[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3276":3,"related-3276":102},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":73,"slug":76,"view_count":68,"doi":77,"paper":78,"created_at":101},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）",null,"人文与社会研究学报","2026-09-22T00:00:00Z","论文",10,false,59,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},8,18,14,9,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","动物福利","禽声识别","小样本学习",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"C3","poultry","家禽","\u002Ftopic\u002Flivestock\u002Fpoultry","C",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,49,55,60,66],{"code":31,"score":18,"supported":44,"title":45,"abstract":47,"tags":48},true,[46],"肉鸡",[33,46],[],{"code":50,"score":51,"supported":44,"title":52,"abstract":53,"tags":54},"C6",4,[],[27],[27],{"code":56,"score":51,"supported":44,"title":57,"abstract":58,"tags":59},"I1",[],[25],[25],{"code":61,"score":51,"supported":44,"title":62,"abstract":63,"tags":65},"I2",[],[64],"深度学习",[26],{"code":67,"score":68,"supported":44,"title":69,"abstract":70,"tags":72},"C1",3,[],[71],"生猪",[],[74,75],"肉鸡叫声 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农业物联网","openalex","2026-09-23T23:30:13.762655Z",{"total":103,"page":20,"page_size":103,"items":104},6,[105,182,272,344,394,439],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":8,"source_name":111,"source_url":108,"published_at":112,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":126,"classification":132,"search_phrases":156,"slug":159,"view_count":160,"doi":161,"paper":162,"created_at":181},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":115,"substance":116,"depth":117,"authority":18,"freshness":16,"relevant":20,"comment":118},15,20,17,"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。",[120],{"name":111,"url":108},[25,26,122,123,124],"边缘计算","家禽养殖","精准孵化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[127],{"code":61,"slug":128,"name":129,"path":130,"parent_code":131},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":133},{"candidates":134,"agricultural_evidence":44},[135,141,147,151],{"code":61,"score":115,"supported":44,"title":136,"abstract":138,"tags":140},[137],"machine 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Sedlar","https:\u002F\u002Forcid.org\u002F0000-0003-2836-5493",{"tldr":177,"method":178,"finding":179,"direction":99,"opportunity":180},"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":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":8,"source_name":111,"source_url":185,"published_at":188,"category":11,"cover_url":8,"hotness":12,"is_selected":44,"score":189,"score_detail":190,"sources":195,"tags":197,"primary_topic":201,"secondary_topics":202,"classification":209,"search_phrases":248,"slug":251,"view_count":68,"doi":252,"paper":253,"created_at":271},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":191,"substance":192,"depth":193,"authority":18,"freshness":19,"relevant":20,"comment":194},22,23,19,"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。",[196],{"name":111,"url":185},[25,26,198,199,200],"计算机视觉","精准畜牧","智能养殖",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[203,204],{"code":61,"slug":128,"name":129,"path":130,"parent_code":131},{"code":205,"slug":206,"name":207,"path":208,"parent_code":35},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants",{"status":38,"locked":13,"version":39,"method":40,"evidence":210},{"candidates":211,"agricultural_evidence":44},[212,218,222,229,234,238,244],{"code":61,"score":193,"supported":44,"title":213,"abstract":215,"tags":217},[214],"computer 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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":142,"substance":17,"depth":280,"authority":18,"freshness":68,"relevant":20,"comment":281},16,"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。",[283],{"name":111,"url":275},[25,26,285,286],"肉鸡养殖","表型检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[289],{"code":61,"slug":128,"name":129,"path":130,"parent_code":131},{"status":38,"locked":13,"version":39,"method":40,"evidence":291},{"candidates":292,"agricultural_evidence":44},[293,298,303],{"code":61,"score":142,"supported":44,"title":294,"abstract":296,"tags":297},[295],"deep 