[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-339":3,"related-339":66},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":32,"classification":33,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":8,"created_at":65},339,"农业智能机器人发展范式转变——从\"以机适种\"到\"种机协同\"","https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350","Tinghao ZHANG、Yue XIE、Mengze SUN、Cao XU在《中国农业科技导报》2026年第28卷第6期发表文章。提出农业智能机器人发展正经历从\"以机适种\"(Robot-to-crop Adaptation)到\"种机协同\"(Crop-robot Synergy)的范式转变,首次系统阐述BAR(Bio-inspired Agricultural Robot)模式:以番茄智能育种机器人\"GEAIR\"为代表,将作物花器官改造与机器人授粉深度结合,实现\"花-机-智\"协同;并构建智能育种工厂全流程示意图,推动农业由单一作物性状改良转向作物与机器人的系统化协同设计。该模式有望成为农业新质生产力形成的重要技术方向,为未来智能农业体系构建提供理论参考。",null,"![Image 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Intelligent Robot Development\n\nTinghao ZHANG 1,2,3([![Image 12](https:\u002F\u002Fnkdb.magtechjournal.com\u002Fimages\u002Femail.png)](mailto:thzhang@genetics.ac.cn)), Yue XIE 1,2,3, Mengze SUN 1,2, Cao XU 1,2,3([![Image 13](https:\u002F\u002Fnkdb.magtechjournal.com\u002Fimages\u002Femail.png)](mailto:caoxu@genetics.ac.cn))\n\n1.   1.State Key Laboratory of Seed Innovation，Institute of Genetics and Developmental Biology，Chinese Academy of Sciences，Beijing 100101，China\n\n2.CAS-JIC Centre of Excellence for Plant and Microbial Science，Institute of Genetics and Developmental Biology，Chinese Academy of Sciences，Beijing 100101，China\n\n3.University of Chinese Academy of Sciences，Beijing 100049，China\n\n*   `Received:`2026-05-27 `Accepted:`2026-06-10 `Online:`2026-06-15 `Published:`2026-07-07 \n*   `Contact:` Tinghao ZHANG,Cao XU \n\n### 从“以机适种”到“种机协同”的农业智能机器人研发范式转变\n\n张廷浩 1,2,3([![Image 14](https:\u002F\u002Fnkdb.magtechjournal.com\u002Fimages\u002Femail.png)](mailto:thzhang@genetics.ac.cn)), 谢跃 1,2,3, 孙萌泽 1,2, 许操 1,2,3([![Image 15](https:\u002F\u002Fnkdb.magtechjournal.com\u002Fimages\u002Femail.png)](mailto:caoxu@genetics.ac.cn))\n\n1.   1.中国科学院遗传与发育生物学研究所，种子创新全国重点实验室，北京 100101\n\n2.中国科学院遗传与发育生物学研究所，CAS-JIC植物与微生物科学卓越创新中心，北京 100101\n\n3.中国科学院大学，北京 100049\n\n*   `通讯作者:` 张廷浩,许操 \n*   `作者简介:`张廷浩 E-mail：[thzhang@genetics.ac.cn](mailto:thzhang@genetics.ac.cn)； \n*   `基金资助:`北京市智能温室蔬菜创新团队项目(BAIC12-2026);北京市乡村振兴农业科技项目(NY2601580000);国家重点研发计划项目(2023YFA0914800) \n\n## [RichHTML 8](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)\n\n## [PDF 24](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#1)\n\n[_![Image 16](https:\u002F\u002Fnkdb.magtechjournal.com\u002Fimages\u002Fknowledge\\_map1.png)_ ## Knowledge 0](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002Flexeme\u002FshowArticleByLexeme.do?articleID=13229)\n\n#### [Abstract](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\n**Abstract:**\n\nDriven