[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3619":3,"related-3619":104},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":103},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",null,"Research Square","2026-09-24T00:00:00Z","论文",10,false,51,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":16,"relevant":20,"comment":21},8,14,15,6,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","无人机","遥感","NDVI","咖啡种植",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,62],{"code":38,"score":50,"supported":51,"title":52,"abstract":54,"tags":55},13,true,[53],"multispectral",[53],[28,27],{"code":31,"score":57,"supported":51,"title":58,"abstract":60,"tags":61},12,[59],"coffee",[59],[29],{"code":63,"score":20,"supported":13,"title":64,"abstract":65,"tags":66},"I1",[],[],[25],[68,69],"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":72,"openalex_id":74,"authors":75,"venue":9,"cited_by_count":99,"oa_url":100,"card":8,"direction":101,"ingested_from":102},"W7214220732",[76,78,80,82,84,87,89,91,94,97],{"name":77,"orcid":8},"Gabriel de Morais Campos",{"name":79,"orcid":8},"Aline Bhering Silva",{"name":81,"orcid":8},"Cileimar Aparecida da Silva",{"name":83,"orcid":8},"Vanda Maria Salles Andrade",{"name":85,"orcid":86},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":88,"orcid":8},"Andressa Barcellos Silva",{"name":90,"orcid":8},"Marco Thúlio Gonçalves Vieira",{"name":92,"orcid":93},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":95,"orcid":96},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":98,"orcid":8},"Flora Maria Melo Villar",0,"https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","农业遥感与作物表型","openalex","2026-09-27T23:30:34.698079Z",{"total":19,"page":20,"page_size":19,"items":105},[106,179,238,313,409,484],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":8,"source_name":112,"source_url":109,"published_at":113,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":114,"score_detail":115,"sources":119,"tags":121,"primary_topic":124,"secondary_topics":125,"classification":127,"search_phrases":153,"slug":156,"view_count":20,"doi":157,"paper":158,"created_at":178},3893,"Modeling the productive potential of coffee trees in the Cerrado of Minas Gerais through hierarchical landscape analysis and remote sensing data","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2516","Brazil stands out as the world’s largest producer and exporter of Arabica coffee, one of the world’s most important commodities, which plays a crucial role in the country’s economy. In this context, this study develops a model to classify the productive potential of coffee trees based on hierarchical landscape analysis and coffee plant metrics, applied to different farms in the Córrego Feio watershed, Patrocínio, Minas Gerais. We employed a model based on landscape metrics (slope, water flow, vegetation, and slope shape), inspired by Ross’s (1994) proposal for Environmental Fragility. Each variable was reclassified according to hierarchical levels from the literature and empirical observations of the area. The data were integrated to generate two products: Factor T, which represents the potential of the area based on static soil and relief variables, and Calculated Potential, which combines Factor T with the NDVI vegetation index for a dynamic assessment. The application of the model demonstrated a strong capacity to interpret the qualitative hierarchy of productive potential, supporting the delineation of differentiated management zones. The results indicated that productive potential is more strongly associated with soil and topographic factors than with the vegetation index. It is concluded that the proposed method is a valuable tool for precision agriculture, allowing the delineation of management zones for the application of inputs in a more efficient and sustainable manner, contributing to resource conservation and environmental protection. Key words: Arabica coffee; environmental fragility; hierarchical analysis; vegetation index; watershed.","巴西是全球最大的阿拉比卡咖啡（Arabica coffee）生产国和出口国，阿拉比卡咖啡作为全球最重要的商品之一，在该国经济中发挥着至关重要的作用。