[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4146":3,"related-4146":74},{"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":69,"slug":72,"view_count":54,"doi":8,"paper":8,"created_at":73},4146,"湖北农科院数智质检对话——农业数智质检为农产品质量安全「守门」","https:\u002F\u002Fwww.toutiao.com\u002Fa7690406073396429338","湖北省农业科学院农业质量标准与检测技术研究所（湖北农科院质标所）党委书记、农业农村部食品质量检验测试中心（武汉）负责人周有祥介绍，研究所打造了AI知识引擎、数据驾驶舱、标准查询数据库、自动化检测设备等创新成果，特别是其牵头研发的《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统，精准破解了行业标准查询难、数据分析弱、应用场景单一的痛点。",null,"**——对话湖北省农业科学院农业质量标准与检测技术研究所党委书记、农业农村部食品质量检验测试中心（武汉）负责人周有祥**\n\n当前，智慧农业、数字农业已成为现代农业高质量发展的重要引擎，农业质检作为农产品质量安全的前端，正加速推进数智技术与传统检测的深度融合，为守护群众 “舌尖上的安全” 注入科技动能。近年来，湖北省农业科学院农业质量标准与检测技术研究所（以下简称“湖北农科院质标所”）打造了AI知识引擎、数据驾驶舱、标准查询数据库、自动化检测设备等一系列创新成果，特别是其牵头研发的《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统，精准破解了行业标准查询难、数据分析弱、应用场景单一的痛点，为农产品质量安全监管、种养殖户便捷查询、产业提质增效提供了数字化支撑。近日，湖北农科院质标所党委书记、农业农村部食品质量检验测试中心（武汉）负责人周有祥就党建与科研融合、数字化质检成果、特色农产品提质、成果推广落地等方面与本报进行了对话。\n\n**本报：农业农村部食品质量检验测试中心（武汉）在发挥党员先锋模范作用、党建与科研深度融合、筑牢科研作风等方面，形成了哪些特色工作机制？**\n\n**周有祥：**近年来，我们聚焦农业数智质检领域，坚持把党建工作深度嵌入科研检测全链条，赋能科研攻关和产业发展。在推动党建与科研深度融合方面，我们创新“三学”工作机制：一方面，围绕中央、湖北省委关于智慧农业、农产品质量安全的重大部署组织集中学习，变“孤立学习”为“贯通学习”；另一方面，积极引导党员骨干锚定农业质检数字化转型目标，主动学习机器视觉等前沿技术，变“纸上学习”为“实干践学”。在发挥党员先锋模范作用方面，我们建强“党员先锋岗”“党员突击队”，组织党员骨干聚焦农产品质量安全“保底线、拉高线”的需求。在筑牢科研作风方面，我们推动党小组建在课题一线、实验室一线，把党性教育融入项目研讨、技术试验的全过程。由党员骨干带头严守科研诚信、恪守学术规范，在样品检测、标准验证、系统开发等环节亮身份、作表率，以严实党风涵养清正学风，锻造出一支作风过硬、善打技术攻坚战的农业质检科研队伍。\n\n**本报：相较于传统查询模式，《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统在数据维度、可视化分析、实操应用上实现了哪些重要突破？目前有哪些渠道便于公众获取和了解？**\n\n**周有祥：**《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）是守护群众“舌尖上的安全”的强制性国家食品安全标准。在相关主管部门的指导下，我们构建了《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统，精准破解了行业标准查询难、数据分析弱、应用场景单一的痛点，为农产品质量安全监管、种养殖户便捷查询、产业提质增效提供了数字化支撑。\n\n在数据维度层面，该系统融合了GB2763限量值与食品类别、农药类别、检测方法、禁限用规定、绿色食品要求以及特色小宗作物等多源数据，尤其破解了组限量情形下的食品限量查询难题。在可视化分析层面，依托商业智能（BI）技术，该系统能将限量数据转化为可联动的数据驾驶舱，直观展示单一食品中的农药限量、单一农药在不同食品中的限量等多维信息，为生产经营、监管执法、检测认证和贸易消费等各类主体提供精准服务。在实操应用层面，该系统支持联动查询，通过单点触发、多维度同步刷新，实现多字段关联检索，节约了跨库检索、人工匹配的时间，可快速完成限量判定。目前，公众可通过农业农村部农产品质量安全中心“农安科普”平台以及湖北省农科院质标所公众号查询使用该系统。\n\n**本报：湖北农科院质标所如何立足检测、标准、评价三大核心职能，系统性推动湖北特色农产品质量安全水平与品质双向提升？**\n\n**周有祥：**我们长期承担湖北省农产品质量安全监测任务，严格开展风险监测和监督抽查，紧盯重点品种、重点区域以及农兽药残留等突出风险，做到及时发现问题、研判风险、服务监管。近年来，湖北省农产品质量安全监测合格率稳定保持在98%以上。湖北农科院质标所围绕湖北稻米、小龙虾、莲藕等优势特色产业开展标准研制。以莲藕为例，我们联合武汉市农业科学院、全省莲藕龙头企业，充分发挥湖北水生蔬菜的科研、产业和资源优势，先后牵头制定了《绿色食品 藕及藕制品》《莲藕生产全程质量控制技术规范》等行业标准，将质量安全要求贯穿产地环境、生产管理、投入品使用、采收加工等全环节，引导产业由‘经验生产’向‘标准化生产’转型。下一步，我们将持续开展优质农产品的品质评价与分等分级研究，推进绿色、有机、名特优新和地理标志等优质农产品的评价认证工作，让更多湖北优质农产品被市场看见、被消费者认可。\n\n**本报：湖北农科院质标所建成了覆盖湖北省十大农业产业链的湖北农业地方标准库、全国“菜篮子”数据驾驶舱、省级抽样监管平台等数字化成果。请问这些数智化成果后续在省内外推广落地、规模化应用方面有哪些具体部署？**\n\n周有祥：一直以来，湖北农科院质标所始终重点推动科研成果的场景化落地应用，着力打通农产品生产、追溯、抽检、监管、认证全流程数据链。