[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4944":3,"related-4944":84},{"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":79,"slug":82,"view_count":21,"doi":8,"paper":8,"created_at":83},4944,"山东农科院联合多家机构发布植物抗逆AI智能体与盐稻芯1号育种芯片","http:\u002F\u002Fgzylhyzx.com\u002Fdetail.html?newsId=58110969","盐碱地综合利用国家技术创新中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所,发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片(盐稻芯1号)。植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境,以水稻等6个物种为研究核心,整合全球逆境文献与多源科研数据。盐稻芯1号以耐盐多组学证据定向选择位点,较全基因组重测序成本仅为其三分之一。",null,"### 两大科研成果重磅发布！盐碱地综合利用国家技术创新中心解锁抗逆育种智能新范式\n\n2026-10-15 04:44:38\n\n**大众网记者 唐梦琳 东营报道**\n\n9月19日，盐碱地综合利用国家技术创新中心（以下简称国家盐碱地中心）在总部举办新闻发布会，正式发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）两项重大科研成果。发布会由中心综合事业部部长石东毅主持，中心副主任杨长军、耐盐碱植物微生物基因挖掘创新团队首席专家商连光发布。会上，杨长军介绍了成果开发背景与意义，商连光发布两项最新研究成果，并回答记者提问。\n\n![Image 1](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F09\u002F19\u002F792a00e9232c00fdcf438be95889fed0.png)\n\n据介绍，两项成果由国家盐碱地中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所共同研发，以人工智能与分子育种技术深度融合，破解作物抗逆选育难题，为盐碱地综合利用、种业振兴和国家粮食安全注入强劲科技动能。\n\n植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境，以水稻等6个物种为研究核心，整合全球逆境文献与多源科研数据，依托6个AI 智能体分工完成资料检索、数据分析、交叉复核、综合研判，能够持续迭代学习科研成果，实现从单一胁迫研究向多逆境耦合研究范式升级。植物抗逆AI智能体可智能挖掘抗逆关键基因、显著压缩分子育种研发周期，作物抗逆上研究性能优于通用科研大模型，并将面向全球科研用户开放共享。\n\n与通用芯片按基因组位置均匀布点不同，耐盐水稻11K液相育种芯片（盐稻芯1号）以耐盐多组学证据定向选择位点，改变了耐盐水稻从生育期表型鉴定向苗期基因型选择的育种变革模式。相较全基因组重测序，该芯片成本仅为其三分之一，核心耐盐表型上，预测精度较同类芯片明显提升，育种科研人员可在苗期完成大规模群体耐盐筛选，为解决水稻耐盐研究中生育期和土地条件制约等问题提供了新的途径。\n\n两项成果取得了五个主要显著进展：一是推动研究范式由单一胁迫走向多逆境耦合；二是育种模式由经验筛选转向数据驱动精准选育；三是科研组织由分散探索升级为 AI 智能协同研究；四是芯片标记由全基因组铺点升级为多组学定向锚定；五是利用芯片将育种选择由大田表型鉴定前移至苗期基因型筛选。\n\n下一步，国家盐碱地中心将持续迭代升级植物抗逆AI智能体，丰富多物种、多场景科研数据集，优化算法模型；打通基础研究到育种应用链条，加速抗逆基因挖掘、种质创制和新品种选育；搭建全球逆境科研协同创新网络，深化与FAO、INSAS等国际组织交流合作，汇聚各方科研力量，联合破解植物逆境响应、气候适应性育种、边际土地高效利用等重大科学问题。\n\n发布会现场还介绍了国家盐碱地中心平台建设、人才集聚、科研攻关、成果转化等方面最新建设成效。\n\n  \n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。\n本文来源：海报新闻\n\n查看更多","大众网 每日热点 2026 年 9 月 19 日(10 月 15 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12:29·[大众网东营](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAWwTcI7rcmoH8YQLif34dXgBEonxq4KbS8is4cb7jJBY\u002F?source=tuwen_detail)\n\n**大众网记者 唐梦琳 东营报道**\n\n9月19日，盐碱地综合利用国家技术创新中心（以下简称国家盐碱地中心）在总部举办新闻发布会，正式发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）两项重大科研成果。发布会由中心综合事业部部长石东毅主持，中心副主任杨长军、耐盐碱植物微生物基因挖掘创新团队首席专家商连光发布。会上，杨长军介绍了成果开发背景与意义，商连光发布两项最新研究成果，并回答记者提问。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F12aca97dece34ce8b4144483f96daca7~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790467387&x-signature=MUmUvD%2B1WCDEzZ59gn8Ck46C%2F6U%3D)\n据介绍，两项成果由国家盐碱地中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所共同研发，以人工智能与分子育种技术深度融合，破解作物抗逆选育难题，为盐碱地综合利用、种业振兴和国家粮食安全注入强劲科技动能。\n\n植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境，以水稻等6个物种为研究核心，整合全球逆境文献与多源科研数据，依托6个AI 智能体分工完成资料检索、数据分析、交叉复核、综合研判，能够持续迭代学习科研成果，实现从单一胁迫研究向多逆境耦合研究范式升级。植物抗逆AI智能体可智能挖掘抗逆关键基因、显著压缩分子育种研发周期，作物抗逆上研究性能优于通用科研大模型，并将面向全球科研用户开放共享。