by demographic change， labor shortages， and climate change， conventional agriculture faces growing constraints in efficiency and resources. Meanwhile， intelligent agriculture continues to encounter major challenges， including the mismatch between crop traits and agricultural robots， and the disconnect between breeding innovation and field production， thereby limiting robotic performance in complex environments. Given this， the paper focused on the BAR model， integrating biotechnology as the foundation， artificial intelligence for empowerment， and robotics for labor， and emphasized the concept of ‘crop-robot co-design’. The application potential of BAR model was explored in key areas such as developing crop traits compatible with robots， optimizing intelligent agricultural decision-making systems， designing multi-task collaborative robots， and building smart breeding factories. Additionally， the practical challenges faced in achieving core technological breakthroughs， establishing an industrial ecosystem and promoting widespread adoption were analyzed. Finally， the agricultural trend toward transforming from ‘robot-to-crop adaptation’ to ‘crop-robot synergy’ was prospected. It was proposed that the BAR model could drive agriculture from single crop trait improvement to systematic co-design of crops and robots. This model should become a key technological direction for developing new forms of agricultural productivity， and provide theoretical guidance for building intelligent agricultural systems in the future.\n\n**Key words:**[BAR model,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[agricultural intelligent robot,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[artificial intelligence,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[crop-robot synergy,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[agricultural new quality productive forces](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)\n\n**摘要：**\n\n在人口结构变化、农业劳动力减少及全球气候变化加剧的背景下，传统高投入、依赖人工经验的农业体系面临效率瓶颈与资源约束等挑战。同时，在智能农业的推进过程中，逐渐暴露出作物农艺性状与智能装备适配性差、育种研发与生产实际脱节等痛点，严重制约了农业智能机器人在复杂场景中的作业效率与规模化推广。鉴于此，围绕生物技术（biotechnology）筑基、人工智能（artificial intelligence）赋能与机器人（robot）劳作深度融合的BAR模式，重点阐述“作物-机器人协同设计”理念，探讨了该模式在创制“机器人友好型”作物性状、优化农业智能决策系统、研发多任务协同机器人及构建智能育种工厂等关键领域的应用潜力，并分析其在核心技术突破、产业生态构建与推广应用中面临的现实挑战。最后，展望了农业由“以机适种”向“种机协同”转型的发展趋势，提出BAR模式可推动农业由单一作物性状改良转向作物与机器人的系统化协同设计。该模式有望成为农业新质生产力形成的重要技术方向，并为未来智能农业体系构建提供理论参考。