湖北农业地方标准库将持续聚焦湖北十大农业产业链，完善标准动态更新、分类检索和场景化应用功能，把标准要求转化为生产主体查得到、看得懂、用得上的技术指引；全国“菜篮子”数据驾驶舱将进一步强化产量统计、多源数据汇聚、风险分析和趋势研判功能，提升监管服务能力；省级抽样监管平台重点推进抽样、审核、追溯等环节在线协同，提升监管工作的精准性和时效性。目前，以GB2763可视化数据库为代表的数智化成果正在主管部门的指导下迭代升级，后续将在全国推广应用。\n\n**本报：当前湖北正加快建设科技农业、绿色农业、质量农业、品牌农业，打造国家优质农产品生产区。下一步，湖北农科院质标所将如何把现有数智化科研成果、标准体系优势、品质评价能力深度结合，进一步拓宽应用场景、加大成果推广力度？**\n\n**周有祥：**湖北农科院质标所将把数智化科研成果、标准体系优势、品质评价能力三者融合贯通，重点做好夯实智能底座、强化场景开发等三方面工作：\n\n一是夯实智能底座，推动算力平台建设，推动法律法规、标准规范、科研文献以及检测数据融合，构建农产品质量安全垂直领域知识图谱，加强智能体工程化技术研究，为技术落地、场景应用、预警研判提供稳定可靠的算力与数据保障；二是强化场景开发，系统推动面向生产、监管、消费主体的农安数智化应用落地，重点在标准示范推广、电子承诺达标合格证应用、追溯体系落地等场景做好赋能支撑，探索构建农产品质量安全人工智能标准体系，提升数智化服务水平；三是做好农检应用，聚焦农产品质量安全检测实验室的自动化、智能化需求，探索开展自动化检测分析、数字化质量管理、智能化报告解读等技术研究，重点破解检测质效难题。**（赵平 叶家茂）**","今日头条·2026年9月底","2026-09-30T00:00:00Z","报道",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},22,20,17,6,1,"省级农科院数智质检成果的深度对话，含GB2763-2026可视化系统等实质信息，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","农产品质量安全","农业标准","农药残留",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,59,64],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},13,true,[48],"农产品质量",[50],"食品安全",[28,30],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"F4",3,[],[57],"农药",[],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"I1",[],[],[26],{"code":65,"score":21,"supported":14,"title":66,"abstract":67,"tags":68},"I2",[],[],[27],[70,71],"湖北农科院 数智质检 GB2763","农药最大残留限量 可视化查询系统","湖北农科院数智质检GB2763-4146","2026-10-04T00:09:08.196478Z",{"total":20,"page":21,"page_size":20,"items":75},[76,176,218,276,313,375],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":91,"tags":93,"primary_topic":96,"secondary_topics":101,"classification":103,"search_phrases":146,"slug":149,"view_count":150,"doi":151,"paper":152,"created_at":175},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z","论文",80,{"impact":87,"substance":17,"depth":87,"authority":88,"freshness":89,"relevant":21,"comment":90},18,14,8,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[92],{"name":82,"url":79},[26,27,94,50,95],"机器学习","文献计量",{"code":65,"slug":97,"name":98,"path":99,"parent_code":100},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[102],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":104},{"candidates":105,"agricultural_evidence":46},[106,117,123,129,136,142],{"code":65,"score":107,"supported":46,"title":108,"abstract":110,"tags":116},19,[109],"artificial intelligence",[111,94,112,113,114,109,115],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[27,94],{"code":32,"score":118,"supported":46,"title":119,"abstract":121,"tags":122},15,[120],"food safety",[50,120],[50],{"code":124,"score":118,"supported":46,"title":125,"abstract":127,"tags":128},"I7",[126],"bibliometric",[95,126],[95],{"code":130,"score":20,"supported":46,"title":131,"abstract":132,"tags":135},"H7",[],[133,134],"气候变化","climate