[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3602":3,"related-3602":75},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":59,"slug":62,"view_count":63,"doi":8,"paper":64,"created_at":74},3602,"Advancing pig lameness detection with a multi-task framework: Leveraging multi-view 3D pose estimation and wavelet convolution（多任务框架推进生猪跛行检测：利用多视角3D姿态估计与小波卷积）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F36981064?&labelLang=hun","Xiang Li、Wang Haidong、Hu Zixuan、Norton Tomas、Jiang Tian、Xue Yueju等在Biosystems Engineering 1537-5110 Vol. 263 Paper: 104393（2026）发表。研究针对生猪跛行检测提出PoseGait-MT多任务分类框架，可同时检测跛行严重程度与受影响肢体。框架从多视角2D视频重建3D骨骼以缓解遮挡与视角依赖问题；提取3D步态时空特征（空间跟踪距离sTRK、头部上下振幅HBA、关节屈曲角JFA），并结合小波卷积（WTConv）增强低频特征提取、抑制高频噪声。实验结果显示：PoseGait-MT在5折交叉验证下对跛行严重程度分类平均准确率94.7%、受影响肢体识别95.7%；独立测试集上分别89%、91.4%。",null,"Biosystems Engineering Vol. 263 2026-09-20","2026-09-20T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"核心期刊论文，方法新颖且实验数据扎实，对智慧养殖中的动物健康监测有实质参考价值，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","计算机视觉","生猪养殖","3D姿态估计","动物健康监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"C1","pigs","生猪","\u002Ftopic\u002Flivestock\u002Fpigs","C",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,54],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[33],[33],[27],{"code":50,"score":20,"supported":13,"title":51,"abstract":52,"tags":53},"I1",[],[],[25],{"code":55,"score":20,"supported":13,"title":56,"abstract":57,"tags":58},"I2",[],[],[26],[60,61],"PoseGait-MT 生猪跛行检测","多视角3D姿态估计 生猪","PoseGait-MT生猪跛行检测-3602",3,{"doi":8,"openalex_id":8,"authors":65,"venue":8,"cited_by_count":66,"oa_url":8,"card":67,"direction":71,"ingested_from":73},[],0,{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"提出PoseGait-MT多任务框架，用多视角3D姿态与小波卷积同时检测生猪跛行程度和患肢。","多视角2D视频重建3D骨骼，提取步态时空特征，结合小波卷积WTConv。","5折交叉验证跛行程度准确率94.7%、患肢识别95.7%，独立测试集为89%和91.4%。","农业人工智能与决策模型","可探索轻量化实时部署与跨农场泛化，并融合多模态数据提升早期跛行预警能力。","agent","2026-09-27T00:05:18.483996Z",{"total":19,"page":20,"page_size":19,"items":76},[77,126,184,238,305,344],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":8,"content":8,"source_name":82,"source_url":8,"published_at":83,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":84,"sources":88,"tags":90,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":114,"slug":117,"view_count":105,"doi":8,"paper":118,"created_at":125},3322,"PB-DETR：群养生猪多行为识别的自适应液体神经网络编码器与动态监督框架","https:\u002F\u002Fjky.yangtzeu.edu.cn\u002Finfo\u002F1265\u002F6884.htm","长江大学计算机科学学院研究生郭宇衡作为第一作者，在计算机与农业交叉领域顶级期刊《Computers and Electronics in Agriculture》发表题为PB-DETR: multi-behaviour recognition of group-housed pigs with adaptive liquid neural network encoder and dynamic supervision的研究论文。长江大学智慧农业交叉创新研究中心主任、计科学院詹炜教授为通讯作者。研究将液体神经网络机制引入Transformer编码器，设计自适应液体时间常数增强模块，构建多尺度特征交互和动态质量感知优化策略，进而降低模型对固定背景信息的依赖。