[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5567":3,"related-5567":128},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":77,"slug":80,"view_count":81,"doi":82,"paper":83,"created_at":127},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。",null,"bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,9,1,"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","育种","遥感","紫花苜蓿",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,63,71],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":57},15,true,[54],"alfalfa",[56,54],"苜蓿",[30],{"code":39,"score":19,"supported":52,"title":59,"abstract":61,"tags":62},[60],"multispectral",[60],[29],{"code":64,"score":65,"supported":52,"title":66,"abstract":67,"tags":70},"I1",7,[],[68,69],"精准农业","precision agriculture",[26],{"code":72,"score":73,"supported":52,"title":74,"abstract":75,"tags":76},"C4",4,[],[56],[30],[78,79],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567",0,"10.64898\u002F2026.10.14.759249",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":81,"oa_url":6,"card":120,"direction":124,"ingested_from":126},"W7223083966",[86,89,92,95,98,100,102,105,108,111,114,117],{"name":87,"orcid":88},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":90,"orcid":91},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":93,"orcid":94},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":96,"orcid":97},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":99,"orcid":9},"Julie Hansen",{"name":101,"orcid":9},"Christopher Pierce",{"name":103,"orcid":104},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":106,"orcid":107},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":109,"orcid":110},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":112,"orcid":113},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":115,"orcid":116},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":118,"orcid":119},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","农业遥感与作物表型","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","openalex","2026-10-22T11:20:32.655394Z",{"total":129,"page":21,"page_size":129,"items":130},6,[131,223,287,378,454,510],{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":9,"source_name":137,"source_url":134,"published_at":138,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":139,"score_detail":140,"sources":144,"tags":146,"primary_topic":150,"secondary_topics":151,"classification":158,"search_phrases":191,"slug":194,"view_count":81,"doi":195,"paper":196,"created_at":222},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",80,{"impact":141,"substance":18,"depth":17,"authority":142,"freshness":13,"relevant":21,"comment":143},16,14,"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。",[145],{"name":137,"url":134},[26,147,148,149,30],"农业人工智能","农业遥感","氮素监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[152,153],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":72,"slug":154,"name":155,"path":156,"parent_code":157},"feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":45,"locked":14,"version":46,"method":47,"evidence":159},{"candidates":160,"agricultural_evidence":52},[161,167,171,177,187],{"code":39,"score":17,"supported":52,"title":162,"abstract":164,"tags":166},[163],"hyperspectral",[29,165,163],"remote sensing",[148],{"code":32,"score":51,"supported":52,"title":168,"abstract":169,"tags":170},[54],[56,54],[30],{"code":72,"score":13,"supported":52,"title":172,"abstract":173,"tags":176},[],[174,56,175],"饲草","forage",[30],{"code":178,"score":13,"supported":52,"title":179,"abstract":180,"tags":186},"I2",[],[181,182,183,184,185],"深度学习","机器学习","machine learning","deep learning","decision support",[147],{"code":64,"score":21,"supported":14,"title":188,"abstract":189,"tags":190},[],[],[26],[192,193],"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":195,"openalex_id":197,"authors":198,"venue":137,"cited_by_count":81,"oa_url":134,"card":217,"direction":124,"ingested_from":126},"W7223154576",[199,201,203,205,207,210,212,215],{"name":200,"orcid":9},"Shuai Fu",{"name":202,"orcid":9},"Yunhao Li",{"name":204,"orcid":9},"Chunli Miao",{"name":206,"orcid":9},"Jie Liu",{"name":208,"orcid":209},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":211,"orcid":9},"Yongjia Hu",{"name":213,"orcid":214},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":216,"orcid":9},"吴彩霞",{"tldr":218,"method":219,"finding":220,"direction":124,"opportunity":221},"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",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":9,"content":9,"source_name":228,"source_url":226,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":234,"tags":236,"primary_topic":240,"secondary_topics":241,"classification":249,"search_phrases":275,"slug":278,"view_count":21,"doi":279,"paper":280,"created_at":286},4084,"Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.31332.74887","Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area。