[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1821":3,"related-1821":79},{"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":22,"tags":24,"primary_topic":9,"secondary_topics":29,"classification":30,"search_phrases":48,"slug":51,"view_count":19,"doi":52,"paper":53,"created_at":78},1821,"Adaptive feature learning and wavelet preservation for forest canopy mapping from UAV imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112403","Adaptive feature learning and wavelet preservation for forest canopy mapping from UAV imagery。Computers and Electronics in Agriculture","基于无人机影像的森林冠层制图的自适应特征学习与小波保持方法。《计算机与电子在农业中的应用》",null,"Computers and Electronics in Agriculture","2026-09-06T00:00:00Z","论文",10,false,54,{"impact":13,"substance":17,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},15,12,2,1,"论文提出自适应特征学习与小波保留方法用于无人机森林冠层制图，方法新颖，但时效性较低，影响范围限于细分领域。",[23],{"name":10,"url":6},[25,26,27,28],"智慧农业","无人机","遥感","森林监测",[],{"status":31,"locked":14,"version":32,"method":33,"evidence":34},"unclassified","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":46,"reason":47},[36,41],{"code":37,"score":20,"supported":14,"title":38,"abstract":39,"tags":40},"I1",[],[],[25],{"code":42,"score":20,"supported":14,"title":43,"abstract":44,"tags":45},"I3",[],[],[27],true,"缺少足够的主题证据",[49,50],"智慧农业 森林监测 无人机 遥感","智慧农业 森林监测","智慧农业森林监测无人机遥感-1821","10.1016\u002Fj.compag.2026.112403",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":70,"oa_url":9,"card":71,"direction":75,"ingested_from":77},"W7210287474",[56,59,61,64,66,68],{"name":57,"orcid":58},"Shikuan Wang","https:\u002F\u002Forcid.org\u002F0000-0003-2780-9590",{"name":60,"orcid":9},"Jianzhou Gong",{"name":62,"orcid":63},"Mengquan Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1822-5980",{"name":65,"orcid":9},"Longxin Liu",{"name":67,"orcid":9},"Mingbin Guo",{"name":69,"orcid":9},"Haiwei Yuan",0,{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"提出自适应特征学习与小波保持方法，用于无人机影像的森林冠层制图。","自适应特征学习结合小波变换，处理无人机影像。","该方法能有效提高森林冠层制图的精度和细节保持。","农业遥感与作物表型","可探索将小波保持与深度学习结合，用于其他作物或复杂地形的遥感制图。","openalex","2026-09-07T23:30:01.926780Z",{"total":80,"page":20,"page_size":80,"items":81},6,[82,189,253,334,388,441],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":96,"tags":98,"primary_topic":101,"secondary_topics":107,"classification":113,"search_phrases":141,"slug":144,"view_count":70,"doi":145,"paper":146,"created_at":188},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":92,"substance":93,"depth":92,"authority":18,"freshness":94,"relevant":20,"comment":95},18,22,9,"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。",[97],{"name":88,"url":85},[25,26,99,27,100],"育种","紫花苜蓿",{"code":102,"slug":103,"name":104,"path":105,"parent_code":106},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[108],{"code":42,"slug":109,"name":110,"path":111,"parent_code":112},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":114,"locked":14,"version":32,"method":33,"evidence":115},"classified",{"candidates":116,"agricultural_evidence":46},[117,123,128,135],{"code":102,"score":17,"supported":46,"title":118,"abstract":120,"tags":122},[119],"alfalfa",[121,119],"苜蓿",[100],{"code":42,"score":18,"supported":46,"title":124,"abstract":126,"tags":127},[125],"multispectral",[125],[27],{"code":37,"score":129,"supported":46,"title":130,"abstract":131,"tags":134},7,[],[132,133],"精准农业","precision agriculture",[25],{"code":136,"score":137,"supported":46,"title":138,"abstract":139,"tags":140},"C4",4,[],[121],[100],[142,143],"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":145,"openalex_id":147,"authors":148,"venue":88,"cited_by_count":70,"oa_url":85,"card":183,"direction":75,"ingested_from":77},"W7223083966",[149,152,155,158,161,163,165,168,171,174,177,180],{"name":150,"orcid":151},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":153,"orcid":154},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":156,"orcid":157},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":159,"orcid":160},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":162,"orcid":9},"Julie