[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3982":3,"related-3982":86},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":57,"slug":60,"view_count":61,"doi":62,"paper":63,"created_at":85},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》",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","无人机","遥感","多光谱",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},15,true,[48],"multispectral",[50,48],"无人机多光谱",[29],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"I1",[],[],[27],[58,59],"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":62,"openalex_id":64,"authors":65,"venue":10,"cited_by_count":77,"oa_url":9,"card":78,"direction":82,"ingested_from":84},"W7215128624",[66,69,72,74],{"name":67,"orcid":68},"Jiayang Xie","https:\u002F\u002Forcid.org\u002F0000-0001-5711-2351",{"name":70,"orcid":71},"Yutao Shen","https:\u002F\u002Forcid.org\u002F0009-0001-4348-125X",{"name":73,"orcid":9},"Wenjuan Li",{"name":75,"orcid":76},"Haiyan Cen","https:\u002F\u002Forcid.org\u002F0000-0003-0266-2123",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"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。","农业遥感与作物表型","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。","openalex","2026-10-02T23:30:03.513918Z",{"total":87,"page":22,"page_size":87,"items":88},6,[89,190,274,347,450,526],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":98,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":114,"classification":116,"search_phrases":142,"slug":145,"view_count":77,"doi":146,"paper":147,"created_at":189},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":99,"substance":100,"depth":99,"authority":17,"freshness":101,"relevant":22,"comment":102},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。",[104],{"name":95,"url":92},[27,28,106,29,107],"育种","紫花苜蓿",{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[115],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":117},{"candidates":118,"agricultural_evidence":46},[119,125,129,136],{"code":109,"score":45,"supported":46,"title":120,"abstract":122,"tags":124},[121],"alfalfa",[123,121],"苜蓿",[107],{"code":32,"score":17,"supported":46,"title":126,"abstract":127,"tags":128},[48],[48],[29],{"code":53,"score":130,"supported":46,"title":131,"abstract":132,"tags":135},7,[],[133,134],"精准农业","precision agriculture",[27],{"code":137,"score":138,"supported":46,"title":139,"abstract":140,"tags":141},"C4",4,[],[123],[107],[143,144],"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":146,"openalex_id":148,"authors":149,"venue":95,"cited_by_count":77,"oa_url":92,"card":184,"direction":82,"ingested_from":84},"W7223083966",[150,153,156,159,162,164,166,169,172,175,178,181],{"name":151,"orcid":152},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":154,"orcid":155},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":157,"orcid":158},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":160,"orcid":161},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":163,"orcid":9},"Julie Hansen",{"name":165,"orcid":9},"Christopher Pierce",{"name":167,"orcid":168},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":170,"orcid":171},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":173,"orcid":174},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":176,"orcid":177},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":179,"orcid":180},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":182,"orcid":183},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":185,"method":186,"finding":187,"direction":82,"opportunity":188},"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":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":215,"search_phrases":247,"slug":250,"view_count":77,"doi":251,"paper":252,"created_