基于PB-DETR框架构建的模型平均精度(mAP)达到0.893，参数量为14.58M，计算量为49.12GFLOPs；在不同养殖场景之间的测试中表现出较好稳定性。","《Computers and Electronics in Agriculture》2026","2026-09-21T00:00:00Z",{"impact":85,"substance":17,"depth":16,"authority":18,"freshness":86,"relevant":20,"comment":87},16,8,"农业人工智能顶刊论文，方法新颖、指标明确，对智慧养殖行为识别有实质参考价值，值得进入每日精选。",[89],{"name":82,"url":80},[25,91,92,93,94],"农业人工智能","智慧养殖","目标检测","生猪行为识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":98},{"candidates":99,"agricultural_evidence":45},[100,104,109],{"code":31,"score":86,"supported":45,"title":101,"abstract":102,"tags":103},[33],[],[],{"code":50,"score":105,"supported":45,"title":106,"abstract":107,"tags":108},4,[],[25],[25],{"code":55,"score":110,"supported":13,"title":111,"abstract":112,"tags":113},2,[],[],[91,93],[115,116],"长江大学 詹炜 生猪行为识别","PB-DETR 群养生猪 多行为识别","长江大学詹炜生猪行为识别-3322",{"doi":8,"openalex_id":8,"authors":119,"venue":8,"cited_by_count":66,"oa_url":8,"card":120,"direction":71,"ingested_from":73},[],{"tldr":121,"method":122,"finding":123,"direction":71,"opportunity":124},"提出PB-DETR框架，实现群养生猪多行为识别，mAP达0.893。","引入液体神经网络改进Transformer编码器，结合多尺度特征交互与动态监督。","模型在群养生猪多行为识别中mAP达0.893，跨场景稳定性较好。","可探索液体神经网络在其它畜禽行为识别中的泛化能力及轻量化部署。","2026-09-24T00:04:02.413623Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":8,"content":131,"source_name":132,"source_url":8,"published_at":10,"category":133,"cover_url":8,"hotness":12,"is_selected":13,"score":134,"score_detail":135,"sources":139,"tags":141,"primary_topic":146,"secondary_topics":147,"classification":148,"search_phrases":179,"slug":182,"view_count":105,"doi":8,"paper":8,"created_at":183},3029,"中国农业大学发布《肉禽智能表型数据采集规范》《猪智能表型数据采集规范》两项团体标准","https:\u002F\u002Fnews.cau.edu.cn\u002Fmtndnew\u002F799e04a34f6f4364bf553f20f41744c6.htm","9-18 WAFI2026国际智能育种论坛重磅发布《肉禽智能表型数据采集规范》《猪智能表型数据采集规范》两项团体标准，系国内畜禽智能表型数据采集首批行业规范。标准由首农食品集团牵头，联合19家科研院所与行业龙头编制，分别聚焦肉禽与生猪两大核心领域，对智能表型数据的采集对象、智能测定技术方法、设备要求、采集流程、数据格式与管理等作出统一规范。肉禽标准采用CT成像、B超回声定位、机器视觉、RFID个体识别等技术，RFID识别准确率达99%；猪标准整合CT成像、3D建模、智能B超和高精度承重四大技术。","**科技日报记者 华凌**\n\n9月18日下午，2026世界农业科技创新大会国际智能育种论坛在北京市平谷区召开。作为大会重要平行论坛，本次论坛以“智能育种·拥抱梦想”为主旨，搭建全球智能育种领域学术交流、技术创新与产业协同平台。中国农业大学校长陈卫，北京市平谷区委副书记葛海滨，平谷区副区长彭石，首农食品集团党委常委、副总经理马俊等出席会议。\n\n![Image 1](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F18\u002F583873_4d01feac-9785-4e17-9e5d-b98bd62a5732.jpeg)\n\n本届论坛由北京首农食品集团有限公司、北京市平谷区人民政府、中信农业科技股份有限公司、北京市农林科学院联合主办，北京生物种业创新联合体（北京智育生物种业创新中心）、北京首农股份有限公司、北京首农平谷农业科技创新投资发展有限公司承办。\n\n![Image 2](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F18\u002F583873_1383d51c-13c5-45c5-943f-dafe45e50d57.jpeg)\n\n论坛聚焦智能育种前沿变革，设置主旨报告、前沿对话、标准发布与项目签约环节。中国工程院院士赵春江、赵书红，以及国内外高校、科研院所专家，围绕AI智慧农业、基因组选择、畜禽与作物智能设计育种等方向作报告。在科技前沿对话环节，孙其信院士带领产学研代表，探讨智能育种对传统育种的革新路径，共谋构建深度融合的种业创新生态。\n\n![Image 3](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F18\u002F583873_35382de5-e682-4172-a4df-2202b07fb261.jpeg)\n\n论坛重磅发布《肉禽智能表型数据采集规范》《猪智能表型数据采集规范》两项团体标准，系国内畜禽智能表型数据采集首批行业规范。该标准由首农食品集团牵头，联合19家科研院所与龙头企业编制。现场，PIC中国与双胞胎集团签署种业振兴战略合作协议，双方整合种质资源与产业优势，助力我国生猪产业现代化发展。