Long-Term Behavior Analysis in Intensive Pig Farms Under Multi-Resource Constraints”的研究论文。\n\n团队在研究和部署应用边云协同框架下的猪只行为分析系统时发现，系统应用时受制于边缘端算力、上行带宽、存储与云端 GPU 吞吐等多重资源约束，使“持续采集-稳定上传-及时推理”难以兼顾。研究团队经过多轮讨论并通过实际验证，在国际上率先提出了在不影响识别精度的前提下采用适度降低视频分辨率和帧率、裁剪视频大小和基于队列上传的融合策略，极大地减少了数据冗余、降低了带宽压力，同时提升数据传输鲁棒性。\n\n系统实际部署应用与实验评估表明，相对基线方案，源端减量与两级ROI设计将单位视频源的 CPU开销降至约1\u002F3、视频数据生成大小降至约1\u002F11.6、平均传输码率降至约1\u002F39.7；同时，传输侧的队列式缓存管理与带宽感知上传在网络波动条件下保障了上传过程的稳定性并提升系统鲁棒性；云端窗口吞吐量实现了约9倍的处理能力提升。\n\n《Computers and Electronics in Agriculture》是农林科学领域的国际知名学术期刊，旨在汇集最先进和高质量的研究成果，促进了包括农学、农业计算机交叉科学等重要领域的研究和应用发展。该期刊发表有关人工智能在动植物农业生产中的应用进展，包括但不限于农业土壤、水、虫害、受控环境，以及植物和动物本身等，2025年影响因子为8.9。\n\n上海市农业科学院农业科技信息研究所郭孝坤为论文的第一作者，徐识溥和王运圣为共同通讯作者。该项研究得到上海市农业科学院AI赋能农业专项（沪农科AI-A（2025）009）、上海市科技兴农攻关项目（2025-02-08-00-12-F00037）和上海市农业科学院2026年度农业科技创新支撑领域研究专项（Grant No.2026 (07)）支持。","上海市农业科学院 2026 年 10 月 09 日 农业科技信息研究所","2026-10-08T16:00:00Z","报道",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"中科院一区论文，提出多资源约束下猪场行为分析云边协同系统，数据详实、方法新颖，具行业参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","边缘计算","云边协同","生猪养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,55,60],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[26],[26],[26],{"code":51,"score":21,"supported":14,"title":52,"abstract":53,"tags":54},"C1",[],[],[30],{"code":56,"score":21,"supported":14,"title":57,"abstract":58,"tags":59},"I2",[],[],[27],{"code":61,"score":21,"supported":14,"title":62,"abstract":63,"tags":64},"I5",[],[],[28],[66,67],"上海农科院 猪场 云边协同","Computers and Electronics in Agriculture 生猪行为分析","上海农科院猪场云边协同-4545",2,"2026-10-10T00:14:58.383720Z",{"total":72,"page":21,"page_size":72,"items":73},6,[74,133,186,233,287,343],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":79,"content":8,"source_name":80,"source_url":77,"published_at":81,"category":82,"cover_url":8,"hotness":83,"is_selected":14,"score":84,"score_detail":85,"sources":88,"tags":92,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":113,"slug":116,"view_count":117,"doi":118,"paper":119,"created_at":132},3609,"EXPLAINABLE EDGE AI FOR EARLY DETECTION OF PLANT DISEASES IN SMART AGRICULTURE USING LIGHTWEIGHT MOBILENETV2 WITH GRADIENT SALIENCY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22972972","EXPLAINABLE EDGE AI FOR EARLY DETECTION OF PLANT DISEASES IN SMART AGRICULTURE USING LIGHTWEIGHT MOBILENETV2 WITH GRADIENT SALIENCY。Zenodo (CERN European Organization for Nuclear Research)","面向智慧农业中植物病害早期检测的可解释边缘人工智能：采用带梯度显著性的轻量级MobileNetV2。Zenodo（CERN欧洲核子研究组织）。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-26T00:00:00Z","论文",25,66,{"impact":45,"substance":17,"depth":86,"authority":45,"freshness":20,"relevant":21,"comment":87},16,"面向智慧农业的轻量可解释边缘AI病害检测论文，方法有创新但属细分技术进展，影响力有限。",[89,90],{"name":80,"url":77},{"name":80,"url":91},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22972971",[26,27,28,93],"植物病害检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":46},[99,105,109],{"code":32,"score":100,"supported":46,"title":101,"abstract":103,"tags":104},15,[102],"smart agriculture",[26,102],[26],{"code":56,"score":21,"supported":14,"title":106,"abstract":107,"tags":108},[],[],[27],{"code":61,"score":21,"supported":14,"title":110,"abstract":111,"tags":112},[],[],[28],[114,115],"MobileNetV2 植物病害 检测","边缘AI 智慧农业 病害","MobileNetV2植物病害检测-3609",3,"10.5281\u002Fzenodo.22972972",{"doi":118,"openalex_id":120,"authors":121,"venue":80,"cited_by_count":124,"oa_url":77,"card":125,"direction":129,"ingested_from":131},"W7214432284",[122],{"name":123,"orcid":8},"Yasir Javaid,Sana Cheema,Akkasha Latif,Atiqa Faiz ur Rehman,Aisha Tariq Khan,Qandeel Nasir,Hafiz Farrukh Abbas",0,{"tldr":126,"method":127,"finding":128,"direction":129,"opportunity":130},"提出轻量MobileNetV2结合梯度显著性实现边缘端植物病害早期可解释检测。","MobileNetV2轻量网络+梯度显著性可视化，部署于边缘设备。","在保证精度的同时实现可解释的早期病害识别，适合资源受限场景。","智慧农业 \u002F 农业物联网","可探索多作物多病害的轻量可解释模型，并优化田间边缘部署的实时性与鲁棒性。","openalex","2026-09-27T23:30:13.798543Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":8,"content":8,"source_name":138,"source_url":136,"published_at":139,"category":82,"cover_url":8,"hotness":13,"is_selected":14,"score":140,"score_detail":141,"sources":144,"tags":146,"primary_topic":150,"secondary_topics":151,"classification":152,"search_phrases":165,"slug":168,"view_count":124,"doi":169,"paper":170,"created_at":185},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":86,"substance":142,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":143},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。",[145],{"name":138,"url":136},[26,27,147,148,149],"精准农业","机器视觉","作物行检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":153},{"candidates":154,"agricultural_evidence":46},[155,161],{"code":32,"score":156,"supported":46,"title":157,"abstract":158,"tags":160},5,[],[159],"precision agriculture",[26,147],{"code":56,"score":69,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[27,148],[166,167],"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":169,"openalex_id":171,"authors":172,"venue":138,"cited_by_count":124,"oa_url":8,"card":8,"direction":8,"ingested_from":131},"W7223117828",[173,176,179,182],{"name":174,"orcid":175},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":177,"orcid":178},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":180,"orcid":181},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":183,"orcid":184},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":80,"source_url":189,"published_at":192,"category":82,"cover_url":8,"hotness":13,"is_selected":14,"score":140,"score_detail":193,"sources":196,"tags":198,"primary_topic":201,"secondary_topics":202,"classification":203,"search_phrases":216,"slug":219,"view_count":124,"doi":220,"paper":221,"created_at":232},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。","2026-10-19T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":45,"freshness":194,"relevant":21,"comment":195},9,"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。",[197],{"name":80,"url":189},[26,27,199,147,200],"数字孪生","植物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,212],{"code":32,"score":207,"supported":46,"title":208,"abstract":210,"tags":211},20,[209],"digital twin",[147,199,159],[199,26,147],{"code":56,"score":21,"supported":14,"title":213,"abstract":214,"tags":215},[],[],[27],[217,218],"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":220,"openalex_id":222,"authors":223,"venue":80,"cited_by_count":124,"oa_url":189,"card":226,"direction":230,"ingested_from":131},"W7222885652",[224],{"name":225,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":227,"method":228,"finding":229,"direction":230,"opportunity":231},"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。","农业遥感与作物表型","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":234,"title":235,"url":236,"summary":237,"summary_zh":8,"content":8,"source_name":238,"source_url":236,"published_at":239,"category":82,"cover_url":8,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":244,"tags":246,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":267,"slug":270,"view_count":69,"doi":271,"paper":272,"created_at":286},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":45,"substance":86,"depth":242,"authority":19,"freshness":20,"relevant":21,"comment":243},17,"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[245],{"name":238,"url":236},[26,27,147,247],"生物防治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":251},{"candidates":252,"agricultural_evidence":46},[253,258,263],{"code":32,"score":254,"supported":46,"title":255,"abstract":256,"tags":257},13,[159],[159],[26,147],{"code":259,"score":21,"supported":14,"title":260,"abstract":261,"tags":262},"F4",[],[],[247],{"code":56,"score":21,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[27],[268,269],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":271,"openalex_id":273,"authors":274,"venue":238,"cited_by_count":124,"oa_url":8,"card":8,"direction":129,"ingested_from":131},"W7221746877",[275,278,281,283],{"name":276,"orcid":277},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":279,"orcid":280},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":282,"orcid":8},"İsmail