[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-819":3,"related-819":56},{"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":50,"slug":53,"view_count":54,"doi":8,"paper":8,"created_at":55},819,"[山东省农科院]小麦遗传育种团队创建\"济麦家族\"——推广7亿亩、增产600多亿斤,济麦61抗5种病害","https:\u002F\u002Fwww.china.com.cn\u002Ftxt\u002F2026-08\u002F21\u002Fcontent_118658490.shtml","山东农业科学院小麦遗传育种团队创建的\"济麦家族\"30年累计推广7亿亩、增产600多亿斤。最近小麦育种专家刘建军团队实现小麦远缘杂交育种重大突破:全球首次直接利用二倍体黑麦与六倍体普通小麦杂交育成的六倍体普通小麦品种\"济麦61\",具有强筋、高产、抗逆等优良性状,兼抗5种病害。山东主要农作物良种覆盖率跃升至98%以上,良种对粮食增产贡献率达47%,2025年农业总产值迈上1.3万亿元台阶。",null,"作物育种工作艰辛备至且周期较长。按照传统育种方式，育成一个小麦新品种大约需要花费10年时间，而构建一个小麦新品种家族更需久久为功。“‘济麦家族’由小至大，山东农业良种工程项目的持久资助功不可没。”刘建军说。\n\n1995年5月，山东启动实施山东省农业良种产业化开发工程（以下简称“良种工程”），开全国良种产业化探索之先河。首创“行政领导+首席专家”协同管理机制，起始阶段每年投入只有几百万元，“十四五”以来每年省级财政支持稳定在2亿元，迄今累计投入财政资金超24亿元。\n\n30年来，“齐鲁良种”为山东农业产业发展夯实了根基。“济麦家族”小麦累计推广7亿多亩，增产小麦600多亿斤；登海良种创造夏玉米亩产1402公斤的全国高产纪录；盐碱地玉米新品种“鲁单510”创全国高产纪录；大豆新品种“齐黄34”在3‰盐碱地亩产突破300公斤。目前，山东主要农作物良种覆盖率跃升至98%以上，良种对粮食增产贡献率达47%。\n\n30年来，山东主要粮食作物品种基本实现自给，80%的蔬菜品种实现国产化，很多育种基础研究由“跟跑”转向“领跑”，生物育种关键核心技术与装备创新也驶入“快车道”，助力该省成为全国首个农业总产值突破万亿元的省份，2025年更是迈上1.3万亿元新台阶。\n\n刘建军回忆，20世纪90年代，科研经费紧张，得益于山东良种工程一次次给予的资助，帮助课题组度过了最困难的时期。后来，随着项目资金的增加，小麦新品种的一个个问世，“济麦家族”得以发展壮大。最近，他带领团队实现了小麦远缘杂交育种的重大突破：全球首次直接利用二倍体黑麦与六倍体普通小麦杂交育成的六倍体普通小麦品种“济麦61”，具有强筋、高产、抗逆等优良性状，兼抗5种病害。","中国网 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分钟，适合制作馒头。","10月19日，济麦1806生产经营权独占实施许可签约仪式在济南举办。作物所理事长兼党委书记赵海军、山东东润种业有限公司总经理刘延福，小麦遗传育种团队、成果转化科以及东润种业相关人员参加活动。\n\n小麦遗传育种团队执行专家李豪圣介绍了济麦1806品种特征特性以及栽培技术要点。双方针对济麦1806的推广进行座谈交流，下一步将以该品种的转让为合作启点，加快新品种推广步伐，同时在项目申报、人才培养、成果培育上开展深入合作。\n\n济麦1806是作物所育成的矮秆、耐逆、高产、广适小麦新品种，2026年通过国家审定。两年区试全生育期234.2天，株高68.3厘米，倒伏达标点率100%；亩穗数35.8万穗，穗粒数35.7粒，千粒重40.7克，亩产435公斤，较对照增产4.6%；生产试验平均亩产346.6公斤，增产8.96%。品质较稳定，适合制作馒头。多年品质分析结果表明，容重792 g\u002FL-814 g\u002FL，蛋白质（干基）12.3%-13.4%，湿面筋（14%湿基）26.5% -29.6%，吸水率60.0%-62.0%，稳定时间5.3-6.4 min。（撰写：郭军 核稿：李峰）","山东省农业科学院 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刘姝彤 通讯员 屈越）","山东省人民政府网 2026-10-06","2026-10-06T00:00:00Z",82,{"impact":18,"substance":18,"depth":161,"authority":70,"freshness":71,"relevant":22,"comment":162},17,"国内首款小麦育种具身机器人原型机亮相，实现感知—决策—执行闭环，属智慧育种领域实质性技术突破，值得进入每日精选。",[164],{"name":157,"url":154},[120,121,76,28,122],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":169},{"candidates":170,"agricultural_evidence":46},[171,175,180,184],{"code":33,"score":20,"supported":46,"title":172,"abstract":173,"tags":174},[35],[35],[28],{"code":142,"score":90,"supported":46,"title":176,"abstract":177,"tags":179},[],[178],"大模型",[121],{"code":89,"score":22,"supported":14,"title":181,"abstract":182,"tags":183},[],[],[76],{"code":137,"score":22,"supported":14,"title":185,"abstract":186,"tags":187},[],[],[120],[189,190],"山东农业大学 小麦育种机器人 WheatX","小麦智能育种 iwheat 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能判断，懂操作！