[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1896":3,"related-1896":110},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":68,"slug":71,"view_count":22,"doi":72,"paper":73,"created_at":109},1896,"Improving Selection Efficiency at the Head Rows Stage of Winter Wheat Breeding by Using the Normalized Difference Vegetation Index","https:\u002F\u002Fdoi.org\u002F10.17557\u002Ftjfc.1860713","Visual selection in early stage of wheat breeding is usually implemented method. This study was conducted to compare breeders’ visual selection with normalized difference vegetation index (NDVI) selection. Three different selection protocols were used at Head Rows (HR) stage: Breeder visual, NDVI+ (high mean NDVI), and NDVI- (low mean NDVI). Some HRs were selected by both Breeder and NDVI+ (Breeder & NDVI+). Performances of HRs selected by each protocol were studied for grain yield and relationship of NDVI with yield in yield trials. 403 HRs of total 1246 HRs derived from 38 populations were selected by protocols at varying rates: 13.2% by Breeder, 10% by NDVI+, 3% by NDVI-, and 6.1% by Breeder & NDVI+. NDVI values measured at the pre-heading stage in the HR and early grain filling in the Preliminary Yield Trial (PYT) showed strong correlations with grain yield (r= 0.55** and r= 0.73**, respectively; p","小麦育种早期阶段的视觉选择通常是常规实施的方法。本研究旨在比较育种者的视觉选择与归一化植被指数（NDVI）选择的效果。在穗行（HR）阶段采用了三种不同的选择方案：育种者视觉选择、NDVI+（高平均NDVI）和NDVI-（低平均NDVI）。部分穗行同时被育种者和NDVI+选中（育种者&NDVI+）。研究了各方案所选穗行在产量试验中的籽粒产量表现，以及NDVI与产量的相关性。在源自38个群体的总计1246个穗行中，有403个穗行按不同比例被各方案选中：育种者选择占13.2%，NDVI+占10%，NDVI-占3%，育种者&NDVI+占6.1%。在穗行阶段抽穗前及初级产量试验（PYT）灌浆早期测定的NDVI值与籽粒产量呈强相关性（r分别为0.55**和0.73**；p",null,"Turkish Journal Of Field Crops","2026-09-06T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,12,3,1,"研究验证NDVI在小麦育种早期选择中的效率，对智慧农业与育种结合有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","种业振兴","小麦育种","遥感","NDVI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,63],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},23,true,[49,34],"winter wheat",[35,34],[29],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I3",2,[],[],[31,30],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"F1",[],[],[28],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[27],[69,70],"小麦育种 智慧农业 种业振兴 遥感","小麦育种 智慧农业","小麦育种智慧农业种业振兴遥感-1896","10.17557\u002Ftjfc.1860713",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":99,"oa_url":100,"card":101,"direction":107,"ingested_from":108},"W7210287349",[76,79,82,85,88,90,93,96],{"name":77,"orcid":78},"Emrah Koç","https:\u002F\u002Forcid.org\u002F0000-0003-3024-8691",{"name":80,"orcid":81},"Beyhan Akın","https:\u002F\u002Forcid.org\u002F0000-0002-2721-9577",{"name":83,"orcid":84},"Savaş Belen","https:\u002F\u002Forcid.org\u002F0000-0001-7357-8127",{"name":86,"orcid":87},"Emel ÖZER","https:\u002F\u002Forcid.org\u002F0000-0002-2320-3105",{"name":89,"orcid":9},"Kemal Subaşi",{"name":91,"orcid":92},"Fatih Özdemir","https:\u002F\u002Forcid.org\u002F0000-0001-7934-2844",{"name":94,"orcid":95},"Mesut Keser","https:\u002F\u002Forcid.org\u002F0000-0001-6316-7981",{"name":97,"orcid":98},"Alexey Morgounov","https:\u002F\u002Forcid.org\u002F0000-0001-7082-5655",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5590561",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"比较NDVI与育种家目视选择在小麦育种早期穗行阶段的效果，发现NDVI选择可提高效率。","使用NDVI进行高、低均值选择，与育种家目视选择对比，并在产量试验中验证。","NDVI与籽粒产量强相关，NDVI+选择效率高，可辅助或替代目视选择。","智慧农业 \u002F 农业物联网","可探索将NDVI与机器学习结合，实现高通量表型选择，提高育种效率。","农业遥感与作物表型","openalex","2026-09-08T23:30:14.947877Z",{"total":111,"page":22,"page_size":111,"items":112},6,[113,159,203,242,311,372],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":9,"content":9,"source_name":118,"source_url":9,"published_at":119,"category":120,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":133,"classification":134,"search_phrases":154,"slug":157,"view_count":99,"doi":9,"paper":9,"created_at":158},4859,"机器人变身小麦育种专家——山东农大小麦育种具身机器人原型机公开亮相","https:\u002F\u002Fnews.sciencenet.cn\u002Fsbhtmlnews\u002F2026\u002F10\u002F391582.shtm","近日在山东泰安举行的小麦智能育种研讨会上,小麦育种具身机器人原型机首次公开亮相。