\n\n**关键词:**[BAR模式,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[农业智能机器人,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[人工智能,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[种机协同,](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)[农业新质生产力](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)\n\n**CLC Number:**\n\n*   [S-1](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#)\n\n#### Cite this article\n\nTinghao ZHANG, Yue XIE, Mengze SUN, Cao XU. A Paradigm Shift from “Robot**-**to**-**crop Adaptation” to “Crop**-**robot Synergy” in Agricultural Intelligent Robot Development[J]. Journal of Agricultural Science and Technology, 2026, 28(6): 1-7.\n\n张廷浩, 谢跃, 孙萌泽, 许操. 从“以机适种”到“种机协同”的农业智能机器人研发范式转变[J]. 中国农业科技导报, 2026, 28(6): 1-7.\n\n#### share this article\n\n**Add to citation manager**[EndNote](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002Farticle\u002FgetTxtFile.do?fileType=EndNote&id=13229)|[Ris](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=13229)|[BibTeX](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=13229)\n\n**URL:**[https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\n[https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002FY2026\u002FV28\u002FI6\u002F1](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002FY2026\u002FV28\u002FI6\u002F1)\n\n#### [Figures\u002FTables 2](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\n[![Image 17](https:\u002F\u002Fnkdb.magtechjournal.com\u002Ffileup\u002F1008-0864\u002FFIGURE\u002F2026-28-6\u002FImages\u002F1008-0864-2026-28-6-1\u002FD1B693C9-83F1-48f0-BD0A-3F7060D8CA33-F001.jpg)](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#figureClass12000)\n\n**Fig. 1**Schematic diagram of crop-robot co-design and working scenario of the intelligent breeding robot ‘GEAIR’ ［7］A： Schematic of floral modification and robotic breeding； B： Floral phenotypes of wild-type and mutant，the scale bar is 1 mm； C： Deep-learning network for flower detection； D： Robotic pollination of tomato in greenhouse \n\n![Image 18](https:\u002F\u002Fnkdb.magtechjournal.com\u002Ffileup\u002F1008-0864\u002FFIGURE\u002F2026-28-6\u002FImages\u002F1008-0864-2026-28-6-1\u002FD1B693C9-83F1-48f0-BD0A-3F7060D8CA33-F001.jpg)\n**Fig. 1**Schematic diagram of crop-robot co-design and working scenario of the intelligent breeding robot ‘GEAIR’ ［7］A： Schematic of floral modification and robotic breeding； B： Floral phenotypes of wild-type and mutant，the scale bar is 1 mm； C： Deep-learning network for flower detection； D： Robotic pollination of tomato in greenhouse\n\n[![Image 19](https:\u002F\u002Fnkdb.magtechjournal.com\u002Ffileup\u002F1008-0864\u002FFIGURE\u002F2026-28-6\u002FImages\u002F1008-0864-2026-28-6-1\u002FD1B693C9-83F1-48f0-BD0A-3F7060D8CA33-F002.jpg)](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#figureClass12001)\n\n**Fig. 