Hansen",{"name":164,"orcid":9},"Christopher Pierce",{"name":166,"orcid":167},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":169,"orcid":170},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":172,"orcid":173},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":175,"orcid":176},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":178,"orcid":179},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":181,"orcid":182},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":184,"method":185,"finding":186,"direction":75,"opportunity":187},"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":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":9,"source_name":194,"source_url":192,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":204,"tags":206,"primary_topic":209,"secondary_topics":213,"classification":214,"search_phrases":235,"slug":238,"view_count":20,"doi":239,"paper":240,"created_at":252},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture","Precision Agriculture","2026-10-14T00:00:00Z",75,{"impact":198,"substance":199,"depth":200,"authority":201,"freshness":202,"relevant":20,"comment":203},16,20,17,14,8,"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[205],{"name":194,"url":192},[25,26,207,27,208],"农业人工智能","大麦",{"code":37,"slug":210,"name":211,"path":212,"parent_code":112},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":114,"locked":14,"version":32,"method":33,"evidence":215},{"candidates":216,"agricultural_evidence":46},[217,221,226,231],{"code":37,"score":137,"supported":46,"title":218,"abstract":219,"tags":220},[],[133],[25],{"code":222,"score":20,"supported":14,"title":223,"abstract":224,"tags":225},"A5",[],[],[208],{"code":227,"score":20,"supported":14,"title":228,"abstract":229,"tags":230},"I2",[],[],[207],{"code":42,"score":20,"supported":14,"title":232,"abstract":233,"tags":234},[],[],[27],[236,237],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":239,"openalex_id":241,"authors":242,"venue":194,"cited_by_count":70,"oa_url":9,"card":9,"direction":9,"ingested_from":77},"W7221758985",[243,246,249],{"name":244,"orcid":245},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":247,"orcid":248},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":250,"orcid":251},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":269,"secondary_topics":270,"classification":275,"search_phrases":307,"slug":310,"view_count":70,"doi":311,"paper":312,"created_at":333},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",68,{"impact":18,"substance":92,"depth":198,"authority":263,"freshness":94,"relevant":20,"comment":264},13,"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。",[266],{"name":259,"url":256},[25,26,207,27,268],"葡萄园管理",{"code":42,"slug":109,"name":110,"path":111,"parent_code":112},[271],{"code":227,"slug":272,"name":273,"path":274,"parent_code":112},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":114,"locked":14,"version":32,"method":33,"evidence":276},{"candidates":277,"agricultural_evidence":46},[278,283,289,293,300],{"code":42,"score":92,"supported":46,"title":279,"abstract":280,"tags":282},[125],[27,281,125],"remote sensing",[27],{"code":227,"score":17,"supported":46,"title":284,"abstract":286,"tags":288},[285],"deep learning",[287,285],"深度学习",[207],{"code":37,"score":129,"supported":46,"title":290,"abstract":291,"tags":292},[],[132,133],[25],{"code":294,"score":80,"supported":46,"title":295,"abstract":296,"tags":299},"B2",[],[297,298],"葡萄","grape",[],{"code":301,"score":80,"supported":46,"title":302,"abstract":303,"tags":306},"I4",[],[304,305],"空间分析","GIS",[],[308,309],"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":311,"openalex_id":313,"authors":314,"venue":259,"cited_by_count":70,"oa_url":256,"card":328,"direction":75,"ingested_from":77},"W7220993218",[315,317,320,322,325],{"name":316,"orcid":9},"Dimitra