OpenAlex","OpenAlex","2026-10-02T00:00:00Z",68,{"impact":19,"substance":17,"depth":141,"authority":232,"freshness":20,"relevant":21,"comment":233},13,"该论文将轮作与遥感结合评估有机土壤水土保持，方法有创新且数据可靠，但属区域研究，影响范围有限，时效性强，值得关注。",[235],{"name":228,"url":226},[26,237,29,238,239],"轮作","水土保持","有机土壤",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[242,243],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":244,"slug":245,"name":246,"path":247,"parent_code":248},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":250},{"candidates":251,"agricultural_evidence":52},[252,257,261,266,271],{"code":32,"score":19,"supported":52,"title":253,"abstract":255,"tags":256},[254],"crop rotation",[254],[237],{"code":39,"score":19,"supported":52,"title":258,"abstract":259,"tags":260},[165],[165],[29],{"code":244,"score":262,"supported":52,"title":263,"abstract":264,"tags":265},11,[245],[245],[],{"code":267,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},"H6",[],[],[238],{"code":64,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},[],[],[26],[276,277],"Everglades 农业区 遥感 轮作","有机土壤 水土保持 遥感","Everglades农业区遥感轮作-4084","10.13140\u002Frg.2.2.31332.74887",{"doi":279,"openalex_id":281,"authors":282,"venue":9,"cited_by_count":81,"oa_url":226,"card":9,"direction":124,"ingested_from":126},"W7216417358",[283],{"name":284,"orcid":285},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033","2026-10-03T23:30:32.356375Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":305,"secondary_topics":306,"classification":308,"search_phrases":325,"slug":328,"view_count":73,"doi":329,"paper":330,"created_at":377},4026,"RTAS: A radiative transfer model for agricultural strip-intercropping structures","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115708","Agricultural strip-intercropping, characterized by alternating tall and short crops, poses substantial challenges for current radiative transfer (RT) modeling due to complex mutual shadowing and heterogeneous canopy optical and structural properties. Most existing geometric-optical models for row crops are restricted to monoculture configurations, unable to account for the asymmetric radiative interactions arising from the coexistence of two distinct crop species. In this study, we developed the Radiative Transfer Model for Agricultural Strip-intercropping Structures (RTAS), a hybrid analytical model integrating geometric-optical (GO) principles and turbid-medium RT theory under periodic boundary conditions. RTAS showed strong agreement with 3D reference simulations from the LESS (Large-scale Remote Sensing Data and Image Simulation Framework) ray-tracing model in both red and near-infrared (NIR) bands ( R 2 ≥ 0.93, RRMSE ≤3.96%) and with multi-angular unmanned aerial vehicle (UAV) observations ( R 2 = 0.94, RMSE ≤0.0252, RRMSE ≤12.44%). Structural sensitivity analysis showed stable performance across the tested intercropping configurations, with the RRMSE remaining below 8%. RTAS also outperformed the Spectral Linear Mixture (SLM) model and GO baseline, whose red-band errors exceeded 14% and reached nearly 27% at large solar zenith angles. Furthermore, component-level analysis revealed a wavelength-dependent difference between geometric visibility and radiative contribution in the NIR band, associated with enhanced multiple scattering and non-negligible contributions from shaded vegetation. RTAS provides an efficient forward simulation framework with potential for future retrieval studies in complex strip-intercropping structures.","农业条带间作以高矮作物交替种植为特征，由于复杂的相互遮蔽以及异质冠层的光学和结构属性，给当前的辐射传输（RT）建模带来了巨大挑战。现有的大多数行播作物几何光学模型仅限于单作配置，无法考虑两种不同作物共存所产生的非对称辐射相互作用。本研究开发了农业条带间作结构辐射传输模型（RTAS），这是一个在周期性边界条件下整合几何光学（GO）原理与浑浊介质辐射传输理论的混合解析模型。RTAS在红光和近红外（NIR）波段均与LESS（大规模遥感数据与图像模拟框架）光线追踪模型的三维参考模拟结果高度一致（R² ≥ 0.93，RRMSE ≤ 3.96%），并与多角度无人机（UAV）观测结果吻合良好（R² = 0.94，RMSE ≤ 0.0252，RRMSE ≤ 12.44%）。