[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2984":3,"related-2984":69},{"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":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},2984,"中国农科院基因组所李奎团队发布\"伯乐\"多智能体AI平台 开启基因组智能育种新范式","https:\u002F\u002Fwap.sciencenet.cn\u002Fblog-3618084-1552418.html","中国农科院深圳基因组所李奎教授团队在Journal of Integrative Agriculture发表的BOLE多智能体AI平台，将原本高度依赖专业知识的复杂工程任务简化为直观的自然语言对话，显著降低技术使用门槛同时提升分析结果可重复性。BOLE已上线并免费开放使用同时支持本地化部署。论文第一作者为广东省农科院王子帅与深圳基因组所梁崇霄，研究得到国家重点研发计划、猪禽种业全国重点实验室基金等项目支持。",null,"随着高通量基因型与表型数据的快速积累，基因组育种已进入数据密集型时代，但如何将异构的生物信息学工具有效整合为连贯的分析工作流仍是一个主要瓶颈。现有平台多依赖预定义的静态流程，要求用户具备深厚的计算遗传学与编程能力，限制了方法的实际应用与规模化推广。\n\n近期，中国农业科学院（深圳）农业基因组研究所 联合 广东省农业科学院农业生物基因研究中心、香港科技大学（广州）等单位，成功开发了基于知识驱动的多智能体AI平台——BOLE（伯乐）。该平台以“聊天对话框”式的极简交互，实现了基因组育种分析的端到端自动化。相关研究成果以“BOLE: A script-knowledge driven multi-agent framework for reproducible llm-assisted genomic breeding”为题 在**_Journal of Integrative A_****_gricultur_****_e（《农业科学学报_（英文）》，JIA）**优先在线 发表。\n\n**从“手动工程”到“自然语言对话”**\n\nBOLE的核心创新在于将基因组育种分析从传统的手动工程模式，转变为自然语言驱动的对话式操作。研究团队构建了结构化的脚本知识库，并将核心功能模块集成于统一的“聊天对话框”交互界面。在大语言模型驱动下，系统通过交互智能体、规划智能体、流程组装智能体和代码生成智能体四个专业化智能体的协同配合，自主完成从用户意图解析到最终数据分析的全流程。\n\n![Image 1: 2.png](http:\u002F\u002Fimage.sciencenet.cn\u002Fhome\u002F202609\u002F14\u002F115137p77o5o9n00bo5pon.png)\n\n图1 伯乐平台的多智能体架构\n\n**从GWAS到全基因组选择的一站式解决方案**\n\nBOLE整合了多个符合产业标准的生物信息学工具，覆盖了基因组育种分析的四大核心任务：全基因组关联分析（GWAS）、遗传力估计、种质资源评价和全基因组选择。该平台将原本高度依赖专业知识的复杂工程任务，简化为直观的自然语言对话，显著降低了技术使用门槛，同时提升了分析结果的可重复性，为人工智能技术在农业育种领域的应用探索了全新路径。\n\n目前，BOLE已上线并免费开放使用（[https:\u002F\u002Fbole.zishuailab.com\u002F](https:\u002F\u002Fbole.zishuailab.com\u002F)），同时支持本地化部署，充分保障育种数据的安全性。\n\n![Image 2: 1.png](http:\u002F\u002Fimage.sciencenet.cn\u002Fhome\u002F202609\u002F14\u002F115124le0bibyslbru320w.png)\n\n图2 伯乐核心功能示意图\n\n论文第一作者：王子帅（广东省农业科学院农业生物基因研究中心）、梁崇霄（中国农业科学院深圳基因组所）\n\n通讯作者：李奎教授（中国农业科学院深圳基因组所）\n\n本研究得到国家重点研发计划、猪禽种业全国重点实验室基金、深圳市优秀人才培养基金和广东省基金等项目支持。\n\nCite the article:\n\nZishuai Wang, Chongxiao Liang, Yanlin Zhang, Rong Zhou, Xiaoai Zhang, Wenkang Wei, Kui Li. 2026. BOLE: A script-knowledge driven multi-agent framework for reproducible llm-assisted genomic breeding.Journal of Integrative Agriculture, Doi:10.1016\u002Fj.jia.2026.07.011\n\n[https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jia.2026.07.011](https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jia.2026.07.011)\n\n_Journal of Integrative Agriculture_（《农 业科学学报（英文）》, JIA）由中华人民共和国农业农村部主管，中国农业科学院与中国农学会主办，中国农业科学院农业信息研究所承办。