该机器人由山东农业大学教授王宏伟、张亮团队联合研发,采用实时动态差分定位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,可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。系统结合智能决策模型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。研究团队与山东极智生物科技有限公司达成合作意向。","中国科学报 科学网 2026 年 10 月 13 日 第 1 版","2026-10-13T00:00:00Z","报道",81,{"impact":123,"substance":18,"depth":124,"authority":125,"freshness":13,"relevant":22,"comment":126},22,16,13,"国内小麦育种具身机器人原型机首次公开亮相，技术路线与产学研合作信息具体，具备行业示范价值，值得进入每日精选。",[128],{"name":118,"url":116},[27,130,28,29,131],"农业人工智能","具身机器人",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":135},{"candidates":136,"agricultural_evidence":47},[137,141,145,149],{"code":33,"score":20,"supported":47,"title":138,"abstract":139,"tags":140},[35],[35],[29],{"code":59,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[28],{"code":64,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[27],{"code":150,"score":22,"supported":14,"title":151,"abstract":152,"tags":153},"I2",[],[],[130],[155,156],"山东农业大学 小麦育种 具身机器人","王宏伟 小麦 表型采集","山东农业大学小麦育种具身机器人-4859","2026-10-14T00:04:08.166418Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":9,"content":164,"source_name":165,"source_url":9,"published_at":166,"category":120,"cover_url":9,"hotness":13,"is_selected":14,"score":167,"score_detail":168,"sources":172,"tags":174,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":198,"slug":201,"view_count":111,"doi":9,"paper":9,"created_at":202},4382,"山东农业大学研发小麦育种具身机器人 WheatX 首次亮相","http:\u002F\u002Fwww.shandong.gov.cn\u002Fart\u002F2026\u002F10\u002F6\u002Fart_97564_765520.html","山东农业大学张亮团队联合山东省农业科学院、中国农业大学等单位研发小麦育种具身机器人 WheatX,集感知、决策与选育功能于一体,可完成大田麦穗快速筛选、对量化达标麦穗喷涂颜料原位标记,实现「感知—决策—执行」全流程闭环;团队已与极智生物达成合作,将首款小麦育种大模型 iwheat 嵌入机器人,推动从原型机向产品级升级。","小麦试验田里，一台长宽高均超过两米的“大家伙”在麦株前稳稳停住。机械臂移动滑台探出，前端圆环装置精准套住一株麦穗缓缓上滑，环内八枚微型摄像头从多个角度对其精细成像。随后，穗粒数、穗长等性状的量化数据全部生成。\n\n这是由山东农业大学、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，近日在该校举办的小麦智能育种研讨会上首次公开亮相。\n\n王宏伟介绍，传统小麦育种中，育种家经年积累了大量宝贵育种经验，能从成千上万的杂交后代里选育出性状优良的品系。但在大田环境下，人工筛选不仅劳动强度大，筛选结果还容易受主观经验差异影响。同时，现有田间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。该研究主要聚焦传统小麦育种主要依赖人工、选育效率低等行业共性问题，将为大田育种智能化提供新的装备与技术路径。\n\n“在模型训练时加入大量育种家先验经验是该款机器人特色之一。”王宏伟说，团队的目标，是让机器人不仅能够“看得见”，还能够“作判断、去操作”，在田间完成类似育种家的选育动作。\n\n为让机器人“学会”育种，团队种植了2000份不同的小麦种质资源，逐一拍摄影像后，请育种家按标准打分标注，再将标注结果输入系统，育种家凭多年经验作出的判断被转化为机器可学习的数据。机器人结合育种家经验构建智能决策模型，具备了初步的自主选育判断能力。\n\n同时，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。\n\n“对于初筛获得的目标植株，机器人还可通过自主研发的多功能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等操作。”张亮解释，对于量化后确认符合标准的麦穗，机器人会像育种家在田间“绑绳子做标记”一样，喷涂颜料原位标记，完成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，实现“感知—决策—执行”的全流程闭环。\n\n该研究获山东省重点研发计划项目支持，并联合山东省农业科学院、中国农业大学等单位协同攻关。国家小麦产业技术体系副首席曹新有表示，该机器人集感知、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“用机器人辅助人工选择，这是很难但很有意义的方向。”张亮坦言，大田场景麦穗密度高、类型多，场景极其复杂，加之天气影响，对机器人精准作业都是极大考验，系统在抓取精准度、学习经验深度等指标上还需进一步升级改良。目前，团队已与极智生物科技有限公司达成合作意向，将我国首款小麦育种大模型iwheat嵌入WheatX育种机器人，联合研发小麦智能育种系统，推动原型机迭代升级向产品级迈进。