change",[],{"code":137,"score":54,"supported":46,"title":138,"abstract":139,"tags":141},"A8",[],[140],"food security",[],{"code":60,"score":21,"supported":14,"title":143,"abstract":144,"tags":145},[],[],[26],[147,148],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400",0,"10.1108\u002Fbfj-06-2025-0819",{"doi":151,"openalex_id":153,"authors":154,"venue":82,"cited_by_count":150,"oa_url":8,"card":168,"direction":172,"ingested_from":174},"W7222920742",[155,157,159,162,165],{"name":156,"orcid":8},"Harang Lee",{"name":158,"orcid":8},"지은서",{"name":160,"orcid":161},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":163,"orcid":164},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":166,"orcid":167},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","农业人工智能与决策模型","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","openalex","2026-10-20T23:30:31.567802Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":8,"content":9,"source_name":181,"source_url":8,"published_at":182,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":188,"tags":190,"primary_topic":194,"secondary_topics":195,"classification":196,"search_phrases":212,"slug":215,"view_count":216,"doi":8,"paper":8,"created_at":217},3741,"聚焦农业数智质检 当好农产品质量安全守门人","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7690406073396429338","湖北省农业科学院农业质量标准与检测技术研究所打造AI知识引擎、数据驾驶舱、标准查询数据库、自动化检测设备等创新成果。牵头研发的食品安全国家标准食品中农药最大残留限量（GB2763—2026）可视化查询和分析系统破解行业标准查询难痛点。","中国食品报融媒体","2026-09-28T02:25:00Z",79,{"impact":17,"substance":185,"depth":19,"authority":13,"freshness":186,"relevant":21,"comment":187},21,9,"省级农科院数智质检成果与GB2763可视化系统的深度对话，信息增量足、专业性强，值得进入每日精选。",[189],{"name":181,"url":179},[27,28,191,192,193],"数智质检","农药残留标准","农业标准库",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":197},{"candidates":198,"agricultural_evidence":46},[199,204,208],{"code":32,"score":200,"supported":46,"title":201,"abstract":202,"tags":203},12,[48],[50],[28],{"code":53,"score":54,"supported":46,"title":205,"abstract":206,"tags":207},[],[57],[],{"code":65,"score":21,"supported":14,"title":209,"abstract":210,"tags":211},[],[],[27],[213,214],"湖北农科院质标所 GB2763 可视化查询系统","湖北农科院 莲藕 全程质量控制","湖北农科院质标所GB2763可视化查询系统-3741",2,"2026-09-29T00:08:24.900017Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":8,"content":223,"source_name":224,"source_url":8,"published_at":225,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":231,"tags":233,"primary_topic":237,"secondary_topics":238,"classification":239,"search_phrases":271,"slug":274,"view_count":150,"doi":8,"paper":8,"created_at":275},2552,"数智助农成服贸会金融专题展亮点 农业银行展示AI数字人农小耘","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685207598819279360\u002F","9月14日服贸会报道。