小麦育种具身机器人原型机公开亮相\n\n近日，在山东农业大学举办的小麦智能育种研讨会上，由山东农业大学小麦育种全国重点实验室王宏伟、张亮教授团队联合研发的小麦育种具身机器人原型机首次公开亮相。该研究获山东省重点研发计划项目（2026LZGC001）支持，并联合山东农业科学院、中国农业大学、北京大学现代农业研究院等单位协同攻关，聚焦传统小麦育种中田间表型鉴定效率低、单株筛选依赖人工等行业共性问题，为田间育种智能化提供新的装备与技术路径。\n\n视频加载中...\n\n长期以来，小麦田间表型鉴定和单株筛选高度依赖人工，不仅作业通量有限、劳动强度较大，筛选结果还容易受育种人员经验差异的影响。与此同时，现有田间表型采集装备多以被动扫描和影像采集为主，能够完成“看”和“测”，但在自主判断、主动干预和选育执行等方面仍存在不足。\n\n针对这些问题，研发团队研制了面向大田小麦育种的高通量主动式机器人系统，并以“主动感知—自主决策—联动执行”为核心设计理念，推动田间表型装备由单纯数据采集向自主选育作业拓展。该机器人采用RTK与视觉融合导航技术，可在试验田小区间自主移动作业；搭载滑台高清相机，对田间小麦进行连续影像采集，并利用视觉算法自动量化株高、穗部性状等表型参数。在此基础上，系统结合育种家先验经验构建智能决策模型，可对田间单株进行高通量初筛，自主识别具有进一步考察价值的候选植株。对于初筛获得的目标植株，机器人还可通过自主研发的机械臂开展进一步精细作业，包括单株抓取、环形多角度精细成像以及原位喷涂标记等操作，实现从大范围快速筛选到目标植株精细评价和标记的连续作业，使机器人不仅能够“看得见”，还能够“做判断、去操作”。\n\n研发团队成员、山东农业大学教授王宏伟介绍：“整套系统突破了现有表型装备以被动扫描为主、缺乏主动干预能力的技术局限，打通了小麦育种机器‘感知—决策—执行’的全流程闭环。”国家小麦产业技术体系副首席曹新有表示，此次发布的机器人集感知、决策与选育功能于一体，为智能育种由算法模型走向田间自主作业提供了关键技术支撑。\n\n此次小麦智能育种研讨会上，来自中国农业科学院、中国农业大学、北京大学现代农业研究院等单位的专家还围绕种质资源数据整合、人工智能算法共享和育种智能装备研发等开展深入交流，并达成进一步合作意向。各方将围绕小麦育种关键环节持续推进数据、算法与智能装备协同创新，探索构建覆盖“感知—分析—决策—执行”的智能育种技术体系，推动小麦育种向数字化、智能化和自主化方向发展。\n\n（大众新闻记者 刘姝彤 通讯员 王宏伟）","今日头条 大众日报 2026-10-03","2026-10-03T12:07:00Z",74,{"impact":18,"substance":115,"depth":69,"authority":13,"freshness":57,"relevant":22,"comment":203},"国内首台小麦育种具身机器人原型机亮相，打通感知—决策—执行闭环，属智能育种装备领域实质性突破，值得进入每日精选。",[205],{"name":199,"url":196},[120,121,76,28,122],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":210},{"candidates":211,"agricultural_evidence":46},[212,216,220,224],{"code":33,"score":20,"supported":46,"title":213,"abstract":214,"tags":215},[35],[35],[28],{"code":89,"score":22,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[76],{"code":137,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[120],{"code":142,"score":22,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[121],[147,229],"小麦育种全国重点实验室 智能育种","山东农业大学小麦育种具身机器人-4262",2,"2026-10-06T00:05:02.013148Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":8,"source_name":239,"source_url":236,"published_at":240,"category":241,"cover_url":8,"hotness":13,"is_selected":14,"score":201,"score_detail":242,"sources":244,"tags":246,"primary_topic":250,"secondary_topics":251,"classification":258,"search_phrases":280,"slug":283,"view_count":90,"doi":284,"paper":285,"created_at":306},3328,"Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102588","Reliable