at":273},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":17,"substance":99,"depth":200,"authority":201,"freshness":101,"relevant":22,"comment":202},16,13,"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。",[204],{"name":196,"url":193},[27,28,206,29,207],"农业人工智能","葡萄园管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[210],{"code":211,"slug":212,"name":213,"path":214,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":216},{"candidates":217,"agricultural_evidence":46},[218,223,229,233,240],{"code":32,"score":99,"supported":46,"title":219,"abstract":220,"tags":222},[48],[29,221,48],"remote sensing",[29],{"code":211,"score":45,"supported":46,"title":224,"abstract":226,"tags":228},[225],"deep learning",[227,225],"深度学习",[206],{"code":53,"score":130,"supported":46,"title":230,"abstract":231,"tags":232},[],[133,134],[27],{"code":234,"score":87,"supported":46,"title":235,"abstract":236,"tags":239},"B2",[],[237,238],"葡萄","grape",[],{"code":241,"score":87,"supported":46,"title":242,"abstract":243,"tags":246},"I4",[],[244,245],"空间分析","GIS",[],[248,249],"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":251,"openalex_id":253,"authors":254,"venue":196,"cited_by_count":77,"oa_url":193,"card":268,"direction":82,"ingested_from":84},"W7220993218",[255,257,260,262,265],{"name":256,"orcid":9},"Dimitra Passa",{"name":258,"orcid":259},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":261,"orcid":9},"Georgios Nektarios Tselos",{"name":263,"orcid":264},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":266,"orcid":267},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":269,"method":270,"finding":271,"direction":82,"opportunity":272},"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":275,"title":276,"url":277,"summary":278,"summary_zh":9,"content":9,"source_name":279,"source_url":277,"published_at":280,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":281,"score_detail":282,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":295,"classification":297,"search_phrases":313,"slug":316,"view_count":317,"doi":318,"paper":319,"created_at":346},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":21,"substance":20,"depth":45,"authority":87,"freshness":21,"relevant":22,"comment":283},"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。",[285],{"name":279,"url":277},[27,28,29,287,288],"NDVI","咖啡种植",{"code":290,"slug":291,"name":292,"path":293,"parent_code":294},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[296],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":298},{"candidates":299,"agricultural_evidence":46},[300,304,309],{"code":32,"score":201,"supported":46,"title":301,"abstract":302,"tags":303},[48],[48],[287,29],{"code":290,"score":17,"supported":46,"title":305,"abstract":307,"tags":308},[306],"coffee",[306],[288],{"code":53,"score":22,"supported":14,"title":310,"abstract":311,"tags":312},[],[],[27],[314,315],"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":318,"openalex_id":320,"authors":321,"venue":279,"cited_by_count":77,"oa_url":345,"card":9,"direction":82,"ingested_from":84},"W7214220732",[322,324,326,328,330,333,335,337,340,343],{"name":323,"orcid":9},"Gabriel de Morais Campos",{"name":325,"orcid":9},"Aline Bhering Silva",{"name":327,"orcid":9},"Cileimar Aparecida da Silva",{"name":329,"orcid":9},"Vanda Maria Salles Andrade",{"name":331,"orcid":332},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":334,"orcid":9},"Andressa Barcellos Silva",{"name":336,"orcid":9},"Marco Thúlio Gonçalves Vieira",{"name":338,"orcid":339},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":341,"orcid":342},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":344,"orcid":9},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":366,"classification":368,"search_phrases":397,"slug":400,"view_count":392,"doi":401,"paper":402,"created_at":449},3354,"Optimizing