中国农业银行以数智助农乡村振兴为主题展示智能化金融服务体系，AI能力嵌入信贷评估、风险核查等环节；农户在村里线上提交申请后系统可辅助完成信息核验与初步授信。北京农商银行展示非接触AI健康检测机器人；北京银行展示的科创企业技术已在智慧农业、农产品溯源等场景落地。","## 数智助农成服贸会金融专题展亮点\n\n2026-09-14 10:12·[中国网三农](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAbAvyMQxxaA9sEwA2aUWsAOJjrH-pK95hBvQUB6GaSEg\u002F?source=tuwen_detail)\n\n近日，北京首钢园迎来2026年中国国际服务贸易交易会（以下简称“服贸会”），金融服务专题展区以“智焕新生 金融聚力”为主题。记者在展区现场看到，各家展台前人头攒动，数智金融如何下沉乡村，成为不少观众关注的焦点。\n\n中国农业银行展台以绿色为主调，“数智助农”“乡村振兴”字样醒目。一位观众朝屏幕招手，AI数字人“农小耘”随即回应。工作人员介绍，农行此次展示了智能化金融服务体系，AI能力可嵌入信贷评估、风险核查等环节，农户在村里线上提交申请后，系统可辅助完成信息核验与初步授信，不必再往返县城网点。服务半径由此延伸到村口，数字技术让金融支农更直达。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fc75fa582de054ded9aaf0da332891c36~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121915&x-signature=%2FPNFaboCnXA%2F%2F0wpGhoOm4cWxLI%3D)\n\n中国农业银行展区，观众正在智能点餐机前扫码点单\n\n另一侧，北京农商银行展区排起小队。观众刘女士坐到一台智能检测机前，无需接触硬件，数秒后健康筛查报告呈现在显示屏上。“这是我们研发的非接触AI健康检测机器人，数秒完成多维度生理指标筛查。”耀眼科技副经理王俊才介绍，该设备已在北京亦庄荣华街道智慧康养机器人养老驿站试点，千余名体验者中农民占三分之一。由于部分农村地区居民健康筛查不便，银行网点正好可以成为服务下沉的入口。北京农商银行与耀眼科技合作，计划将AI健康检测机器人嵌入网点，依托养老助残卡、第三代社保卡，构建“金融网点+健康服务”便民模式。\n\n北京银行展台同样热闹。环抱式“金融会客厅”里，中轴线手绘长卷与科技产品相映成趣。环廊陈列的25件前沿科技产品，多来自该行长期支持的科创企业。记者了解到，这些企业的技术已在智慧农业、农产品溯源等场景落地，部分科创企业还获得该行专项信贷支持，将AI、物联网技术带入设施农业园区。从支持科创企业到技术反哺农业，金融正成为连接科技与乡村的桥梁。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002F21c30bfe934940439449f459252e41d8~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121915&x-signature=9CqAJQPWYzu3ZBgbNjWmOZ%2FwyxA%3D)\n\n北京银行展区，观众驻足观看机器人演奏\n\n穿行展区，记者感到，本次服贸会金融服务专题展示的不只是技术，更是服务民生、助力“三农”的路径探索。资金、科技与乡村加速融合，数智技术正让服务更贴近乡土，为乡村全面振兴提供持久支撑。\n\n来源：农民日报","中国网三农 2026-09-14","2026-09-14T02:00:00Z",55,{"impact":200,"substance":45,"depth":200,"authority":228,"freshness":229,"relevant":21,"comment":230},11,7,"服贸会金融展上的数智助农展示，属企业宣传性综合报道，有AI数字人、非接触健康检测等具体案例，但无政策条款或新数据，适合作为主题页聚合素材而非每日精选头条。",[232],{"name":224,"url":221},[234,26,27,235,236],"数字乡村","乡村振兴","数字金融",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":240},{"candidates":241,"agricultural_evidence":46},[242,248,252,257,262,266],{"code":243,"score":244,"supported":46,"title":245,"abstract":246,"tags":247},"G",4,[],[235],[235],{"code":60,"score":244,"supported":46,"title":249,"abstract":250,"tags":251},[],[26],[26],{"code":32,"score":54,"supported":46,"title":253,"abstract":254,"tags":256},[],[255],"农产品溯源",[],{"code":258,"score":21,"supported":14,"title":259,"abstract":260,"tags":261},"G3",[],[],[236],{"code":65,"score":21,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[27],{"code":267,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},"I6",[],[],[234],[272,273],"农业人工智能 乡村振兴 数字乡村 数字金融","农业人工智能 