[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1264":3,"related-1264":74},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":52,"slug":55,"view_count":20,"doi":56,"paper":57,"created_at":73},1264,"Integrated Production and Financial Records Enable Cost Visibility but Not Environmental Measurement in Poultry Farming: A Design Science Study","https:\u002F\u002Fdoi.org\u002F10.59535\u002Fsehati.v4i3.702","Small and medium poultry farms often manage production, costs, and sales in separate records, limiting timely margin analysis and obscuring the data needed to assess environmental performance. This study designs and evaluates a web-based poultry farm management information system. Following Design Science Research Methodology, operational requirements were translated into role-based production, cost, sales, dashboard, and profit-or-loss modules. Evaluation used requirements-to-feature traceability and scenario-based inspection of the implemented workflows; causal cost savings, user acceptance, and environmental impacts were not tested. The artifact integrates role-based access, production records, cost categories, sales transactions, summaries, product-period income statements, and export functions. The central finding is that this integration creates descriptive cost visibility and accounting traceability, but it does not yet constitute closed-loop cost control because budget, standard-cost, and variance-alert functions are absent. Environmental sustainability also cannot be inferred from digitization alone because the prototype does not capture physical energy, water, feed, manure, waste, or emissions data. This boundary motivates a dual-ledger extension in which physical resource records share time, coop, product, and batch identifiers with the financial ledger. The study contributes an evidence-bounded evaluation model that separates implemented functionality from expected benefits and provides a staged roadmap for sustainability-ready poultry farm information systems.",null,"Socio-economic and Humanistic Aspects for Township and Industry.","2026-08-29T00:00:00Z","论文",10,false,59,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},12,18,16,3,1,"论文提出家禽养殖业财务与生产数据整合系统，虽未实现环境测量，但为可持续农业信息化提供了阶段性框架。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","家禽养殖","成本核算","环境监测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"C3","poultry","家禽","\u002Ftopic\u002Flivestock\u002Fpoultry","C",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":43},[42,47],{"code":30,"score":16,"supported":43,"title":44,"abstract":45,"tags":46},true,[31],[31],[26],{"code":48,"score":20,"supported":13,"title":49,"abstract":50,"tags":51},"I1",[],[],[25],[53,54],"家禽养殖 成本核算 智慧农业 环境监测","家禽养殖 成本核算","家禽养殖成本核算智慧农业环境监测-1264","10.59535\u002Fsehati.v4i3.702",{"doi":56,"openalex_id":58,"authors":59,"venue":9,"cited_by_count":63,"oa_url":64,"card":65,"direction":71,"ingested_from":72},"W7204628245",[60],{"name":61,"orcid":62},"Fransisca Hanita Rusgowanto","https:\u002F\u002Forcid.org\u002F0000-0003-3909-3374",0,"https:\u002F\u002Fjurnal.tintaemas.id\u002Findex.php\u002Fsehati\u002Farticle\u002Fdownload\u002F702\u002F575",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"设计并评估了家禽养殖管理信息系统，实现成本可见但无法测量环境绩效。","设计科学研究方法，开发基于Web的养殖管理系统，包含生产、成本、销售等模块。","系统实现成本可见和会计可追溯，但缺乏预算和标准成本控制，且未捕获物理资源数据，无法推断环境可持续性。","农业人工智能与决策模型","可扩展双账本系统，将物理资源记录与财务账本关联，实现环境绩效测量，填补养殖业环境数据管理空白。