[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3014":3,"related-3014":127},{"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":45,"search_phrases":85,"slug":88,"view_count":89,"doi":90,"paper":91,"created_at":126},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。",null,"Remote Sensing of Environment","2026-09-19T00:00:00Z","论文",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,23,19,15,9,1,"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业遥感","棉花","精准农业","无人机遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,57,64,70,78],{"code":33,"score":21,"supported":52,"title":53,"abstract":55,"tags":56},true,[54],"cotton",[],[29],{"code":58,"score":59,"supported":52,"title":60,"abstract":61,"tags":63},"I1",8,[],[30,62],"precision agriculture",[27,30],{"code":40,"score":59,"supported":52,"title":65,"abstract":66,"tags":69},[],[67,68],"遥感","remote sensing",[28,31],{"code":71,"score":72,"supported":52,"title":73,"abstract":74,"tags":77},"H3",6,[],[75,76],"灌溉","irrigation",[],{"code":79,"score":72,"supported":52,"title":80,"abstract":81,"tags":84},"I2",[],[82,83],"深度学习","deep learning",[],[86,87],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014",4,"10.1016\u002Fj.rse.2026.115674",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":118,"oa_url":6,"card":119,"direction":123,"ingested_from":125},"W7213661690",[94,97,99,101,103,105,108,111,113,116],{"name":95,"orcid":96},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":98,"orcid":9},"Dalei Hao",{"name":100,"orcid":9},"Jan Pisek",{"name":102,"orcid":9},"Zicheng Ji",{"name":104,"orcid":9},"Yanan Wei",{"name":106,"orcid":107},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":109,"orcid":110},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":112,"orcid":9},"Yangmin Feng",{"name":114,"orcid":115},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":117,"orcid":9},"Yelu Zeng",0,{"tldr":120,"method":121,"finding":122,"direction":123,"opportunity":124},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","农业遥感与作物表型","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","openalex","2026-09-20T23:30:21.262698Z",{"total":72,"page":22,"page_size":72,"items":128},[129,183,242,318,371,415],{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":135,"source_url":132,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":142,"tags":144,"primary_topic":147,"secondary_topics":148,"classification":150,"search_phrases":166,"slug":169,"view_count":170,"doi":171,"paper":172,"created_at":182},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",63,{"impact":59,"substance":139,"depth":140,"authority":13,"freshness":21,"relevant":22,"comment":141},20,16,"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[143],{"name":135,"url":132},[27,145,29,67,146],"产量预测","NDVI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[149],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":151},{"candidates":152,"agricultural_evidence":52},[153,157,162],{"code":33,"score":20,"supported":52,"title":154,"abstract":155,"tags":156},[54],[29,54],[29],{"code":40,"score":158,"supported":52,"title":159,"abstract":160,"tags":161},11,[68],[],[146,145,67],{"code":58,"score":22,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[27],[167,168],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838",3,"10.52171\u002Fherald.342",{"doi":171,"openalex_id":173,"authors":174,"venue":135,"cited_by_count":118,"oa_url":132,"card":177,"direction":123,"ingested_from":125},"W7214748696",[175],{"name":176,"orcid":9},"B.H. Babashli",{"tldr":178,"method":179,"finding":180,"direction":123,"opportunity":181},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":196,"tags":198,"primary_topic":201,"secondary_topics":202,"classification":204,"search_phrases":219,"slug":222,"view_count":223,"doi":224,"paper":225,"created_at":241},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z",58,{"impact":59,"substance":193,"depth":20,"authority":194,"freshness":21,"relevant":22,"comment":195},14,12,"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[197],{"name":189,"url":186},[199,29,67,200],"农业人工智能","作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[203],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":205},{"candidates":206,"agricultural_evidence":52},[207,211,215],{"code":40,"score":140,"supported":52,"title":208,"abstract":209,"tags":210},[68],[67,68],[200,67],{"code":33,"score":20,"supported":52,"title":212,"abstract":213,"tags":214},[54],[29,54],[29],{"code":79,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[199],[220,221],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179",5,"10.1007\u002Fs12524-026-02541-8",{"doi":224,"openalex_id":226,"authors":227,"venue":189,"cited_by_count":118,"oa_url":9,"card":236,"direction":123,"ingested_from":125},"W7213944040",[228,230,232,234],{"name":229,"orcid":9},"G. Suresh",{"name":231,"orcid":9},"G. Bhuvaneswari",{"name":233,"orcid":9},"G. Manikandan",{"name":235,"orcid":9},"M. Robinson