Passa",{"name":318,"orcid":319},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":321,"orcid":9},"Georgios Nektarios Tselos",{"name":323,"orcid":324},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":326,"orcid":327},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":329,"method":330,"finding":331,"direction":75,"opportunity":332},"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":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":9,"source_name":340,"source_url":337,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":9,"secondary_topics":349,"classification":350,"search_phrases":369,"slug":372,"view_count":20,"doi":373,"paper":374,"created_at":387},4300,"Road surface material classification from unmanned aerial vehicle red, green, blue and multispectral imagery using spectral features and ensemble machine learning model","https:\u002F\u002Fdoi.org\u002F10.12913\u002F22998624\u002F226062","The aim of this study was to evaluate the impact of incorporating multispectral data acquired from an unmanned aerial vehicle (UAV) as an extension of standard RGB imagery on the accuracy of road surface material classification using machine learning algorithms.The research was conducted on six road sections differing in pavement type and technical condition.The performance of three ensemble learning algorithms: Random Forest, XGBoost, and LightGBM was investigated.Three input data configurations were considered: RGB imagery, a seven-band dataset comprising RGB channels and four multispectral bands (MS7), and an extended dataset (MS9) additionally including the NDVI and NDRE vegetation indices.For the best-performing model, the influence of texture features calculated within a moving 5 × 5 pixel window and edge information derived using the Sobel operator was also evaluated.The results demonstrated that the inclusion of multispectral data significantly improved classification performance compared with RGB imagery alone.The highest accuracy was achieved by the LightGBM model using the MS9 dataset, reaching an overall accuracy (OA) of 81.31%.The incorporation of texture features further increased the classification accuracy to 93.30% OA.The findings confirm the positive contribution of UAVacquired multispectral data to the effectiveness of road surface material classification.","本研究旨在评估将无人机（UAV）获取的多光谱数据作为标准RGB影像的扩展，对基于机器学习算法的路面材料分类精度的影响。研究在六段路面类型和技术状况不同的道路上进行，考察了三种集成学习算法——随机森林（Random Forest）、XGBoost和LightGBM的性能。研究考虑了三种输入数据配置：RGB影像、包含RGB通道和四个多光谱波段的七波段数据集（MS7），以及额外加入NDVI和NDRE植被指数的扩展数据集（MS9）。对于表现最佳的模型，还评估了在5×5像素移动窗口内计算的纹理特征以及利用Sobel算子提取的边缘信息的影响。结果表明，与仅使用RGB影像相比，加入多光谱数据显著提升了分类性能。使用MS9数据集的LightGBM模型取得了最高精度，总体精度（OA）达到81.31%。引入纹理特征后，分类精度进一步提升至93.30% OA。研究结果证实了无人机获取的多光谱数据对路面材料分类效果具有积极贡献。","Advances in Science and Technology – Research Journal","2026-10-04T00:00:00Z",64,{"impact":202,"substance":199,"depth":198,"authority":18,"freshness":202,"relevant":20,"comment":344},"该研究利用无人机多光谱影像与集成机器学习提升路面材料分类精度，方法新颖、数据详实，对农业遥感与智慧农业有参考价值，但属细分领域论文，公共影响有限。",[346],{"name":340,"url":337},[25,26,207,27,348],"路面分类",[],{"status":351,"locked":14,"version":32,"method":33,"evidence":352},"needs_review",{"candidates":353,"agricultural_evidence":14,"reason":368},[354,360,364],{"code":227,"score":17,"supported":46,"title":355,"abstract":357,"tags":359},[356],"machine learning",[358,356],"机器学习",[207],{"code":42,"score":18,"supported":46,"title":361,"abstract":362,"tags":363},[125],[125],[27],{"code":37,"score":20,"supported":14,"title":365,"abstract":366,"tags":367},[],[],[25],"农业相关性需要复核",[370,371],"无人机 多光谱 路面材料 分类","LightGBM 道路 遥感 分类","无人机多光谱路面材料分类-4300","10.12913\u002F22998624\u002F226062",{"doi":373,"openalex_id":375,"authors":376,"venue":340,"cited_by_count":70,"oa_url":380,"card":381,"direction":386,"ingested_from":77},"W7219783179",[377],{"name":378,"orcid":379},"Marcin