在此背景下，本研究开发了一个基于层次景观分析和咖啡植株指标的咖啡树生产潜力分类模型，并将其应用于米纳斯吉拉斯州帕特罗西尼奥市Córrego Feio流域的不同农场。我们采用了一个基于景观指标（坡度、水流、植被和坡形）的模型，其灵感来源于Ross（1994）提出的环境脆弱性方案。每个变量根据文献和该区域经验观察所得的层次等级进行重新分类。数据经过整合生成了两个产品：因子T，代表基于静态土壤和地形变量的区域潜力；以及计算潜力，将因子T与NDVI植被指数相结合以进行动态评估。模型的应用展现出强大的能力，能够解释生产潜力的定性层次，支持差异化经营区的划分。结果表明，生产潜力与土壤和地形因子的关联性强于与植被指数的关联性。研究认为，所提出的方法是一种有价值的精准农业工具，能够以更高效和可持续的方式划定管理区以施用投入品，有助于资源保护和环境保护。关键词：阿拉比卡咖啡；环境脆弱性；层次分析；植被指数；流域。","Coffee Science","2026-09-30T00:00:00Z",70,{"impact":57,"substance":116,"depth":117,"authority":50,"freshness":16,"relevant":20,"comment":118},20,17,"基于景观分析与NDVI的咖啡生产潜力分级模型，方法新颖、结论可靠，对精准农业管理分区有实用价值，但属区域性研究，影响范围有限。",[120],{"name":112,"url":109},[25,122,27,123,29],"精准农业","管理分区",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[126],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":128},{"candidates":129,"agricultural_evidence":51},[130,135,141,146],{"code":31,"score":18,"supported":51,"title":131,"abstract":132,"tags":134},[59],[133,59],"咖啡",[29],{"code":38,"score":136,"supported":51,"title":137,"abstract":139,"tags":140},9,[138],"remote sensing",[],[27],{"code":63,"score":16,"supported":51,"title":142,"abstract":143,"tags":145},[],[122,144],"precision agriculture",[25,122],{"code":147,"score":19,"supported":51,"title":148,"abstract":149,"tags":152},"H2",[],[150,151],"土壤","soil",[],[154,155],"Minas Gerais 咖啡 遥感","Córrego Feio 流域 咖啡 生产潜力","MinasGerais咖啡遥感-3893","10.25186\u002F.v21i.2516",{"doi":157,"openalex_id":159,"authors":160,"venue":112,"cited_by_count":99,"oa_url":109,"card":173,"direction":101,"ingested_from":102},"W7214888407",[161,164,167,170],{"name":162,"orcid":163},"Jorge Eduardo Ferreira Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6376",{"name":165,"orcid":166},"Mateus Duarte Segismundo","https:\u002F\u002Forcid.org\u002F0009-0001-1289-406X",{"name":168,"orcid":169},"George Deroco Martins","https:\u002F\u002Forcid.org\u002F0000-0001-9738-7325",{"name":171,"orcid":172},"João Vítor Meza Bravo","https:\u002F\u002Forcid.org\u002F0000-0002-5457-3192",{"tldr":174,"method":175,"finding":176,"direction":101,"opportunity":177},"基于景观指标与NDVI构建模型，对巴西塞拉多咖啡树生产潜力进行分级分类。","层次化景观分析（坡度、水流、植被、坡形）结合NDVI，生成因子T与计算潜力。","生产潜力与土壤和地形因素关联强于植被指数，可划分差异化精准管理区。","可引入多时相遥感与机器学习，动态预测咖啡生产潜力并优化管理区划分。","2026-10-01T23:30:28.485534Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":8,"content":8,"source_name":184,"source_url":8,"published_at":185,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":186,"score_detail":187,"sources":191,"tags":193,"primary_topic":196,"secondary_topics":197,"classification":204,"search_phrases":225,"slug":228,"view_count":20,"doi":8,"paper":229,"created_at":237},1367,"基于无人机多源遥感特征的茶树生长与氮素状况深度学习监测","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F17\u002F2923","南京农业大学园艺学院团队提出融合光谱、纹理与谐波特征的多域融合指标，结合无人机数据与深度学习模型，用于茶树生物量与氮素积累量估算。实验表明多域融合指数显著优于传统光谱指标，可实现对茶园生物量与氮素状况的高精度反演，为近实时茶园监测提供工具。","Remote Sensing · MDPI · 2026-09-01","2026-08-31T16:00:00Z",72,{"impact":18,"substance":116,"depth":188,"authority":57,"freshness":189,"relevant":20,"comment":190},18,7,"研究提出多域融合指标，提升茶树氮素监测精度，对智慧茶园有实用价值。",[192],{"name":184,"url":182},[25,26,27,194,195],"氮素监测","茶树",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[198,203],{"code":199,"slug":200,"name":201,"path":202,"parent_code":42},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":205},{"candidates":206,"agricultural_evidence":51},[207,212,217,221],{"code":31,"score":208,"supported":51,"title":209,"abstract":210,"tags":211},11,[195],[195],[],{"code":199,"score":208,"supported":51,"title":213,"abstract":215,"tags":216},[214],"深度学习",[214],[],{"code":38,"score":136,"supported":51,"title":218,"abstract":219,"tags":220},[27],[],[27],{"code":63,"score":20,"supported":13,"title":222,"abstract":223,"tags":224},[],[],[25],[226,227],"智慧农业 氮素监测 无人机 茶树","智慧农业 