2**Schematic diagram of intelligent breeding factory process integrated with BAR model［7］ \n\n![Image 20](https:\u002F\u002Fnkdb.magtechjournal.com\u002Ffileup\u002F1008-0864\u002FFIGURE\u002F2026-28-6\u002FImages\u002F1008-0864-2026-28-6-1\u002FD1B693C9-83F1-48f0-BD0A-3F7060D8CA33-F002.jpg)\n**Fig. 2**Schematic diagram of intelligent breeding factory process integrated with BAR model［7］\n\n#### [References 25](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\n[1]LI G, AN L, YANG W, et al.. 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Plant Commun., 2026, 7(5): 101820 [2026-05-02]. .\n\n#### [Related Articles 8](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\n[1]Han GAO, Feifei GUAN, Wenchang PENG, Tao TU, Huiying LUO, Jian TIAN, Bin YAO. [Strain-specific Antimicrobial Peptide： Biological Basis， Intelligent Design and Challenge](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0312)[J]. Journal of Agricultural Science and Technology, 2026, 28(5): 1-6.\n[2]Xiaofeng GU, Lichao ZHANG, Huihui LI, Junjie FU, Hai WANG, Meixia WANG, Nian WANG, Qiang HE, Jun ZHENG, Youzhi MA, Luxiang LIU, Xinhai LI, Jianmin WAN. [Agro-biological Intelligent Design Breeding](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2025.0934)[J]. Journal of Agricultural Science and Technology, 2025, 27(12): 1-13.\n[3]Yuan LEI, Caixia GAO. 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[Study on the Development of Pork Freshness Detection Techniques](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F)[J]. , 2009, 11(S1): 72-74.\n\n#### [Recommended Articles](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\n#### [Metrics](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350)\n\nViewed\n\n**Full text**\n\n32**HTML****PDF** **Just accepted****Online first****Issue****Just accepted****Online first****Issue** 0 0 8 0 0 24 **From****Others****local** **Times**0 32 **Rate**0%100%\n\n**Abstract**\n\n83**Just accepted****Online first****Issue** 0 0 83 **From****local** **Times**83 **Rate**100%\n\n*   [Abstract](javascript:;)\n*   [Figures\u002FTables](javascript:;)\n*   [References](javascript:;)\n*   [Related Articles](javascript:;)\n*   [Recommended Articles](javascript:;)\n*   [Metrics](javascript:;)\n\n[TOP](https:\u002F\u002Fnkdb.magtechjournal.com\u002FEN\u002F10.13304\u002Fj.nykjdb.2026.0350#goTop \"TOP\")\n\n[京ICP备05021913号-17](https:\u002F\u002Fbeian.miit.gov.cn\u002F)![Image 21](https:\u002F\u002Fnkdb.magtechjournal.com\u002Fimages\u002F1008-0864\u002Fimages\u002Fbeian.png)[京公网安备11010802021197号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802021197)\n\n Copyright © Journal of Agricultural Science and Technology, All Rights Reserved.