[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2960":3,"related-2960":44},{"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":18,"tags":20,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":43},2960,"Мониторинг трансформации руслового рельефа при селевых воздействиях с применением беспилотных авиационных систем","https:\u002F\u002Fdoi.org\u002F10.46698\u002Fvnc.2026.73.60.001","В работе показан опыт применения цифровых методов дистанционного зондирования для количественной оценки морфометрических последствий схода селевого потока в русле горной реки. По материалам разновременной БПЛА-аэрофотосъёмки участка р. Нальчик у с. Хасанья (до и после события) построены ортофотопланы и цифровые модели местности, на основе которых рассчитаны объёмы денудации и аккумуляции грунта. Вынос грунта в зоне разрушения автодороги составил 4,3 тыс. м³, накопление наносов выше автодорожного моста – 4,6 тыс. м³. Полученные данные интегрированы в базу расчётов для разработки инженерно-защитных мероприятий. Показана высокая эффективность метода для оперативного мониторинга горных территорий при минимальных затратах. The paper shows the experience of using digital remote sensing methods to quantify the morphometric consequences of a mudflow in a mountain riverbed. Based on multi-temporal UAV aerial surveys of a section of the Nalchik River near the village of Khasanya conducted before and after the event, orthophotomaps and digital terrain models (DTMs) were generated, on the basis of which the volumes of denudation and sediment accumulation were calculated. The volume of material in the area where the highway was destroyed amounted to 4.3 thousand m3 , while sediment accumulation upstream of the highway bridge amounted to 4.6 thousand m3 . The data obtained were integrated into a calculation database for developing engineering protection measures. The study demonstrates the high efficiency of the method for operational monitoring of mountainous areas at relatively low cost. Keywords: digital terrain model, orthophotomap, mudflow, riverchannel, morphometry, unmanned aerial system, remote sensing","本文展示了应用数字遥感方法对山区河流河床泥石流（селевой поток）地貌形态后果进行定量评估的实践。基于对哈萨尼亚村附近纳利奇克河某河段在事件前后开展的无人机（БПЛА）多时相航空摄影资料，构建了正射影像图和数字地形模型，并据此计算了土壤剥蚀量和堆积量。公路破坏区的土体输出量为4300立方米，公路桥上游的泥沙堆积量为4600立方米。所得数据已整合至工程防护措施设计计算数据库。研究表明，该方法在以最低成本对山区进行实时监测方面具有高效性。",null,"Вестник Владикавказского научного центра","2026-09-18T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究山地泥石流河床地形变化的无人机遥感监测，属于地质灾害与遥感测绘领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"无人机遥感","泥石流监测","山地灾害","数字地形模型",[26,27],"Нальчик 泥石流 无人机","泥石流 河床 地形监测","Нальчик泥石流无人机-2960","10.46698\u002Fvnc.2026.73.60.001",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":6,"card":35,"direction":41,"ingested_from":42},"W7213550177",[33],{"name":34,"orcid":9},"З.