结构敏感性分析表明，该模型在测试的间作配置下性能稳定，RRMSE保持在8%以下。RTAS还优于光谱线性混合（SLM）模型和GO基线模型，后两者在红光波段的误差超过14%，在大太阳天顶角时接近27%。此外，组分层面分析揭示了近红外波段中几何可见度与辐射贡献之间存在波长依赖性差异，这与增强的多重散射及阴影植被不可忽略的贡献有关。RTAS提供了一个高效的前向模拟框架，有望用于未来复杂条带间作结构的反演研究。","Remote Sensing of Environment","2026-10-01T00:00:00Z",83,{"impact":17,"substance":18,"depth":297,"authority":51,"freshness":20,"relevant":21,"comment":298},19,"提出面向条带间作结构的混合辐射传输模型RTAS，验证充分、精度可靠，对农业遥感定量反演有实质推进。",[300],{"name":293,"url":290},[26,29,302,303,304],"间作","作物表型","辐射传输模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[307],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":309},{"candidates":310,"agricultural_evidence":52},[311,316,321],{"code":32,"score":51,"supported":52,"title":312,"abstract":314,"tags":315},[313],"intercropping",[302,313],[302],{"code":39,"score":317,"supported":52,"title":318,"abstract":319,"tags":320},8,[],[29,165],[303,29],{"code":64,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[26],[326,327],"RTAS 辐射传输模型 间作","条带间作 遥感 冠层","RTAS辐射传输模型间作-4026","10.1016\u002Fj.rse.2026.115708",{"doi":329,"openalex_id":331,"authors":332,"venue":293,"cited_by_count":81,"oa_url":290,"card":372,"direction":124,"ingested_from":126},"W7215043648",[333,336,339,341,344,347,349,351,354,356,359,361,363,366,369],{"name":334,"orcid":335},"Run Zhong","https:\u002F\u002Forcid.org\u002F0009-0003-3067-388X",{"name":337,"orcid":338},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":340,"orcid":9},"Zhihong Li",{"name":342,"orcid":343},"Carlos Camino","https:\u002F\u002Forcid.org\u002F0000-0001-5188-4406",{"name":345,"orcid":346},"Si Gao","https:\u002F\u002Forcid.org\u002F0000-0001-6726-5693",{"name":348,"orcid":9},"Qing Li",{"name":350,"orcid":9},"Yanan Wei",{"name":352,"orcid":353},"Higor  de Queiroz Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0002-8715-3290",{"name":355,"orcid":9},"Hong Sun",{"name":357,"orcid":358},"Feng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-7499-9422",{"name":360,"orcid":9},"Matheus Pinheiro Ferreirad",{"name":362,"orcid":9},"Haoran Liu",{"name":364,"orcid":365},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":367,"orcid":368},"Durval Dourado Neto","https:\u002F\u002Forcid.org\u002F0000-0002-8452-8288",{"name":370,"orcid":371},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"tldr":373,"method":374,"finding":375,"direction":124,"opportunity":376},"提出RTAS混合辐射传输模型，模拟条带间作农田的反射率并验证其精度。","融合几何光学与浑浊介质理论，用LESS三维模拟和无人机多角度观测验证。","RTAS精度高（R²≥0.93），优于线性混合和几何光学基线，并揭示近红外几何可见度与辐射贡献的差异","可基于RTAS发展条带间作结构参数反演，并耦合多物种冠层异质性提升遥感监测能力。","2026-10-02T23:30:24.513706Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":9,"source_name":384,"source_url":381,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":386,"sources":388,"tags":390,"primary_topic":392,"secondary_topics":393,"classification":398,"search_phrases":427,"slug":430,"view_count":81,"doi":431,"paper":432,"created_at":453},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",{"impact":19,"substance":17,"depth":141,"authority":232,"freshness":20,"relevant":21,"comment":387},"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[389],{"name":384,"url":381},[26,27,147,29,391],"葡萄园管理",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[394],{"code":178,"slug":395,"name":396,"path":397,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":45,"locked":14,"version":46,"method":47,"evidence":399},{"candidates":400,"agricultural_evidence":52},[401,405,409,413,420],{"code":39,"score":17,"supported":52,"title":402,"abstract":403,"tags":404},[60],[29,165,60],[29],{"code":178,"score":51,"supported":52,"title":406,"abstract":407,"tags":408},[184],[181,184],[147],{"code":64,"score":65,"supported":52,"title":410,"abstract":411,"tags":412},[],[68,69],[26],{"code":414,"score":129,"supported":52,"title":415,"abstract":416,"tags":419},"B2",[],[417,418],"葡萄","grape",[],{"code":421,"score":129,"supported":52,"title":422,"abstract":423,"tags":426},"I4",[],[424,425],"空间分析","GIS",[],[428,429],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":431,"openalex_id":433,"authors":434,"venue":384,"cited_by_count":81,"oa_url":381,"card":448,"direction":124,"ingested_from":126},"W7220993218",[435,437,440,442,445],{"name":436,"orcid":9},"Dimitra Passa",{"name":438,"orcid":439},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":441,"orcid":9},"Georgios Nektarios