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:11.766648Z",{"total":75,"page":20,"page_size":75,"items":76},6,[77,156,210,322,391,478],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":8,"source_name":83,"source_url":80,"published_at":84,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":85,"score_detail":86,"sources":93,"tags":95,"primary_topic":99,"secondary_topics":100,"classification":107,"search_phrases":130,"slug":133,"view_count":134,"doi":135,"paper":136,"created_at":155},2115,"EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112380","EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device。Computers and Electronics in Agriculture","EdgeHatch：基于边缘设备的机器学习鸡孵化阶段检测。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-10T00:00:00Z",74,{"impact":87,"substance":88,"depth":89,"authority":90,"freshness":91,"relevant":20,"comment":92},15,20,17,14,8,"核心期刊论文，将机器学习与边缘设备用于鸡孵化阶段识别，方法新颖、面向家禽精准养殖，具备行业参考价值。",[94],{"name":83,"url":80},[25,96,97,26,98],"农业人工智能","边缘计算","精准孵化",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[101],{"code":102,"slug":103,"name":104,"path":105,"parent_code":106},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":13,"version":38,"method":39,"evidence":108},{"candidates":109,"agricultural_evidence":43},[110,116,121,125],{"code":102,"score":87,"supported":43,"title":111,"abstract":113,"tags":115},[112],"machine learning",[114,112],"机器学习",[96],{"code":30,"score":16,"supported":43,"title":117,"abstract":119,"tags":120},[118],"chicken",[118],[26],{"code":48,"score":20,"supported":13,"title":122,"abstract":123,"tags":124},[],[],[25],{"code":126,"score":20,"supported":13,"title":127,"abstract":128,"tags":129},"I5",[],[],[97],[131,132],"农业人工智能 家禽养殖 智慧农业 精准孵化","农业人工智能 家禽养殖","农业人工智能家禽养殖智慧农业精准孵化-2115",2,"10.1016\u002Fj.compag.2026.112380",{"doi":135,"openalex_id":137,"authors":138,"venue":83,"cited_by_count":63,"oa_url":80,"card":150,"direction":71,"ingested_from":72},"W7212107612",[139,142,144,147],{"name":140,"orcid":141},"Luka Mali","https:\u002F\u002Forcid.org\u002F0009-0008-6224-527X",{"name":143,"orcid":8},"Matjaž Gobec",{"name":145,"orcid":146},"Anton Gradišek","https:\u002F\u002Forcid.org\u002F0000-0001-6480-9587",{"name":148,"orcid":149},"Urban Sedlar","https:\u002F\u002Forcid.org\u002F0000-0003-2836-5493",{"tldr":151,"method":152,"finding":153,"direction":71,"opportunity":154},"提出EdgeHatch，在边缘设备上用机器学习检测鸡蛋孵化阶段。","边缘设备部署机器学习模型，分析孵化期图像\u002F数据。","可在边缘端实时识别鸡胚孵化阶段，无需云端。","可扩展至多物种孵化监测，优化边缘模型轻量化与多阶段连续识别。","2026-09-11T23:30:01.667648Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":8,"content":8,"source_name":161,"source_url":159,"published_at":162,"category":11,"cover_url":8,"hotness":12,"is_selected":43,"score":163,"score_detail":164,"sources":168,"tags":170,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":195,"slug":198,"view_count":134,"doi":199,"paper":200,"created_at":209},4327,"The