[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2552":3,"related-2552":83},{"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":77,"slug":80,"view_count":81,"doi":8,"paper":8,"created_at":82},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。",null,"## 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","报道",10,false,55,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},12,13,11,7,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农业人工智能","乡村振兴","数字金融",{"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":47},[44,51,56,62,67,72],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},"G",4,true,[],[29],[29],{"code":52,"score":46,"supported":47,"title":53,"abstract":54,"tags":55},"I1",[],[27],[27],{"code":32,"score":57,"supported":47,"title":58,"abstract":59,"tags":61},3,[],[60],"农产品溯源",[],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"G3",[],[],[30],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"I2",[],[],[28],{"code":73,"score":21,"supported":14,"title":74,"abstract":75,"tags":76},"I6",[],[],[26],[78,79],"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",0,"2026-09-16T00:03:45.025672Z",{"total":84,"page":21,"page_size":84,"items":85},6,[86,187,235,296,336,398],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":8,"source_name":92,"source_url":89,"published_at":93,"category":94,"cover_url":8,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":102,"tags":104,"primary_topic":108,"secondary_topics":113,"classification":115,"search_phrases":158,"slug":161,"view_count":81,"doi":162,"paper":163,"created_at":186},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":97,"substance":98,"depth":97,"authority":99,"freshness":100,"relevant":21,"comment":101},18,22,14,8,"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。",[103],{"name":92,"url":89},[27,28,105,106,107],"机器学习","食品安全","文献计量",{"code":68,"slug":109,"name":110,"path":111,"parent_code":112},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[114],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":116},{"candidates":117,"agricultural_evidence":47},[118,129,135,141,148,154],{"code":68,"score":119,"supported":47,"title":120,"abstract":122,"tags":128},19,[121],"artificial intelligence",[123,105,124,125,126,121,127],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[28,105],{"code":32,"score":130,"supported":47,"title":131,"abstract":133,"tags":134},15,[132],"food safety",[106,132],[106],{"code":136,"score":130,"supported":47,"title":137,"abstract":139,"tags":140},"I7",[138],"bibliometric",[107,138],[107],{"code":142,"score":84,"supported":47,"title":143,"abstract":144,"tags":147},"H7",[],[145,146],"气候变化","climate change",[],{"code":149,"score":57,"supported":47,"title":150,"abstract":151,"tags":153},"A8",[],[152],"food security",[],{"code":52,"score":21,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[27],[159,160],"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":162,"openalex_id":164,"authors":165,"venue":92,"cited_by_count":81,"oa_url":8,"card":179,"direction":183,"ingested_from":185},"W7222920742",[166,168,170,173,176],{"name":167,"orcid":8},"Harang Lee",{"name":169,"orcid":8},"지은서",{"name":171,"orcid":172},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":174,"orcid":175},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":177,"orcid":178},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":180,"method":181,"finding":182,"direction":183,"opportunity":184},"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":188,"title":189,"url":190,"summary":191,"summary_zh":8,"content":192,"source_name":193,"source_url":8,"published_at":194,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":202,"primary_topic":206,"secondary_topics":207,"classification":208,"search_phrases":230,"slug":233,"view_count":57,"doi":8,"paper":8,"created_at":234},