[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4640":3,"related-4640":65},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":59,"slug":62,"view_count":63,"doi":8,"paper":8,"created_at":64},4640,"浙江省农科院质营所联合江山市举办鸡蛋质量安全风险防控与高质量发展大会","https:\u002F\u002Fwww.rmzxw.com.cn\u002Fc\u002F2026-10-08\u002F3985226.shtml","由浙江省农科院农产品质量安全与营养研究所(简称质营所)与江山市农业农村局联合主办的浙江省重点农产品(鸡蛋)质量安全风险防控与高质量发展大会在江山举行,省农科院质营所所长王新全、省农业农村厅农产品质量安全监管处祝侃、国家蛋鸡产业技术体系岗位科学家唐宏刚等近90人参会。培训会系统宣贯农产品质量安全监管政策,围绕鸡蛋全产业链质量安全管控与品质提升策略、蛋壳品质形成机制与精准营养调控技术开展专题报告,并现场指导快检实操演练与规范开具承诺达标合格证。",null,"2026年9月29日，由浙江省农科院农产品质量安全与营养研究所（简称“质营所”）与江山市农业农村局联合主办的浙江省重点农产品（鸡蛋）质量安全风险防控与高质量发展大会在江山举行。省农科院质营所所长王新全、省农业农村厅农产品质量安全监管处祝侃、国家蛋鸡产业技术体系岗位科学家唐宏刚、江山市农业农村局党委委员郑章良，以及专家学者、基层监管人员和养殖主体代表近90人参会。\n\n培训会上，江山市农业农村局叶方伟副科长宣贯了农产品质量安全监管政策。省农科院质营所吉小凤副研究员、江苏福润德李亚奎教授分别围绕“鸡蛋全产业链质量安全管控与品质提升策略”、“蛋壳品质形成机制与精准营养调控技术”作专题报告。随后，杭州傲敏刘成安工程师就“禽蛋中药物残留快速检测卡”的检测原理和操作进行了讲解培训，技术团队现场手把手指导养殖户开展快检实操演练，并指导养殖主体规范开具承诺达标合格证。\n\n期间，参会领导、专家及监管人员先后赴浙江汇农农业开发有限公司、江山翰音农业产业基地实地考察蛋禽养殖、源头品控与数字化管理情况。会场同步设置成果展示区，金大康、杭州傲敏、江苏福润德等企业重点推介了蛋鸡健康养殖与减抗替抗方案，浙江汇农、圣迪乐村江山分公司展示了品牌蛋品与全程质量追溯体系，为参会养殖主体规范生产、提升鸡蛋品质提供了直观样板。\n\n本次活动融政策解读、技术指导、实操演练、实地调研与示范推广于一体，推动了科研成果与生产实践有效衔接，为全省蛋鸡产业安全与高质量发展提供了有力支持。","人民政协网 2026-10-08","2026-10-08T00:00:00Z","报道",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,17,14,9,6,1,"省级科研机构与地方联合举办的鸡蛋质量安全防控会议，含政策宣贯、技术报告与快检实操，内容较实但属行业会议报道，时效性一般。",[25],{"name":10,"url":6},[27,28,29,30,31],"减抗替抗","蛋鸡养殖","农产品追溯","承诺达标合格证","鸡蛋质量安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},4,true,[],[50],"农产品质量",[29],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"C3",3,[],[57],"蛋鸡",[],[60,61],"浙江省农科院 鸡蛋 质量安全","江山市 蛋鸡 高质量发展","浙江省农科院鸡蛋质量安全-4640",0,"2026-10-11T00:09:42.543886Z",{"total":21,"page":22,"page_size":21,"items":66},[67,112,152,253,296,359],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":8,"content":8,"source_name":72,"source_url":8,"published_at":73,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":80,"tags":82,"primary_topic":87,"secondary_topics":88,"classification":89,"search_phrases":107,"slug":110,"view_count":22,"doi":8,"paper":8,"created_at":111},5294,"云南省农科院质标所举办\"特色农产品数字化品控及标准体系构建\"成果推介会","https:\u002F\u002Fwww.yn.xinhuanet.com\u002F20261016\u002F4e2d2462c42e41f8b2d9d5bf525cbed7\u002Fc.html","10月16日云南省农科院质标所发布由云南省科技厅支持的\"特色农产品全产业链可信数字化技术研究\"项目下产出的3个数字化平台系统(产地环境质量监测调控系统、特色农产品质量可信追溯系统、特色农产品可信供应订单定制系统),覆盖产地环境、质量追溯、订单供应三大环节;相关数字化成果已在元阳梯田红米、绥江半边红李、香格里拉草莓、盈江优质软米中应用。","新华网云南频道(2026-10-16)","2026-10-16T00:00:00Z",72,{"impact":17,"substance":76,"depth":19,"authority":77,"freshness":78,"relevant":22,"comment":79},20,12,8,"省级科研机构发布三大数字化平台并落地多品类应用，信息增量与实操价值较高，值得进入每日精选。",[81],{"name":72,"url":70},[83,84,29,85,86],"数字乡村","标准体系","数字化品控","一物一码",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":90},{"candidates":91,"agricultural_evidence":47},[92,96,102],{"code":33,"score":46,"supported":47,"title":93,"abstract":94,"tags":95},[],[50],[29],{"code":97,"score":54,"supported":47,"title":98,"abstract":99,"tags":101},"B1",[],[100],"草莓",[],{"code":103,"score":22,"supported":14,"title":104,"abstract":105,"tags":106},"I6",[],[],[83],[108,109],"云南省农科院 质标所 数字化品控","特色农产品 可信追溯 元阳梯田红米","云南省农科院质标所数字化品控-5294","2026-10-19T00:07:42.258537Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":8,"content":117,"source_name":118,"source_url":8,"published_at":119,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":125,"tags":127,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":147,"slug":150,"view_count":63,"doi":8,"paper":8,"created_at":151},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","2026-10-19T00:00:00Z",65,{"impact":122,"substance":18,"depth":123,"authority":123,"freshness":21,"relevant":22,"comment":124},16,13,"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。",[126],{"name