\n\n![Image 4](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F18\u002F583873_3a626cc8-632b-4f5c-86ea-224d258c2684.jpeg)\n\n同期举办的世界农业科技博览会上，首农食品集团集中展示育种核心部件、核心种源等创新成果。首农集团联动多方主体，探索“政府+科研机构+企业”协同机制，助力北京“种业之都”建设。\n\n**（主办方供图）**\n\n[中国科技网2026年9月18日](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F18\u002Fcontent_583873.html)","中国农业大学新闻网","报道",85,{"impact":136,"substance":17,"depth":85,"authority":18,"freshness":137,"relevant":20,"comment":138},24,9,"国内首批畜禽智能表型数据采集团体标准发布，由首农牵头联合19家机构编制，对智慧育种与种业振兴具行业规范意义，值得进入每日精选。",[140],{"name":132,"url":129},[25,142,143,144,145],"行业标准","种业振兴","智能育种","畜禽智能表型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":149},{"candidates":150,"agricultural_evidence":45},[151,156,160,165,170,174],{"code":35,"score":63,"supported":45,"title":152,"abstract":153,"tags":155},[],[154],"畜禽",[],{"code":31,"score":63,"supported":45,"title":157,"abstract":158,"tags":159},[],[33],[],{"code":161,"score":20,"supported":13,"title":162,"abstract":163,"tags":164},"F1",[],[],[143],{"code":166,"score":20,"supported":13,"title":167,"abstract":168,"tags":169},"F2",[],[],[144],{"code":50,"score":20,"supported":13,"title":171,"abstract":172,"tags":173},[],[],[25],{"code":175,"score":20,"supported":13,"title":176,"abstract":177,"tags":178},"J1",[],[],[142],[180,181],"中国农业大学 肉禽智能表型数据采集规范","首农 猪智能表型数据采集规范","中国农业大学肉禽智能表型数据采集规范-3029","2026-09-21T00:04:32.166834Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":8,"content":189,"source_name":190,"source_url":8,"published_at":191,"category":133,"cover_url":8,"hotness":12,"is_selected":13,"score":192,"score_detail":193,"sources":197,"tags":199,"primary_topic":203,"secondary_topics":204,"classification":205,"search_phrases":233,"slug":236,"view_count":63,"doi":8,"paper":8,"created_at":237},2977,"新华财经：中国-东盟AI农业合作提速 算力变农力","https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL776IV4S05346RC6.html","第23届中国-东盟博览会数字经济展区捷佳润科技集团展示\"农业数字大脑\"：整合气象站、虫情仪、土壤监测设备等终端完成12项环境数据实时采集，借助AI视觉技术监测叶绿素含量、挂果状态等8项作物生长指标，可精准识别60余种病虫害与杂草类型。系统已应用在柬埔寨、老挝、泰国等多个东盟国家。2025年广西扬翔集团联合越南企业、深圳数影科技达成跨境智能养猪战略合作落地楼房养猪\"云端猪场\"，5-6人可管理1000头母猪，养殖效率提升约30%。","[![Image 3](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL776IV4S05346RC6.html)](javascript:void(0))\n\n[网易首页](https:\u002F\u002Fwww.163.com\u002F 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[举报](https:\u002F\u002Fwww.163.com\u002Fspecial\u002F0077jt\u002Ftipoff.html?title=%E2%80%9C%E7%AE%97%E5%8A%9B%E2%80%9D%E5%8F%98%E2%80%9C%E5%86%9C%E5%8A%9B%E2%80%9D%EF%BC%9A%E4%B8%AD%E5%9B%BD%E2%80%94%E4%B8%9C%E7%9B%9FAI%E5%86%9C%E4%B8%9A%E5%90%88%E4%BD%9C%E6%8F%90%E9%80%9F)\n\n[](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL776IV4S05346RC6.html#post_comment_area \"快速发贴\")[0](https:\u002F\u002Fcomment.tie.163.com\u002FL776IV4S05346RC6.html \"点击查看跟贴\")\n\n分享至\n*   [](javascript:;)\n*   [](javascript:;)\n*   [](javascript:;)\n\n[](javascript:; \"关闭\")\n\n![Image 6: Scan me!](blob:http:\u002F\u002Flocalhost\u002Fd5f5af2332dba78cb4d883700b0417aa)\n\n用微信扫码二维码\n\n分享至好友和朋友圈\n\n新华社南宁9月19日电（黄庆刚、王亮）环境数据实时采集、精准识别病虫害，支持多个东盟国家语言……在第23届中国—东盟博览会的数字经济展区，捷佳润科技集团股份有限公司的工作人员正在展示“农业数字大脑”，吸引不少人驻足观看。