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。","**——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",75,{"impact":98,"substance":197,"depth":198,"authority":13,"freshness":84,"relevant":21,"comment":199},20,17,"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。",[201],{"name":193,"url":190},[27,28,203,204,205],"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},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":209},{"candidates":210,"agricultural_evidence":47},[211,216,222,226],{"code":32,"score":18,"supported":47,"title":212,"abstract":214,"tags":215},[213],"农产品质量",[106],[203,205],{"code":217,"score":57,"supported":47,"title":218,"abstract":219,"tags":221},"F4",[],[220],"农药",[],{"code":52,"score":21,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[27],{"code":68,"score":21,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[28],[231,232],"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",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":8,"content":240,"source_name":241,"source_url":8,"published_at":242,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":243,"score_detail":244,"sources":246,"tags":248,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":291,"slug":294,"view_count":81,"doi":8,"paper":8,"created_at":295},2976,"活力中国调研行·湖北嘉鱼：科技兴农绘就荆楚丰收画卷","https:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260919_3224382.shtml","中国经济网9-19报道湖北嘉鱼县蔬菜科技示范园与中国农科院、湖北省农科院等多个科研团队合作，配套建成农业数字化服务站对蔬菜生产实施全天候监测，实时采集气象、土壤、水肥等农情信息实现水肥精准调控、病虫害绿色防控及生产全过程溯源。嘉鱼整合农作物生长数据建成400亩农产品溯源基地给蔬菜赋予\"二维码身份证\"，直供上海、广州、武汉等商超并远销日本、俄罗斯及东南亚市场。","新华社武汉9月19日电 题：科技兴农绘就荆楚丰收画卷\n\n新华社记者宋立崑、高敬、曹佩弦\n\n鱼肥鳝美，莲藕盈塘，9月的湖北，处处涌动着丰收气息。\n\n近日，记者跟随“活力中国调研行”采访团走访了解到，湖北正持续推动农业科技创新与产业创新深度融合，形成多项核心技术，培育出一批特色优质农产品，农业产业提质增效成果丰硕。\n\n在仙桃市张沟镇先锋村，记者来到仙桃市黄鳝农产品供应链有限公司的培苗间，只见蓝色培育箱整齐排布，一条条黄鳝幼苗在箱内缓缓游动。\n\n“等长到筷子长短，这些鳝苗就能分发到农户手里。”先锋村党总支书记朱传宝说，“过去村里黄鳝种苗大多依赖外地引种，养殖成本高。如今本地种苗自给率持续提升，养殖户的投入压力也减轻了。”\n\n黄鳝规模化全人工繁育，长期以来都是世界性难题。仙桃市联合湖北黄鳝产业集团、湖北省农科院组建湖北省黄鳝产业技术研究院，成功攻克这一技术瓶颈，培育的鳝苗存活率明显提升。\n\n![Image 1](http:\u002F\u002Fi.ce.cn\u002Fce\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002FW020260919732410114462_ORIGIN.jpg)\n\n这是9月16日在仙桃市黄鳝农产品供应链有限公司拍摄的黄鳝培苗间。新华社记者鲁金博 摄\n\n与此同时，仙桃市还联合高校、科研院所研发出开口饵料、液氮速冻、自动化卤制等10余项专利技术，覆盖黄鳝养殖、冷链运输、精深加工全链条，撑起完整的本地产业体系。目前，全市黄鳝产业年产值突破120亿元。\n\n仙桃黄鳝产业的蓬勃发展，正是湖北依托农业科技创新，持续壮大特色农业产业的生动缩影。近年来，一大批农业科技成果在荆楚大地不断涌现、落地生根。ARC生物耦合技术破解花生、大豆提质固氮等难题，相关成果在湖北就地转化；科研团队在洪湖围绕煲汤、清炒、凉拌等不同烹饪场景与口感需求，培育出多个莲藕新品种并实现规模化推广种植……\n\n湖北省农业农村厅相关负责人表示，近年来，湖北持续推动农业企业与科研院校深度协同，建成一批产学研合作创新基地，累计形成专利2000余件。这些科技成果，不仅显著提升农业综合生产能力，还帮助农户化解多项风险，让乡村沃土引得来人、留得住人、能够致富。\n\n“小菜小菜，请告诉我5号地块的实时数据。”在嘉鱼县蔬菜科技示范园，湖北省农科院首席科学家邱正明站在田埂上，对着数字大屏发出指令，AI语音助手即刻播报土壤温度、湿度等田间监测数据。\n\n近年来，园区与中国农科院、湖北省农科院等多个科研团队合作，配套建成农业数字化服务站，对蔬菜生产实施全天候监测，实时采集气象、土壤、水肥等农情信息，实现水肥精准调控、病虫害绿色防控以及生产全过程溯源，有效提升田间管理效能。\n\n![Image 2](http:\u002F\u002Fi.ce.cn\u002Fce\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002FW020260919732410241881_ORIGIN.jpg)\n\n这是9月15日在嘉鱼县潘家湾镇拍摄的农业数字化服务站显示屏。新华社记者鲁金博 摄\n\n邱正明介绍，蔬菜生长周期短，抗极端天气能力弱，暴雨内涝、高温干旱、寒潮霜冻都可能导致减产甚至绝收；部分病虫害在田间扩散速度快，若防控不及时，将给农户带来难以挽回的损失。依托这套数智化管护体系，农户能第一时间捕捉田间异常，心里更有底。\n\n有了数智基础，嘉鱼整合农作物生长数据，建成400亩农产品溯源基地，给蔬菜赋予“二维码身份证”，让好生态、好技术、好产品能被看见并验证。如今，嘉鱼蔬菜直供上海、广州、武汉等多地大型商超，还远销日本、俄罗斯及东南亚市场。\n\n“湖北既是科教大省，也是农业大省，具备以科技赋能农业的独特优势。”中国农科院农业经济与发展研究所研究员麻吉亮表示，以种苗技术攻关、新品种研发为引领，湖北大力发展智慧种植与农产品精深加工，走出了一条符合荆楚特色的现代农业发展之路。","中国经济网","2026-09-19T01:00:00Z",84,{"impact":98,"substance":197,"depth":198,"authority":130,"freshness":13,"relevant":21,"comment":245},"央媒调研报道，呈现湖北种苗攻关、数智化管护与溯源基地等实质进展，产业级参考价值较高。",[247],{"name":241,"url":238},[26,27,249,60,250,251],"种业振兴","嘉鱼蔬菜","黄鳝产业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":255},{"candidates":256,"agricultural_evidence":47},[257,261,267,272,278,283,287],{"code":32,"score":46,"supported":47,"title":258,"abstract":259,"tags":260},[],[60],[60],{"code":262,"score":57,"supported":47,"title":263,"abstract":264,"tags":266},"B1",[],[265],"蔬菜",[],{"code":217,"score":57,"supported":47,"title":268,"abstract":269,"tags":271},[],[270],"绿色防控",[],{"code":273,"score":57,"supported":47,"title":274,"abstract":275,"tags":277},"H2",[],[276],"土壤",[],{"code":279,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},"F1",[],[],[249],{"code":52,"score":21,"supported":14,"title":284,"abstract":285,"tags":286},[],[],[27],{"code":73,"score":21,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[26],[292,293],"湖北嘉鱼 