Ж. Гергокова",{"tldr":36,"method":37,"finding":38,"direction":39,"opportunity":40},"用无人机多期航测量化泥石流对山区河床地形的侵蚀与堆积变化。","前后两期无人机航拍生成正射影像与数字地形模型，计算侵蚀堆积体积。","公路破坏区冲出泥沙4.3千立方米，桥上游堆积4.6千立方米，方法高效低成本。","农业遥感与作物表型","可将无人机多期地形监测拓展至农业流域泥沙输移与农田保护工程评估。","数字乡村与农业信息化","openalex","2026-09-19T23:30:45.161114Z",{"total":45,"page":46,"page_size":45,"items":47},6,1,[48,81,113,140,183,207],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":9,"content":9,"source_name":53,"source_url":9,"published_at":54,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":55,"score_detail":56,"sources":61,"tags":63,"search_phrases":68,"slug":71,"view_count":15,"doi":9,"paper":72,"created_at":80},2997,"基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别方法","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685741381196825088","江苏省农业科学院农业信息研究所梁万杰等联合中国农科院农业环境与可持续发展研究所、湖北粮作所、扬州大学等团队，针对小麦渍害防控提出基于无人机多光谱图像和VGG21模型的快速无损识别方法。在小麦拔节-抽穗和抽穗-灌浆两个阶段开展对照、渍水胁迫、硅肥调控和氨基酸调控4个类别数据集，大疆精灵4多光谱无人机采集小麦冠层多光谱图像，测产评估调控效果。","智慧农业(中英文)2026,8(4):60-69","2026-09-15T12:42:00Z",78,{"impact":57,"substance":58,"depth":57,"authority":59,"freshness":45,"relevant":46,"comment":60},18,22,14,"多机构协作提出无人机多光谱结合VGG21的小麦渍害无损识别方法，方法新颖、数据扎实，对智慧农业植保监测有参考价值。",[62],{"name":53,"url":51},[64,65,21,66,67],"智慧农业","农业人工智能","小麦渍害","多光谱成像",[69,70],"江苏省农科院 小麦渍害 无人机多光谱","VGG21 小麦 渍害识别","江苏省农科院小麦渍害无人机多光谱-2997",{"doi":9,"openalex_id":9,"authors":73,"venue":9,"cited_by_count":15,"oa_url":9,"card":74,"direction":39,"ingested_from":79},[],{"tldr":75,"method":76,"finding":77,"direction":39,"opportunity":78},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4多光谱无人机采集冠层图像，构建VGG21分类模型。","该方法可快速无损识别渍害及硅肥、氨基酸调控效果。","可探索多光谱与深度学习结合评估其他逆境调控措施，并迁移至多作物场景。","agent","2026-09-20T00:03:07.858899Z",{"id":82,"title":83,"url":84,"summary":85,"summary_zh":9,"content":9,"source_name":86,"source_url":9,"published_at":87,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":89,"sources":96,"tags":98,"search_phrases":101,"slug":104,"view_count":15,"doi":9,"paper":105,"created_at":112},2854,"面向冬小麦水分含量的无人机遥感自动机器学习预测——MDPI Remote Sensing","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3161","本研究探索了无人机遥感快速准确评估冬小麦水分含量的潜力。在开花期和灌浆期使用配备多光谱、RGB和热红外相机的无人机获取高分辨率冠层遥感图像。集成地面真值采样数据与无人机遥感数据,使用自动机器学习(AutoML)框架建立回归模型预测冬小麦水分含量(MC)。结果表明,MC预测在灌浆期表现最佳,TIR传感器精度最高(R²=0.812,MAE=0.0204,RMSE=0.0274)。多传感器融合相比单传感器方法进一步提升预测性能,MC预测的R²达0.876、MAE 0.0191、RMSE 0.0259。来自中国农业科学院农田灌溉研究所。","MDPI Remote Sensing","2026-09-15T00:00:00Z",74,{"impact":90,"substance":91,"depth":92,"authority":93,"freshness":94,"relevant":46,"comment":95},15,21,17,13,8,"中国农科院团队用AutoML融合多光谱、RGB与热红外无人机数据预测冬小麦水分含量，多传感器融合R²达0.876，方法新颖、结论可靠，对精准灌溉有实用价值，值得进入每日精选。",[97],{"name":86,"url":84},[64,65,21,99,100],"冬小麦","多传感器融合",[102,103],"农业人工智能 多传感器融合 无人机遥感 智慧农业","农业人工智能 