Tselos",{"name":443,"orcid":444},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":446,"orcid":447},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":449,"method":450,"finding":451,"direction":124,"opportunity":452},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":466,"tags":468,"primary_topic":470,"secondary_topics":471,"classification":472,"search_phrases":484,"slug":487,"view_count":488,"doi":489,"paper":490,"created_at":509},3982,"Direct surface reflectance retrieval from UAV multispectral imagery using onboard upward-looking spectrometer with MLR under variable illuminations","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112470","Direct surface reflectance retrieval from UAV multispectral imagery using onboard upward-looking spectrometer with MLR under variable illuminations。Computers and Electronics in Agriculture","基于机载向上观测光谱仪与多元线性回归（MLR）在变化光照条件下从无人机多光谱影像直接反演地表反射率。《计算机与电子农业》","Computers and Electronics in Agriculture",71,{"impact":19,"substance":463,"depth":464,"authority":142,"freshness":317,"relevant":21,"comment":465},20,17,"发表于农业信息领域核心期刊的方法类论文，提出机载上行光谱仪结合多元线性回归在多变光照下直接反演地表反射率，对无人机遥感定量监测有实用价值，但属细分技术进展，公共影响有限。",[467],{"name":460,"url":457},[26,27,29,469],"多光谱",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":473},{"candidates":474,"agricultural_evidence":52},[475,480],{"code":39,"score":51,"supported":52,"title":476,"abstract":477,"tags":479},[60],[478,60],"无人机多光谱",[29],{"code":64,"score":21,"supported":14,"title":481,"abstract":482,"tags":483},[],[],[26],[485,486],"无人机 多光谱 地表反射率","机载 光谱仪 反射率反演","无人机多光谱地表反射率-3982",2,"10.1016\u002Fj.compag.2026.112470",{"doi":489,"openalex_id":491,"authors":492,"venue":460,"cited_by_count":81,"oa_url":9,"card":504,"direction":124,"ingested_from":126},"W7215128624",[493,496,499,501],{"name":494,"orcid":495},"Jiayang Xie","https:\u002F\u002Forcid.org\u002F0000-0001-5711-2351",{"name":497,"orcid":498},"Yutao Shen","https:\u002F\u002Forcid.org\u002F0009-0001-4348-125X",{"name":500,"orcid":9},"Wenjuan Li",{"name":502,"orcid":503},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",{"tldr":505,"method":506,"finding":507,"direction":124,"opportunity":508},"利用无人机搭载向上光谱仪与多元线性回归，从多光谱影像直接反演地表反射率。","无人机多光谱影像、机载向上光谱仪、多元线性回归（MLR）。","在光照变化条件下，该方法能有效直接反演地表反射率。","可探索复杂光照下多光谱辐射校正的普适模型，并验证对作物表型参数反演的提升。","2026-10-02T23:30:03.513918Z",{"id":511,"title":512,"url":513,"summary":514,"summary_zh":9,"content":9,"source_name":515,"source_url":513,"published_at":516,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":517,"score_detail":518,"sources":520,"tags":522,"primary_topic":525,"secondary_topics":531,"classification":533,"search_phrases":549,"slug":552,"view_count":553,"doi":554,"paper":555,"created_at":582},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","Research Square","2026-09-24T00:00:00Z",51,{"impact":317,"substance":142,"depth":51,"authority":129,"freshness":317,"relevant":21,"comment":519},"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[521],{"name":515,"url":513},[26,27,29,523,524],"NDVI","咖啡种植",{"code":526,"slug":527,"name":528,"path":529,"parent_code":530},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[532],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":534},{"candidates":535,"agricultural_evidence":52},[536,540,545],{"code":39,"score":232,"supported":52,"title":537,"abstract":538,"tags":539},[60],[60],[523,29],{"code":526,"score":19,"supported":52,"title":541,"abstract":543,"tags":544},[542],"coffee",[542],[524],{"code":64,"score":21,"supported":14,"title":546,"abstract":547,"tags":548},[],[],[26],[550,551],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619",5,"10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":554,"openalex_id":556,"authors":557,"venue":515,"cited_by_count":81,"oa_url":581,"card":9,"direction":124,"ingested_from":126},"W7214220732",[558,560,562,564,566,569,571,573,576,579],{"name":559,"orcid":9},"Gabriel de Morais Campos",{"name":561,"orcid":9},"Aline Bhering Silva",{"name":563,"orcid":9},"Cileimar Aparecida da Silva",{"name":565,"orcid":9},"Vanda Maria Salles Andrade",{"name":567,"orcid":568},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":570,"orcid":9},"Andressa Barcellos Silva",{"name":572,"orcid":9},"Marco Thúlio Gonçalves Vieira",{"name":574,"orcid":575},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":577,"orcid":578},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":580,"orcid":9},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z"]