impact of smart farming on livestock eco-efficiency: An empirical analysis of meat duck farmers in 10 Chinese provinces","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105003","The impact of smart farming on livestock eco-efficiency: An empirical analysis of meat duck farmers in 10 Chinese provinces。Agricultural Systems","Agricultural Systems","2026-10-06T00:00:00Z",85,{"impact":165,"substance":165,"depth":17,"authority":90,"freshness":166,"relevant":20,"comment":167},22,9,"基于中国10省肉鸭养殖户实证数据，量化智慧农业对畜牧生态效率的影响，方法规范、结论可靠，对智慧畜牧与绿色养殖具有参考价值。",[169],{"name":161,"url":159},[171,25,172,173,174],"数字农业","畜牧养殖","农业生态效率","肉鸭养殖",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":178},{"candidates":179,"agricultural_evidence":43},[180,186,191],{"code":34,"score":181,"supported":43,"title":182,"abstract":184,"tags":185},11,[183],"livestock",[183],[],{"code":30,"score":181,"supported":43,"title":187,"abstract":189,"tags":190},[188],"duck",[188],[],{"code":48,"score":134,"supported":13,"title":192,"abstract":193,"tags":194},[],[],[171,25],[196,197],"肉鸭养殖 智慧农业 生态效率","中国十省 肉鸭养殖 实证分析","肉鸭养殖智慧农业生态效率-4327","10.1016\u002Fj.agsy.2026.105003",{"doi":199,"openalex_id":201,"authors":202,"venue":161,"cited_by_count":63,"oa_url":159,"card":8,"direction":8,"ingested_from":72},"W7220714215",[203,205,207],{"name":204,"orcid":8},"Guiren Tao",{"name":206,"orcid":8},"Yuhao Wang",{"name":208,"orcid":8},"Lingzhi Liu","2026-10-07T23:30:03.988823Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":216,"source_url":213,"published_at":217,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":218,"score_detail":219,"sources":222,"tags":224,"primary_topic":229,"secondary_topics":230,"classification":231,"search_phrases":270,"slug":273,"view_count":63,"doi":274,"paper":275,"created_at":321},4207,"Evaluating on-line media and survey data to understand beliefs and sentiment associated with pastured and integrated poultry production among farmers and stakeholders","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1860985","Commercial pastured and integrated poultry production (PIPP) represents an emerging area of poultry and crop production. As a “niche” area of commercial production, there is little overall knowledge about public opinion associated with PIPP. Here we utilized two complementary approaches to better understand current sentiment and associated challenges with PIPP-based production. Specifically, to understand farmer PIPP sentiment and challenges we surveyed commercial PIPP farmers. In parallel we captured stakeholder sentiment toward integrative farming (a slightly broader classification that includes PIPP) via the extraction of selected data (i.e., web-scraping) on the internet. The methodology combines web-scraping of various media and Natural Language Processing (NLP) to understand sentiment in a longitudinal fashion. Here we compared a farmer survey of 53 