canopy nitrogen retrieval in wheat from high-resolution UAV hyperspectral data using RTM-based Gaussian process regression","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01904167.2026.2736627","The canopy nitrogen content (CNC) is one of the vital crop health indicators directly influencing the crop growth, yield, and nutritional status. Unmanned aerial vehicles (UAVs) that acquired hyperspectral imagery opened a new path for nondestructive, accurate, and timely estimation of CNC, facilitating the adoption of smart agro practices in precision agriculture for site-specific nutrient management. With the purpose of processing such hyperspectral data into CNC maps, two hybrid retrieval approaches, protein-based (CNCprot) and chlorophyll-based (CNCchl) models, were built by combining a radiative transfer model with a machine learning Gaussian process regression (GPR) algorithm. The GPR retrieval models were trained using synthetic spectral data from the PROSAIL-PRO model, and the sampling was optimized using the Euclidean distance-based diversity (EBD) active learning (AL) technique. The CNC models were applied to a UAV-borne hyperspectral image in the spectral range of 400-1000 nm with an ultrahigh spatial resolution of 4 cm, acquired over the experimental wheat field of Indian Council of Agricultural Research (ICAR) -Indian Agricultural Research Institute (IARI), New Delhi, India. The GPR models yielded good prediction accuracies with R2 values of 0.74 and 0.56 and NRMSE of 13.62 and 23.82% for CNCchl and CNCprot, respectively. The consistent prediction accuracies and low associated uncertainties revealed that CNC was more accurately mapped using chlorophyll-based CNC retrieval applied to a Vis-NIR-based hyperspectral data. These promising results demonstrated the possibility of applying hybrid GPR models at ultrahigh resolution and the operational viability of UAV datasets for regular CNC monitoring.","冠层氮含量（CNC）是直接影响作物生长、产量和营养状况的重要作物健康指标之一。无人机（UAV）获取的高光谱影像为无损、精准、及时地估算CNC开辟了新途径，有助于在精准农业中采用智能农业实践进行定点养分管理。为了将此类高光谱数据处理为CNC分布图，本研究通过将辐射传输模型与机器学习高斯过程回归（GPR）算法相结合，构建了两种混合反演方法：基于蛋白质的模型（CNCprot）和基于叶绿素的模型（CNCchl）。GPR反演模型使用PROSAIL-PRO模型生成的合成光谱数据进行训练，并采用基于欧氏距离的多样性（EBD）主动学习（AL）技术优化采样。CNC模型被应用于一幅无人机载高光谱影像，光谱范围为400-1000 nm，空间分辨率高达4 cm，该影像获取自印度农业研究理事会（ICAR）-印度农业研究所（IARI）位于印度新德里的实验麦田。GPR模型取得了良好的预测精度，CNCchl和CNCprot的R²值分别为0.74和0.56，NRMSE分别为13.62%和23.82%。一致的预测精度和较低的相关不确定性表明，将基于叶绿素的CNC反演应用于基于可见光-近红外（Vis-NIR）的高光谱数据能够更准确地制图CNC。这些有前景的结果证明了在超高分辨率下应用混合GPR模型的可能性，以及无人机数据集用于常规CNC监测的可行性。","Journal of Plant Nutrition","2026-09-23T00:00:00Z",{"impact":99,"substance":100,"depth":99,"authority":201,"freshness":21,"relevant":22,"comment":356},"将辐射传输模型与高斯过程回归结合，用4厘米超高分辨率无人机高光谱实现小麦冠层氮素精准反演，方法新颖、结论可靠，对精准农业养分管理有实操参考价值。",[358],{"name":353,"url":350},[27,28,360,29,361],"小麦","氮素监测",{"code":363,"slug":364,"name":360,"path":365,"parent_code":113},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[367],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":369},{"candidates":370,"agricultural_evidence":46},[371,375,380,384,390],{"code":363,"score":17,"supported":46,"title":372,"abstract":373,"tags":374},[364],[364],[360],{"code":32,"score":17,"supported":46,"title":376,"abstract":378,"tags":379},[377],"hyperspectral",[377],[29],{"code":53,"score":130,"supported":46,"title":381,"abstract":382,"tags":383},[],[133,134],[27],{"code":211,"score":87,"supported":46,"title":385,"abstract":386,"tags":389},[],[387,388],"机器学习","machine learning",[],{"code":391,"score":392,"supported":46,"title":393,"abstract":394,"tags":396},"F3",3,[],[395],"养分管理",[],[398,399],"无人机 高光谱 小麦 氮素","PROSAIL 高斯过程回归 