氮素监测","智慧农业氮素监测无人机茶树-1367",{"doi":8,"openalex_id":8,"authors":230,"venue":8,"cited_by_count":99,"oa_url":8,"card":231,"direction":101,"ingested_from":236},[],{"tldr":232,"method":233,"finding":234,"direction":101,"opportunity":235},"融合无人机多源遥感特征与深度学习，高精度估算茶树生物量与氮素积累量。","融合光谱、纹理、谐波特征的多域融合指标，结合无人机数据与深度学习模型。","多域融合指数显著优于传统光谱指标，实现茶园生物量与氮素高精度反演。","可探索多域融合特征在其他作物或不同生长阶段的适用性，或结合时序数据预测氮素动态。","agent","2026-09-02T00:05:07.021728Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":8,"source_name":244,"source_url":241,"published_at":245,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":246,"score_detail":247,"sources":250,"tags":252,"primary_topic":256,"secondary_topics":257,"classification":259,"search_phrases":283,"slug":286,"view_count":20,"doi":287,"paper":288,"created_at":312},4598,"Seasonal compatibility of RGB and multispectral vegetation indices in Amazon Cacao","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00815-x","Abstract Precision agriculture in cacao cultivation is constrained by multispectral sensor costs, limiting accessibility for smallholder farmers. This study evaluated the seasonal compatibility between RGB-derived visible reflectance and multispectral vegetation indices (NDVI, GNDVI, NDRE) to establish a low-cost monitoring approach for Eastern Amazon cacao plantations. We collected spectral data from 1,431 random sampling points across a 0.9-ha commercial cacao plantation during four seasonal periods (February, May, August, and November 2025) using UAV-mounted RGB and multispectral sensors. Pearson correlations and linear regression models assessed relationships between mean visible reflectance [(R + G+B)\u002F3] and multispectral indices. Geostatistical analysis characterized spatial dependence structure across seasons. GNDVI demonstrated superior compatibility across all seasons (mean R² = 59.3%; range: 52.5–62.9%), outperforming NDRE (44.2%) and NDVI (40.8%) by 15–19% points. Temporal stability was 2.9× higher for GNDVI (SD = 4.7 pp) than NDRE (14.1 pp) and NDVI (12.7 pp). Peak compatibility occurred in May (R² = 62.9%) during wet-to-dry transition. The conversion equation (GNDVI = 0.509–0.000012 × Visible Reflectance; RMSE = 0.077) enables reliable estimation, retaining 59% of multispectral information. Strong spatial dependence (Co\u002F(Co + C) \u003C 10%) was confirmed across all periods, with August providing optimal structure (range = 11.4 m) for management zone delineation. GNDVI is the most reliable RGB proxy for cacao monitoring, democratizing precision agriculture access through affordable technology while maintaining robust spatial and temporal performance year-round.","摘要 可可种植中的精准农业受到多光谱传感器成本的制约，限制了小农户的可及性。本研究评估了RGB衍生的可见光反射率与多光谱植被指数（NDVI、GNDVI、NDRE）之间的季节性兼容性，以建立一种面向东亚马逊可可种植园的低成本监测方法。我们使用无人机搭载的RGB和多光谱传感器，在四个季节时段（2025年2月、5月、8月和11月）从一片0.9公顷商业可可种植园的1，431个随机采样点采集了光谱数据。采用Pearson相关分析和线性回归模型评估平均可见光反射率[（R + G + B）\u002F3]与多光谱指数之间的关系。地统计学分析刻画了各季节的空间依赖结构。GNDVI在所有季节均表现出最优兼容性（平均R² = 59.3%；范围：52.5–62.9%），较NDRE（44.2%）和NDVI（40.8%）高出15–19个百分点。GNDVI的时间稳定性（SD = 4.7个百分点）是NDRE（14.1个百分点）和NDVI（12.7个百分点）的2.9倍。最高兼容性出现在5月（R² = 62.9%），即湿季向干季过渡期。转换方程（GNDVI = 0.509–0.000012 × 可见光反射率；RMSE = 0.077）可实现可靠估算，保留了59%的多光谱信息。所有时期均确认存在强空间依赖性（Co\u002F(Co + C) \u003C 10%），其中8月提供了最优结构（变程 = 11.4 m），适合用于管理分区划分。GNDVI是可可监测中最可靠的RGB替代指标，通过可负担的技术使精准农业更广泛可及，同时全年保持稳健的空间和时间性能。","Applied Geomatics","2026-10-08T00:00:00Z",68,{"impact":16,"substance":248,"depth":117,"authority":50,"freshness":136,"relevant":20,"comment":249},21,"以低成本RGB替代多光谱监测亚马逊可可，数据扎实、结论明确，对精准农业降本有参考价值，但属细分作物研究，公共影响有限。",[251],{"name":244,"url":241},[25,27,253,254,255],"植被指数","可可种植","无人机监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[258],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":260},{"candidates":261,"agricultural_evidence":51},[262,268,272,276],{"code":31,"score":17,"supported":51,"title":263,"abstract":265,"tags":267},[264],"cacao",[266,264],"可可",[],{"code":38,"score":50,"supported":51,"title":269,"abstract":270,"tags":271},[53],[53],[253,27],{"code":63,"score":189,"supported":51,"title":273,"abstract":274,"tags":275},[],[122,144],[25],{"code":277,"score":19,"supported":51,"title":278,"abstract":279,"tags":282},"G2",[],[280,281],"小农户","smallholder",[],[284,285],"亚马逊 