多传感器融合","农业人工智能多传感器融合无人机遥感智慧农业-2854",{"doi":9,"openalex_id":9,"authors":106,"venue":9,"cited_by_count":15,"oa_url":9,"card":107,"direction":39,"ingested_from":79},[],{"tldr":108,"method":109,"finding":110,"direction":39,"opportunity":111},"用无人机多传感器遥感结合AutoML预测冬小麦水分含量。","无人机多光谱、RGB、热红外图像+地面真值，AutoML回归建模。","灌浆期热红外精度最高，多传感器融合将R²提升至0.876。","可探索不同生育期与品种的泛化性，及将水分预测接入灌溉决策系统。","2026-09-18T00:03:30.758419Z",{"id":114,"title":115,"url":116,"summary":117,"summary_zh":9,"content":9,"source_name":118,"source_url":9,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":123,"tags":125,"search_phrases":128,"slug":131,"view_count":15,"doi":9,"paper":132,"created_at":139},2611,"无人机遥感在水稻高通量表型分析中的研究进展 系统综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260911171723051.htm","发表于Smart Agricultural Technology。依据PRISMA 2020规范系统检索文献最终纳入199项研究(2014–2026年)。研究发现：先进传感、特征集成和建模技术日益支持氮素和叶绿素估算及产量预测；轻量级模型和边缘计算系统在倒伏和病害监测任务中显示出实时部署的可行性；跨区域泛化受环境背景干扰以及地点品种偏倚制约；199项研究中有6项(3.0%)将UAV衍生性状与遗传关联分析联系起来。","Smart Agricultural Technology","2026-09-11T01:00:00Z",77,{"impact":57,"substance":58,"depth":57,"authority":93,"freshness":45,"relevant":46,"comment":122},"基于PRISMA规范纳入199项研究的系统综述，方法严谨、数据规模大，对水稻表型与智慧育种有实质参考价值，但属细分领域学术进展，公共影响有限。",[124],{"name":118,"url":116},[64,65,126,21,127],"水稻","高通量表型",[129,130],"农业人工智能 无人机遥感 高通量表型 智慧农业","农业人工智能 无人机遥感","农业人工智能无人机遥感高通量表型智慧农业-2611",{"doi":9,"openalex_id":9,"authors":133,"venue":9,"cited_by_count":15,"oa_url":9,"card":134,"direction":39,"ingested_from":79},[],{"tldr":135,"method":136,"finding":137,"direction":39,"opportunity":138},"系统综述199项研究，梳理无人机遥感在水稻高通量表型分析中的应用进展与瓶颈。","依据PRISMA 2020系统检索2014–2026年199项研究并归纳分析。","传感与建模支撑氮素、产量预测，跨区域泛化受环境与品种偏倚制约，基因关联研究仅占3%。","UAV表型与遗传关联分析严重不足，可探索跨区域泛化建模及表型-基因型融合方向。","2026-09-16T00:03:52.470505Z",{"id":141,"title":142,"url":143,"summary":144,"summary_zh":145,"content":9,"source_name":146,"source_url":143,"published_at":147,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":148,"sources":150,"tags":152,"search_phrases":157,"slug":160,"view_count":15,"doi":161,"paper":162,"created_at":182},2418,"Artificial Intelligence-Driven Anti-Poaching Surveillance Systems: A Review of Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.14445\u002F23488549\u002Fijece-v13i8p103","The UN Environment Programme reports an average 69% decline in wildlife population since 1970, with around 4000 species illegally poached, trafficked, and traded globally. The World Wildlife Crime Report shows a significant population revival for certain species, but overall protection remains inadequate. The 5-step approach to understanding wildlife decline can improve methods. Emerging smart technologies, such as computer vision, IoT systems, and AI, can help manage wildlife populations and prevent poaching. Drones, bio-loggers, camera traps, and GPS collars provide real-time data for wildlife monitoring and tracking. Unmanned Aerial Vehicles (UAVs) have also shown promise in wildlife welfare. This article aims to analyze the decline of wildlife populations, study smart technologies for anti-poaching efforts, and evaluate emerging technology-driven solutions to traditional wildlife welfare challenges. Human-induced factors like hunting, poaching, pollution, and habitat loss have compromised biodiversity. Poaching has led to population reduction and ecological imbalance. Traditional conservation practices have failed to revive wildlife populations. Integrating smart technologies, IoT, AI, and predictive analysis can help prevent poaching and promote ecological balance.","联合国环境规划署报告称，自1970年以来，野生动物种群平均减少了69%，全球约有4000个物种遭到非法偷猎、贩运和交易。