producers and 2,399 English language web-scraped online posts (e.g., from online forums, news sites, and blogs) from 1,089 unique authors between 2019 and 2021 to understand opinions of and experiences with PIPP. The survey data and online posts both highlighted soil health, animal welfare, and weed control as perceived benefits of PIPP. In addition, survey respondents identified food safety regulations or incidents, processing, and certifications as barriers to PIPP whereas these topics rarely appeared in online conversations (i.e., on-line discourse capturing news and social media). Furthermore, topics mentioned in online conversations but not included in the survey include challenges related to soil, yield, land efficiency, autonomy, and mental health benefits. Comparing online posts with surveys has the potential to offer a more robust approach toward understanding opportunities and challenges among PIPP and other niche agricultural spaces. The parallel approach of analyzing both farmer survey and on-line stakeholder sentiment provides broader context for PIPP farmers and stakeholders with respect to farmer and stakeholder perspective. This approach provides insights that can be useful for PIPP farmers and other agricultural entities with respect to linking surveys and web-based analysis.","商业放牧与综合家禽生产（PIPP）是家禽与作物生产中的一个新兴领域。作为一个商业生产的“利基”领域，关于PIPP相关的公众舆论总体认知甚少。本研究采用两种互补方法，以更好地理解当前对PIPP生产的看法及相关挑战。具体而言，为了解养殖户对PIPP的看法与挑战，我们对商业PIPP养殖户进行了调查。与此同时，我们通过提取互联网上的选定数据（即网络爬虫）来捕捉利益相关者对综合养殖（一个稍宽泛的分类，包含PIPP）的看法。该方法结合了多种媒体的网络爬虫与自然语言处理（NLP），以纵向方式理解情感倾向。本研究比较了一项涵盖53名生产者的养殖户调查与2019年至2021年间来自1,089位独特作者的2,399条英语网络爬取在线帖子（如来自在线论坛、新闻网站和博客），以了解对PIPP的看法与经验。调查数据与在线帖子均强调土壤健康、动物福利和杂草控制为PIPP的感知益处。此外，调查受访者将食品安全法规或事件、加工和认证列为PIPP的障碍，而这些话题在在线对话中很少出现（即涵盖新闻和社交媒体的在线话语）。此外，在线对话中提到但调查中未包含的话题包括与土壤、产量、土地效率、自主性和心理健康益处相关的挑战。将在线帖子与调查进行比较，有望为理解PIPP及其他利基农业领域的机遇与挑战提供更稳健的方法。同时分析养殖户调查与在线利益相关者情感的并行方法，为PIPP养殖户和利益相关者提供了关于养殖户与利益相关者视角的更广泛背景。该方法提供的见解对于PIPP养殖户及其他农业实体在联系调查与基于网络的分析方面具有参考价值。","Frontiers in Sustainable Food Systems","2026-10-05T00:00:00Z",67,{"impact":91,"substance":88,"depth":18,"authority":220,"freshness":12,"relevant":20,"comment":221},13,"以网络爬取与NLP结合农户调查分析散养家禽养殖舆情，方法新颖但属小众细分领域，公共影响有限。",[223],{"name":216,"url":213},[26,225,226,227,228],"有机农业","林下养殖","农业舆情","自然语言处理",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":232},{"candidates":233,"agricultural_evidence":43},[234,238,245,252,259,265],{"code":30,"score":87,"supported":43,"title":235,"abstract":236,"tags":237},[31],[32,31],[26],{"code":239,"score":75,"supported":43,"title":240,"abstract":241,"tags":244},"C6",[],[242,243],"动物福利","animal welfare",[],{"code":246,"score":75,"supported":43,"title":247,"abstract":248,"tags":251},"E4",[],[249,250],"食品安全","food safety",[],{"code":253,"score":75,"supported":43,"title":254,"abstract":255,"tags":258},"H2",[],[256,257],"土壤","soil",[],{"code":260,"score":19,"supported":43,"title":261,"abstract":262,"tags":264},"F4",[],[263],"weed control",[],{"code":266,"score":20,"supported":13,"title":267,"abstract":268,"tags":269},"H8",[],[],[225],[271,272],"自然语言处理 