\n\n![Image 7](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http%3A%2F%2Fdingyue.ws.126.net%2F2026%2F0919%2Fb907a07aj00tllw5h01o1d004ao02v4g.jpg&thumbnail=660x2147483647&quality=80&type=jpg)  \n 观众在中国—东盟博览会智慧农业展区品尝水果。新华社发\n\n记者在公司展区的互动屏上看到，“AI巡园助手”运行逻辑清晰呈现：通过整合气象站、虫情仪、土壤监测设备等终端，系统可完成温度、湿度、土壤pH值等12项环境数据的实时采集；借助AI视觉技术，监测叶绿素含量、挂果状态等8项作物生长指标，甚至能精准识别60余种病虫害与杂草类型。\n\n“数据采集频率达每日2次，相当于给作物配备了24小时‘私人医生’。”公司企管中心总监陈秀娟说，用户只需通过云平台设定巡园范围与频率，系统便能自动生成动态报告或分析文档，关键指标与风险预警一目了然。目前该系统已应用在柬埔寨、老挝、泰国等多个东盟国家。\n\n“过去主要靠经验，现在可以让数据辅助判断。”陈秀娟说，借助物联网设备和AI技术，农业生产中的部分环节可以实现实时监测和精准管理。\n\n![Image 8](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http%3A%2F%2Fdingyue.ws.126.net%2F2026%2F0919%2F6f1e3580j00tllw5l01b8d004ao02v4g.jpg&thumbnail=660x2147483647&quality=80&type=jpg)  \n 这是9月17日拍摄的广西南宁国际会展中心。新华社发\n\n近年来，广西持续深化与东盟的农业经贸与技术合作，立足毗邻东盟的区位优势，不断拓宽智慧农业出海赛道。\n\n本届东博会也更加突出人工智能等新技术应用。展区不再只是展示“有什么技术”，而是进一步围绕东盟市场需求展示“技术能解决什么问题”，推动人工智能与农业、制造业、物流等产业结合。\n\n在农业领域，这种变化尤其明显。从一块试验田到更多农业生产场景，算力正在转化为农业生产力。\n\n2025年，依托“中国—东盟生猪产业创新联盟”，广西扬翔集团股份有限公司联合越南企业、深圳数影科技有限公司达成跨境智能养猪战略合作，在越南落地全链路数字化养猪示范项目，提供楼房养猪管理体系与未来猪场解决方案，并与越南和广西的高校联合开展专业人才培养。\n\n“我们与扬翔集团联合研发未来猪场智能养猪整体解决方案，将物联网、人工智能、大数据等技术应用于猪场生产管理，对养殖环境、生产过程和生猪生长情况进行实时监测分析，形成实时可控的‘云端猪场’。”深圳数影科技有限公司董事长张岩说，项目实施后，养殖效率提升约30%，5至6人即可管理1000头母猪，进一步提升生猪疫病防控和生产管理能力。\n\n在张岩看来，随着东盟农业加快向规模化、集约化、智能化发展，农业生产对技术和管理的需求也在发生变化，市场关注点正由单一设备采购逐步转向更加系统、完整的智能解决方案。他们还联动越南、泰国等东盟国家高校，常态化推动全产业链智能化种养经验与技术体系，助力东盟农业产业转型发展。\n\n广西大学农学院副教授唐小付表示，人工智能帮助农业生产增加“数字助手”。对中国企业而言，这意味着新的市场空间；对东盟国家而言，则意味着传统农业数字化转型获得了新的技术选择。从技术展示到场景应用，从单项产品到整体解决方案，中国与东盟智慧农业合作正在进入更加具体的阶段。\n\n特别声明：以上内容(如有图片或视频亦包括在内)为自媒体平台“网易号”用户上传并发布，本平台仅提供信息存储服务。\n\nNotice: The content above (including the pictures and videos if any) is uploaded and posted by a user of NetEase Hao, which is a social media platform 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[哈兰德与女友长相出众，女友长得像洋娃娃，十分登对，浪漫幸福](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL5JNT5JN05562BI4.html?f=post1603_tab_news \"哈兰德与女友长相出众，女友长得像洋娃娃，十分登对，浪漫幸福\")\n\n娱你同欢\n\n2026-08-30 18:30:43\n\n[![Image 34: 广东热上了全国第一！多地午后体感温度超40℃，广州下周将迎雷雨天气](https:\u002F\u002Fnimg.ws.126.net\u002F?url=http:\u002F\u002Fbjnewsrec-cv.ws.126.net\u002Flittle845cd074f07j00tllu90001gd000hs00dug.jpg&thumbnail=140y88&quality=90&type=jpg)](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL774QBR40534AAOK.html?f=post1603_tab_news \"广东热上了全国第一！