\n\n与通用芯片按基因组位置均匀布点不同，耐盐水稻11K液相育种芯片（盐稻芯1号）以耐盐多组学证据定向选择位点，改变了耐盐水稻从生育期表型鉴定向苗期基因型选择的育种变革模式。相较全基因组重测序，该芯片成本仅为其三分之一，核心耐盐表型上，预测精度较同类芯片明显提升，育种科研人员可在苗期完成大规模群体耐盐筛选，为解决水稻耐盐研究中生育期和土地条件制约等问题提供了新的途径。\n\n两项成果取得了五个主要显著进展：一是推动研究范式由单一胁迫走向多逆境耦合；二是育种模式由经验筛选转向数据驱动精准选育；三是科研组织由分散探索升级为 AI 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盐碱地综合利用国家技术创新中心召开新闻发布会，正式发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）两项重大科研成果。植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境，以水稻等6个物种为研究核心，整合全球逆境文献与多源科研数据，依托6个AI智能体分工完成资料检索、数据分析、交叉复核、综合研判。耐盐水稻11K液相育种芯片以耐盐多组学证据定向选择位点，相较全基因组重测序成本仅为其三分之一。两项成果由国家盐碱地中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所共同研发。","东营市农业农村局","2026-09-20T00:00:00Z",{"impact":97,"substance":98,"depth":19,"authority":151,"freshness":152,"relevant":22,"comment":153},14,9,"国家级科研机构发布全球首个植物抗逆AI智能体与国内首个耐盐水稻育种芯片，属种业与农业AI领域重大突破，信息增量与权威性俱佳，值得进入每日精选。",[155],{"name":148,"url":146},[27,29,30,157,31],"盐碱地治理",{"code":52,"slug":104,"name":56,"path":105,"parent_code":106},[160],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":162},{"candidates":163,"agricultural_evidence":46},[164,168,172,176,180,184],{"code":33,"score":113,"supported":46,"title":165,"abstract":166,"tags":167},[49],[49],[157],{"code":52,"score":118,"supported":46,"title":169,"abstract":170,"tags":171},[56],[56],[],{"code":59,"score":53,"supported":46,"title":173,"abstract":174,"tags":175},[],[62],[],{"code":65,"score":22,"supported":14,"title":177,"abstract":178,"tags":179},[],[],[29],{"code":70,"score":22,"supported":14,"title":181,"abstract":182,"tags":183},[],[],[30],{"code":75,"score":22,"supported":14,"title":185,"abstract":186,"tags":187},[],[],[27],[139,189],"盐稻芯1号 耐盐水稻育种芯片","盐碱地国家技术创新中心植物抗逆AI智能体-3219",7,"2026-09-23T00:04:29.516454Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":8,"content":198,"source_name":199,"source_url":8,"published_at":8,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":204,"tags":206,"primary_topic":207,"secondary_topics":208,"classification":209,"search_phrases":236,"slug":239,"view_count":240,"doi":8,"paper":8,"created_at":241},5341,"中国农科院联合发布植物抗逆 AI 智能体与盐稻芯 1 号耐盐水稻 11K 液相育种芯片","https:\u002F\u002Fwww.md2we.com\u002Fitems\u002F4944","国家盐碱地中心发布植物抗逆 AI 智能体与耐盐水稻 11K 液相育种芯片(盐稻芯 1 号)。植物抗逆 AI 智能体聚焦盐碱、干旱、高温、低温四大非生物逆境,以水稻等 6 个物种为研究核心,整合全球逆境文献与多源科研数据,依托 6 个 AI 智能体分工完成资料检索、数据分析、交叉复核、综合研判。盐稻芯 1 号以耐盐多组学证据定向选择位点,改变了耐盐水稻从生育期表型鉴定向苗期基因型选择的育种变革模式,相较全基因组重测序,该芯片成本仅为其三分之一。","### 两大科研成果重磅发布！盐碱地综合利用国家技术创新中心解锁抗逆育种智能新范式\n\n2026-10-15 04:44:38\n\n**大众网记者 唐梦琳 东营报道**\n\n9月19日，盐碱地综合利用国家技术创新中心（以下简称国家盐碱地中心）在总部举办新闻发布会，正式发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）两项重大科研成果。发布会由中心综合事业部部长石东毅主持，中心副主任杨长军、耐盐碱植物微生物基因挖掘创新团队首席专家商连光发布。会上，杨长军介绍了成果开发背景与意义，商连光发布两项最新研究成果，并回答记者提问。\n\n![Image 1: Image 1](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fdzngimage.dzwww.com%2F2026%2F09%2F19%2F792a00e9232c00fdcf438be95889fed0.png)\n\n据介绍，两项成果由国家盐碱地中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所共同研发，以人工智能与分子育种技术深度融合，破解作物抗逆选育难题，为盐碱地综合利用、种业振兴和国家粮食安全注入强劲科技动能。