\n\n Tel: 010-82109848 E-mail: nykjdb@163.com; nkdb@caas.cn\n\n Powered by [Beijing Magtech Co., Ltd.](http:\u002F\u002Fwww.magtech.com.cn\u002FCN\u002Fmodel\u002Findex.shtml)","中国农业科技导报 2026, 28(6): 1-7","论文",10,false,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},25,22,18,15,4,1,"核心期刊论文提出农业机器人研发范式转变，具有前瞻性和产业影响，但发表时间较早，时效性不足。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智能育种","农业智能机器人","种机协同","BAR模式","GEAIR","中国农业科技导报",[],{"status":34,"locked":13,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42,"reason":58},[39,47,53],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"B1",3,true,[],[45],"番茄",[],{"code":48,"score":41,"supported":42,"title":49,"abstract":50,"tags":52},"F6",[],[51],"agricultural robot",[],{"code":54,"score":21,"supported":13,"title":55,"abstract":56,"tags":57},"F2",[],[],[26],"多个产业证据相当，需要确认主归属",[60,61],"中国农业科技导报 农业智能机器人 智能育种 种机协同","中国农业科技导报 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Plant》。该智能体模拟科研团队协作流程，通过多个智能体协同自主完成资料检索、数据分析和结果核查。团队借此筛选出多个水稻耐盐候选基因，经实验验证其中一个与水稻耐盐响应相关，为作物抗逆改良提供了人工智能辅助工具。","近日，中国农业科学院农业基因组研究所超级稻种质创新团队联合国内科研单位发布了AI智能体“绿洲”。该智能体能自动查阅植物抗逆研究领域的海量文献和科研数据，帮助研究人员快速找到值得实验验证的新线索。相关研究成果发表在《分子植物（Molecular Plant）》上。\n\n植物抗逆研究涉及海量的文献和生物数据，如何从复杂信息中快速发现关键线索并提出可验证假设一直是研究难点。\n\n研究团队开发了一套模拟科研团队协作流程的智能系统，通过多个智能体协同，可自主完成资料检索、数据分析和结果核查，并在任务反馈中不断优化。研究团队借助该系统成功筛选出多个水稻耐盐候选基因，经实验验证，其中一个候选基因与水稻耐盐响应相关，展现了人工智能辅助植物抗逆研究的应用潜力。该研究为植物抗逆机制研究和作物抗逆改良提供辅助工具。\n\n该研究得到国家重点研发计划、国家自然科学基金、中国农业科学院科技创新工程等项目资助。（通讯员  马昕怡）","中国农业科学院 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万余条实体关系,覆盖水稻、玉米、小麦和拟南芥四类作物。","10月14日至16日，中国农学会图书情报分会2026年学术年会在河南洛阳举办。会议由中国农学会图书情报分会主办，中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)、河南省农业科学院农业信息技术研究所、洛阳市农林科学院联合承办。中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)副所长赵瑞雪在会上介绍农知育芯（BRIS，Breeding Research Intelligent Service）阶段性研究成果。农知育芯融合育种文献、权威数据库、知识图谱和分析算法，集成8项分析工具、3项自研算法，为研究人员提供知识获取、任务规划、证据推理全链条智能科研服务。\n\n智能育种是AI+农业的重要场景。随着高通量测序技术快速发展，育种科研数据快速增长，基因、性状和调控通路等知识分散于海量文献和数据库，候选基因筛选、功能解析和多组学研究面临知识检索繁琐、数据分析复杂、验证周期长和试错成本高等挑战。而通用大模型也难以充分满足专业知识、结果依据和溯源能力等科研要求。\n\n面向这一需求，农知育芯打通问答与计算、分析与决策、知识与工具三类能力。科研人员可通过自然语言提出问题或发起任务，系统自动调度工具与算法，汇聚文献、数据库、知识图谱及用户数据，形成分析结果与科研建议。在关键候选基因发现等任务中，平台可辅助科研人员将筛选过程由“大海捞针”转向“精准发现”，堪称智能育种的“科研副脑”。\n\n该平台以高质量知识图谱引导推理，结合分析工具计算、模型研判和知识增强开展协同验证，并对文献、数据库、工具和参数进行全链路核查，形成“问题—数据—结论”的科研闭环。\n\n据悉，农知育芯已汇聚2011年至2025年育种相关核心期刊文献，整合6800万条NCBI基因索引、57万条UniProt专家校验蛋白信息；自主构建的育种知识图谱包含7.9万余个知识实体、12.6万余条实体关系，覆盖水稻、玉米、小麦和拟南芥四类作物，形成文献、权威数据库和知识图谱互为校验的证据网络。