assessment of yield models in breeding trials requires evaluation on lines that have not contributed observations to model development. We examined this problem in an advanced spring wheat yield nursery in northern Kazakhstan during the 2024 and 2025 seasons. Of 300 plots, 176 from 22 entries formed the model development set, while 124 plots representing 31 breeding lines were completely excluded from training. Eleven variables were retained from 82 candidates using the training data alone; the candidates were derived from UAV multispectral imagery, ERA5-Land reanalysis, LiDAR-derived static plot microtopography, phenology and maturity group. LightGBM yielded a pooled R² of 0.587 for the excluded lines; resampling breeding lines within season gave a mean R² of 0.564 with a 95 % confidence interval from 0.252 to 0.776. The pooled value partly reflected the strong contrast between seasons rather than discrimination among lines within a season. Random division of plots produced an R² of 0.890, indicating that apparent predictive accuracy depended strongly on validation design. Agreement between observed and predicted line rankings was moderate, with Spearman correlations of 0.612 in 2024 and 0.650 in 2025, although uncertainty was wide because only 14 and 17 independent lines were available. Six modelling approaches produced overlapping uncertainty intervals, with no clear evidence of superiority, and the complete combination of data sources was not separable from simpler configurations retaining the multispectral block. A separate analysis in which screening, selection and fitting used 2024 data alone and evaluation used 2025 data showed a severe loss of performance, with R² = −8.81 and a Spearman correlation of −0.082 that was indistinguishable from zero. Poor transfer persisted among entries grown in both seasons, so genotype novelty alone was insufficient to explain the failure. Breeding lines absent from training could therefore be ranked with moderate consistency when both observed seasonal regimes were represented during model development, whereas prediction in an unobserved season remained unreliable. The study covers one nursery complex and two seasons, so transfer across locations and broader conditions remains to be established.","在育种试验中，要对产量模型进行可靠评估，必须在未参与模型开发的品系上进行评价。我们在2024年和2025年生长季于哈萨克斯坦北部的一个高级春小麦产量圃中考察了这一问题。在300个小区中，来自22个品系的176个小区构成模型开发集，而代表31个育种品系的124个小区则完全排除在训练之外。仅使用训练数据，从82个候选变量中保留了11个变量；这些候选变量来源于无人机多光谱影像、ERA5-Land再分析数据、LiDAR衍生的静态小区微地形、物候和成熟期组。LightGBM对被排除品系给出的合并R²为0.587；在生长季内对育种品系进行重采样得到的平均R²为0.564，95%置信区间为0.252至0.776。