Alper Susurluk",{"name":284,"orcid":285},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":8,"content":8,"source_name":138,"source_url":290,"published_at":239,"category":82,"cover_url":8,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":295,"tags":297,"primary_topic":301,"secondary_topics":302,"classification":303,"search_phrases":325,"slug":328,"view_count":21,"doi":329,"paper":330,"created_at":342},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture",75,{"impact":86,"substance":207,"depth":242,"authority":19,"freshness":20,"relevant":21,"comment":294},"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[296],{"name":138,"url":290},[26,298,27,299,300],"无人机","遥感","大麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":304},{"candidates":305,"agricultural_evidence":46},[306,311,316,320],{"code":32,"score":307,"supported":46,"title":308,"abstract":309,"tags":310},4,[],[159],[26],{"code":312,"score":21,"supported":14,"title":313,"abstract":314,"tags":315},"A5",[],[],[300],{"code":56,"score":21,"supported":14,"title":317,"abstract":318,"tags":319},[],[],[27],{"code":321,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},"I3",[],[],[299],[326,327],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":329,"openalex_id":331,"authors":332,"venue":138,"cited_by_count":124,"oa_url":8,"card":8,"direction":8,"ingested_from":131},"W7221758985",[333,336,339],{"name":334,"orcid":335},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":337,"orcid":338},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":340,"orcid":341},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":8,"content":8,"source_name":348,"source_url":346,"published_at":349,"category":82,"cover_url":8,"hotness":13,"is_selected":14,"score":18,"score_detail":350,"sources":352,"tags":354,"primary_topic":356,"secondary_topics":357,"classification":358,"search_phrases":369,"slug":372,"view_count":124,"doi":373,"paper":374,"created_at":391},4659,"Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22742.61769","Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation。OpenAlex","OpenAlex","2026-10-10T00:00:00Z",{"impact":117,"substance":69,"depth":69,"authority":72,"freshness":194,"relevant":21,"comment":351},"仅有标题与来源、无实质摘要内容的论文条目，信息增量极低，不建议进入每日精选。",[353],{"name":348,"url":346},[26,27,355],"科研平台",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":359},{"candidates":360,"agricultural_evidence":46},[361,365],{"code":32,"score":45,"supported":46,"title":362,"abstract":363,"tags":364},[102],[102],[26],{"code":56,"score":21,"supported":14,"title":366,"abstract":367,"tags":368},[],[],[27],[370,371],"农业人工智能 智慧农业 科研平台","农业人工智能 智慧农业","农业人工智能智慧农业科研平台-4659","10.13140\u002Frg.2.2.22742.61769",{"doi":373,"openalex_id":375,"authors":376,"venue":8,"cited_by_count":124,"oa_url":346,"card":8,"direction":129,"ingested_from":131},"W7221337880",[377,379,381,383,385,387,389],{"name":378,"orcid":8},"Ing José Antonio",{"name":380,"orcid":8},"Valles Romero",{"name":382,"orcid":8},"Gerardo Manuel",{"name":384,"orcid":8},"Alonzo Medina",{"name":386,"orcid":8},"Emilio Raymundo Morales Maldonado",{"name":388,"orcid":8},"Ing José",{"name":390,"orcid":8},"Antonio Valles","2026-10-11T23:30:07.700109Z"]