\n\n植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境，以水稻等6个物种为研究核心，整合全球逆境文献与多源科研数据，依托6个AI 智能体分工完成资料检索、数据分析、交叉复核、综合研判，能够持续迭代学习科研成果，实现从单一胁迫研究向多逆境耦合研究范式升级。植物抗逆AI智能体可智能挖掘抗逆关键基因、显著压缩分子育种研发周期，作物抗逆上研究性能优于通用科研大模型，并将面向全球科研用户开放共享。\n\n与通用芯片按基因组位置均匀布点不同，耐盐水稻11K液相育种芯片（盐稻芯1号）以耐盐多组学证据定向选择位点，改变了耐盐水稻从生育期表型鉴定向苗期基因型选择的育种变革模式。相较全基因组重测序，该芯片成本仅为其三分之一，核心耐盐表型上，预测精度较同类芯片明显提升，育种科研人员可在苗期完成大规模群体耐盐筛选，为解决水稻耐盐研究中生育期和土地条件制约等问题提供了新的途径。\n\n两项成果取得了五个主要显著进展：一是推动研究范式由单一胁迫走向多逆境耦合；二是育种模式由经验筛选转向数据驱动精准选育；三是科研组织由分散探索升级为 AI 智能协同研究；四是芯片标记由全基因组铺点升级为多组学定向锚定；五是利用芯片将育种选择由大田表型鉴定前移至苗期基因型筛选。\n\n下一步，国家盐碱地中心将持续迭代升级植物抗逆AI智能体，丰富多物种、多场景科研数据集，优化算法模型；打通基础研究到育种应用链条，加速抗逆基因挖掘、种质创制和新品种选育；搭建全球逆境科研协同创新网络，深化与FAO、INSAS等国际组织交流合作，汇聚各方科研力量，联合破解植物逆境响应、气候适应性育种、边际土地高效利用等重大科学问题。\n\n发布会现场还介绍了国家盐碱地中心平台建设、人才集聚、科研攻关、成果转化等方面最新建设成效。\n\n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。  \n 本文来源：海报新闻\n\n查看更多","AgriHot 转发 2026-10",83,{"impact":97,"substance":18,"depth":202,"authority":20,"freshness":53,"relevant":22,"comment":203},17,"全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片同期发布，属种业与农业AI领域的重大突破，但发布已逾一月，时效性偏弱。",[205],{"name":199,"url":196},[27,28,29,30,31],{"code":52,"slug":104,"name":56,"path":105,"parent_code":106},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":210},{"candidates":211,"agricultural_evidence":46},[212,216,220,224,228,232],{"code":52,"score":118,"supported":46,"title":213,"abstract":214,"tags":215},[56],[56],[],{"code":33,"score":21,"supported":46,"title":217,"abstract":218,"tags":219},[],[49],[28],{"code":59,"score":53,"supported":46,"title":221,"abstract":222,"tags":223},[],[62],[],{"code":65,"score":22,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[29],{"code":70,"score":22,"supported":14,"title":229,"abstract":230,"tags":231},[],[],[30],{"code":75,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[27],[237,238],"盐稻芯1号 耐盐水稻","农业人工智能 盐碱地利用 种业振兴 耐盐水稻","盐稻芯1号耐盐水稻-5341",0,"2026-10-20T02:47:43.721822Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":8,"content":247,"source_name":248,"source_url":8,"published_at":249,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":250,"score_detail":251,"sources":256,"tags":258,"primary_topic":262,"secondary_topics":267,"classification":269,"search_phrases":287,"slug":290,"view_count":240,"doi":8,"paper":8,"created_at":291},5045,"山东农科院联合育种团队选育耐盐碱耐密植大豆新品种 在河口盐碱地亩产近300公斤","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7694921927810564648","山东省重点研发计划(农业良种工程)\"耐盐碱耐密植大豆新品种选育\"项目在河口区完成田间现场测产。该项目由渤海圣丰种业牵头,联合山东农业大学、青岛农业大学、山东省农业科学院作物研究所、德州市农业科学院组建跨单位育种团队。在土壤含盐量3‰~5‰的盐碱大豆试验基地,8个参试品系群体密度突破2万株\u002F亩,青农豆密2308亩产297.86公斤、德豆10号289.60公斤。","**爱东营讯** 近日，山东省重点研发计划（农业良种工程）“耐盐碱耐密植大豆新品种选育”项目在河口区完成田间现场测产评估。该项目由渤海圣丰种业科技有限公司牵头实施，联合山东农业大学、青岛农业大学、山东省农业科学院作物研究所、德州市农业科学院组建跨单位育种创新团队，由多名正高级大豆育种专家，对自主选育的8个大豆新品种（系）开展田间产量鉴定。\n\n在土壤含盐量3‰—5‰的河口盐碱大豆试验示范基地，系统评价各参试品系在盐碱胁迫环境下的农艺性状、群体结构与产能潜力。经理论测产核算，全部参试品系群体密度突破2万株\u002F亩，部分品系单株平均粒数超70粒。