":118,"url":115},[128,129,130,131,132],"国际合作","农产品质量安全","产地溯源","农业污染物","跨境供应链",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":136},{"candidates":137,"agricultural_evidence":47},[138,142],{"code":33,"score":20,"supported":47,"title":139,"abstract":140,"tags":141},[50],[],[129],{"code":143,"score":22,"supported":14,"title":144,"abstract":145,"tags":146},"J3",[],[],[128],[148,149],"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":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":8,"source_name":158,"source_url":155,"published_at":119,"category":159,"cover_url":8,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":164,"tags":166,"primary_topic":172,"secondary_topics":178,"classification":180,"search_phrases":224,"slug":227,"view_count":63,"doi":228,"paper":229,"created_at":252},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","论文",80,{"impact":17,"substance":162,"depth":17,"authority":19,"freshness":78,"relevant":22,"comment":163},22,"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。",[165],{"name":158,"url":155},[167,168,169,170,171],"智慧农业","农业人工智能","机器学习","食品安全","文献计量",{"code":173,"slug":174,"name":175,"path":176,"parent_code":177},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[179],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":181},{"candidates":182,"agricultural_evidence":47},[183,194,200,206,213,219],{"code":173,"score":184,"supported":47,"title":185,"abstract":187,"tags":193},19,[186],"artificial intelligence",[188,169,189,190,191,186,192],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[168,169],{"code":33,"score":195,"supported":47,"title":196,"abstract":198,"tags":199},15,[197],"food safety",[170,197],[170],{"code":201,"score":195,"supported":47,"title":202,"abstract":204,"tags":205},"I7",[203],"bibliometric",[171,203],[171],{"code":207,"score":21,"supported":47,"title":208,"abstract":209,"tags":212},"H7",[],[210,211],"气候变化","climate change",[],{"code":214,"score":54,"supported":47,"title":215,"abstract":216,"tags":218},"A8",[],[217],"food security",[],{"code":220,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},"I1",[],[],[167],[225,226],"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","10.1108\u002Fbfj-06-2025-0819",{"doi":228,"openalex_id":230,"authors":231,"venue":158,"cited_by_count":63,"oa_url":8,"card":245,"direction":249,"ingested_from":251},"W7222920742",[232,234,236,239,242],{"name":233,"orcid":8},"Harang Lee",{"name":235,"orcid":8},"지은서",{"name":237,"orcid":238},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":240,"orcid":241},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":243,"orcid":244},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":246,"method":247,"finding":248,"direction":249,"opportunity":250},"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":254,"title":255,"url":256,"summary":257,"summary_zh":8,"content":8,"source_name":258,"source_url":8,"published_at":259,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":264,"tags":266,"primary_topic":270,"secondary_topics":271,"classification":272,"search_phrases":291,"slug":294,"view_count":63,"doi":8,"paper":8,"created_at":295},5060,"云南省农科院质标所参与2026年云南省食品安全宣传周主场活动 自主研发快检技术现场演示","https:\u002F\u002Fwww.yn.xinhuanet.com\u002F20261010\u002F59bfd10da16b4a36ad8de9272595ce5e\u002Fc.html","10月9日,云南省食品安全宣传周在昆明启动,云南省农科院质标所受邀参加主场活动,围绕\"从农田到餐桌\"的源头检测提供技术支撑。