《世界野生动物犯罪报告》显示，某些物种种群显著恢复，但总体保护仍然不足。理解野生动物减少的5步方法可以改进相关措施。新兴智能技术，如计算机视觉、物联网系统和人工智能，有助于管理野生动物种群并防止偷猎。无人机、生物记录器、相机陷阱和GPS项圈为野生动物监测和追踪提供实时数据。无人驾驶飞行器（UAVs）在野生动物福利方面也展现出前景。本文旨在分析野生动物种群的减少，研究反偷猎的智能技术，并评估新兴技术驱动的解决方案以应对传统野生动物福利挑战。狩猎、偷猎、污染和栖息地丧失等人为因素破坏了生物多样性。偷猎导致种群减少和生态失衡。传统保护实践未能恢复野生动物种群。整合智能技术、物联网、人工智能和预测分析有助于防止偷猎并促进生态平衡。","International Journal of Electronics and Communication Engineering","2026-09-11T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":149},"主题为野生动物反盗猎智能监测，属生态保护领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[151],{"name":146,"url":143},[153,154,21,155,156],"计算机视觉","物联网","野生动物保护","反盗猎监测",[158,159],"野生动物保护 反盗猎监测 无人机遥感 计算机视觉","野生动物保护 反盗猎监测","野生动物保护反盗猎监测无人机遥感计算机视觉-2418","10.14445\u002F23488549\u002Fijece-v13i8p103",{"doi":161,"openalex_id":163,"authors":164,"venue":146,"cited_by_count":15,"oa_url":175,"card":176,"direction":180,"ingested_from":42},"W7212319763",[165,167,169,171,173],{"name":166,"orcid":9},"Rohit Samkaria",{"name":168,"orcid":9},"Rajesh Singh",{"name":170,"orcid":9},"Anita Gehlot",{"name":172,"orcid":9},"Charvi Joshi",{"name":174,"orcid":9},"Rahul Mahala","https:\u002F\u002Fwww.internationaljournalssrg.org\u002F..\u002FIJECE\u002F2026\u002FVolume13-Issue8\u002FIJECE-V13I8P103.pdf",{"tldr":177,"method":178,"finding":179,"direction":180,"opportunity":181},"综述AI、物联网与无人机等智能技术在反盗猎监测中的进展、挑战与未来方向。","综述计算机视觉、IoT、无人机、相机陷阱、GPS项圈等实时监测技术。","传统保护措施失效，AI与IoT融合可有效预防盗猎并促进生态平衡。","智慧农业 \u002F 农业物联网","可探索AI预测模型与多源传感数据融合在反盗猎实时预警中的落地验证。","2026-09-14T23:30:12.295479Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":9,"content":188,"source_name":189,"source_url":9,"published_at":190,"category":191,"cover_url":9,"hotness":13,"is_selected":192,"score":193,"score_detail":194,"sources":198,"tags":200,"search_phrases":203,"slug":205,"view_count":46,"doi":9,"paper":9,"created_at":206},1499,"中国农大马韫韬教授团队 AI 驱动棉花高通量表型解析三项系列论文：覆盖花器官检测、单株表型、吐絮动态评估","https:\u002F\u002Fnews.cau.edu.cn\u002Fkxyj\u002F287ffb5af8464b76bd61d1db9d408af3.htm","中国农业大学土地科学与技术学院马韫韬教授团队围绕棉花关键生育时期的表型智能识别与量化分析，在 Computers and Electronics in Agriculture、Plant Phenomics 和 Precision Agriculture 等国际权威期刊上连续发表三篇研究论文，题目分别为 MS-R outperforms RGB: Lightweight CYOLO-BiCNet enables efficient detection and storage optimization of cotton flowers，Scalable phenotyping and yield estimation via stability index and single-plant variability using a vision-based large model framework 和 Assessing Cotton Boll-opening Concentration for Harvest Decision-making via Foundation Model-enhanced Cross-scale Phenotyping。","**中国农大新闻网讯**近日，中国农业大学土地科学与技术学院马韫韬教授团队在AI驱动的棉花高通量表型解析领域取得系列重要进展。