（记者 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":123,"substance":123,"depth":169,"authority":125,"freshness":170,"relevant":22,"comment":171},17,8,"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。",[173],{"name":165,"url":162},[27,130,28,29,131],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,184,190,194],{"code":33,"score":20,"supported":47,"title":181,"abstract":182,"tags":183},[35],[35],[29],{"code":150,"score":185,"supported":47,"title":186,"abstract":187,"tags":189},4,[],[188],"大模型",[130],{"code":59,"score":22,"supported":14,"title":191,"abstract":192,"tags":193},[],[],[28],{"code":64,"score":22,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[27],[199,200],"山东农业大学 小麦育种机器人 WheatX","小麦智能育种 iwheat 大模型","山东农业大学小麦育种机器人WheatX-4382","2026-10-08T00:06:07.051280Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":9,"content":208,"source_name":209,"source_url":9,"published_at":210,"category":120,"cover_url":9,"hotness":13,"is_selected":14,"score":211,"score_detail":212,"sources":214,"tags":216,"primary_topic":217,"secondary_topics":218,"classification":219,"search_phrases":238,"slug":240,"view_count":54,"doi":9,"paper":9,"created_at":241},4262,"山东农业大学发布小麦育种具身机器人原型机","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7692412028073361958","山东农业大学小麦育种全国重点实验室王宏伟、张亮教授团队联合山东农科院、中国农业大学、北京大学现代农业研究院研发的小麦育种具身机器人原型机公开亮相。机器人采用RTK与视觉融合导航，可自主移动作业，搭载滑台高清相机连续影像采集，结合育种家先验经验构建智能决策模型，对田间单株高通量初筛与精细作业。","## 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":123,"substance":18,"depth":124,"authority":13,"freshness":111,"relevant":22,"comment":213},"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。",[215],{"name":209,"url":206},[27,130,28,29,131],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":220},{"candidates":221,"agricultural_evidence":47},[222,226,230,234],{"code":33,"score":20,"supported":47,"title":223,"abstract":224,"tags":225},[35],[35],[29],{"code":59,"score":22,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[28],{"code":64,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[27],{"code":150,"score":22,"supported":14,"title":235,"abstract":236,"tags":237},[],[],[130],[155,239],"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","2026-10-06T00:05:02.013148Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":249,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":211,"score_detail":250,"sources":253,"tags":255,"primary_topic":258,"secondary_topics":259,"classification":265,"search_phrases":286,"slug":289,"view_count":185,"doi":290,"paper":291,"created_at":310},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",{"impact":20,"substance":123,"depth":19,"authority":125,"freshness":251,"relevant":22,"comment":252},9,"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。",[254],{"name":248,"url":245},[27,256,257,29,30],"产量预测","机器学习",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[260],{"code":53,"slug":261,"name":262,"path":263,"parent_code":264},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":266},{"candidates":267,"agricultural_evidence":47},[268,272,278,282],{"code":33,"score":17,"supported":47,"title":269,"abstract":270,"tags":271},[34],[35,34],[29],{"code":53,"score":170,"supported":47,"title":273,"abstract":274,"tags":277},[],[275,276],"无人机多光谱","multispectral",[256,30],{"code":64,"score":22,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[27],{"code":150,"score":22,"supported":14,"title":283,"abstract":284,"tags":285},[],[],[257],[287,288],"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":290,"openalex_id":292,"authors":293,"venue":248,"cited_by_count":99,"oa_url":245,"card":305,"direction":107,"ingested_from":108},"W7214044328",[294,297,300,303],{"name":295,"orcid":296},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":298,"orcid":299},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":301,"orcid":302},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":304,"orcid":9},"Bakhtiyar