\n\n该平台自主研发差异表达分析、GWAS、QTL定位、WGCNA共表达网络等8项经典生物信息与统计遗传分析工具，并构建多效基因挖掘、基因功能预测、通路与网络挖掘3项自研育种算法。\n\n依托上述知识资源、分析工具和算法能力，农知育芯面向育种科研人员提供智能问答和六大科研场景分析工具，涵盖关键候选基因发现、候选基因功能解析、分子机制与调控通路、基因型—表型—转录组联合研判、种质群体遗传解析和分析结果综合研判。\n\n该平台能提供快速问答、结构化分析和深度研究三类任务模式，可适配知识查询、多组学数据分析和综合育种前景评估等不同需求。系统可自动完成数据分析，汇聚多源文献证据，一键导出标准化PDF科研报告，并提供候选基因优先级排序和后续实验建议，支持PC端科研分析和移动端田间使用，服务育种科研人员和相关专业学生。","中国农业科学院 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1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F2397644cc26144379c7e432950a2deec~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792627757&x-signature=o8pNXOWnEqlIl4mnK12LZ8rHkeA%3D)\n**水稻育种：一个“数据富矿”**\n\n杂交水稻育种是一个典型的“数据富矿、决策洼地”领域。全球积累了海量品种、性状、基因和文献数据，但长期分散、难以检索，育种更多依赖科研人员的经验与记忆。为破解这一难题，今年7月2日，中国科学院自动化研究所、北京中科闻歌科技股份有限公司与湖南省农业科学院正式签约，围绕“人工智能+农业科研”开展联合攻关。“袁稻思源”正是这一合作的阶段性成果，平台名称取自袁隆平杂交水稻创新团队，寓意“饮水思源”，感怀袁隆平院士对中国水稻杂交育种作出的突出贡献。\n\n湖南省农科院院长余应弘介绍，“袁稻思源”是湖南落实国家种业振兴行动、推进育种数字化智能化转型的关键举措，旨在传承袁隆平院士科学精神，把数据资源和智能技术转化为育种创新的现实生产力。在中科闻歌的战略版图里，这次入局农业科研，是其“决策大模型”“AI国家队”能力向产业纵深的又一次延伸。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F328723c57ddce965a8efda71ad02f21a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792627757&x-signature=0csP6GpZya22ien7EebcU64%2FxRw%3D)\n**“袁稻思源”四大核心功能**\n\n“袁稻思源”平台构建四大核心功能模块，形成完整的智能育种知识和能力。\n\n数据资源库，将散落的育种数据统一“上牌”。目前平台收纳35570个水稻品种、689项性状、28648条基因数据和624757篇中英文文献。不育系按光敏、温敏、光温敏等维度精细分类，支持育种专种家像检索商品一样，快速定位目标种质。该数据资源库是“袁稻思源”平台的核心底座。\n\n基于资源库建立图谱，让品种、基因、文献等彼此“建联”。品种图谱勾画出每个品种的选育谱系和材料关系，基因图谱把基因、性状、品种与文献证据编织成一张可推理的网络。例如输入Y58S，系统可直接呈现该品种的千粒重、抗病性及相关基因。\n\n智能问答“小稻”，作为育种专家的对话式检索入口。专业科研人员只需自然语言，也可向平台提出专业问题，系统从文献和真实数据中生成答案，每条结论都标明来源，不凭空编造。在发布会现场，专家提问“第三代杂交水稻产业化有哪些障碍”，“小稻”当即从监管、生物安全、成本、知识产权等维度给出专业分析，并提示品种审定前需补充哪些数据。\n\n最后一个能力模块是育种智能体。品种审定智能体可根据品种性状，帮助科研人员预判审定通过率，杂交配组智能体可基于基因型数据预测候选组合的表现。育种家从“翻资料猜结果”变成“让AI算概率”，大幅提升工作效率。\n\n湖南省农科院副院长李莉现场演示了平台在育种资源查询、品种系谱呈现、基因关联展示等方面的能力，一套组合下来，育种专家从“盲试”变成“有据可依”。目前，平台已在第三代杂交水稻育种中开展应用示范，取得良好成效。\n\n**专家视角：这场变革会改变什么**\n\n平台发布当天，中国工程院院士、湖南省农科院党委书记柏连阳直言，“拥抱人工智能，是今天科研人员的必答题”。平台目前虽为初级版本，但已迈出AI赋能杂交水稻育种关键而可喜的一步。\n\n中国科学院院士、崖州湾国家实验室主任李家洋的判断更为前瞻：当前全球种业竞争加剧，育种正从“经验驱动”转向“数据驱动、设计育种”，“谁率先突破智能育种，谁就赢得下一轮农业产业革命的主动”。李家洋建议，平台可强化智能决策、深化协同创新、坚持服务产业，最终建设成为面向全国乃至全球的水稻智能育种公共产品。\n\n据中科闻歌方面表示，“袁稻思源”是决策智能第一次系统嵌入国家粮食安全的核心环节。从金融到政务，再到农业科研，AI行业正在把“数据决策”能力，嵌入更多关乎国计民生的领域。对育种行业而言，当AI助力科研人员更快、更准地“排雷”和“选路”，中国水稻育种的效率天花板或许才刚刚被打开。（心月）","环球网 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Index）生命科学领域排名以及基本科学指标数据库（ESI）全球前1‰、1%学科数量均跃居全球农业科研院校首位。\n\n**科技支撑稳产保供有力有效。**培育一批粮油畜禽新品种，研发系列农业装备，多个自主品种突破国外种源垄断，一大批新品种、新技术、新产品落地示范、转化推广。组建18个产业专家团，派出科技小分队下沉一线，“科技包县”推广模式、组团式援疆援藏机制助力产业不断升级。\n\n**学科团队整体水平持续提升。**“农科英才”“四横四纵”人才体系基本成型，高层次人才规模持续扩容。