可可 无人机 遥感","GNDVI RGB 植被指数 可可","亚马逊可可无人机遥感-4598","10.1007\u002Fs12518-026-00815-x",{"doi":287,"openalex_id":289,"authors":290,"venue":244,"cited_by_count":99,"oa_url":306,"card":307,"direction":101,"ingested_from":102},"W7221039911",[291,294,297,300,303],{"name":292,"orcid":293},"Charlyngton da Silva e Silva","https:\u002F\u002Forcid.org\u002F0009-0003-9332-4302",{"name":295,"orcid":296},"Luiz Antônio Soares Cardoso","https:\u002F\u002Forcid.org\u002F0000-0001-6868-7847",{"name":298,"orcid":299},"Ronilson de Souza Santos","https:\u002F\u002Forcid.org\u002F0000-0002-2683-456X",{"name":301,"orcid":302},"Carlos Rodrigo Tanajura Caldeira","https:\u002F\u002Forcid.org\u002F0000-0001-7102-7735",{"name":304,"orcid":305},"Norberto Cornejo Noronha","https:\u002F\u002Forcid.org\u002F0000-0001-7216-8687","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00815-x.pdf",{"tldr":308,"method":309,"finding":310,"direction":101,"opportunity":311},"评估RGB与多光谱植被指数在亚马逊可可园的季节性兼容性，以低成本监测替代多光谱传感器。","无人机RGB与多光谱传感器采集1431个样点，用相关、回归和地统计分析四季数据。","GNDVI兼容性最佳（R²=59.3%），时空稳定性最高，5月表现最优，可保留59%多光谱信息。","可探索RGB代理指数在其他热带作物和不同冠层结构下的普适性，并优化低成本管理分区方法。","2026-10-10T23:30:31.142887Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":320,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":321,"score_detail":322,"sources":325,"tags":327,"primary_topic":330,"secondary_topics":336,"classification":338,"search_phrases":361,"slug":364,"view_count":99,"doi":365,"paper":366,"created_at":408},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。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":188,"substance":323,"depth":188,"authority":57,"freshness":136,"relevant":20,"comment":324},22,"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。",[326],{"name":319,"url":316},[25,26,328,27,329],"育种","紫花苜蓿",{"code":331,"slug":332,"name":333,"path":334,"parent_code":335},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[337],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":339},{"candidates":340,"agricultural_evidence":51},[341,347,351,355],{"code":331,"score":18,"supported":51,"title":342,"abstract":344,"tags":346},[343],"alfalfa",[345,343],"苜蓿",[329],{"code":38,"score":57,"supported":51,"title":348,"abstract":349,"tags":350},[53],[53],[27],{"code":63,"score":189,"supported":51,"title":352,"abstract":353,"tags":354},[],[122,144],[25],{"code":356,"score":357,"supported":51,"title":358,"abstract":359,"tags":360},"C4",4,[],[345],[329],[362,363],"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","10.64898\u002F2026.10.14.759249",{"doi":365,"openalex_id":367,"authors":368,"venue":319,"cited_by_count":99,"oa_url":316,"card":403,"direction":101,"ingested_from":102},"W7223083966",[369,372,375,378,381,383,385,388,391,394,397,400],{"name":370,"orcid":371},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":373,"orcid":374},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":376,"orcid":377},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":379,"orcid":380},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":382,"orcid":8},"Julie Hansen",{"name":384,"orcid":8},"Christopher