Bilski","https:\u002F\u002Forcid.org\u002F0000-0002-1445-5749","https:\u002F\u002Fwww.astrj.com\u002Fpdf-226062-148948?filename=Road-surface-material-cla.pdf",{"tldr":382,"method":383,"finding":384,"direction":75,"opportunity":385},"用无人机RGB与多光谱影像结合集成机器学习，分类路面材料并验证多光谱与纹理特征的增益。","无人机RGB\u002F多光谱影像、NDVI\u002FNDRE、纹理与Sobel边缘，随机森林\u002FX","多光谱数据显著优于纯RGB，LightGBM用MS9达81.31%，加入纹理特征后提升至93.30%","可迁移到农田道路、机耕道与田间通行性识别，结合时序影像与轻量模型服务农机导航。","智慧农业 \u002F 农业物联网","2026-10-06T23:30:09.562617Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":10,"source_url":391,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":402,"secondary_topics":403,"classification":404,"search_phrases":416,"slug":419,"view_count":19,"doi":420,"paper":421,"created_at":440},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》","2026-10-01T00:00:00Z",71,{"impact":18,"substance":199,"depth":200,"authority":201,"freshness":202,"relevant":20,"comment":397},"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。",[399],{"name":10,"url":391},[25,26,27,401],"多光谱",{"code":42,"slug":109,"name":110,"path":111,"parent_code":112},[],{"status":114,"locked":14,"version":32,"method":33,"evidence":405},{"candidates":406,"agricultural_evidence":46},[407,412],{"code":42,"score":17,"supported":46,"title":408,"abstract":409,"tags":411},[125],[410,125],"无人机多光谱",[27],{"code":37,"score":20,"supported":14,"title":413,"abstract":414,"tags":415},[],[],[25],[417,418],"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","10.1016\u002Fj.compag.2026.112470",{"doi":420,"openalex_id":422,"authors":423,"venue":10,"cited_by_count":70,"oa_url":9,"card":435,"direction":75,"ingested_from":77},"W7215128624",[424,427,430,432],{"name":425,"orcid":426},"Jiayang Xie","https:\u002F\u002Forcid.org\u002F0000-0001-5711-2351",{"name":428,"orcid":429},"Yutao Shen","https:\u002F\u002Forcid.org\u002F0009-0001-4348-125X",{"name":431,"orcid":9},"Wenjuan Li",{"name":433,"orcid":434},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",{"tldr":436,"method":437,"finding":438,"direction":75,"opportunity":439},"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":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":447,"source_url":444,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":452,"tags":454,"primary_topic":9,"secondary_topics":456,"classification":457,"search_phrases":475,"slug":478,"view_count":19,"doi":479,"paper":480,"created_at":497},3900,"ANÁLISE COMPARATIVA DE SENSORES RGB E LiDAR APLICADOS À TOPOGRAFIA COM DRONES EM ÁREAS AGRÍCOLAS","https:\u002F\u002Fdoi.org\u002F10.24980\u002Fucm.v15i17.6682","O sensoriamento remoto tem se consolidado como uma ferramenta fundamental no monitoramento ambiental e no manejo agrícola, sobretudo com o avanço dos Veículos Aéreos Não Tripulados (VANTs). Este trabalho teve como objetivo realizar uma análise comparativa entre os sensores RGB e LiDAR aplicados ao levantamento topográfico em áreas de cultivo de cana-de-açúcar, atividade que demanda alta precisão altimétrica para a sistematização do terreno. O estudo foi conduzido na Fazenda Brasil, localizada em Suzanápolis-SP, utilizando drones equipados com câmera fotogramétrica RGB e sensor LiDAR. Foram avaliados parâmetros como altitude de voo, sobreposição de imagens, densidade de pontos, tempo de processamento, presença de GCPs e capacidade de penetração na vegetação. Os resultados demonstraram que o sensor LiDAR proporciona maior precisão na obtenção do Modelo Digital do Terreno (MDT), especialmente, em áreas com vegetação densa e relevo acidentado, devido à sua capacidade de atravessar a cobertura vegetal e captar informações diretamente do solo e o sensor RGB apresentou melhor desempenho em áreas abertas, com menor custo operacional, porém com limitações na definição altimétrica em regiões vegetadas. Conclui-se que, embora ambas as tecnologias apresentem potencial para aplicação na agricultura de precisão, o LiDAR mostra-se mais eficiente para levantamentos que exigem maior acurácia em ambientes complexos, enquanto o RGB pode ser