创新推出“青10条”，完善分类评价、引育并举、岗位聘用、考核激励等人才机制。国家现代农业产业技术体系首席科学家约占全国1\u002F3。作物基因组学、遗传育种、动植物病害防控等领域一批科研团队跻身国际一流。\n\n**科研保障能力不断增强。**牵头建设水稻生物育种等8个全国重点实验室，参与共建畜禽生物育种等全国重点实验室，建设数量占农业领域总数的1\u002F3。获批盐碱地综合利用国家技术创新中心等国家级创新平台，建成国家作物种质库等重大基础设施。构建起功能衔接、层次完备的科技平台体系。\n\n**国际农业科技影响力显著提升。**中非农业科技创新联盟纳入元首外交成果，荣获联合国粮农组织成就奖，牵头组织国际大科学计划。主办高水平国际学术会议，在国际组织、权威学术期刊任职专家稳步增加。\n\n**锚定农业强国建设目标，构建“一二三四”工作体系**\n\n习近平总书记指出，中国要强盛、要复兴，就一定要大力发展科学技术，努力成为世界主要科学中心和创新高地。党的二十大明确到2035年进入创新型国家前列、建成科技强国的战略目标。立足新形势新任务，中国农业科学院深入贯彻习近平总书记关于“三农”工作和科技创新的重要论述，在系统总结发展成效、研判发展形势、对标国家顶层部署、广泛征集多方意见基础上，完成《规划》编制，为全院“十五五”改革创新绘就宏伟蓝图、明晰行动路径。\n\n立足农业强国建设战略任务，胸怀国家发展大局、把握时代发展大势，中国农业科学院秉持**“以科学为中心，以奋斗者为本，做有组织科研，全产业链创新”**的理念，加快构建**“一二三四”工作体系。**坚守**一个根本遵循**，以习近平新时代中国特色社会主义思想、习近平总书记关于“三农”工作和科技创新重要论述及致我院建院60周年贺信精神为指引；聚力**“两个一流”建设****，**打造世界一流学科团队和一流现代院所；构建“七大学部—十三支全产业链科研集团军—九个区域中心”**三维有组织科研体系；**打造“接天线、接地气、接企业、接国际”的**“四个衔接”农业科技创新体系。**\n\n着眼中长期发展愿景，中国农业科学院设定阶段性发展目标：力争到**2030**年，原创性、引领性研究能力显著增强，关键核心技术实现系列突破，创新生态与国际影响大幅提升，基本实现布局体系化、科研创新组织化、人员队伍高质化、条件平台高能化、产业支撑高效化、开放合作国际化、治理体系现代化，推动国家农业科技创新效能整体提升。展望**2035**年，全面建成世界一流学科和一流科研院所，农业基础原始创新能力国际先进，关键核心技术自主可控，助力我国成为世界农业主要科学中心，为农业强国建设提供坚实科技支撑。\n\n**部署重点任务，锻造自立自强核心硬实力**\n\n锚定“四个面向”战略导向，中国农业科学院明确“十五五”科技攻关与院所建设重点任务，统筹集聚各类资源，加快产出更多原创性重大科技成果，以高水平成果赋能农业农村现代化。\n\n**一是深入推进三维有组织科研布局，加快高水平成果供给，赋能农业农村高质量发展。**构建“学部—科研集团军—区域中心”矩阵式体系化科研布局，设立前沿交叉、作物园艺植保、畜牧兽医、资源环境、装备信息、农业经济与乡村发展、食品营养七大学部，组建覆盖育种、种养、动植物保护、收储、加工全链条的十三支科研集团军，围绕主要农业区布局九个区域中心，推动科研力量下沉农业主产区、扎根产业主战场，形成贯通基础研究、应用开发、成果转化的体系化攻关格局。\n\n**二是做实“四个衔接”，支撑国家农业科技创新体系整体效能提升。**“接天线”引入上游学科前沿成果，在农业场景中融合、应用、转化；“接地气”扎根农业农村主战场，强化稳产保供、乡村振兴科技支撑；“接企业”打通技术成果转移转化堵点，推动科技创新与产业创新深度融合；“接国际”参与和引领全球农业科技治理，打造农业科技公共产品矩阵。\n\n**三是坚持教育科技人才一体发展，打造新时代人才高地。**启动战略科学家梯队培育计划，完善科研首席接续机制，全力支持青年人才潜心攻坚重大科学问题与前沿技术。探索优秀博士后流动机制，盘活内部人才资源，激发人才创新活力。统筹实施人才引进计划，培育一批具备国际视野的高层次人才。稳步扩大研究生招生规模，提高博士研究生占比，探索本硕博贯通培养新模式。强化农业拔尖创新人才培养，打造“留学农科”国际化办学品牌。优化科研团队结构，重点补强基础研究、前沿交叉领域科研力量，打造一支结构合理、素质优良的高水平农科人才队伍。\n\n**四是建设高水平条件平台，加快人工智能赋能农业科研。**聚焦农业科技前沿赛道，全面升级科研硬件设施与数字化支撑体系。面向智能育种、合成生物等前沿方向布局重大科研基础设施，提质全国重点实验室、部级重点实验室等高能级平台，完善试验基地与资源库圃，建设概念验证与中试熟化平台，夯实科研硬件底座。搭建全院人工智能算力基座，构建农业科学模型矩阵，促进人工智能与农业科技创新深度融合，打造一体化数据信息支撑体系。统筹优化院所基础设施，全力建成开放协同、数据互通、智能引领的现代科研设施保障体系。\n\n**五是优化农科治理体系，以高质量党建引领高质量发展。**深化党的创新理论武装，推动党建工作与科研攻坚、成果转化、人才培育等中心工作深度融合。坚持严的基调正风肃纪，建设过硬领导班子和高素质干部队伍。深化现代院所制度建设，推行全域创新绩效管理模式，统筹优化财务资源配置。严守科研诚信底线，健全科技伦理治理体系，常态化普及农业科学知识。大力弘扬农科精神，厚植农科创新文化。\n\n大潮奔涌，千帆竞发，使命如山，催人奋进。站在“十五五”新起点，全院将以《规划》为总牵引，坚守强农初心、勇担兴农使命，围绕组织领导、执行落实、资源统筹、监测评估，构建常态化实施保障机制。层层压实责任，切实把宏伟蓝图转化为发展实效，把战略目标转化为实干实绩。全院同心、尽锐出战，向基础前沿无人区挺进，向关键核心技术深水区攻坚，向产业主战场第一线扎根，奋力抢占农业科技制高点，锻造国家战略科技力量主力军，在推进高水平农业科技自立自强、建设农业强国的壮阔征程中，书写浓墨重彩的农科院新篇章！","中国农业科学院 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