小麦","无人机高光谱小麦氮素-3354","10.1080\u002F01904167.2026.2736627",{"doi":401,"openalex_id":403,"authors":404,"venue":353,"cited_by_count":77,"oa_url":9,"card":443,"direction":448,"ingested_from":84},"W7214104831",[405,407,409,412,415,417,419,422,425,428,430,433,435,437,440],{"name":406,"orcid":9},"R. G. Rejith",{"name":408,"orcid":9},"Rabi N. Sahoo",{"name":410,"orcid":411},"Shalini Gakhar","https:\u002F\u002Forcid.org\u002F0000-0001-5717-1714",{"name":413,"orcid":414},"Jochem Verrelst","https:\u002F\u002Forcid.org\u002F0000-0002-6313-2081",{"name":416,"orcid":9},"Amrita Bhandari",{"name":418,"orcid":9},"Tarun Kondraju",{"name":420,"orcid":421},"Rajeev Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-2233-9147",{"name":423,"orcid":424},"Mahesh C. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-5386-883X",{"name":426,"orcid":427},"Abir Dey","https:\u002F\u002Forcid.org\u002F0000-0002-5009-9518",{"name":429,"orcid":9},"Joydeep Mukherjee",{"name":431,"orcid":432},"Sudhir Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-1089-7435",{"name":434,"orcid":9},"Mahesh Kumar",{"name":436,"orcid":9},"Raju Dhandapani",{"name":438,"orcid":439},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":441,"orcid":442},"Viswanathan Chinnusamy","https:\u002F\u002Forcid.org\u002F0000-0003-2174-9064",{"tldr":444,"method":445,"finding":446,"direction":82,"opportunity":447},"用PROSAIL-PRO与高斯过程回归从无人机高光谱数据反演小麦冠层氮含量。","PROSAIL-PRO合成光谱训练GPR，EBD主动学习采样，4cm无人机高光谱","叶绿素基模型精度更高，R²达0.74，NRMSE为13.62%，优于蛋白基模型。","可探索多生育期、多品种迁移能力及主动学习采样策略对反演精度的提升空间。","智慧农业 \u002F 农业物联网","2026-09-24T23:30:10.311254Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":9,"source_name":10,"source_url":453,"published_at":456,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":457,"score_detail":458,"sources":460,"tags":462,"primary_topic":464,"secondary_topics":465,"classification":466,"search_phrases":492,"slug":495,"view_count":21,"doi":496,"paper":497,"created_at":525},2500,"Estimation of crop canopy nitrogen content using deep transfer learning with PROSAIL-PRO model and UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112385","Canopy nitrogen content (CNC) is a key indicator for assessing crop nitrogen status. The non-destructive and rapid estimation of CNC can be performed using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing on large-scale; however, it is often constrained by the scarcity of ground truth data relative to model complexity, which can lead to overfitting and poor generalizability. To address these challenges, a novel inversion framework was proposed by integrating the PROSAIL-PRO physical model with deep transfer learning. Generalized physical features were extracted using pre-trained deep neural network on large-scale PROSAIL-PRO simulated spectra. Subsequently, a fine-tuning strategy was applied in which shallow-level parameters were frozen while deep-level parameters were updated, thereby effectively decoupling intrinsic physical features from site-specific variations. Four deep learning architectures including convolutional neural network (CNN), Residual Neural Network (ResNet18), and their respective channel attention-enhanced variants were evaluated and compared with six classical statistical models including a backpropagation neural network (BPNN). The results demonstrated that transfer-learning-optimized deep models significantly outperformed both baseline and traditional methods in terms of accuracy and generalizability. Specifically, the CNN-based transfer model achieved the best performance on the wheat dataset ( R 2 = 0.8597 and RMSECV = 1.7642), whereas the ResNet18-based transfer model achieved the best performance on the maize dataset ( R 2 = 0.6921 and RMSECV = 2.7944). These findings confirmed that PROSAIL-PRO driven deep transfer learning effectively mitigated overfitting in small-sample hyperspectral datasets. By ensuring high-precision inversion and robust generalization, the proposed physics-guided data-driven fusion approach provides a promising solution for crop nitrogen monitoring.","冠层氮含量（Canopy Nitrogen Content, CNC）是评估作物氮素状况的关键指标。