农业舆情 家禽养殖 有机农业","自然语言处理 农业舆情","自然语言处理农业舆情家禽养殖有机农业-4207","10.3389\u002Ffsufs.2026.1860985",{"doi":274,"openalex_id":276,"authors":277,"venue":216,"cited_by_count":63,"oa_url":213,"card":315,"direction":319,"ingested_from":72},"W7219843982",[278,280,283,286,288,290,293,296,298,301,303,306,309,312],{"name":279,"orcid":8},"Faye Duan",{"name":281,"orcid":282},"Arlene E. Enderton","https:\u002F\u002Forcid.org\u002F0009-0004-7416-6203",{"name":284,"orcid":285},"Joseph D. Gendreau","https:\u002F\u002Forcid.org\u002F0000-0001-8797-2686",{"name":287,"orcid":8},"Jingwen Zhang",{"name":289,"orcid":8},"Brock Riggs",{"name":291,"orcid":292},"Alejandro Plastina","https:\u002F\u002Forcid.org\u002F0000-0001-7333-5459",{"name":294,"orcid":295},"David J. Gonthier","https:\u002F\u002Forcid.org\u002F0000-0001-5977-2054",{"name":297,"orcid":8},"Dong Ahn",{"name":299,"orcid":300},"Marshall D. McDaniel","https:\u002F\u002Forcid.org\u002F0000-0001-6267-7293",{"name":302,"orcid":8},"Ann Baier",{"name":304,"orcid":305},"Anne M. Carey","https:\u002F\u002Forcid.org\u002F0009-0006-4122-6083",{"name":307,"orcid":308},"Viktor Halmos","https:\u002F\u002Forcid.org\u002F0009-0001-4636-9730",{"name":310,"orcid":311},"Ajay Nair","https:\u002F\u002Forcid.org\u002F0000-0002-6360-9556",{"name":313,"orcid":314},"Maurice E. Pitesky","https:\u002F\u002Forcid.org\u002F0000-0003-0084-6404",{"tldr":316,"method":317,"finding":318,"direction":319,"opportunity":320},"结合农户调查与网络文本挖掘，对比分析散养与综合家禽生产的认知与情感。","调查53位农户，并爬取2019-2021年2399条英文网络帖子，用NLP做情感","双方均认可土壤健康、动物福利和除草；农户关注食品安全法规与认证，网络讨论则涉及产量、土地效率与心理健","数字乡村与农业信息化","可将网络情感分析与农户调查结合，用于小众农业领域舆情监测与决策支持研究。","2026-10-05T23:30:07.432535Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":8,"content":8,"source_name":327,"source_url":325,"published_at":328,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":329,"sources":331,"tags":333,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":366,"slug":369,"view_count":19,"doi":370,"paper":371,"created_at":390},3276,"基于梅尔频谱与卷积神经网络的肉鸡叫声小样本识别实现与教学研究","https:\u002F\u002Fdoi.org\u002F10.65436\u002Fhssj.v1i9.64","摘 要：肉鸡叫声能够直观反映其采食、应激、求救等生理与行为状态,准确识别肉鸡叫声对规模化养殖中的动物福利监测和异常预警具有重要意义。然而,肉鸡叫声标注样本获取困难,现有研究多针对单一网络模型展开,缺少在小样本条件下对不同骨干网络及迁移学习策略的系统比较,模型选型缺乏依据。为此,本文构建了包含求救呼叫、愉悦音、短促啾啾、颤音和其他发声共5类、54条原始音频的肉鸡叫声数据集,将音频统一转为128×128维梅尔频谱图作为输入,采用四种典型卷积神经网络,并分别在特征提取式与微调式两种迁移学习策略下进行对比实验,采用分层5折交叉验证评估性能。实验结果表明：AlexNet特征提取式方案取得最优识别效果,总体准确率为59.54%、宏平均F1值为49.10%,其中求救呼叫的F1值达到0.94；轻量级网络MobileNet-V2在微调式策略下性能提升显著,而结构较复杂的网络在小样本下采用冻结骨干的特征提取式训练更为稳定。研究表明,在小样本肉鸡叫声识别任务中,骨干网络与迁移学习策略应根据模型复杂度协同选择,该结论可为面向养殖场景的禽声自动识别系统提供方法选型参考。 基金项目：安徽省教育厅优秀青年教师培育项目（YQYB2026117）；安徽省智慧农业技术与装备重点实验室开放基金（AEC2026016；AEC2025009）；安徽省教育厅自然科学研究项目（重大）（2024AH040217）；省级质量工程-教学研究项目（2023jyxm1009；2023xjzlts117；2023sdxx145）;基于人工智能下的家禽康养与疾病预警技术研究（S202413620055）；基于自监督学习与对抗增强的小样本信号的智能感知研究（2025SK019）；基于深度学习的生猪识别与康养研究（S202513620083）","人文与社会研究学报","2026-09-22T00:00:00Z",{"impact":91,"substance":17,"depth":90,"authority":12,"freshness":166,"relevant":20,"comment":330},"小样本肉鸡叫声识别方法选型研究，数据规模有限但结论具参考价值，属细分领域技术进展。",[332],{"name":327,"url":325},[25,96,242,334,