Joel",{"tldr":237,"method":238,"finding":239,"direction":123,"opportunity":240},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","2026-09-22T23:30:23.811658Z",{"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":250,"score_detail":251,"sources":255,"tags":257,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":294,"slug":297,"view_count":22,"doi":298,"paper":299,"created_at":317},4563,"Seeing Crops in 3D: Remote Sensing for Smarter Precision Farming","https:\u002F\u002Fdoi.org\u002F10.12688\u002Fopenreseurope.25171.1","The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm\u002Fpixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.","数字技术在农业中的日益普及，正通过数据驱动的决策为提升生产力、可持续性和资源效率开辟新的机遇。本文介绍了RFSAT有限公司在H2020 AgriBIT项目中开发的综合精准农业框架，该框架目前正通过ENACT项目进一步完善。该框架将物联网（IoT）传感、厘米级精度的全球导航卫星系统（GNSS）定位、高分辨率多光谱遥感以及人工智能（AI）分析整合为一个智慧农业解决方案。一个关键的使能因素是由RFSAT开发的低成本、厘米级精度GNSS接收器，它能够对航空和地面观测进行精确的地理参照，进而生成空间分辨率超过1厘米\u002F像素的作物冠层高分辨率多光谱三维模型。这些数字表示支持在整个生长季对作物生长、冠层发育、生物量分布和植物健康进行详细监测。通过与由环境和小气候传感器组成的物联网监测基础设施相融合，地理参照的遥感产品形成了农业环境的综合数字孪生，机器学习技术在此基础上自动识别和定位作物异常，包括水分胁迫、养分缺乏、病害暴发和虫害侵扰。这些技术已在希腊、葡萄牙和意大利的运营试点基地进行了验证，涵盖果园、葡萄园和其他高价值作物，其中厘米级定位、多光谱三维建模、物联网监测和AI分析的结合大幅提高了监测精度、降低了投入品消耗并改善了管理决策。","Open Research Europe","2026-10-09T00:00:00Z",80,{"impact":17,"substance":252,"depth":19,"authority":253,"freshness":59,"relevant":22,"comment":254},22,13,"欧盟H2020项目论文，融合厘米级GNSS、多光谱3D建模与AI分析，方法新颖且多国验证，对智慧农业技术路线有参考价值。",[256],{"name":248,"url":245},[27,199,28,258,30],"数字孪生",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":262},{"candidates":263,"agricultural_evidence":52},[264,269,274,281,288],{"code":40,"score":17,"supported":52,"title":265,"abstract":266,"tags":268},[68],[67,68,267],"multispectral",[28],{"code":58,"score":194,"supported":52,"title":270,"abstract":271,"tags":273},[],[27,30,258,272],"digital twin",[258,27,30],{"code":79,"score":275,"supported":52,"title":276,"abstract":277,"tags":280},7,[],[278,279],"机器学习","artificial intelligence",[199],{"code":282,"score":72,"supported":52,"title":283,"abstract":284,"tags":287},"I5",[],[285,286],"物联网","IoT",[],{"code":289,"score":170,"supported":52,"title":290,"abstract":291,"tags":293},"B2",[],[292],"葡萄",[],[295,296],"AgriBIT 精准农业 遥感","GNSS 多光谱 3D 作物","AgriBIT精准农业遥感-4563","10.12688\u002Fopenreseurope.25171.1",{"doi":298,"openalex_id":300,"authors":301,"venue":248,"cited_by_count":118,"oa_url":245,"card":311,"direction":316,"ingested_from":125},"W7221178461",[302,305,308],{"name":303,"orcid":304},"Artur Krukowski","https:\u002F\u002Forcid.org\u002F0000-0002-6635-7585",{"name":306,"orcid":307},"Emmanouela Vogiatzaki","https:\u002F\u002Forcid.org\u002F0000-0001-9239-2045",{"name":309,"orcid":310},"İzzet Kale","https:\u002F\u002Forcid.org\u002F0000-0001-5562-6885",{"tldr":312,"method":313,"finding":314,"direction":123,"opportunity":315},"提出融合物联网、厘米级GNSS、多光谱遥感和AI的精准农业框架，构建作物数字孪生。","低成本厘米级GNSS、多光谱3D建模、IoT传感器与机器学习，在希腊、葡萄牙、意","厘米定位与多光谱3D建模结合AI可提升监测精度、减少投入并改善管理决策。","可探索低成本厘米级GNSS与多光谱3D数字孪生在更多作物和区域的可扩展性及实时决策优化。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:07.913557Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":9,"content":9,"source_name":323,"source_url":321,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":335,"classification":336,"search_phrases":347,"slug":350,"view_count":22,"doi":351,"paper":352,"created_at":370},4288,"Estimation of leaf nitrogen content of typical fruit trees in arid regions based on fusion of UAV multispectral information and texture features","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10465-z","Estimation of leaf nitrogen content of typical fruit trees in arid regions based on fusion of UAV multispectral information and texture features。