团队围绕棉花关键生育时期的表型智能识别与量化分析，在 _Computers and Electronics in Agriculture_、_Plant Phenomics_ 和 _Precision Agriculture_ 等国际权威期刊上连续发表三篇研究论文，题目分别为[_MS-R outperforms RGB: Lightweight CYOLO-BiCNet enables efficient detection and storage optimization of cotton flowers_](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11119-026-10396-9)_，[Scalable phenotyping and yield estimation via stability index and single-plant variability using a vision-based large model framework](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0168169925014061)_ 和[_Assessing Cotton Boll-opening Concentration for Harvest Decision-making via Foundation Model-enhanced Cross-scale Phenotyping_](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS264365152600107X)。三篇论文构建了覆盖“花器官检测—单株表型解析—吐絮动态评估”的棉花全生育期AI表型技术链条，为推动棉花智慧育种和机械化收获决策提供了重要的方法学支撑与技术方案。\n\n团队在作物表型组学与智能农业方向具有深厚的研究积累。近年来，又将研究视野从大田粮食作物拓展至经济作物领域，与中国农业科学院棉花研究所杜雄明研究员、何守朴研究员团队建立了紧密的合作关系。合作团队围绕棉花种质资源评价、株型调控与机械化适配等核心问题，联合开展了大量田间表型试验与数据采集工作，为AI表型技术的落地提供了丰富的多源、多尺度、多时段数据基础和育种应用场景。在此基础上，马韫韬教授团队充分发挥在深度学习建模、无人机遥感与高通量表型解析方面的技术优势，逐步形成了面向棉花全生育期的智能化表型研究体系。\n\n此次发表的三篇系列论文，分别从棉花不同生育阶段的关键表型需求出发，构建了一条由花器官快速检测、单株尺度多维表型解析到吐絮动态评估与收获决策的技术闭环。论文一针对棉花花期花朵检测的效率与存储瓶颈，提出了轻量化检测网络CYOLO-BiCNet，证实独立获取的多光谱红波段（MS-R）在花朵检测精度与存储效率上均显著优于传统RGB图像，为大田尺度花器官高通量监测提供了低成本、高效率的数据-模型组合方案。\n\n![Image 1: 图片1.png](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002F8ed95462d59c45a8ad68d69bb913d44e.png)\n\n图1 基于CYOLO-BiCNet模型对不同数据类型、多个时期棉花花的推断结果分析\n\n图1展示了CYOLO-BiCNet模型在MS-R、RGB及RGB-R三种数据上的可视化推理结果。整体来看，MS-R数据的漏检花朵数量最少，检测效果最优；RGB与 RGB-R数据漏检相对较多。从不同采集日期来看，7月30日三种数据的漏检数分别为11、16、19朵；8月8日降至4、6、7朵；8月15日为3、7、5朵。可视化结果直观表明，MS-R数据在各时段均表现出最佳的花朵检测性能，漏检情况显著少于RGB和RGB-R数据。\n\n![Image 2: 图片2.png](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002F31e657d8287840d198aff233328adebe.png)\n\n图2 基于视觉大模型的棉花单株多维表型解析与高产种质筛选技术流程\n\n论文二聚焦单株尺度表型解析这一育种核心需求，开发了视觉大模型框架TopoRefineSAM，融合YOLOv12检测与SAM2分割能力，实现了复杂田间条件下棉花单株的精准实例分割与多性状反演；同时创新性地提出稳定性指标组（SIG），将单株间的差异转化为小区尺度的稳定性特征，显著提升了产量估算和品种筛选的准确性与可解释性。\n\n![Image 3: 图片3.png](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002F76d4db4425564fd28a022cfd32166839.png)\n\n图3 有无稳定性指数组时，产量反演精度和特征重要性之间的差异\n\n图3展示了稳定性指数组（SIG）对小区产量反演精度的提升效果。引入SIG后，全生育期决定系数R²由0.344–0.629提升至0.512–0.701，RMSE与MAE同步下降。生育前期精度偏低，7月3日与10日R²分别由0.473、0.440升至0.512、0.520；随生育进程推进，模型表现持续改善，8月23日R²达全季最高0.701（无稳定性指数组时R²为0.629）。特征重要性分析显示，RHa_mean始终为最关键预测因子，而Ta_stability、Tl_stability、RHa_stability与 Tc_stability均进入前十，稳定性特征整体贡献超过61%。结果表明，SIG有效整合了株间变异信息，显著增强了模型对产量差异的解释能力与全生育期预测精度。\n\n![Image 4: 图片4.png](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002F1395532013ea4c71a7612cd15fbd7773.png)\n\n图4 棉花吐絮进程动态监测与基于CTSI指数的机械化收获决策框架\n\n论文三则瞄准棉花机械化收获中吐絮集中度评价这一产业痛点，开发了基于视觉基础模型的DINO-BollGX检测框架，结合两年期383个品种的多时相无人机影像，重建了小区尺度吐絮动态时序曲线，提出了棉花吐絮时间稳定性指数（CTSI），并将其与时间风险函数耦合生成收获决策曲线，为品种适宜机收性评价和最优收获窗口确定提供了定量化工具。\n\n![Image 5: 图片5.png](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002F6711154c55a84f5c8dd2f980c6a7f9e3.png)\n\n图5 383个品种棉花吐絮时空分布特征及代表性品种动态吐絮曲线聚类分析\n\n图5展示了多时相无人机棉铃吐絮计数的群体与品种尺度动态。