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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":17,"substance":347,"depth":198,"authority":348,"freshness":100,"relevant":21,"comment":349},21,9,"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。",[351],{"name":342,"url":343},[27,106,353,354,355],"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":359},{"candidates":360,"agricultural_evidence":47},[361,365,371],{"code":32,"score":20,"supported":47,"title":362,"abstract":363,"tags":364},[],[106,132],[106],{"code":52,"score":20,"supported":47,"title":366,"abstract":367,"tags":370},[],[368,369],"精准农业","precision agriculture",[27],{"code":372,"score":84,"supported":47,"title":373,"abstract":374,"tags":377},"H3",[],[375,376],"灌溉","irrigation",[],[379,380],"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":382,"openalex_id":384,"authors":385,"venue":342,"cited_by_count":81,"oa_url":389,"card":390,"direction":396,"ingested_from":185},"W7221576059",[386],{"name":387,"orcid":388},"Panot Sripakarach","https:\u002F\u002Forcid.org\u002F0000-0002-7861-5406","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Frmutsvrj\u002Farticle\u002Fdownload\u002F271208\u002F182844",{"tldr":391,"method":392,"finding":393,"direction":394,"opportunity":395},"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。","农业绿色发展与碳","2026-10-14T23:31:07.397575Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":8,"content":8,"source_name":403,"source_url":8,"published_at":404,"category":94,"cover_url":8,"hotness":13,"is_selected":14,"score":405,"score_detail":406,"sources":408,"tags":410,"primary_topic":415,"secondary_topics":416,"classification":417,"search_phrases":432,"slug":435,"view_count":21,"doi":436,"paper":437,"created_at":446},4644,"农业产业链可信数据空间:理论框架、运行机制与实施路径(Trusted Data Space for the Agricultural Industry Chain: Theoretical Framework, Operating Mechanism, and Implementation Path)","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202605005","北京工商大学许继平团队基于《可信数据空间技术架构》标准,构建农业产业链可信数据空间框架,涵盖本体-元数据-知识图谱语义互操作、连接器受控交互、隐私计算与区块链存证。依托2023—2025年某市约12万条食品安全抽检与有机农场业务流程开展场景化验证,形成数据可信流转闭环。局限:仅在某市单场景验证,跨域推广效果待评估。","智慧农业(中英文) 2026年第4期 2026-10-09","2026-10-09T00:00:00Z",81,{"impact":97,"substance":98,"depth":119,"authority":99,"freshness":100,"relevant":21,"comment":407},"核心期刊论文，提出农业产业链可信数据空间框架并以约12万条抽检数据验证，方法新颖、数据扎实，但仅单市单场景验证，推广性待观察。",[409],{"name":403,"url":401},[27,411,412,413,414],"区块链","隐私计算","可信数据空间","食品安全追溯",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":418},{"candidates":419,"agricultural_evidence":47},[420,424,428],{"code":32,"score":57,"supported":47,"title":421,"abstract":422,"tags":423},[],[106],[],{"code":52,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[27],{"code":73,"score":21,"supported":14,"title":429,"abstract":430,"tags":431},[],[],[413],[433,434],"农业产业链 可信数据空间","许继平 食品安全抽检","农业产业链可信数据空间-4644","10.12133\u002Fj.smartag.sa202605005",{"doi":436,"openalex_id":8,"authors":438,"venue":8,"cited_by_count":81,"oa_url":8,"card":439,"direction":443,"ingested_from":445},[],{"tldr":440,"method":441,"finding":442,"direction":443,"opportunity":444},"构建农业产业链可信数据空间框架，并在某市食品安全场景验证数据可信流转闭环。","基于可信数据空间标准，融合语义互操作、连接器、隐私计算与区块链，用约12万条抽检","框架可实现农业产业链数据可信流转闭环，但仅单场景验证，跨域推广效果待评估。","数字乡村与农业信息化","可研究跨域、多产业链场景下的可信数据空间推广机制与互操作标准适配问题。","agent","2026-10-11T00:09:47.452615Z"]