Precision Agriculture","Precision Agriculture","2026-10-05T00:00:00Z",78,{"impact":140,"substance":327,"depth":17,"authority":193,"freshness":21,"relevant":22,"comment":328},21,"核心期刊论文，将无人机多光谱与纹理特征融合用于干旱区果树叶片氮素估测，方法有创新、结论可靠，对果园精准施肥有参考价值，但属细分领域技术进展，影响范围有限。",[330],{"name":323,"url":321},[27,28,31,332,333],"果树种植","氮素营养诊断",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":337},{"candidates":338,"agricultural_evidence":52},[339,343],{"code":40,"score":253,"supported":52,"title":340,"abstract":341,"tags":342},[267],[267],[28,31],{"code":58,"score":89,"supported":52,"title":344,"abstract":345,"tags":346},[],[62],[27],[348,349],"无人机 多光谱 果树 氮含量","干旱区 果树 遥感 估测","无人机多光谱果树氮含量-4288","10.1007\u002Fs11119-026-10465-z",{"doi":351,"openalex_id":353,"authors":354,"venue":323,"cited_by_count":118,"oa_url":9,"card":9,"direction":9,"ingested_from":125},"W7220458084",[355,357,360,362,364,366,368],{"name":356,"orcid":9},"Ran Pang",{"name":358,"orcid":359},"Mamat Sawut","https:\u002F\u002Forcid.org\u002F0009-0004-9961-7381",{"name":361,"orcid":9},"Eniwar Sidik",{"name":363,"orcid":9},"Shanshan Liu",{"name":365,"orcid":9},"Weichen Tian",{"name":367,"orcid":9},"Reheman Rouzi",{"name":369,"orcid":9},"Xiaolong Zhang","2026-10-06T23:30:02.240820Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":9,"content":376,"source_name":377,"source_url":9,"published_at":378,"category":379,"cover_url":9,"hotness":13,"is_selected":52,"score":380,"score_detail":381,"sources":384,"tags":386,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":410,"slug":413,"view_count":89,"doi":9,"paper":9,"created_at":414},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",89,{"impact":382,"substance":382,"depth":139,"authority":253,"freshness":59,"relevant":22,"comment":383},24,"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。",[385],{"name":377,"url":374},[27,199,29,31,387],"作物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":391},{"candidates":392,"agricultural_evidence":52},[393,397,401,406],{"code":33,"score":20,"supported":52,"title":394,"abstract":395,"tags":396},[29],[29,54],[29],{"code":58,"score":89,"supported":52,"title":398,"abstract":399,"tags":400},[],[62],[27],{"code":40,"score":402,"supported":14,"title":403,"abstract":404,"tags":405},2,[],[],[387,31],{"code":79,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[199],[411,412],"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","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":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":250,"score_detail":423,"sources":425,"tags":427,"primary_topic":430,"secondary_topics":435,"classification":443,"search_phrases":473,"slug":476,"view_count":118,"doi":477,"paper":478,"created_at":504},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",{"impact":140,"substance":252,"depth":17,"authority":193,"freshness":13,"relevant":22,"comment":424},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[426],{"name":421,"url":418},[27,199,28,428,429],"氮素监测","紫花苜蓿",{"code":431,"slug":432,"name":433,"path":434,"parent_code":37},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[436,437],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":438,"slug":439,"name":440,"path":441,"parent_code":442},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":46,"locked":14,"version":47,"method":48,"evidence":444},{"candidates":445,"agricultural_evidence":52},[446,451,457,463,469],{"code":40,"score":17,"supported":52,"title":447,"abstract":449,"tags":450},[448],"hyperspectral",[67,68,448],[28],{"code":431,"score":20,"supported":52,"title":452,"abstract":454,"tags":456},[453],"alfalfa",[455,453],"苜蓿",[429],{"code":438,"score":13,"supported":52,"title":458,"abstract":459,"tags":462},[],[460,455,461],"饲草","forage",[429],{"code":79,"score":13,"supported":52,"title":464,"abstract":465,"tags":468},[],[82,278,466,83,467],"machine learning","decision support",[199],{"code":58,"score":22,"supported":14,"title":470,"abstract":471,"tags":472},[],[],[27],[474,475],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":477,"openalex_id":479,"authors":480,"venue":421,"cited_by_count":118,"oa_url":418,"card":499,"direction":123,"ingested_from":125},"W7223154576",[481,483,485,487,489,492,494,497],{"name":482,"orcid":9},"Shuai Fu",{"name":484,"orcid":9},"Yunhao Li",{"name":486,"orcid":9},"Chunli Miao",{"name":488,"orcid":9},"Jie Liu",{"name":490,"orcid":491},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":493,"orcid":9},"Yongjia Hu",{"name":495,"orcid":496},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":498,"orcid":9},"吴彩霞",{"tldr":500,"method":501,"finding":502,"direction":123,"opportunity":503},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z"]