群体尺度上，吐絮在时间分布上呈后期集中态势：观测初期（8月中下旬）各小区吐絮量极低，中位数均不足峰值的5%，箱线图四分位距狭窄，表明吐絮进程缓慢且表达微弱；进入9月中下旬后中位数快速攀升，四分位距显著拓宽，标志着吐絮进入加速期；10月14日至20日为吐絮高峰窗口，中位数由131跃升至277铃\u002F小区，约半数季节最大吐絮量在此短期内完成；11月初中位数小幅回落至244，提示吐絮基本完成并伴有少量损耗或遮挡。品种尺度上，383个品种整体表现为前期缓慢、后期快速增长的动态模式，但吐絮起始时间与后期增速差异显著。基于标准化动态特征的K-means聚类进一步将品种划分为两类：紧凑高量型吐絮窗口短（中位6天）、峰值高（中位460铃\u002F小区）、速率快（35铃\u002F天）；延续型窗口长（约60天）、峰值低（144铃\u002F小区）、速率缓（2.4铃\u002F天），两组差异均达极显著水平（p\u003C0.001）。\n\n三篇论文沿棉花“花—株—铃”的生育主线层层递进，形成了从器官识别、个体解析到群体决策的完整技术体系。这一系列成果的核心意义在于，将人工智能与高通量表型技术深度融合，为棉花育种从传统的经验选择向数据驱动的精准筛选转型提供了系统性解决方案。当前，我国棉花种质资源丰富但表型评价手段仍相对滞后，大量优异种质因缺乏高效、标准化的表型鉴定而未能被充分发掘利用。未来，团队将聚焦更多元化的棉花种质资源群体，结合新疆、河南等不同生态区的目标环境特征，拓展表型解析框架在不同棉区和种植制度下的适用性与泛化能力；同时，进一步整合数字孪生、作物生长模型与基因组信息，构建贯通“表型—基因型—环境—管理”的棉花智能设计体系，为我国棉花种业振兴和智慧农业的高质量发展提供核心技术支撑。\n\n上述三篇论文的第一作者均为土地科学与技术学院博士研究生陈勉，通讯作者为马韫韬教授。合作者包括中国农业科学院棉花研究所杜雄明研究员、何守朴研究员、耿晓丽副研究员、胡道武副研究员；中国农业科学院生物技术研究所张锐研究员；中国农业大学农学院田晓莉教授等，研究得到了国家重点研发计划、国家自然科学基金等项目的资助支持。","中国农业大学新闻网","2026-09-01T00:00:00Z","报道",true,89,{"impact":195,"substance":195,"depth":196,"authority":93,"freshness":94,"relevant":46,"comment":197},24,20,"中国农大团队在棉花AI表型领域发表三篇系列论文，构建全生育期技术链条，方法新颖、数据详实，对智慧育种和机收决策有重要支撑。",[199],{"name":189,"url":186},[64,65,201,21,202],"棉花","作物表型",[204,130],"农业人工智能 无人机遥感 作物表型 智慧农业","农业人工智能无人机遥感作物表型智慧农业-1499","2026-09-03T00:06:43.550281Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":219,"tags":221,"search_phrases":226,"slug":229,"view_count":15,"doi":230,"paper":231,"created_at":270},1406,"Rice leaf structural adjustment to sustain photosynthetic traits under drought and produce more grain: evidence from field studies of the 3K indica panel","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cpb.2026.100660","Understanding plant traits that contribute to maintenance of physiological activities under drought is crucial for sustainable rice production. Leaf morphological characters together with UAV-based HTP measurements of canopy temperature (CT), NDVI, leaf water potential ( ψ Leaf ), photosynthetic traits like A, g s E and Ci with drought response index (DRI) were collected from >600 indica genotypes in two field dry seasons. This dataset from the 3 K rice sequenced genomes under well-watered (WW) and managed drought stress (MDS) revealed drought-induced leaf morpho-functional interactive changes related to grain yield under drought. Onset of drought caused ~14-20% reduction in Ci , 35 – 56% reduction in A causing serious loss in yield. Correlations analyses from two years field dry seasons dataset revealed that the combination of initially broader leaves capable of increasing leaf thickness in response to drought, as evidenced by changes in SLA, together with maintenance of ψ Leaf , supporting a higher A net can collectively drive towards a higher DRI. The SLA changes up to even ~50% in one of the best performing genotypes with high DRI values upon severe drought stress. GWAS identified seven QTLs for DRI