335],"禽声识别","小样本学习",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":339},{"candidates":340,"agricultural_evidence":43},[341,346,351,355,360],{"code":30,"score":90,"supported":43,"title":342,"abstract":344,"tags":345},[343],"肉鸡",[32,343],[],{"code":239,"score":347,"supported":43,"title":348,"abstract":349,"tags":350},4,[],[242],[242],{"code":48,"score":347,"supported":43,"title":352,"abstract":353,"tags":354},[],[25],[25],{"code":102,"score":347,"supported":43,"title":356,"abstract":357,"tags":359},[],[358],"深度学习",[96],{"code":361,"score":19,"supported":43,"title":362,"abstract":363,"tags":365},"C1",[],[364],"生猪",[],[367,368],"肉鸡叫声 识别 梅尔频谱","卷积神经网络 小样本 禽声","肉鸡叫声识别梅尔频谱-3276","10.65436\u002Fhssj.v1i9.64",{"doi":370,"openalex_id":372,"authors":373,"venue":327,"cited_by_count":63,"oa_url":325,"card":385,"direction":71,"ingested_from":72},"W7213999479",[374,376,378,380,382],{"name":375,"orcid":8},"赵永才",{"name":377,"orcid":8},"李慢",{"name":379,"orcid":8},"郜静茹",{"name":381,"orcid":8},"关曼玉",{"name":383,"orcid":384},"Yanbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-5144-2309",{"tldr":386,"method":387,"finding":388,"direction":69,"opportunity":389},"构建5类54条肉鸡叫声数据集，比较四种CNN与两种迁移学习策略的小样本识别性能。","梅尔频谱图输入，四种CNN，特征提取与微调迁移学习，分层5折交叉验证。","AlexNet特征提取式最优，准确率59.54%，求救呼叫F1达0.94；轻量网络微调提升显著。","可探索自监督预训练与数据增强，解决小样本禽声识别中复杂网络迁移策略选型问题。","2026-09-23T23:30:13.762655Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":8,"source_name":83,"source_url":394,"published_at":397,"category":11,"cover_url":8,"hotness":12,"is_selected":43,"score":398,"score_detail":399,"sources":403,"tags":405,"primary_topic":409,"secondary_topics":410,"classification":417,"search_phrases":454,"slug":457,"view_count":19,"doi":458,"paper":459,"created_at":477},2017,"Two Decades of Computer Vision in Precision Livestock Farming: an Umbrella Review of Applications, Benchmarking Evidence, and the Performance-to-Deployment Gap Across Cattle, Pig, and Poultry Systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112419","Computer vision is becoming a core technology in PLF, enabling non-contact monitoring, phenotyping, and decision support at both animal and group levels. This umbrella review synthesised review-level evidence from 112 reviews on cattle, pig, and poultry systems, covering visual applications for identification, phenotyping, health, behaviour, locomotion, body condition, growth, reproduction, mortality, and resource use. To preserve the review as the bibliographic unit while capturing multi-topic evidence, multi-label coding generated 328 review-domain assignments across six PLF computer vision domains. Health\u002Fstress and posture\u002Factivity were the most frequently reviewed areas; however, these patterns reflect review coverage rather than evidence strength. Benchmarking reporting averaged 11.09 out of 14 criteria, corresponding to 79.2%, but validation-critical items, including class balance, annotation protocols, validation splits, and farm or site reporting, remained less complete. Methodological confidence was limited: AMSTAR 