综合性英文学术期刊，月刊。创刊于2002年，现任主编为中国科学院院士陈化兰。JIA主要栏目有作物科学、园艺、植物保护、动物科学、动物医学、农业生态环境、食品科学、农业经济与管理等。刊稿类型有综述、研究论文、简报以及评述等。全部论文在Elsevier-ScienceDirect (SD) 平台OA出版。最新SCI影响因子5.7，位于SCI-JCR农业综合学科Q1区。中国科学院分区农林科学1区。2016年以来先后获得中国科协等部委 “提升计划”“登峰计划”“卓越计划”项目支持。\n\n转载本文请联系原作者获取授权，同时请注明本文来自王宁科学网博客。  \n链接地址： [https:\u002F\u002Fwap.sciencenet.cn\u002Fblog-3618084-1552418.html](https:\u002F\u002Fwap.sciencenet.cn\u002Fblog-3618084-1552418.html)\n\n上一篇：[JIA | 四川农业大学小麦研究所马建教授课题组鉴定并遗传解析一个新的小麦粒长位点](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-3618084-1552417.html)  \n下一篇：[JIA优先上线｜多单位联合综述从“传统棉作”到“智能设计”：中国棉花产业十年的跃迁](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-3618084-1552981.html)\n\n[欢迎参加科学网十佳博文评选活动！](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1520553.html)\n\n主办单位： [![Image 3](https:\u002F\u002Fimage.sciencenet.cn\u002Fhome\u002F202606\u002F03\u002F170346g6h6jzlddzxnvgk1.jpg)](https:\u002F\u002Fwww.sciencenet.cn\u002F)支持单位：[![Image 4](https:\u002F\u002Fimage.sciencenet.cn\u002Fhome\u002F202606\u002F04\u002F150033i1jb7dpbv12pbsbs.jpg)](https:\u002F\u002Fais.cn\u002Fu\u002FQnia6z)","科学网博客","2026-09-18T00:00:00Z","报道",10,true,86,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,22,18,14,8,1,"国家级科研机构发布的多智能体AI育种平台，方法新颖、开放可用，对智慧育种有实质推动，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","种业振兴","多智能体","智能育种","基因组选择",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,52,58],{"code":33,"score":47,"supported":14,"title":48,"abstract":49,"tags":51},4,[],[50],"种业",[28],{"code":53,"score":54,"supported":41,"title":55,"abstract":56,"tags":57},"F2",2,[],[],[31,30],{"code":59,"score":54,"supported":41,"title":60,"abstract":61,"tags":62},"I2",[],[],[27,29],[64,65],"中国农科院基因组所 伯乐 育种平台","BOLE 多智能体 基因组育种","中国农科院基因组所伯乐育种平台-2984",5,"2026-09-20T00:03:03.016540Z",{"total":70,"page":22,"page_size":70,"items":71},6,[72,131,175,225,270,325],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":8,"content":77,"source_name":78,"source_url":8,"published_at":79,"category":12,"cover_url":8,"hotness":13,"is_selected":41,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":91,"secondary_topics":97,"classification":99,"search_phrases":126,"slug":129,"view_count":113,"doi":8,"paper":8,"created_at":130},2690,"第十八届中国国际种业博览会聚焦智能育种——中种集团发布水稻AI育种家1.0智能体","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7684583875766452772\u002F","第十八届中国国际种业博览会暨第二十三届全国种子信息交流与产品交易会在合肥举行。