基于无人机（UAV）高光谱遥感可以在大尺度上对CNC进行无损快速估测；然而，该任务常受限于地面真值数据相对模型复杂度而言较为稀缺的问题，这会导致过拟合和泛化能力较差。为应对这些挑战，该研究提出了一种将PROSAIL-PRO物理模型与深度迁移学习相结合的新型反演框架。利用在大规模PROSAIL-PRO模拟光谱上预训练的深度神经网络提取广义物理特征。随后，采用冻结浅层参数、更新深层参数的微调策略，从而有效解耦内在物理特征与站点特异性变异。研究评估了四种深度学习架构，包括卷积神经网络（CNN）、残差神经网络（ResNet18）及其各自的通道注意力增强变体，并与包括反向传播神经网络（BPNN）在内的六种经典统计模型进行了比较。结果表明，经迁移学习优化的深度模型在精度和泛化能力方面均显著优于基线和传统方法。具体而言，基于CNN的迁移模型在小麦数据集上取得了最佳性能（R² = 0.8597，RMSECV = 1.7642），而基于ResNet18的迁移模型在玉米数据集上取得了最佳性能（R² = 0.6921，RMSECV = 2.7944）。这些发现证实，PROSAIL-PRO驱动的深度迁移学习有效缓解了小样本高光谱数据集中的过拟合问题。通过确保高精度反演和稳健泛化，所提出的物理引导的数据驱动融合方法为作物氮素监测提供了一种有前景的解决方案。","2026-09-14T00:00:00Z",81,{"impact":99,"substance":100,"depth":99,"authority":20,"freshness":101,"relevant":22,"comment":459},"将PROSAIL-PRO物理模型与深度迁移学习结合，缓解小样本高光谱反演过拟合，方法新颖且验证扎实，对作物氮素遥感监测有实用价值。",[461],{"name":10,"url":453},[27,28,206,29,463],"作物氮素监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":467},{"candidates":468,"agricultural_evidence":46},[469,473,477,481,488],{"code":32,"score":99,"supported":46,"title":470,"abstract":471,"tags":472},[377],[29,221,377],[29],{"code":211,"score":130,"supported":46,"title":474,"abstract":475,"tags":476},[],[227,225],[206],{"code":363,"score":87,"supported":46,"title":478,"abstract":479,"tags":480},[],[360,364],[],{"code":482,"score":87,"supported":46,"title":483,"abstract":484,"tags":487},"A3",[],[485,486],"玉米","maize",[],{"code":53,"score":22,"supported":14,"title":489,"abstract":490,"tags":491},[],[],[27],[493,494],"作物氮素监测 农业人工智能 智慧农业 无人机","作物氮素监测 农业人工智能","作物氮素监测农业人工智能智慧农业无人机-2500","10.1016\u002Fj.compag.2026.112385",{"doi":496,"openalex_id":498,"authors":499,"venue":10,"cited_by_count":77,"oa_url":453,"card":520,"direction":82,"ingested_from":84},"W7212964562",[500,502,504,506,508,511,513,515,518],{"name":501,"orcid":9},"Jing Zhao",{"name":503,"orcid":9},"Hong Li",{"name":505,"orcid":9},"Junping Liu",{"name":507,"orcid":9},"Wei Chen",{"name":509,"orcid":510},"Xin Guo","https:\u002F\u002Forcid.org\u002F0000-0002-1583-5364",{"name":512,"orcid":9},"Yunlong Wu",{"name":514,"orcid":9},"Menglong Zhao",{"name":516,"orcid":517},"Junaid Nawaz Chauhdary","https:\u002F\u002Forcid.org\u002F0000-0001-7398-5646",{"name":519,"orcid":9},"Zhaoxia Yan",{"tldr":521,"method":522,"finding":523,"direction":82,"opportunity":524},"融合PROSAIL-PRO物理模型与深度迁移学习，用无人机高光谱估算作物冠层氮含量。","PROSAIL-PRO模拟光谱预训练CNN\u002FResNet18，冻结浅层微调深层，","迁移学习模型精度与泛化性最优，小麦R²=0.86、玉米R²=0.69，缓解小样本过拟合。","可探索多作物多生育期迁移、物理模型参数不确定性传播及跨传感器泛化能力。","2026-09-15T23:30:01.518007Z",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":532,"source_url":529,"published_at":533,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":534,"score_detail":535,"sources":537,"tags":539,"primary_topic":541,"secondary_topics":546,"classification":549,"search_phrases":571,"slug":574,"view_count":138,"doi":575,"paper":576,"created_at":608},2320,"Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183121","Geospatial artificial intelligence (GeoAI) integrates multimodal remote sensing with deep learning to model complex agricultural systems at scale. Within this framework, accurate and non-destructive prediction of seed composition from in-season standing crops is essential for breeding and precision agriculture. This study developed an end-to-end convolutional neural network (CNN) framework to estimate eight seed traits (protein, oil, sucrose, fiber, starch, ash, complex and simple carbohydrates) from UAV-based