uma alternativa viável em áreas menos exigentes em termos de detalhamento topográfico. COMPARATIVE ANALYSIS OF RGB AND LiDAR SENSORS APPLIED TO UAV-BASED TOPOGRAPHIC SURVEYS IN AGRICULTURAL AREAS ABSTRACT Remote sensing has become an essential tool for environmental monitoring and agricultural management, particularly with the advancement of Unmanned Aerial Vehicles (UAVs). This study aimed to conduct a comparative analysis of RGB and LiDAR sensors applied to topographic surveys in sugarcane cultivation areas, an activity that requires high altimetric accuracy for land leveling. The study was conducted at Fazenda Brasil, located in Suzanápolis, São Paulo, Brazil, using UAVs equipped with an RGB photogrammetric camera and a LiDAR sensor. Parameters such as flight altitude, image overlap, point density, processing time, presence of Ground Control Points (GCPs), and vegetation penetration capability were evaluated. The results demonstrated that the LiDAR sensor provides greater accuracy in generating the Digital Terrain Model (DTM), particularly in areas with dense vegetation and rugged terrain, due to its ability to penetrate vegetation cover and acquire information directly from the ground. The RGB sensor performed better in open areas and had lower operational costs, but showed limitations in altimetric definition in vegetated areas. It is concluded that, although both technologies have potential applications in precision agriculture, LiDAR is more efficient for surveys requiring greater accuracy in complex environments, whereas RGB may be a viable alternative in areas with lower topographic detail requirements. Keywords: precision agriculture; remote sensing; RGB; LiDAR.","遥感技术已巩固为环境监测和农业管理中的基本工具，尤其是随着无人机（VANTs）的发展。本研究旨在对应用于甘蔗种植区地形测量的RGB和LiDAR传感器进行比较分析，该活动需要高海拔精度以进行土地平整。研究在位于圣保罗州苏扎纳波利斯的巴西农场进行，使用配备RGB摄影测量相机和LiDAR传感器的无人机。评估了飞行高度、图像重叠度、点密度、处理时间、地面控制点（GCPs）的存在以及植被穿透能力等参数。结果表明，LiDAR传感器在获取数字地形模型（MDT）方面提供了更高的精度，特别是在植被密集和地形崎岖的区域，因为它能够穿透植被覆盖并直接从地面捕获信息，而RGB传感器在开阔区域表现更好，运营成本较低，但在植被覆盖区域的altimetric定义上存在局限性。结论是，尽管两种技术在精准农业中都具有应用潜力，但LiDAR在需要更高精度的复杂环境中显示出更高的效率，而RGB在地形细节要求较低的区域可以成为一种可行的替代方案。RGB和LiDAR传感器应用于农业区域无人机地形测量的比较分析 摘要 遥感已成为环境监测和农业管理的重要工具，特别是随着无人机（UAVs）的进步。本研究旨在对应用于甘蔗种植区地形测量的RGB和LiDAR传感器进行比较分析，该活动需要高海拔精度以进行土地平整。研究在位于巴西圣保罗州苏扎纳波利斯的巴西农场进行，使用配备RGB摄影测量相机和LiDAR传感器的无人机。评估了飞行高度、图像重叠度、点密度、处理时间、地面控制点（GCPs）的存在以及植被穿透能力等参数。结果表明，LiDAR传感器在生成数字地形模型（DTM）方面提供了更高的精度，特别","Unifunec Científica Multidisciplinar","2026-09-30T00:00:00Z",55,{"impact":202,"substance":92,"depth":17,"authority":80,"freshness":202,"relevant":20,"comment":451},"巴西期刊对RGB与LiDAR无人机地形测绘的对比研究，方法扎实、结论明确，对精准农业地形平整有参考价值，但属细分技术论文，公共影响有限。",[453],{"name":447,"url":444},[25,26,455,132,27],"甘蔗",[],{"status":351,"locked":14,"version":32,"method":33,"evidence":458},{"candidates":459,"agricultural_evidence":46,"reason":474},[460,464,470],{"code":37,"score":202,"supported":46,"title":461,"abstract":462,"tags":463},[],[132,133],[25,132],{"code":465,"score":129,"supported":46,"title":466,"abstract":467,"tags":469},"A6",[],[455,468],"sugarcane",[455],{"code":42,"score":129,"supported":46,"title":471,"abstract":472,"tags":473},[],[27,281],[27],"多个产业证据相当，需要确认主归属",[476,477],"RGB LiDAR 无人机 地形测量","甘蔗 精准农业 无人机","RGBLiDAR无人机地形测量-3900","10.24980\u002Fucm.v15i17.6682",{"doi":479,"openalex_id":481,"authors":482,"venue":447,"cited_by_count":70,"oa_url":444,"card":492,"direction":75,"ingested_from":77},"W7214933645",[483,485,487,489],{"name":484,"orcid":9},"Alexandre Gabriel LUCHETE",{"name":486,"orcid":9},"Eduardo Sanches CARDOSO",{"name":488,"orcid":9},"Fernando Alves TROMBETA",{"name":490,"orcid":491},"Camila Fernandes Ferreira APARECIDO","https:\u002F\u002Forcid.org\u002F0000-0002-8429-950X",{"tldr":493,"method":494,"finding":495,"direction":75,"opportunity":496},"对比无人机RGB与LiDAR传感器在甘蔗田地形测绘中的精度与适用性。","在巴西甘蔗田用无人机搭载RGB相机与LiDAR，评估飞行参数、点密度与植被穿透性","LiDAR在植被密集和崎岖地形中生成DTM精度更高，RGB在开阔区成本更低但高程受限。","可探索RGB与LiDAR点云融合，在低成本下提升复杂农田地形建模精度。","2026-10-01T23:30:29.092979Z"]