Pierce",{"name":386,"orcid":387},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":389,"orcid":390},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":392,"orcid":393},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":395,"orcid":396},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":398,"orcid":399},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":401,"orcid":402},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":404,"method":405,"finding":406,"direction":101,"opportunity":407},"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。","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。","2026-10-22T11:20:32.655394Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":8,"source_name":415,"source_url":412,"published_at":245,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":246,"score_detail":416,"sources":419,"tags":421,"primary_topic":424,"secondary_topics":425,"classification":427,"search_phrases":457,"slug":460,"view_count":99,"doi":461,"paper":462,"created_at":483},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)",{"impact":57,"substance":188,"depth":417,"authority":50,"freshness":136,"relevant":20,"comment":418},16,"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。",[420],{"name":415,"url":412},[25,26,422,27,423],"农业人工智能","葡萄园管理",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[426],{"code":199,"slug":200,"name":201,"path":202,"parent_code":42},{"status":44,"locked":13,"version":45,"method":46,"evidence":428},{"candidates":429,"agricultural_evidence":51},[430,434,439,443,450],{"code":38,"score":188,"supported":51,"title":431,"abstract":432,"tags":433},[53],[27,138,53],[27],{"code":199,"score":18,"supported":51,"title":435,"abstract":437,"tags":438},[436],"deep learning",[214,436],[422],{"code":63,"score":189,"supported":51,"title":440,"abstract":441,"tags":442},[],[122,144],[25],{"code":444,"score":19,"supported":51,"title":445,"abstract":446,"tags":449},"B2",[],[447,448],"葡萄","grape",[],{"code":451,"score":19,"supported":51,"title":452,"abstract":453,"tags":456},"I4",[],[454,455],"空间分析","GIS",[],[458,459],"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":461,"openalex_id":463,"authors":464,"venue":415,"cited_by_count":99,"oa_url":412,"card":478,"direction":101,"ingested_from":102},"W7220993218",[465,467,470,472,475],{"name":466,"orcid":8},"Dimitra Passa",{"name":468,"orcid":469},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":471,"orcid":8},"Georgios Nektarios Tselos",{"name":473,"orcid":474},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":476,"orcid":477},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":479,"method":480,"finding":481,"direction":101,"opportunity":482},"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":485,"title":486,"url":487,"summary":488,"summary_zh":489,"content":8,"source_name":490,"source_url":487,"published_at":491,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":492,"score_detail":493,"sources":495,"tags":497,"primary_topic":499,"secondary_topics":500,"classification":501,"search_phrases":521,"slug":524,"view_count":525,"doi":526,"paper":527,"created_at":544},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":188,"substance":323,"depth":188,"authority":17,"freshness":136,"relevant":20,"comment":494},"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[496],{"name":490,"url":487},[25,422,27,28,498],"土地利用",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[],{"status":44,"locked":13,"version":45,"method":46,"evidence":502},{"candidates":503,"agricultural_evidence":51},[504,508,512,517],{"code":38,"score":50,"supported":51,"title":505,"abstract":506,"tags":507},[53],[53],[28,27],{"code":199,"score":189,"supported":51,"title":509,"abstract":510,"tags":511},[],[214,436],[422],{"code":513,"score":20,"supported":13,"title":514,"abstract":515,"tags":516},"H6",[],[],[498],{"code":63,"score":20,"supported":13,"title":518,"abstract":519,"tags":520},[],[],[25],[522,523],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497",2,"10.1038\u002Fs41598-026-67985-x",{"doi":526,"openalex_id":528,"authors":529,"venue":490,"cited_by_count":99,"oa_url":537,"card":538,"direction":543,"ingested_from":102},"W7220869344",[530,532,534],{"name":531,"orcid":8},"Neeraj Kumar",{"name":533,"orcid":8},"Navin Rajpal",{"name":535,"orcid":536},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":539,"method":540,"finding":541,"direction":101,"opportunity":542},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:12.234854Z"]