乡村振兴","农业人工智能乡村振兴数字乡村数字金融-2552","2026-09-16T00:03:45.025672Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":8,"content":281,"source_name":282,"source_url":8,"published_at":83,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":287,"tags":289,"primary_topic":294,"secondary_topics":295,"classification":296,"search_phrases":308,"slug":311,"view_count":150,"doi":8,"paper":8,"created_at":312},5517,"上海农科院代表团赴马来西亚与新西兰 深化农产品质量安全与溯源合作","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57130","10月7日至12日院党委委员姚训率代表团一行4人出访，与马来西亚国立大学签署合作谅解备忘录并出席食品质量安全联合实验室揭牌，交流农业新兴污染物监测与农产品多源指纹真实性鉴别成果。赴新期间访问威世物流新西兰公司、新西兰生物经济科学研究所与梅西大学，调研有机食品认证与追溯管理经验。","2026年10月7-12日，院党委委员、纪委书记姚训率代表团一行4人赴马来西亚和新西兰参加国际学术会议并开展访问交流。代表团成员包括院农业生物安全研究中心副主任董茂锋、刘星副研究员和王献礼助理研究员。此次出访重点围绕农产品质量安全、真实性与产地溯源、农业新兴污染物监测及农食产品跨境供应链管理等领域，深化与两国高校、科研机构及企业的交流合作。\n\n7-9日，姚训代表我院参加与马来西亚国立大学合作谅解备忘录（MOU）交换仪式，出席双方食品质量安全联合实验室揭牌仪式并致辞。联合实验室的揭牌标志着双方合作进一步深化，为推动食品质量安全领域的联合科研、人才培养和成果转化搭建了合作平台。双方围绕科研项目合作、人员互访、研究生联合培养及科研资源共享等事项进行了深入交流。\n\n代表团参加了第三届马来西亚国立大学-宋卡王子大学亚洲科学技术国际研讨会（ISSTUP-Asia 2026）。会议期间，我院宋卫国研究员以线上方式作学术报告，介绍农业新兴污染物环境行为与食品安全风险防控方面的研究进展；刘星现场分享了农产品多源指纹真实性鉴别与产地溯源研究成果；王献礼通过学术墙报展示了绿色超分子溶剂在全氟和多氟烷基物质（PFAS）提取分析中的研究成果，并与参会专家开展交流。此外，代表团还参访了马来西亚国立大学食品科学相关实验室、甜食技术创新中心及马来西亚可可局可可创新与技术中心，并调研当地有机食品市场，了解食品质量安全检测、产品研发、成果转化及质量追溯管理等方面的实践经验。\n\n10-12日，代表团赴新西兰开展科研与产业交流。代表团访问了威世物流新西兰公司，围绕农产品跨境运输、冷链仓储、质量控制及供应链追溯等开展交流，了解新西兰农食产品出口及进入中国市场的物流与质量管理经验。代表团还与我院柔性引进专家Karyne Rogers教授就农产品真实性鉴别与产地溯源等领域的合作进展及后续研究计划进行交流。随后，代表团访问新西兰生物经济科学研究所及梅西大学马纳瓦图校区相关科研平台，围绕农产品质量安全、环境污染物监测研究及科研成果转化等开展学术交流，并探讨联合研究与人才培养等合作方向。访问期间，代表团还调研了新西兰有机食品市场的认证、质量保障及追溯管理情况。\n\n此次出访进一步深化了我院与马来西亚、新西兰相关高校、科研机构和企业的交流合作，展示了我院在农产品质量安全、真实性与产地溯源及农业新兴污染物研究方面的科技创新成果。下一步，我院将以食品质量安全联合实验室等合作平台为依托，推动联合科研、人才交流及成果转化，促进国际合作取得更多实质性成效，为提升农业科技创新能力提供支持。","上海市农业科学院 2026-10-19",65,{"impact":285,"substance":19,"depth":45,"authority":45,"freshness":20,"relevant":21,"comment":286},16,"省级农科院出访交流，有联合实验室揭牌与学术报告等实质内容，但属机构外事动态，公共影响有限。",[288],{"name":282,"url":279},[290,28,291,292,293],"国际合作","产地溯源","农业污染物","跨境供应链",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":297},{"candidates":298,"agricultural_evidence":46},[299,303],{"code":32,"score":186,"supported":46,"title":300,"abstract":301,"tags":302},[48],[],[28],{"code":304,"score":21,"supported":14,"title":305,"abstract":306,"tags":307},"J3",[],[],[290],[309,310],"上海农科院 马来西亚 联合实验室","农产品 真实性鉴别 产地溯源","上海农科院马来西亚联合实验室-5517","2026-10-22T00:07:04.117432Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":320,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":325,"tags":327,"primary_topic":332,"secondary_topics":333,"classification":334,"search_phrases":353,"slug":356,"view_count":150,"doi":357,"paper":358,"created_at":374},4978,"Fixed compliance costs and producer selection in regional food production: evidence from China's 2006 agricultural product quality and safety law","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1959282","Food-safety law protects