2 classified 95 reviews as critically low and 17 as moderate, while ROBIS classified 78 as high, 17 as unclear, and 17 as low risk of bias. The corpus-level candidate-reference overlap estimate was 0.122% and is retained only as a sensitivity estimate, not as formal primary-study CCA or evidence of independence. Review-derived performance and readiness tiers identified a mean descriptive performance-to-deployment gap of 0.63 across 328 assignments. Overall, reported model performance continues to exceed documented farm-deployment evidence, highlighting the need for stronger external validation, transparent benchmarking, workflow integration, economic assessment, and user-readiness evidence before routine deployment claims can be supported.","计算机视觉正成为精准畜牧养殖（PLF）中的核心技术，能够在个体和群体层面实现非接触式监测、表型分析和决策支持。本伞状综述综合了112篇综述的综述级证据，涵盖牛、猪和家禽养殖系统，涉及识别、表型分析、健康、行为、运动、体况、生长、繁殖、死亡率和资源利用等视觉应用。为在保留综述作为文献计量单元的同时捕捉多主题证据，采用多标签编码在六个PLF计算机视觉领域中生成了328个综述-领域分配。健康\u002F应激和姿态\u002F活动是综述覆盖最多的领域；然而，这些模式反映的是综述覆盖情况而非证据强度。基准报告平均得分为14项标准中的11.09项，对应79.2%，但验证关键条目，包括类别平衡、标注方案、验证集划分以及农场或场地报告，仍不够完整。方法学可信度有限：AMSTAR 2将95篇综述评为极低质量，17篇评为中等质量；ROBIS将78篇评为高偏倚风险，17篇评为不明确，17篇评为低偏倚风险。语料库层面的候选参考文献重叠估计值为0.122%，仅作为敏感性估计保留，而非正式的一手研究CCA或独立性证据。基于综述推导的性能与就绪度分级在328个分配中识别出平均描述性性能-部署差距为0.63。总体而言，所报告的性能持续超过已记录的农场部署证据，凸显了在支持常规部署声明之前，需要更强的外部验证、透明的基准测试、工作流程整合、经济评估和用户就绪度证据。","2026-09-09T00:00:00Z",87,{"impact":165,"substance":400,"depth":401,"authority":90,"freshness":166,"relevant":20,"comment":402},23,19,"基于112篇综述的伞式综述，系统揭示计算机视觉在牛猪禽精准养殖中性能与落地之间的差距，方法学与证据分级扎实，对智慧畜牧研发与部署具有较高参考价值。",[404],{"name":83,"url":394},[25,96,406,407,408],"计算机视觉","精准畜牧","智能养殖",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[411,412],{"code":102,"slug":103,"name":104,"path":105,"parent_code":106},{"code":413,"slug":414,"name":415,"path":416,"parent_code":34},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants",{"status":37,"locked":13,"version":38,"method":39,"evidence":418},{"candidates":419,"agricultural_evidence":43},[420,426,430,435,440,444,450],{"code":102,"score":401,"supported":43,"title":421,"abstract":423,"tags":425},[422],"computer vision",[406,422,424],"decision support",[96,406],{"code":30,"score":90,"supported":43,"title":427,"abstract":428,"tags":429},[31],[32,31],[],{"code":34,"score":181,"supported":43,"title":431,"abstract":432,"tags":434},[183],[433],"畜牧",[],{"code":413,"score":181,"supported":43,"title":436,"abstract":438,"tags":439},[437],"cattle",[437],[],{"code":239,"score":347,"supported":43,"title":441,"abstract":442,"tags":443},[],[407],[407],{"code":445,"score":19,"supported":43,"title":446,"abstract":447,"tags":449},"I7",[],[448],"文献计量",[],{"code":48,"score":20,"supported":13,"title":451,"abstract":452,"tags":453},[],[],[25],[455,456],"农业人工智能 计算机视觉 智慧农业 智能养殖","农业人工智能 计算机视觉","农业人工智能计算机视觉智慧农业智能养殖-2017","10.1016\u002Fj.compag.2026.112419",{"doi":458,"openalex_id":460,"authors":461,"venue":83,"cited_by_count":63,"oa_url":394,"card":472,"direction":71,"ingested_from":72},"W7212062011",[462,464,467,469],{"name":463,"orcid":8},"Alexey Ruchay",{"name":465,"orcid":466},"Vladimir