多地午后体感温度超40℃，广州下周将迎雷雨天气\")\n### [广东热上了全国第","新华社·新华财经","2026-09-19T10:08:00Z",73,{"impact":17,"substance":194,"depth":195,"authority":18,"freshness":137,"relevant":20,"comment":196},15,13,"央媒报道中国-东盟AI农业合作与智能养殖落地案例，具产业级参考价值，时效性强，可入每日精选。",[198],{"name":190,"url":187},[200,25,91,201,202],"数字乡村","智能养殖","中国东盟合作",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":206},{"candidates":207,"agricultural_evidence":45},[208,214,220,224,228],{"code":31,"score":19,"supported":45,"title":209,"abstract":210,"tags":213},[],[211,212],"养猪","母猪",[],{"code":215,"score":63,"supported":45,"title":216,"abstract":217,"tags":219},"H2",[],[218],"土壤",[],{"code":50,"score":20,"supported":13,"title":221,"abstract":222,"tags":223},[],[],[25],{"code":55,"score":20,"supported":13,"title":225,"abstract":226,"tags":227},[],[],[91],{"code":229,"score":20,"supported":13,"title":230,"abstract":231,"tags":232},"I6",[],[],[200],[234,235],"中国东盟 AI农业 合作","扬翔集团 智能养猪","中国东盟AI农业合作-2977","2026-09-20T00:03:00.663448Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":8,"source_name":244,"source_url":241,"published_at":245,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":66,"score_detail":246,"sources":248,"tags":250,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":285,"slug":286,"view_count":63,"doi":287,"paper":288,"created_at":304},1385,"Estimating carbon emissions from pig farming in China: spatial–temporal differentiation and influencing factors","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1908712","The pig farming industry is an important source of greenhouse gas emissions in China, the world’s largest pork producer and consumer. However, existing studies have paid limited attention to the provincial differences, dynamic evolution, and spatial spillover effects of carbon emissions from pig farming. Based on panel data from 31 provinces from 2008 to 2020, this study used the life cycle assessment method to estimate carbon emissions from feed input, pig breeding, and energy consumption. Kernel density estimation and the Global Moran’s I index are used to examine the spatial–temporal evolution of carbon emissions, and the spatial Durbin model is applied to identify the influencing factors and their spatial effects. It is found that (1) between 2008 and 2020, carbon emissions from pig farming fluctuated across four stages but declined by the end of the study period, while carbon emission intensity showed a clear downward trend. (2) Pig farming showed a trend to shift from coastal regions to inland regions, while national carbon emission intensity decreased and inter-provincial disparities narrowed. (3) Carbon emissions from China’s pig farming industry exhibited significant spatial aggregation and positive spatial autocorrelation. (4) Environmental regulation directly reduced local carbon emissions, while industrial agglomeration level, pork share in meat structure, scale, and environmental regulation showed significant spatial spillover effects. Overall, economic development level, industrial agglomeration level, technological progress, and environmental regulation contributed to carbon emission reduction. These findings indicate that regional cooperation, optimized industrial layout, and targeted environmental regulation are essential for promoting the low-carbon transition of China’s pig farming industry.","生猪养殖业是中国作为全球最大猪肉生产国和消费国的重要温室气体排放源。