multisensor imagery and associated genotype and phenological metadata. A total of 372 soybean samples were collected over two growing seasons (2020–2021) from two fields in Missouri, with UAV flights capturing multispectral (MSI), thermal (THR), and LiDAR (LDR) data at four time points spanning vegetative to reproductive growth stages. CNN models were trained in single- and multi-date configurations, incorporating genotype (GEN) and days after sowing (DAS) as additional features. The highest accuracy was achieved for sucrose (R2 = 0.80), followed by simple carbohydrate (R2 = 0.70) and starch (R2 = 0.55), with notable gains from GEN and DAS. Multi-date models incorporating earlier acquisitions often matched or outperformed later or all-date combinations. Among modalities, MSI provided the most robust estimates, with limited added value from LDR or THR. Unlike feature-based pipelines prone to multicollinearity, this image-to-trait approach enables automated, scalable prediction of soybean seed composition for in-season, field-level assessment.","地理空间人工智能（GeoAI）将多模态遥感与深度学习相结合，以在大尺度上对复杂农业系统进行建模。在该框架下，对当季未收获作物进行准确且无损的种子成分预测，对于育种和精准农业至关重要。本研究开发了一种端到端的卷积神经网络（CNN）框架，利用基于无人机（UAV）的多传感器影像以及相关的基因型和物候元数据，估算八种种子性状（蛋白质、油分、蔗糖、纤维、淀粉、灰分、复杂碳水化合物和简单碳水化合物）。研究在2020—2021两个生长季内，从密苏里州两块田地共采集372份大豆样本，无人机飞行在从营养生长期到生殖生长期的四个时间点获取了多光谱（MSI）、热红外（THR）和激光雷达（LDR）数据。CNN模型以单日期和多日期配置进行训练，并将基因型（GEN）和播种后天数（DAS）作为附加特征纳入。蔗糖的估算精度最高（R² = 0.80），其次为简单碳水化合物（R² = 0.70）和淀粉（R² = 0.55），且GEN和DAS的加入带来了显著提升。纳入早期采集数据的多日期模型往往与后期或全日期组合的表现相当，甚至更优。在各模态中，MSI提供了最稳健的估算结果，而LDR或THR的附加价值有限。与易受多重共线性影响的特征工程流程不同，这种从图像到性状的方法能够实现大豆种子成分的自动化、可扩展预测，用于当季田块尺度的评估。","Remote Sensing","2026-09-11T00:00:00Z",80,{"impact":99,"substance":100,"depth":99,"authority":20,"freshness":21,"relevant":22,"comment":536},"基于多模态无人机影像与基因型信息的大豆籽粒成分无损预测研究，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[538],{"name":532,"url":529},[27,28,206,29,540],"大豆育种",{"code":542,"slug":543,"name":544,"path":545,"parent_code":113},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[547,548],{"code":211,"slug":212,"name":213,"path":214,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":550},{"candidates":551,"agricultural_evidence":46},[552,558,563,567],{"code":542,"score":45,"supported":46,"title":553,"abstract":555,"tags":557},[554],"soybean",[556,554],"大豆",[540],{"code":211,"score":13,"supported":46,"title":559,"abstract":560,"tags":562},[],[227,225,561],"artificial intelligence",[206],{"code":32,"score":13,"supported":46,"title":564,"abstract":565,"tags":566},[],[29,221,48],[29],{"code":53,"score":130,"supported":46,"title":568,"abstract":569,"tags":570},[],[133,134],[27],[572,573],"农业人工智能 大豆育种 智慧农业 无人机","农业人工智能 大豆育种","农业人工智能大豆育种智慧农业无人机-2320","10.3390\u002Frs18183121",{"doi":575,"openalex_id":577,"authors":578,"venue":532,"cited_by_count":77,"oa_url":529,"card":603,"direction":82,"ingested_from":84},"W7212223520",[579,582,585,588,591,593,595,598,601],{"name":580,"orcid":581},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":583,"orcid":584},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":586,"orcid":587},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":589,"orcid":590},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":592,"orcid":9},"Aviskar Giri",{"name":594,"orcid":9},"Ashutosh Pawar",{"name":596,"orcid":597},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":599,"orcid":600},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":602,"orcid":9},"Felix Fritschi",{"tldr":604,"method":605,"finding":606,"direction":82,"opportunity":607},"用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。","372份大豆样本，多光谱\u002F热红外\u002FLiDAR影像加基因型与播后天数，端到端CNN","蔗糖预测最优(R²=0.80)，基因型与播期提升精度，多光谱最稳健。","可探索基因型-表型-环境交互建模，并迁移至其他作物与多时相早期预测。","2026-09-13T23:30:22.776177Z"]