consumers, but the mandatory standards, input restrictions, record-keeping, and pre-sale testing it imposes are largely fixed costs that fall hardest on the smallest producers. Whether that incidence survives aggregation, that is, whether output shed by marginal producers is absorbed by survivors or genuinely lost to a region, remains unsettled. We study China's 2006 Agricultural Product Quality and Safety Law, a nationally synchronous mandate, across 284 prefecture-level cities from 2001 to 2018. A continuous-intensity difference-in-differences design links predetermined pre-law exposure to post-law changes in per-capita animal-source food output, measured at the point of production rather than consumption, treating 2006 as a transition year and 2007 as the first full year of implementation. In the baseline specification, more exposed cities produced roughly 5% less animal-source food after implementation, with the contraction emerging over the post-law period. Because exposure is built from a pre-law outcome level, we re-estimate the relationship on a window excluding the years used to measure exposure; the negative gradient persists. Three findings locate the burden. The contraction covers the activities the statute regulates, animal-source food, grain, and primary-sector value added alike, and is absent from secondary and tertiary activity, making a general regional decline less consistent with the observed pattern. Within animal-source food, it concentrates in aquaculture, the most compliance-intensive segment. Further, it weakens as baseline farm scale rises, a gradient that persists under province-by-year fixed effects and under year-specific trends in baseline economic structure, both of which absorb the average exposure gradient entirely. Because the design works on regional quantities rather than producer histories, it identifies output a region no longer produces rather than the departure of identified producers. The findings are therefore consistent with uniform safety obligations having uneven production-side incidence across regions with different producer structures, which points to shared compliance infrastructure as a way to ease fixed compliance burdens without weakening substantive safety standards.","食品安全法保护消费者，但其规定的强制性标准、投入品限制、记录保存和售前检测在很大程度上属于固定成本，对最小规模的生产者冲击最大。这种负担分布能否在加总层面继续成立，即边际生产者减少的产出是被幸存者吸收还是真正从某一地区流失，仍无定论。我们研究中国2006年《农产品质量安全法》，这是一项全国同步实施的强制要求，覆盖2001年至2018年间284个地级市。我们采用连续强度双重差分设计，将法律实施前预先确定的暴露程度与法律实施后人均动物源食品产出的变化联系起来，产出在生产端而非消费端度量，并将2006年视为过渡年份、2007年视为首个完整实施年份。在基准设定中，暴露程度更高的城市在实施后动物源食品产量约减少5%，且收缩在法律实施后逐步显现。由于暴露程度基于法律实施前的产出水平构建，我们在排除用于度量暴露程度的年份的窗口上重新估计该关系；负向梯度依然存在。三项发现定位了负担所在。收缩覆盖了该法规所监管的活动，动物源食品、粮食和第一产业增加值均如此，而第二和第三产业则没有出现收缩，这使得一般性地区衰退与观察到的模式不太一致。在动物源食品内部，收缩集中于水产养殖，这是合规强度最高的部分。