合并值部分反映了生长季之间的强烈差异，而非生长季内品系之间的区分能力。对小区进行随机划分得到的R²为0.890，表明表观预测精度在很大程度上取决于验证设计。观测品系排名与预测品系排名之间的一致性为中等，2024年和2025年的Spearman相关系数分别为0.612和0.650，但由于仅有14个和17个独立品系可用，不确定性范围较宽。六种建模方法产生了相互重叠的不确定性区间，没有明确证据表明哪一种更优，并且完整的数据源组合与保留多光谱模块的较简单配置无法区分。另一项分析中，筛选、选择和拟合仅使用2024年数据，而评估使用2025年数据，结果显示性能严重下降，R² = −8.81，Spearman相关系数为−0.082，与零无法区分。在两个生长季均种植的品系之间，较差的迁移性依然存在，因此仅用品系新颖性不足以解释这种失败。因此，当模型开发过程中涵盖了所观测到的两种生长季情形时，未参与训练的育种品系可以以中等一致性进行排名，而在未观测生长季中的预测仍然不可靠。本研究仅涵盖一个圃系复合体和两个生长季，因此跨地点和更广泛条件下的迁移性仍有待确立。","Smart Agricultural Technology","2026-09-22T00:00:00Z","论文",{"impact":20,"substance":18,"depth":19,"authority":70,"freshness":21,"relevant":22,"comment":243},"基于无人机多光谱与气象数据的春小麦育种品系产量预测研究，验证设计严谨、结论审慎，对智慧育种与遥感估产有参考价值，但属单点试验、地域性强，未达重大突破层级。",[245],{"name":239,"url":236},[120,247,248,28,249],"产量预测","机器学习","遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[252],{"code":253,"slug":254,"name":255,"path":256,"parent_code":257},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":259},{"candidates":260,"agricultural_evidence":46},[261,266,272,276],{"code":33,"score":262,"supported":46,"title":263,"abstract":264,"tags":265},15,[34],[35,34],[28],{"code":253,"score":71,"supported":46,"title":267,"abstract":268,"tags":271},[],[269,270],"无人机多光谱","multispectral",[247,249],{"code":137,"score":22,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[120],{"code":142,"score":22,"supported":14,"title":277,"abstract":278,"tags":279},[],[],[248],[281,282],"哈萨克斯坦 春小麦 产量预测","UAV 多光谱 育种试验","哈萨克斯坦春小麦产量预测-3328","10.1016\u002Fj.atech.2026.102588",{"doi":284,"openalex_id":286,"authors":287,"venue":239,"cited_by_count":104,"oa_url":236,"card":299,"direction":303,"ingested_from":305},"W7214044328",[288,291,294,297],{"name":289,"orcid":290},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":292,"orcid":293},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":295,"orcid":296},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":298,"orcid":8},"Bakhtiyar Zhanzakov",{"tldr":300,"method":301,"finding":302,"direction":303,"opportunity":304},"评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。","无人机多光谱、ERA5-Land、LiDAR与物候数据，LightGBM等六种模","两季均参与训练时品系排名中等一致，但预测未观测季节完全失效。","农业遥感与作物表型","需研究跨地点、跨年份可迁移的表型预测模型与验证设计，避免随机划分高估精度。","openalex","2026-09-24T23:30:03.201775Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":8,"content":312,"source_name":313,"source_url":8,"published_at":8,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":341,"slug":344,"view_count":345,"doi":8,"paper":8,"created_at":346},2700,"扬麦53入选十四五国家农作物育种联合攻关突破性小麦品种——缘两优816入选核心展示","https:\u002F\u002Fyz.jaas.ac.cn\u002Fxww\u002Fxwzx\u002Fart\u002F2026\u002Fart_a207b00c31aa4fafa8dd9a93a06fcc1f.html","第十八届中国国际种业博览会集中发布十四五国家农作物育种联合攻关代表性成果90项,其中突破性品种73项(小麦4项)。