中国工程院院士赵春江、玉米产业技术体系首席科学家李新海等表示,智能育种通过整合基因组学、人工智能和大数据提高育种精准度。中种集团发布水稻AI育种家1.0智能体,实现从经验驱动向数据驱动智能精准设计的范式转变。","## 第十八届中国国际种业博览会合肥举行 聚焦智能育种\n\n2026-09-12 17:50·[中国新闻网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAvOdgu_VhRab2uUfrYPedveCA9KSEdd4AfYLyl9vkh2s\u002F?source=tuwen_detail)\n\n中新社合肥9月12日电 (记者 储玮玮)第十八届中国国际种业博览会暨第二十三届全国种子信息交流与产品交易会12日在安徽合肥举行。多位院士专家在会上表示，智能育种技术通过整合基因组学、人工智能(AI)和大数据分析等前沿科技提高育种效率和精准度。\n\n中国工程院院士赵春江表示，智能设计育种是人类对生命科学认知提升和作物驯化能力提升的标志，在蛋白设计与全基因组选择等众多领域需要AI技术支撑。\n\n赵春江认为，智能设计的品种仍然离不开大量的田间鉴定筛选。而在测试品种的环境响应与胁迫应答能力方面，同样离不开AI技术进行数据分析与表型预测。\n\n中国农业科学院科技管理局局长、国家玉米产业技术体系首席科学家李新海说，全球种业市场加速向头部企业集中，全球品种培育呈现出耐逆广适、营养优质、绿色低碳三大趋势。\n\n企业界的探索也在加速落地。当日，中国种子集团有限公司发布“水稻AI育种家1.0”智能体，将育种中关键方法模型化，通过AI算法实现从“经验驱动的田间目测筛选”向“数据驱动的智能精准设计”的范式转变。\n\n袁隆平农业高科技股份有限公司亚华水稻事业部副总经理史勇敢说，公司依托分子辅助育种与AI设计育种实现工厂化流水式高效选育。而在推广端，农户通过小程序即可获取种植信息、田间监控及预警服务，以智能服务替代部分人工现场指导，大幅提升服务效率。\n\n与会专家普遍认为，国际作物智能育种技术正朝着多学科交叉融合的方向发展。(完)","中国新闻网 2026年9月12日","2026-09-12T00:00:00Z",82,{"impact":82,"substance":83,"depth":84,"authority":20,"freshness":70,"relevant":22,"comment":85},26,20,16,"全国性种业博览会叠加中种集团水稻AI育种家1.0发布，院士与企业多方信源支撑，智能育种范式转变信息增量足，值得进入每日精选。",[87],{"name":78,"url":75},[27,28,30,89,90],"水稻育种","AI育种家",{"code":92,"slug":93,"name":94,"path":95,"parent_code":96},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[98],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":100},{"candidates":101,"agricultural_evidence":14},[102,107,111,118,122],{"code":92,"score":103,"supported":14,"title":104,"abstract":105,"tags":106},12,[94],[94],[89],{"code":33,"score":103,"supported":14,"title":108,"abstract":109,"tags":110},[50],[50],[28],{"code":112,"score":113,"supported":14,"title":114,"abstract":115,"tags":117},"A3",3,[],[116],"玉米",[],{"code":53,"score":22,"supported":41,"title":119,"abstract":120,"tags":121},[],[],[30],{"code":59,"score":22,"supported":41,"title":123,"abstract":124,"tags":125},[],[],[27],[127,128],"农业人工智能 智能育种 水稻育种 种业振兴","农业人工智能 智能育种","农业人工智能智能育种水稻育种种业振兴-2690","2026-09-17T00:04:35.208458Z",{"id":132,"title":133,"url":134,"summary":135,"summary_zh":8,"content":8,"source_name":136,"source_url":8,"published_at":137,"category":12,"cover_url":8,"hotness":13,"is_selected":41,"score":138,"score_detail":139,"sources":142,"tags":144,"primary_topic":147,"secondary_topics":148,"classification":149,"search_phrases":170,"slug":173,"view_count":113,"doi":8,"paper":8,"created_at":174},2258,"北京大学种业大会发布\"神农育种Studio\"一体化智能育种工作台","https:\u002F\u002Fwww.gskjb.cn\u002Fgskjb\u002F20260907\u002Fhtml\u002Fcontent_20260907003001.htm","9月4日,\"神农育种Studio\"正式发布,数据底座汇集超1000万条农业知识图谱数据、超5000万条农业生产数据。