此外，随着基线农场规模上升，收缩减弱，这一梯度在省份-年份固定效应以及基线经济结构的年份特定趋势下依然存在，而这两者都完全吸收了平均暴露梯度。由于该设计基于地区总量而非生产者历史，它识别的是某一地区不再生产的产出，而非可识别生产者的退出。因此，这些发现与以下观点一致：统一的安全义务在不同生产者结构的地区之间产生了不均衡的生产端负担分布，这表明共享合规基础设施是一种在不削弱实质性安全标准的前提下缓解固定合规负担的途径。","Frontiers in Sustainable Food Systems","2026-10-14T00:00:00Z",86,{"impact":17,"substance":323,"depth":107,"authority":45,"freshness":186,"relevant":21,"comment":324},23,"基于284个地级市2001—2018年数据的准自然实验研究，揭示食品安全法规固定合规成本对区域畜牧水产产出的收缩效应，方法扎实、结论有政策含义，值得进入每日精选。",[326],{"name":319,"url":316},[28,328,329,330,331],"水产养殖","小农户","食品安全法","合规成本",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":335},{"candidates":336,"agricultural_evidence":46},[337,344,348],{"code":338,"score":229,"supported":46,"title":339,"abstract":340,"tags":343},"D",[],[341,342],"水产","aquaculture",[328],{"code":32,"score":229,"supported":46,"title":345,"abstract":346,"tags":347},[],[50,48],[28],{"code":349,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},"G2",[],[],[329],[354,355],"中国 地级市 动物源食品 产出","农产品质量安全 食品安全法 合规成本 水产养殖","中国地级市动物源食品产出-4978","10.3389\u002Ffsufs.2026.1959282",{"doi":357,"openalex_id":359,"authors":360,"venue":319,"cited_by_count":150,"oa_url":316,"card":368,"direction":372,"ingested_from":174},"W7221767515",[361,364,366],{"name":362,"orcid":363},"Xi Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8652-5980",{"name":365,"orcid":8},"毕军涛",{"name":367,"orcid":8},"Mengwei Yang",{"tldr":369,"method":370,"finding":371,"direction":372,"opportunity":373},"研究中国2006年农产品质量安全法，发现合规固定成本使高暴露地区动物源食品产量下降约5%。","连续强度双重差分，2001-2018年284个地级市面板数据，以法律前暴露度识别","合规成本集中于水产养殖，且随农场规模上升而减弱，说明小生产者承担更重固定成本。","农业绿色发展与碳","可研究共享合规基础设施（如区域检测、记录平台）如何降低小农固定合规成本而不放松安全标准。","2026-10-15T23:30:06.488813Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":8,"source_name":381,"source_url":382,"published_at":383,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":394,"classification":395,"search_phrases":415,"slug":418,"view_count":150,"doi":419,"paper":420,"created_at":433},4919,"การออกแบบและพฒนาระบบควบคมดวยไมโครคอนโทรลเลอรสำหรบเครองปลกถวงอกกงอตโนมต","https:\u002F\u002Fdoi.org\u002F10.65411\u002Frst.2026.271208","The cultivation of high-quality bean sprouts is often hindered by inconsistent environmental control and hazardous chemical contamination. This research develops a closed-loop environmental control system, utilizing an Arduino Mega 2560 microcontroller to actively manage the microclimate within a semi-automatic cultivation machine. The system integrates thermoelectric (Peltier) cooling to effectively dissipate accumulated metabolic heat, combined with a precision humidity control mechanism. Performance was evaluated in triplicate (n = 3) against traditional timer-based irrigation systems. Statistical analysis (one-way