which include known drought-responsive aquaporin, dehydrin, and heat shock protein genes, which coincide with the GWAS interval identified for CT on chromosome 7. This study identified a tentative pathway linking genomic loci to water balance, leaf development, and photosynthesis-related traits under drought. Our results provide opportunities to select the best-performing rice genotypes, enhance understanding about how to improve grain yield of rice under drought by mechanistic exploration whereas aid drought breeding efforts by selecting optimal haplotype combinations or gene edits .","了解植物在干旱条件下维持生理活动的性状特征，对于实现水稻可持续生产至关重要。本研究在两年田间旱季条件下，对超过600份籼稻基因型进行了叶片形态特征、基于无人机的高通量表型（HTP）测量（包括冠层温度（CT）、归一化植被指数（NDVI）、叶片水势（ψLeaf））、光合性状（如净光合速率A、气孔导度gs、蒸腾速率E和胞间CO₂浓度Ci）以及干旱响应指数（DRI）的测定。该数据集来源于3K水稻基因组测序计划材料，在充分灌溉（WW）和人工控水干旱胁迫（MDS）条件下获取，揭示了干旱诱导的叶片形态-功能交互变化及其与干旱条件下籽粒产量的关系。干旱发生导致Ci降低约14%~20%，A降低35%~56%，从而造成严重的产量损失。基于两年田间旱季数据的相关性分析表明，初始叶片较宽且能在干旱响应中增加叶片厚度（以比叶面积SLA的变化为证据）的性状组合，加上维持较高的ψLeaf以支持更高的净光合速率A，可共同驱动更高的DRI值。在表现最优的高DRI基因型中，严重干旱胁迫下SLA变化幅度甚至可达约50%。全基因组关联分析（GWAS）鉴定出7个与DRI相关的数量性状位点（QTL），其中包含已知的干旱响应水通道蛋白、脱水素和热激蛋白基因，这些位点与第7号染色体上CT的GWAS区间重叠。本研究初步揭示了一条将基因组位点与干旱条件下水分平衡、叶片发育及光合相关性状联系起来的潜在通路。我们的研究结果为筛选优良水稻基因型提供了机会，通过机制性探索加深了对干旱条件下提高水稻籽粒产量的理解，同时通过选择最优单倍型组合或基因编辑为抗旱育种工作提供支持。","Current Plant Biology",79,{"impact":196,"substance":58,"depth":57,"authority":216,"freshness":217,"relevant":46,"comment":218},12,7,"基于3K水稻基因组的大规模田间研究，揭示叶片结构适应干旱的机制，对育种有重要指导意义。",[220],{"name":213,"url":210},[222,21,223,224,225],"水稻育种","抗旱性","光合作用","基因组学",[227,228],"无人机遥感 光合作用 基因组学 水稻育种","无人机遥感 光合作用","无人机遥感光合作用基因组学水稻育种-1406","10.1016\u002Fj.cpb.2026.100660",{"doi":230,"openalex_id":232,"authors":233,"venue":213,"cited_by_count":15,"oa_url":264,"card":265,"direction":180,"ingested_from":42},"W7204905867",[234,237,239,241,243,245,247,249,251,254,256,259,262],{"name":235,"orcid":236},"Jolly Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0003-2370-4185",{"name":238,"orcid":9},"Mary Jacqueline Dionora",{"name":240,"orcid":9},"Ma. Rebecca Laza",{"name":242,"orcid":9},"Marjorie De Ocampo",{"name":244,"orcid":9},"Pauline M. Muyco",{"name":246,"orcid":9},"Maria Elizabeth B. Naredo",{"name":248,"orcid":9},"Mignon Natividad",{"name":250,"orcid":9},"Marinell Ramirez-Quintana",{"name":252,"orcid":253},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":255,"orcid":9},"Stephen Klassen",{"name":257,"orcid":258},"W. Paul Quick","https:\u002F\u002Forcid.org\u002F0000-0002-6327-1962",{"name":260,"orcid":261},"Amelia Henry","https:\u002F\u002Forcid.org\u002F0000-0001-6255-5480",{"name":263,"orcid":9},"Kenneth L. McNally","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2214662826000824\u002Fpdf",{"tldr":266,"method":267,"finding":268,"direction":39,"opportunity":269},"通过田间试验和无人机高通量表型分析，研究水稻叶片结构在干旱下维持光合并增产的机制。","利用3K水稻基因组、无人机表型、GWAS分析叶片形态与光合性状。","干旱下叶片增厚与维持水势可维持光合，提高抗旱指数和产量，发现7个QTL。","可进一步研究叶片结构可塑性相关基因调控网络，结合基因编辑优化抗旱育种策略。","2026-09-02T23:30:11.007351Z"]