北京将依托农业中关村核心区打造北京农业中关村智能育种创新基地,计划建设智能育种共性技术创新中心和装备创新中心。","甘肃科技报 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农业中关村","农业人工智能农业中关村智慧农业智能育种-2258","2026-09-13T00:04:02.973675Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":8,"content":180,"source_name":181,"source_url":8,"published_at":182,"category":12,"cover_url":8,"hotness":13,"is_selected":41,"score":183,"score_detail":184,"sources":188,"tags":190,"primary_topic":192,"secondary_topics":193,"classification":194,"search_phrases":220,"slug":223,"view_count":22,"doi":8,"paper":8,"created_at":224},1841,"给育种装上智慧大脑——北京以数智引领种业未来（神农育种Studio正式发布）","https:\u002F\u002Fwww.agri.cn\u002Fzt\u002Fzhzz\u002Fzxdt\u002F202609\u002Ft20260904_8869248.htm","9月4日，神农育种Studio正式发布，把分散研究环节自动串联。以设计一套小麦育种方案为例，过去需投入10人月，借助Studio可降至0.5人月。神农大模型数据底座已汇集超过1000万条农业知识图谱数据、超5000万条农业生产数据；北京市将依托农业中关村核心区打造智能育种创新基地，建设智能育种共性技术创新中心和智能育种共性装备创新中心。","田野里，无人车沿玉米行前行，机器一边“看”，算法一边“算”；鸡舍里，种鸡的产蛋、体重等数据不断汇入系统，基因芯片与算法共同寻找更有潜力的个体；实验室里，育种人员用自然语言提出目标，人工智能开始检索资料、调用模型、生成方案。\n\n这些正在北京发生的新场景，勾勒出同一个方向：育种正在从主要依靠经验，走向数据与经验结合、人脑与机器协同。\n\n9月4日至5日，第三十三届中国北京种业大会将在北京举行。大会以“AI赋能智慧育种·数智引领种业未来”为主题，首次以智慧育种为主线。人工智能怎样进入田间、鸡舍和实验室？它究竟改变了育种的哪一步？答案藏在正在采集的数据、实际的选种过程和不断落地的技术中。\n\n**机器“看”玉米**\n\n在北京通州区于家务乡的通州国际种业科技园区基地，20亩试验田里的约7万株玉米，正在接受一场“全周期体检”。环绕式无人车拍下植株的颜色、纹理和株型，激光雷达勾勒三维结构，多光谱和热成像设备捕捉长势与温度变化。一台不足半米宽的机器人则钻入冠层之下，从另一个视角补上从上方看不到的信息，近距离记录茎秆粗细、植株疏密和吐丝进度。\n\n从6叶期到9叶期，再到大喇叭口期和吐丝期，设备目前已完成4个阶段的数据采集，后续还将跟踪至成熟期。一次作业由两人操作，每小时可完成约4000株的数据采集。1000株以内的小批量数据，4至6小时可以形成结果，全区域数据可在24小时内完成解析和质控。在海南崖州南繁基地，同类装备还与无人机协同作业，在240亩试验田完成了超过36万株玉米的全生育期数据采集。\n\n交到育种人员手中的，不只是田间照片，还有性状数据表、三维点云模型、品系对比图表和单株表型档案。阶段性数据已能初步区分不同品系的株高增长速度、叶夹角和冠层透光性差异。团队随机抽取100株玉米，以人工测量的株高作对照，机器测量结果的一致性超过95%，误差低于5厘米。\n\n“过去依靠人工作业和经验判断，现在通过机器标准化采集、算法自动化处理，获取的指标更多、维度更多，精度也更高。”北京市农林科学院信息技术研究中心研究员郭新宇介绍，表型是基因型与环境互作的结果，只有把表型数据与基因组数据结合起来，才能更好地认识不同基因型在真实田间究竟会有怎样的表现。\n\n表型大数据工厂所做的，就是把作物真实的“长相”和“长势”变成AI可以学习的语言，帮助育种人员判断哪些材料在耐密、抗倒伏和高产方面更具潜力。\n\n**算法“评”种鸡**\n\n一只尚未产蛋的雏鸡，能不能提前判断它将来产多少蛋、蛋重多大、蛋壳结不结实？在北京市华都峪口禽业有限责任公司，一管血可以帮助育种人员更早读出候选种鸡的遗传潜力。\n\n研发人员闫奕源把选种比作选学生：传统方法要等母鸡产蛋，取得产蛋数、蛋重、蛋品质等“考试成绩”后才能判断。有了基因数据和预测模型，则可以在候选种鸡尚无自身生产记录时，提前评价其遗传潜能。\n\n雏鸡出生后采集血液、提取DNA，再用蛋鸡育种芯片检测基因变异位点。