然而，现有研究对生猪养殖碳排放的省际差异、动态演变及空间溢出效应关注有限。基于2008—2020年31个省份的面板数据，本研究采用生命周期评估法核算饲料投入、生猪饲养及能源消耗环节的碳排放量，运用核密度估计和全局莫兰指数考察碳排放的时空演变特征，并借助空间杜宾模型识别影响因素及其空间效应。研究发现：（1）2008—2020年间，生猪养殖碳排放经历四个阶段的波动变化，但研究期末较期初有所下降，碳排放强度呈现明显下降趋势；（2）生猪养殖呈现由沿海地区向内陆地区转移的趋势，全国碳排放强度下降且省际差距缩小；（3）中国生猪养殖业碳排放表现出显著的空间集聚性和正向空间自相关性；（4）环境规制直接降低了本地碳排放，而产业集聚水平、猪肉占肉类结构比重、养殖规模及环境规制均呈现显著的空间溢出效应。总体而言，经济发展水平、产业集聚水平、技术进步和环境规制对碳排放减排具有促进作用。上述研究结果表明，区域协作、优化产业布局和精准环境规制对于推动中国生猪养殖业低碳转型至关重要。","Frontiers in Sustainable Food Systems","2026-09-02T00:00:00Z",{"impact":66,"substance":66,"depth":66,"authority":66,"freshness":66,"relevant":66,"comment":247},"该论文聚焦生猪养殖碳排放，虽涉及农业，但未直接关联农业信息化、智慧农业或数字乡村主题，相关性不足。",[249],{"name":244,"url":241},[25],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":254},{"candidates":255,"agricultural_evidence":45},[256,261,268,275,281],{"code":31,"score":18,"supported":45,"title":257,"abstract":259,"tags":260},[258],"pig farming",[33,258],[],{"code":262,"score":19,"supported":45,"title":263,"abstract":264,"tags":267},"B7",[],[265,266],"温室","greenhouse",[],{"code":269,"score":19,"supported":45,"title":270,"abstract":271,"tags":274},"H4",[],[272,273],"碳排放","carbon emission",[],{"code":276,"score":63,"supported":45,"title":277,"abstract":278,"tags":280},"C4",[],[279],"饲料",[],{"code":50,"score":20,"supported":13,"title":282,"abstract":283,"tags":284},[],[],[25],[],"Estimatingcarbonemissionsfrompigfarmingi-1385","10.3389\u002Ffsufs.2026.1908712",{"doi":287,"openalex_id":289,"authors":290,"venue":244,"cited_by_count":66,"oa_url":241,"card":297,"direction":301,"ingested_from":303},"W7204987136",[291,293,295],{"name":292,"orcid":8},"Lilan Chen",{"name":294,"orcid":8},"Youxuan Sun",{"name":296,"orcid":8},"Yunjing Tang",{"tldr":298,"method":299,"finding":300,"direction":301,"opportunity":302},"估算中国生猪养殖碳排放时空差异及影响因素，发现碳排放下降但存在空间溢出效应。","生命周期评估、核密度估计、全局莫兰指数、空间杜宾模型。","碳排放呈下降趋势，空间集聚显著，环境规制和产业集聚有空间溢出效应。","农业绿色发展与碳","可研究生猪养殖碳排放的跨区域转移机制，或结合遥感数据细化县域尺度排放核算。","openalex","2026-09-02T23:30:07.683394Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":8,"content":310,"source_name":311,"source_url":8,"published_at":312,"category":133,"cover_url":8,"hotness":12,"is_selected":13,"score":313,"score_detail":314,"sources":317,"tags":319,"primary_topic":325,"secondary_topics":326,"classification":327,"search_phrases":339,"slug":342,"view_count":66,"doi":8,"paper":8,"created_at":343},5033,"生猪市场动态|10月第2周全国活猪11.07元\u002F公斤 猪肉20.98元\u002F公斤","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F10\u002F14\u002F991030728.html","据农业农村部对500个县集贸市场和采集点监测,上周(10月第2周,10月5日—11日)全国活猪价格为11.07元\u002F公斤,环比跌0.5%;猪肉集贸市场价格为20.98元\u002F公斤,环比跌0.4%。据农业农村部对200个批发市场监测调度,上周全国猪肉批发市场均价为15.92元\u002F公斤,环比跌1.8%,同比低15.5%。专家分析认为上周生猪供给较为充裕、终端市场消费阶段性下降,生猪和猪肉价格有所下跌,展望后市预计本周生猪和猪肉价格小幅震荡。","据农业农村部对500个县集贸市场和采集点监测，上周（10月第2周，10月5日—11日，下同），全国活猪价格为11.07元\u002F公斤，环比跌0.5%；猪肉集贸市场价格为20.98元\u002F公斤，环比跌0.4%。