ANOVA, p \u003C 0.05) demonstrated that the prototype successfully stabilized water temperature (22–25 °C) and relative humidity (80–90%) throughout the cultivation period. These optimal conditions led to a 31–65% increase in total net yield compared to traditional chemical-free methods. While this yield remains slightly lower than intensive commercial practices that rely on growth-accelerating chemicals, laboratory tests by an ISO\u002FIEC 17025 accredited facility confirmed undetectable levels of formaldehyde and sulfur dioxide. This study provides a novel engineering framework for chemical-free precision agriculture, demonstrating significant potential for enhancing food safety and reducing manual labor in commercial-scale production.","优质豆芽的培育常受环境控制不稳定和有害化学污染的制约。本研究开发了一套闭环环境控制系统，利用Arduino Mega 2560微控制器主动调控半自动培育机内的微气候。该系统集成了热电（珀尔帖）冷却以有效散除积累的代谢热，并结合精密湿度控制机制。性能评估以传统定时灌溉系统为对照，进行三次重复试验（n = 3）。统计分析（单因素方差分析，p \u003C 0.05）表明，该原型在整个培育期内成功将水温稳定在22–25 °C、相对湿度稳定在80–90%。这些最优条件使总净产量较传统无化学方法提高了31–65%。尽管该产量仍略低于依赖促生长化学品的集约化商业实践，但由ISO\u002FIEC 17025认可实验室进行的检测确认，甲醛和二氧化硫均未检出。本研究为无化学精准农业提供了一种新颖的工程框架，展示了在商业规模生产中提升食品安全和减少人工劳动的巨大潜力。","Recent Science and Technology","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Frmutsvrj\u002Farticle\u002Fview\u002F271208","2026-10-12T00:00:00Z",67,{"impact":200,"substance":185,"depth":19,"authority":186,"freshness":89,"relevant":21,"comment":386},"论文提出基于Arduino的闭环环境控制系统，实现豆芽无化学精准栽培并显著增产，方法新颖、数据可靠，但属细分领域技术进展，公共影响有限。",[388],{"name":381,"url":382},[26,50,390,391,392],"环境调控","无土栽培","豆芽生产",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":46},[398,402,408],{"code":32,"score":229,"supported":46,"title":399,"abstract":400,"tags":401},[],[50,120],[50],{"code":60,"score":229,"supported":46,"title":403,"abstract":404,"tags":407},[],[405,406],"精准农业","precision agriculture",[26],{"code":409,"score":20,"supported":46,"title":410,"abstract":411,"tags":414},"H3",[],[412,413],"灌溉","irrigation",[],[416,417],"Arduino Mega 2560 豆芽机","豆芽 无化学 栽培 环境控制","ArduinoMega2560豆芽机-4919","10.65411\u002Frst.2026.271208",{"doi":419,"openalex_id":421,"authors":422,"venue":381,"cited_by_count":150,"oa_url":426,"card":427,"direction":372,"ingested_from":174},"W7221576059",[423],{"name":424,"orcid":425},"Panot Sripakarach","https:\u002F\u002Forcid.org\u002F0000-0002-7861-5406","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Frmutsvrj\u002Farticle\u002Fdownload\u002F271208\u002F182844",{"tldr":428,"method":429,"finding":430,"direction":431,"opportunity":432},"设计基于Arduino Mega 2560的闭环环境控制系统，用于半自动豆芽机，实现无化学添加的高产","Arduino Mega 2560、热电（Peltier）冷却、湿度控制，与定时","系统稳定水温22–25°C、湿度80–90%，净产量提高31–65%，甲醛和二氧化硫未检出。","智慧农业 \u002F 农业物联网","可探索多环境因子协同优化与低成本传感器集成，并验证规模化商业生产中的能耗与产量平衡。","2026-10-14T23:31:07.397575Z"]