Zhanzakov",{"tldr":306,"method":307,"finding":308,"direction":107,"opportunity":309},"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。","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。","2026-09-24T23:30:03.201775Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":9,"source_name":317,"source_url":314,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":326,"secondary_topics":327,"classification":335,"search_phrases":356,"slug":359,"view_count":185,"doi":360,"paper":361,"created_at":371},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",61,{"impact":20,"substance":19,"depth":17,"authority":13,"freshness":111,"relevant":22,"comment":321},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[323],{"name":317,"url":314},[27,325,35,30,31],"精准施肥",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[328,334],{"code":329,"slug":330,"name":331,"path":332,"parent_code":333},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":53,"slug":261,"name":262,"path":263,"parent_code":264},{"status":40,"locked":14,"version":41,"method":42,"evidence":336},{"candidates":337,"agricultural_evidence":47},[338,342,347,352],{"code":33,"score":17,"supported":47,"title":339,"abstract":340,"tags":341},[34],[35,34],[35],{"code":329,"score":20,"supported":47,"title":343,"abstract":345,"tags":346},[344],"fertilizer",[344],[325],{"code":53,"score":170,"supported":47,"title":348,"abstract":349,"tags":351},[],[30,350],"remote sensing",[31,30],{"code":64,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[27],[357,358],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":360,"openalex_id":362,"authors":363,"venue":317,"cited_by_count":99,"oa_url":9,"card":366,"direction":107,"ingested_from":108},"W7212289620",[364],{"name":365,"orcid":9},"Mohammed A. Naser",{"tldr":367,"method":368,"finding":369,"direction":107,"opportunity":370},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":9,"content":377,"source_name":378,"source_url":9,"published_at":379,"category":120,"cover_url":9,"hotness":13,"is_selected":47,"score":380,"score_detail":381,"sources":386,"tags":388,"primary_topic":390,"secondary_topics":391,"classification":392,"search_phrases":422,"slug":425,"view_count":185,"doi":9,"paper":9,"created_at":426},1465,"中国农业大学王耀君团队\"神农育种 Studio\"将于 9 月 4 日发布：1 套小麦育种方案效率提升 20 倍","https:\u002F\u002Fnews.cau.edu.cn\u002Fmtndnew\u002F021c96341130432394f118cd42f2f462.htm","中国农业大学副教授王耀君介绍：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、超过 5000 万条农业生产数据；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\"。9 月 4 日\"神农育种 Studio\"将正式发布——以设计一套小麦育种方案为例，过去需要投入 10 人月，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分散工作可由平台连续完成。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。","![Image 1: 微信截图_20260902172646.png](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002Fe4600f17fe5c4b24885135b75827105b.png)\n\n**农民日报全媒体记者 芦晓春 赵天浩**\n\n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\n**9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。\n\n[《农民日报》2026年8月28日 1版](https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260828\u002F20260828_1\u002Fnmrb_20260828_13390_1_2093084220427702292.html)","中国农业大学新闻网","2026-08-28T00:00:00Z",90,{"impact":382,"substance":383,"depth":19,"authority":384,"freshness":111,"relevant":22,"comment":385},28,24,14,"报道详实，展示AI在育种全链条的应用，发布平台效率提升显著，具产业级影响。",[387],{"name":378,"url":375},[27,130,28,389,29],"智能育种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":393},{"candidates":394,"agricultural_evidence":47},[395,399,403,409,413,418],{"code":33,"score":20,"supported":47,"title":396,"abstract":397,"tags":398},[35],[35],[29],{"code":150,"score":185,"supported":47,"title":400,"abstract":401,"tags":402},[],[188],[130],{"code":404,"score":21,"supported":47,"title":405,"abstract":406,"tags":408},"B1",[],[407],"生菜",[],{"code":59,"score":22,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[28],{"code":414,"score":22,"supported":14,"title":415,"abstract":416,"tags":417},"F2",[],[],[389],{"code":64,"score":22,"supported":14,"title":419,"abstract":420,"tags":421},[],[],[27],[423,424],"农业人工智能 小麦育种 智慧农业 智能育种","农业人工智能 小麦育种","农业人工智能小麦育种智慧农业智能育种-1465","2026-09-03T00:06:40.464354Z"]