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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":191,"substance":191,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":353},"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。",[355],{"name":349,"url":347},[357,25,358,359,360],"数字农业","畜牧养殖","农业生态效率","肉鸭养殖",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":364},{"candidates":365,"agricultural_evidence":44},[366,370,375],{"code":35,"score":223,"supported":44,"title":367,"abstract":368,"tags":369},[225],[225],[],{"code":31,"score":223,"supported":44,"title":371,"abstract":373,"tags":374},[372],"duck",[372],[],{"code":56,"score":160,"supported":13,"title":376,"abstract":377,"tags":378},[],[],[357,25],[380,381],"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":383,"openalex_id":385,"authors":386,"venue":349,"cited_by_count":92,"oa_url":347,"card":8,"direction":8,"ingested_from":100},"W7220714215",[387,389,391],{"name":388,"orcid":8},"Guiren Tao",{"name":390,"orcid":8},"Yuhao Wang",{"name":392,"orcid":8},"Lingzhi Liu","2026-10-07T23:30:03.988823Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":8,"content":8,"source_name":399,"source_url":397,"published_at":400,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":401,"sources":403,"tags":405,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":421,"slug":424,"view_count":20,"doi":425,"paper":426,"created_at":438},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.","Socio-economic and Humanistic Aspects for Township and Industry.","2026-08-29T00:00:00Z",{"impact":142,"substance":17,"depth":280,"authority":12,"freshness":68,"relevant":20,"comment":402},"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。",[404],{"name":399,"url":397},[25,123,406,407],"成本核算","环境监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":411},{"candidates":412,"agricultural_evidence":44},[413,417],{"code":31,"score":142,"supported":44,"title":414,"abstract":415,"tags":416},[32],[32],[123],{"code":56,"score":20,"supported":13,"title":418,"abstract":419,"tags":420},[],[],[25],[422,423],"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":425,"openalex_id":427,"authors":428,"venue":399,"cited_by_count":92,"oa_url":432,"card":433,"direction":99,"ingested_from":100},"W7204628245",[429],{"name":430,"orcid":431},"Fransisca Hanita Rusgowanto","https:\u002F\u002Forcid.org\u002F0000-0003-3909-3374","https:\u002F\u002Fjurnal.tintaemas.id\u002Findex.php\u002Fsehati\u002Farticle\u002Fdownload\u002F702\u002F575",{"tldr":434,"method":435,"finding":436,"direction":97,"opportunity":437},"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。","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。","2026-09-01T04:03:11.766648Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":8,"content":444,"source_name":445,"source_url":8,"published_at":446,"category":447,"cover_url":8,"hotness":12,"is_selected":13,"score":448,"score_detail":449,"sources":451,"tags":453,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":474,"slug":477,"view_count":92,"doi":8,"paper":8,"created_at":478},5424,"中国农科院北京畜牧兽医所揭示葛根素改善产蛋后期肉种鸡繁殖性能机制","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20261019A046U200","中国农业科学院北京畜牧兽医研究所鸡营养代谢与调控创新团队揭示葛根素通过改善「肠-肝-卵巢轴」功能提高产蛋后期肉种鸡繁殖性能的机制，成果发表在《Microbiome》。研究发现在产蛋后期种鸡基础日粮中添加葛根素，可提高回肠乳酸杆菌丰度、激活 PPAR 信号通路增强肝脏脂质代谢与卵黄蛋白原合成、缓解卵巢氧化损伤与细胞凋亡。该研究首次明确了肠道菌群、肝脂质代谢与卵巢功能间的协同调控关系，牧医所博士研究生黄振吾与已毕业博士研究生马丹丹为共同第一作者。","近日，中国农业科学院北京畜牧兽医研究所鸡营养代谢与调控创新团队围绕调控产蛋后期肉种鸡繁殖性能衰退开展深入研究，揭示了葛根素（Puerarin）可通过改善“肠-肝-卵巢轴”功能，提高产蛋后期肉种鸡繁殖性能的机制。相关成果发表在《微生物组（Microbiome）》。\n\n葛根素是天然植物黄酮类提取物，具有抗氧化、抗炎、调节脂代谢及增强免疫等功能。种母鸡作为肉鸡养殖的起点，产蛋后期生理性衰老会导致繁殖性能快速下降，直接影响商品肉鸡的生产效率和整个产业竞争力。种鸡繁殖性能下降与肠道功能受损、肝脏代谢紊乱以及卵巢衰老密切相关，但以往研究多孤立解析肠道、肝脏、卵巢等单器官影响机制，但三者潜在的关联调控途径以及葛根素对种鸡繁殖性能的影响尚不明确。\n\n研究团队发现，在产蛋后期种鸡基础日粮中添加葛根素，可提高回肠乳酸杆菌丰度，改善肠道菌群结构；激活PPAR信号通路，增强肝脏脂质代谢和卵黄蛋白原合成；缓解卵巢氧化损伤和细胞凋亡，促进孕酮等生殖激素分泌和卵泡发育。该研究首次明确了葛根素可通过多途径关联实现“肠道菌群—肝脂质代谢—卵巢功能”间协同调控，进而提高产蛋后期母鸡繁殖性能，为葛根素等天然功能性成分在产蛋后期肉种鸡生产中的应用提供了重要理论依据。\n\n牧医所博士研究生黄振吾和已毕业博士研究生马丹丹为论文共同第一作者，张敏红研究员为论文通讯作者。该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。","中国农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",81,{"impact":17,"substance":191,"depth":17,"authority":115,"freshness":16,"relevant":20,"comment":450},"国家级科研机构在《Microbiome》发表的新机制研究，首次阐明葛根素经肠-肝-卵巢轴改善产蛋后期肉种鸡繁殖性能，专业增量明确，值得进入每日精选。",[452],{"name":445,"url":442},[454,455,456,457,458],"饲料添加剂","畜禽养殖","肉种鸡","葛根素","繁殖性能",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":462},{"candidates":463,"agricultural_evidence":44},[464,470],{"code":31,"score":465,"supported":44,"title":466,"abstract":468,"tags":469},24,[456,467],"产蛋",[456,467],[455,456],{"code":35,"score":142,"supported":44,"title":471,"abstract":472,"tags":473},[227],[227],[455],[475,476],"北京畜牧兽医所 葛根素 肉种鸡","葛根素 肠肝卵巢轴 繁殖性能","北京畜牧兽医所葛根素肉种鸡-5424","2026-10-21T00:10:00.250037Z"]