里下河所扬麦53入选十四五国家农作物育种联合攻关突破性小麦品种,缘两优816入选2026年国家农作物新品种核心展示综合表现优良品种。","扬麦 53、缘两优 816 分别入选国家育种联合攻关突破性品种和国家农作物新品种核心展示优良品种\n\n作者：科研管理科 文章来源：里下河所 点击数：次 更新时间：2026-09-16 14:42\n\n近日，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安徽合肥举办。大会期间，集中发布了“十四五”国家农作物育种联合攻关代表性成果90项，其中突破性品种73项（小麦4项），以及2025年度农作物品种推广信息和2026年国家农作物新品种核心展示结果。我所“扬麦53”入选“十四五”国家农作物育种联合攻关突破性小麦品种，“缘两优816”入选2026年国家农作物新品种核心展示综合表现优良品种，彰显了我所在稻麦新品种选育领域的创新实力。\n\n“扬麦53”聚合多个抗赤霉病、抗白粉病及优质相关基因，在国家小麦育种联合攻关试验中，连续三年赤霉病抗性达到抗（R）级，实现了抗赤霉病、高产和优质性状的协同突破。该品种入选国家农业重大科技专项标志性成果、江苏省现代农业领域十大科技进展、国家农作物优良品种推广目录、中国重大科技新成果。“缘两优816”分蘖能力强，茎杆粗壮，早熟抗倒，中抗稻瘟病，耐高温性好，区试平均亩产631.6千克，比对照增产5.2%，米质达到行业标准优质二级。\n\n下一步，我所将继续面向农业生产一线和产业发展需求，强化有组织科研和协同攻关，加快新品种选育、示范与推广，为保障粮食安全和推动农业高质量发展提供更加有力的科技支撑。\n\n[![Image 1: 2.png](https:\u002F\u002Fyz.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F99318008\u002Fpicture\u002F20268\u002Fd16d8c9bc2f34fd3b35e74d9b8caa602.png)](https:\u002F\u002Fyz.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F99318008\u002Fpicture\u002F20268\u002Fd16d8c9bc2f34fd3b35e74d9b8caa602.png)[](https:\u002F\u002Fyz.jaas.ac.cn\u002Fxww\u002Fxwzx\u002Fart\u002F2026\u002Fart_a207b00c31aa4fafa8dd9a93a06fcc1f.html)\n\n[![Image 2: 1.png](https:\u002F\u002Fyz.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F99318008\u002Fpicture\u002F20268\u002FSa52902179ba042f18d926bd20d3c0cdd-800.png)](https:\u002F\u002Fyz.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F99318008\u002Fpicture\u002F20268\u002Fa52902179ba042f18d926bd20d3c0cdd.png)[](https:\u002F\u002Fyz.jaas.ac.cn\u002Fxww\u002Fxwzx\u002Fart\u002F2026\u002Fart_a207b00c31aa4fafa8dd9a93a06fcc1f.html)","江苏省农科院里下河所 2026年9月",73,{"impact":18,"substance":19,"depth":20,"authority":70,"freshness":71,"relevant":22,"comment":316},"国家级育种联合攻关突破性品种入选信息，含抗病性与产量等实质数据，对种业振兴主题有聚合价值。",[318],{"name":313,"url":310},[76,28,320,321,322],"品种推广","水稻品种","抗赤霉病",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":326},{"candidates":327,"agricultural_evidence":46},[328,332,336],{"code":33,"score":20,"supported":46,"title":329,"abstract":330,"tags":331},[35],[35],[28],{"code":89,"score":54,"supported":46,"title":333,"abstract":334,"tags":335},[],[93],[320,76],{"code":337,"score":22,"supported":14,"title":338,"abstract":339,"tags":340},"A1",[],[],[321],[342,343],"品种推广 小麦育种 抗赤霉病 水稻品种","品种推广 小麦育种","品种推广小麦育种抗赤霉病水稻品种-2700",3,"2026-09-17T00:04:37.183499Z"]