重点演示薄层色谱法(2分钟快速鉴别肥料中隐性添加的禁限用农药)、自主研发的丙溴磷等金纳米抗体侧流层析核酸试纸条(灵敏度较商品试纸条提升10倍)以及合成诱导圆偏振发光探针和微针贴片技术(10秒内捕捉稳定信号)。","新华网云南频道 2026年10月10日","2026-10-10T00:00:00Z",68,{"impact":122,"substance":76,"depth":195,"authority":77,"freshness":262,"relevant":22,"comment":263},5,"省级食品安全宣传周活动，含自主研发快检技术现场演示，技术细节具体，具备一定行业参考价值。",[265],{"name":258,"url":256},[267,170,268,269],"云南省农科院","农业科技","快检技术",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":273},{"candidates":274,"agricultural_evidence":47},[275,279,285],{"code":33,"score":77,"supported":47,"title":276,"abstract":277,"tags":278},[170],[170],[170],{"code":280,"score":54,"supported":47,"title":281,"abstract":282,"tags":284},"F3",[],[283],"肥料",[],{"code":286,"score":54,"supported":47,"title":287,"abstract":288,"tags":290},"F4",[],[289],"农药",[],[292,293],"云南农科院 质标所 快检技术","食品安全宣传周 云南","云南农科院质标所快检技术-5060","2026-10-16T00:13:12.742863Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":8,"source_name":302,"source_url":299,"published_at":303,"category":159,"cover_url":8,"hotness":13,"is_selected":14,"score":304,"score_detail":305,"sources":308,"tags":310,"primary_topic":315,"secondary_topics":316,"classification":317,"search_phrases":337,"slug":340,"view_count":63,"doi":341,"paper":342,"created_at":358},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":162,"substance":306,"depth":184,"authority":123,"freshness":20,"relevant":22,"comment":307},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。",[309],{"name":302,"url":299},[129,311,312,313,314],"水产养殖","小农户","食品安全法","合规成本",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":318},{"candidates":319,"agricultural_evidence":47},[320,328,332],{"code":321,"score":322,"supported":47,"title":323,"abstract":324,"tags":327},"D",7,[],[325,326],"水产","aquaculture",[311],{"code":33,"score":322,"supported":47,"title":329,"abstract":330,"tags":331},[],[170,50],[129],{"code":333,"score":22,"supported":14,"title":334,"abstract":335,"tags":336},"G2",[],[],[312],[338,339],"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":341,"openalex_id":343,"authors":344,"venue":302,"cited_by_count":63,"oa_url":299,"card":352,"direction":356,"ingested_from":251},"W7221767515",[345,348,350],{"name":346,"orcid":347},"Xi Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8652-5980",{"name":349,"orcid":8},"毕军涛",{"name":351,"orcid":8},"Mengwei Yang",{"tldr":353,"method":354,"finding":355,"direction":356,"opportunity":357},"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":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":8,"source_name":365,"source_url":366,"published_at":367,"category":159,"cover_url":8,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":372,"tags":374,"primary_topic":378,"secondary_topics":379,"classification":380,"search_phrases":400,"slug":403,"view_count":63,"doi":404,"paper":405,"created_at":418},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":77,"substance":370,"depth":18,"authority":20,"freshness":78,"relevant":22,"comment":371},21,"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。",[373],{"name":365,"url":366},[167,170,375,376,377],"环境调控","无土栽培","豆芽生产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":381},{"candidates":382,"agricultural_evidence":47},[383,387,393],{"code":33,"score":322,"supported":47,"title":384,"abstract":385,"tags":386},[],[170,197],[170],{"code":220,"score":322,"supported":47,"title":388,"abstract":389,"tags":392},[],[390,391],"精准农业","precision agriculture",[167],{"code":394,"score":21,"supported":47,"title":395,"abstract":396,"tags":399},"H3",[],[397,398],"灌溉","irrigation",[],[401,402],"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":404,"openalex_id":406,"authors":407,"venue":365,"cited_by_count":63,"oa_url":411,"card":412,"direction":356,"ingested_from":251},"W7221576059",[408],{"name":409,"orcid":410},"Panot Sripakarach","https:\u002F\u002Forcid.org\u002F0000-0002-7861-5406","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Frmutsvrj\u002Farticle\u002Fdownload\u002F271208\u002F182844",{"tldr":413,"method":414,"finding":415,"direction":416,"opportunity":417},"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"]