其中，青农豆密2308综合抗逆与产量性能最优，亩株数达21470.85株，理论亩产297.86公斤；德豆10号表现突出，亩株数20557.20株，理论亩产289.60公斤；其他多个品系也均表现优异。专家组现场研判，本次参试的多个大豆新品系实现耐盐、耐密、高产性状协同聚合。\n\n本次田间测产验收，标志着该省级农业良种攻关项目取得关键性阶段性突破。这批筛选创制的耐盐碱、耐密植、高产稳产大豆新品种（系），可为全省乃至全国盐碱耕地大豆产能提升提供核心种质支撑，对降低我国大豆对外依存度、筑牢种业种源安全屏障具有重要战略价值。\n\n下一步，河口区将持续做好良种攻关项目全流程技术服务，推动各参与单位深化产学研协同创新，加速优良品系多点试验、示范与产业化推广，推动育种科研成果高效转化为盐碱地农业现实生产力。\n\n（记者 李萍 通讯员 施国军）","东营市新媒体中心 爱东营讯 2026年10月10日","2026-10-10T06:27:00Z",71,{"impact":252,"substance":252,"depth":253,"authority":85,"freshness":254,"relevant":22,"comment":255},22,16,5,"省级良种攻关项目在盐碱地实现耐盐耐密高产协同突破，数据具体、战略价值明确，值得进入每日精选。",[257],{"name":248,"url":245},[28,29,259,260,261],"产学研协同","大豆育种","耐盐碱大豆",{"code":263,"slug":264,"name":265,"path":266,"parent_code":106},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[268],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":270},{"candidates":271,"agricultural_evidence":46},[272,277,282],{"code":263,"score":113,"supported":46,"title":273,"abstract":275,"tags":276},[274],"大豆",[274],[260],{"code":33,"score":113,"supported":46,"title":278,"abstract":279,"tags":281},[49],[280],"土壤",[28],{"code":65,"score":21,"supported":46,"title":283,"abstract":284,"tags":286},[],[285],"种业",[29],[288,289],"山东农科院 耐盐碱大豆 河口","青农豆密2308 德豆10号 测产","山东农科院耐盐碱大豆河口-5045","2026-10-16T00:13:11.498967Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":8,"source_name":298,"source_url":295,"published_at":299,"category":300,"cover_url":8,"hotness":13,"is_selected":46,"score":200,"score_detail":301,"sources":305,"tags":307,"primary_topic":312,"secondary_topics":313,"classification":314,"search_phrases":386,"slug":389,"view_count":240,"doi":390,"paper":391,"created_at":422},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z","论文",{"impact":252,"substance":302,"depth":19,"authority":151,"freshness":303,"relevant":22,"comment":304},21,8,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[306],{"name":298,"url":295},[308,27,29,309,310,311],"智慧农业","精准农业","土壤健康","多组学",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":315},{"candidates":316,"agricultural_evidence":46},[317,325,329,333,340,347,354,360,367,372,378,382],{"code":318,"score":319,"supported":46,"title":320,"abstract":322,"tags":324},"I1",19,[321],"precision agriculture",[309,323,321],"数字孪生",[308,309],{"code":33,"score":191,"supported":46,"title":326,"abstract":327,"tags":328},[],[280,34],[310],{"code":52,"score":85,"supported":46,"title":330,"abstract":331,"tags":332},[],[56,104],[],{"code":334,"score":85,"supported":46,"title":335,"abstract":336,"tags":339},"A2",[],[337,338],"小麦","wheat",[],{"code":341,"score":85,"supported":46,"title":342,"abstract":343,"tags":346},"A3",[],[344,345],"玉米","maize",[],{"code":348,"score":85,"supported":46,"title":349,"abstract":350,"tags":353},"A8",[],[351,352],"粮食安全","food security",[],{"code":59,"score":85,"supported":46,"title":355,"abstract":356,"tags":359},[],[357,358],"气候变化","climate change",[],{"code":361,"score":85,"supported":46,"title":362,"abstract":363,"tags":366},"H8",[],[364,365],"可持续农业","sustainable