Kolpakov","https:\u002F\u002Forcid.org\u002F0000-0001-9658-7034",{"name":468,"orcid":8},"Hao Guo",{"name":470,"orcid":471},"Andrea Pezzuolo","https:\u002F\u002Forcid.org\u002F0000-0002-9955-9896",{"tldr":473,"method":474,"finding":475,"direction":71,"opportunity":476},"伞式综述112篇综述，梳理牛猪禽精准养殖计算机视觉应用与部署差距。","伞式综述，多标签编码328项，AMSTAR 2与ROBIS评估方法学质量。","模型性能普遍高于农场部署证据，平均性能-部署差距0.63，验证与基准报告不足。","可研究外部验证、标准化基准与农场工作流集成，弥合性能到部署的鸿沟。","2026-09-10T23:30:01.497855Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":8,"content":8,"source_name":83,"source_url":481,"published_at":483,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":484,"score_detail":485,"sources":487,"tags":489,"primary_topic":492,"secondary_topics":493,"classification":495,"search_phrases":512,"slug":515,"view_count":134,"doi":516,"paper":517,"created_at":548},1199,"A deep learning framework for non-invasive quantification of wing and foot phenotypes in live broiler chickens","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112327","A deep learning framework for non-invasive quantification of wing and foot phenotypes in live broiler chickens。Computers and Electronics in Agriculture","2026-08-28T00:00:00Z",63,{"impact":16,"substance":17,"depth":18,"authority":90,"freshness":19,"relevant":20,"comment":486},"提出深度学习框架用于活体肉鸡翅足表型非侵入量化，方法新颖，数据可靠，对家禽精准养殖有参考价值。",[488],{"name":83,"url":481},[25,96,490,491],"肉鸡养殖","表型检测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[494],{"code":102,"slug":103,"name":104,"path":105,"parent_code":106},{"status":37,"locked":13,"version":38,"method":39,"evidence":496},{"candidates":497,"agricultural_evidence":43},[498,503,508],{"code":102,"score":16,"supported":43,"title":499,"abstract":501,"tags":502},[500],"deep learning",[500],[96],{"code":30,"score":181,"supported":43,"title":504,"abstract":506,"tags":507},[505],"broiler",[505],[],{"code":48,"score":20,"supported":13,"title":509,"abstract":510,"tags":511},[],[],[25],[513,514],"农业人工智能 智慧农业 肉鸡养殖 表型检测","农业人工智能 智慧农业","农业人工智能智慧农业肉鸡养殖表型检测-1199","10.1016\u002Fj.compag.2026.112327",{"doi":516,"openalex_id":518,"authors":519,"venue":83,"cited_by_count":63,"oa_url":8,"card":543,"direction":69,"ingested_from":72},"W7204530838",[520,523,525,528,530,533,535,538,541],{"name":521,"orcid":522},"Yakun Yang","https:\u002F\u002Forcid.org\u002F0000-0002-4772-4263",{"name":524,"orcid":8},"Xuehai Ge",{"name":526,"orcid":527},"Wei Wu","https:\u002F\u002Forcid.org\u002F0000-0002-0286-0720",{"name":529,"orcid":8},"Qiancheng Shen",{"name":531,"orcid":532},"Xupeng Kou","https:\u002F\u002Forcid.org\u002F0000-0002-3246-6820",{"name":534,"orcid":8},"Xu Hu",{"name":536,"orcid":537},"Longhe Wang","https:\u002F\u002Forcid.org\u002F0000-0001-9176-8063",{"name":539,"orcid":540},"Hongcheng Xue","https:\u002F\u002Forcid.org\u002F0000-0001-9024-9632",{"name":542,"orcid":8},"Lin Li",{"tldr":544,"method":545,"finding":546,"direction":69,"opportunity":547},"提出深度学习框架，非侵入性量化活体肉鸡翅膀和脚部表型。","深度学习框架，结合图像处理与关键点检测。","实现活体肉鸡翅膀和脚部表型的自动量化，减少人工测量。","可扩展至其他家禽或牲畜表型，结合多视角图像与3D重建，提高精度和鲁棒性。","2026-09-01T04:03:01.950868Z"]