据农业农村部对200个批发市场监测调度，上周全国猪肉批发市场均价为15.92元\u002F公斤，环比跌1.8%，同比低15.5%。\n\n专家分析认为，上周生猪供给较为充裕，终端市场消费阶段性下降，生猪和猪肉价格有所下跌。展望后市，预计本周生猪和猪肉价格小幅震荡。","农业农村部市场与信息化司 畜牧兽医局 中国农网 2026 年 10 月 14 日","2026-10-14T07:48:00Z",79,{"impact":17,"substance":315,"depth":18,"authority":194,"freshness":86,"relevant":20,"comment":316},20,"农业农村部官方周度监测数据，含活猪、猪肉集贸与批发价格及后市研判，属全国性产业级行情信息，时效性较好，值得进入每日精选。",[318],{"name":311,"url":308},[320,321,322,323,324],"畜牧兽医","生猪价格","猪肉价格","市场监测","农产品价格",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":328},{"candidates":329,"agricultural_evidence":45},[330,335],{"code":31,"score":331,"supported":45,"title":332,"abstract":333,"tags":334},11,[33],[33],[],{"code":35,"score":20,"supported":13,"title":336,"abstract":337,"tags":338},[],[],[320],[340,341],"农业农村部 生猪价格 周报","农产品价格 市场监测 猪肉价格 生猪价格","农业农村部生猪价格周报-5033","2026-10-16T00:13:03.166129Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":8,"source_name":350,"source_url":347,"published_at":351,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":192,"score_detail":352,"sources":355,"tags":357,"primary_topic":359,"secondary_topics":364,"classification":365,"search_phrases":392,"slug":395,"view_count":105,"doi":396,"paper":397,"created_at":427},2511,"Evaluation of hybrid models based on image segmentation and inference for pig weight estimation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1876396","Introduction Accurate weight estimation in pig production is essential for optimizing management, feeding, and commercialization decisions; however, traditional weighing methods are invasive, time-consuming, and prone to operational errors. This study proposes a non-invasive computer vision–based approach to estimate pig weight under real farm conditions in San Martín, Peru. Methods A dataset of 3,800 lateral images paired with their corresponding ground-truth weights was collected. A computational pipeline was implemented, including geometric standardization, instance segmentation using YOLOv8n-seg, and feature extraction through EfficientNet-B0. The resulting embeddings were used as input for supervised regression models (SVR, XGBoost, and CatBoost), evaluated using repeated stratified cross-validation and an independent test set, with MAE, RMSE, and R² as performance metrics. Statistical comparisons were conducted using the Friedman test followed by Wilcoxon post hoc analysis with Holm correction. Results The results demonstrated strong predictive performance, with the SVR model achieving the best results (RMSE = 2.68 kg, MAE = 1.81 kg, R 2 = 0.85), showing statistically significant differences compared to the other models. Discussion These findings indicate that combining computer vision techniques with models capable of capturing non-linear relationships effectively models the relationship between animal morphology and body weight, providing a low-cost, non-invasive solution applicable to real-world production systems and supporting the advancement of precision livestock farming.","引言 在生猪生产中，准确的体重估测对于优化管理、饲喂和商业化决策至关重要；然而，传统称重方法具有侵入性、耗时且易产生操作误差。