亲本和群体的历史数据中，既有基因型，也有产蛋数、蛋重、体重、蛋壳颜色和强度等表型数据。模型从两类数据的对应关系中学习，再对新一批候选种鸡逐只预测，形成涵盖不同性状的“成绩单”和综合排名。\n\n在这套体系中，芯片负责检测基因变异，算法模型则接过大规模比较、遗传潜能预测和排序等工作，帮助育种人员更早看出哪些个体更有育种潜力。“选得准，才能更快实现育种目标。”闫奕源说。\n\n目前，“京粉6号”已在百万只规模养殖条件下稳定实现“700天产500枚蛋”，相关成果获得2025年度国家科学技术进步奖二等奖。接续这一成熟品种，本届大会将发布“京红2号”“京粉8号”“沃德178”“京翠绿壳”等自主研发蛋（肉）鸡新品种，呈现“京字号”种鸡持续迭代的新成果。\n\n**平台“串”流程**\n\n如果说田间表型工厂解决的是“数据从哪里来”，种鸡精准育种展示的是“算法怎样辅助选择”，那么即将在本届大会发布的“神农育种Studio”，要解决的是分散的数据、模型和软件怎样被育种人员更方便地用起来。\n\n过去，育种人员常常需要在数据库、表格和多种软件间反复切换，有些环节还要请信息技术人员编写程序。现在，育种人员用自然语言提出目标，Studio先识别任务所需的数据、模型和软件，再按需调用相应工具，完成资料检索、亲本筛选、品系评价、多环境试验分析等任务。各环节自动衔接，前一步的结果成为下一步的输入，最后汇成附有资料来源的报告。\n\n支撑平台运行的，是团队多年积累的农业知识、生产数据和育种模型。神农大模型的数据底座已汇集超过1000万条农业知识图谱数据、超过5000万条农业生产数据。在北京市怀柔区，团队借助平台从数百个生菜品种中海选出“雁栖1号”“雁栖2号”。中国农业大学副教授王耀君介绍，过去一个蔬菜新品种往往要五六年才能培育出来，借助海量数据分析，可将时间缩短至两三年。\n\n9月4日，“神农育种Studio”将正式发布。Studio把原本分散的研究环节自动串联起来，效率变化随之显现。以设计一套小麦育种方案为例，王耀君介绍，过去需要投入10人月——即1个人工作10个月，或10个人工作1个月；在育种人员把目标和逻辑梳理清楚后，借助Studio可降至0.5人月。资料检索、数据处理、模型计算和初步方案生成等原本分散的工作，可由平台连续完成。\n\n从机器进田采数据，到算法辅助选材料，再到大模型串起研究环节，3种场景勾勒出智能育种的一条数据链路，也对算力、数据、装备和公共服务提出新要求。\n\n北京市农业农村局有关负责人介绍，北京将依托农业中关村核心区，打造北京农业中关村智能育种创新基地，计划建设智能育种共性技术创新中心和智能育种共性装备创新中心，提升育种专用算力、数据治理、装备研发中试、基因检测、标准规范和成果转化等共性条件；面向作物、畜禽、水产、微生物打造四类智能育种测试平台与产业应用场景。目前，作物逆境表型鉴定平台、作物智能分子育种创新平台、畜禽智能育种技术共性平台等重大平台正在建设中。\n\n育种仍是一场以年为单位的长跑，人工智能不能让种子跳过生长季，却能接过大量重复、耗时的前期工作。它可以把田间观察变成更完整的数据，把海量候选材料排出次序，把分散的研究环节自动串联起来，让每一轮选择少花一些时间、少走一些弯路。当机器真正读懂田间，数据、算法与育种经验形成合力，一粒好种子走向生产的脚步，便能迈得更快、更稳。（农民日报全媒体记者 芦晓春 赵天浩）","农民日报全媒体\u002F中国农业农村信息网 2026-09-04","2026-09-04T00:00:00Z",84,{"impact":185,"substance":18,"depth":19,"authority":186,"freshness":70,"relevant":22,"comment":187},25,13,"北京种业大会聚焦AI赋能智慧育种，报道详实，展示表型工厂、基因芯片与神农育种Studio等实际应用，具有产业引领价值。",[189],{"name":181,"url":178},[145,27,28,191,30],"神农大模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":195},{"candidates":196,"agricultural_evidence":14},[197,201,206,212,216],{"code":33,"score":153,"supported":14,"title":198,"abstract":199,"tags":200},[50],[],[28],{"code":59,"score":67,"supported":14,"title":202,"abstract":203,"tags":205},[],[204],"大模型",[27,191],{"code":207,"score":113,"supported":14,"title":208,"abstract":209,"tags":211},"A2",[],[210],"小麦",[],{"code":53,"score":22,"supported":41,"title":213,"abstract":214,"tags":215},[],[],[30],{"code":162,"score":22,"supported":41,"title":217,"abstract":218,"tags":219},[],[],[145],[221,222],"农业人工智能 神农大模型 智慧农业 智能育种","农业人工智能 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