agriculture",[],{"code":75,"score":21,"supported":46,"title":368,"abstract":369,"tags":371},[],[370],"artificial intelligence",[27],{"code":373,"score":53,"supported":46,"title":374,"abstract":375,"tags":377},"F3",[],[376],"肥料",[],{"code":65,"score":22,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[29],{"code":70,"score":22,"supported":14,"title":383,"abstract":384,"tags":385},[],[],[311],[387,388],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":390,"openalex_id":392,"authors":393,"venue":298,"cited_by_count":240,"oa_url":295,"card":414,"direction":420,"ingested_from":421},"W7221567543",[394,397,399,402,404,406,409,412],{"name":395,"orcid":396},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":398,"orcid":8},"Wang Qingxia",{"name":400,"orcid":401},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":403,"orcid":8},"Shuzhen Ye",{"name":405,"orcid":8},"Zhijuan Ji",{"name":407,"orcid":408},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":410,"orcid":411},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":413,"orcid":8},"Younan Ouyang",{"tldr":415,"method":416,"finding":417,"direction":418,"opportunity":419},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","农业人工智能与决策模型","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","openalex","2026-10-13T23:30:29.504594Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":8,"source_name":429,"source_url":426,"published_at":430,"category":300,"cover_url":8,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":441,"secondary_topics":442,"classification":448,"search_phrases":463,"slug":466,"view_count":240,"doi":467,"paper":468,"created_at":482},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":303,"substance":433,"depth":202,"authority":152,"freshness":13,"relevant":22,"comment":434},20,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[436],{"name":429,"url":426},[308,27,438,439,440],"主动学习","线虫检测","土壤采样",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[443],{"code":75,"slug":444,"name":445,"path":446,"parent_code":447},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":449},{"candidates":450,"agricultural_evidence":46},[451,455,459],{"code":33,"score":151,"supported":46,"title":452,"abstract":453,"tags":454},[34],[280,34],[],{"code":75,"score":152,"supported":46,"title":456,"abstract":457,"tags":458},[370],[],[27],{"code":318,"score":191,"supported":46,"title":460,"abstract":461,"tags":462},[],[309,321],[308],[464,465],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":467,"openalex_id":469,"authors":470,"venue":429,"cited_by_count":240,"oa_url":426,"card":477,"direction":418,"ingested_from":421},"W7223082195",[471,473,475],{"name":472,"orcid":8},"Amine Ben Salah",{"name":474,"orcid":8},"Ines Trabelsi",{"name":476,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":478,"method":479,"finding":480,"direction":418,"opportunity":481},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z"]