本研究提出了一种基于非侵入式计算机视觉的方法，用于在秘鲁圣马丁的实际农场条件下估测生猪体重。方法 收集了3，800张侧向图像及其对应的真实体重数据集。实施了计算流程，包括几何标准化、使用YOLOv8n-seg进行实例分割，以及通过EfficientNet-B0进行特征提取。所得嵌入向量被用作监督回归模型（SVR、XGBoost和CatBoost）的输入，采用重复分层交叉验证和独立测试集进行评估，以MAE、RMSE和R²作为性能指标。统计比较采用Friedman检验，随后进行Wilcoxon事后分析并应用Holm校正。结果 结果表明预测性能良好，SVR模型取得了最佳结果（RMSE = 2.68 kg，MAE = 1.81 kg，R² = 0.85），与其他模型相比显示出统计学显著差异。讨论 这些发现表明，将计算机视觉技术与能够捕捉非线性关系的模型相结合，可以有效建模动物形态与体重之间的关系，提供一种适用于实际生产系统的低成本、非侵入式解决方案，并支持精准畜牧养殖的发展。","Frontiers in Artificial Intelligence","2026-09-14T00:00:00Z",{"impact":194,"substance":315,"depth":353,"authority":195,"freshness":86,"relevant":20,"comment":354},17,"基于YOLOv8分割与EfficientNet特征提取的生猪无接触称重研究，3800张图像、RMSE 2.68kg，方法组合与统计验证扎实，对精准畜牧有实用价值，但属细分技术进展，影响面有限。",[356],{"name":350,"url":347},[25,91,26,27,358],"精准畜牧",{"code":55,"slug":360,"name":361,"path":362,"parent_code":363},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":366},{"candidates":367,"agricultural_evidence":45},[368,373,379,383,388],{"code":55,"score":86,"supported":45,"title":369,"abstract":370,"tags":372},[],[26,371],"computer vision",[91,26],{"code":35,"score":19,"supported":45,"title":374,"abstract":375,"tags":378},[],[376,377],"畜牧","livestock",[],{"code":31,"score":105,"supported":45,"title":380,"abstract":381,"tags":382},[],[33],[27],{"code":384,"score":105,"supported":45,"title":385,"abstract":386,"tags":387},"C6",[],[358],[358],{"code":50,"score":20,"supported":13,"title":389,"abstract":390,"tags":391},[],[],[25],[393,394],"农业人工智能 计算机视觉 智慧农业 生猪养殖","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生猪养殖-2511","10.3389\u002Ffrai.2026.1876396",{"doi":396,"openalex_id":398,"authors":399,"venue":350,"cited_by_count":66,"oa_url":420,"card":421,"direction":426,"ingested_from":303},"W7212747795",[400,403,406,408,411,413,415,418],{"name":401,"orcid":402},"Miguel Angel Valles-Coral","https:\u002F\u002Forcid.org\u002F0000-0002-8806-2892",{"name":404,"orcid":405},"Kelvin Lleins Rojas-Córdova","https:\u002F\u002Forcid.org\u002F0009-0001-4097-3961",{"name":407,"orcid":8},"Lloy Pinedo",{"name":409,"orcid":410},"Richard Injante","https:\u002F\u002Forcid.org\u002F0000-0002-2449-8937",{"name":412,"orcid":8},"Pierre Vidaurre-Rojas",{"name":414,"orcid":8},"Jorge Saavedra-Ramírez",{"name":416,"orcid":417},"Fernando Ruiz-Saavedra","https:\u002F\u002Forcid.org\u002F0000-0003-4664-4867",{"name":419,"orcid":8},"Williams Ramirez","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1876396\u002Fpdf",{"tldr":422,"method":423,"finding":424,"direction":71,"opportunity":425},"用YOLOv8n-seg分割猪体并提取EfficientNet特征，结合回归模型实现非侵入式猪体重估","3800张侧视图像，YOLOv8n-seg实例分割+EfficientNet-B","SVR表现最佳（RMSE=2.68kg，MAE=1.81kg，R²=0.85），显著优于其他模型。","可探索多视角、多品种及轻量化边缘部署，提升复杂农场环境下的泛化能力。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:08.535413Z"]