[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4300":3,"related-4300":81},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":59,"slug":62,"view_count":21,"doi":63,"paper":64,"created_at":80},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。",null,"Advances in Science and Technology – Research Journal","2026-10-04T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,16,12,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","农业人工智能","遥感","路面分类",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":58},[38,47,53],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":46},"I2",15,true,[43],"machine learning",[45,43],"机器学习",[28],{"code":48,"score":20,"supported":41,"title":49,"abstract":51,"tags":52},"I3",[50],"multispectral",[50],[29],{"code":54,"score":21,"supported":14,"title":55,"abstract":56,"tags":57},"I1",[],[],[26],"农业相关性需要复核",[60,61],"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":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":70,"oa_url":71,"card":72,"direction":78,"ingested_from":79},"W7219783179",[67],{"name":68,"orcid":69},"Marcin Bilski","https:\u002F\u002Forcid.org\u002F0000-0002-1445-5749",0,"https:\u002F\u002Fwww.astrj.com\u002Fpdf-226062-148948?filename=Road-surface-material-cla.pdf",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"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%","农业遥感与作物表型","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","openalex","2026-10-06T23:30:09.562617Z",{"total":82,"page":21,"page_size":82,"items":83},6,[84,147,235,317,401,469],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":9,"content":9,"source_name":89,"source_url":87,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":96,"tags":98,"primary_topic":100,"secondary_topics":105,"classification":106,"search_phrases":129,"slug":132,"view_count":21,"doi":133,"paper":134,"created_at":146},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":19,"substance":18,"depth":93,"authority":94,"freshness":17,"relevant":21,"comment":95},17,14,"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。",[97],{"name":89,"url":87},[26,27,28,29,99],"大麦",{"code":54,"slug":101,"name":102,"path":103,"parent_code":104},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":107,"locked":14,"version":34,"method":35,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":41},[110,116,121,125],{"code":54,"score":111,"supported":41,"title":112,"abstract":113,"tags":115},4,[],[114],"precision agriculture",[26],{"code":117,"score":21,"supported":14,"title":118,"abstract":119,"tags":120},"A5",[],[],[99],{"code":39,"score":21,"supported":14,"title":122,"abstract":123,"tags":124},[],[],[28],{"code":48,"score":21,"supported":14,"title":126,"abstract":127,"tags":128},[],[],[29],[130,131],"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":133,"openalex_id":135,"authors":136,"venue":89,"cited_by_count":70,"oa_url":9,"card":9,"direction":9,"ingested_from":79},"W7221758985",[137,140,143],{"name":138,"orcid":139},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":141,"orcid":142},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":144,"orcid":145},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":161,"tags":163,"primary_topic":165,"secondary_topics":169,"classification":174,"search_phrases":208,"slug":211,"view_count":70,"doi":212,"paper":213,"created_at":234},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":20,"substance":157,"depth":19,"authority":158,"freshness":159,"relevant":21,"comment":160},18,13,9,"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。",[162],{"name":153,"url":150},[26,27,28,29,164],"葡萄园管理",{"code":48,"slug":166,"name":167,"path":168,"parent_code":104},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[170],{"code":39,"slug":171,"name":172,"path":173,"parent_code":104},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":107,"locked":14,"version":34,"method":35,"evidence":175},{"candidates":176,"agricultural_evidence":41},[177,182,188,194,201],{"code":48,"score":157,"supported":41,"title":178,"abstract":179,"tags":181},[50],[29,180,50],"remote sensing",[29],{"code":39,"score":40,"supported":41,"title":183,"abstract":185,"tags":187},[184],"deep learning",[186,184],"深度学习",[28],{"code":54,"score":189,"supported":41,"title":190,"abstract":191,"tags":193},7,[],[192,114],"精准农业",[26],{"code":195,"score":82,"supported":41,"title":196,"abstract":197,"tags":200},"B2",[],[198,199],"葡萄","grape",[],{"code":202,"score":82,"supported":41,"title":203,"abstract":204,"tags":207},"I4",[],[205,206],"空间分析","GIS",[],[209,210],"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":212,"openalex_id":214,"authors":215,"venue":153,"cited_by_count":70,"oa_url":150,"card":229,"direction":76,"ingested_from":79},"W7220993218",[216,218,221,223,226],{"name":217,"orcid":9},"Dimitra Passa",{"name":219,"orcid":220},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":222,"orcid":9},"Georgios Nektarios Tselos",{"name":224,"orcid":225},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":227,"orcid":228},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":230,"method":231,"finding":232,"direction":76,"opportunity":233},"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":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":9,"source_name":241,"source_url":238,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":243,"score_detail":244,"sources":247,"tags":249,"primary_topic":251,"secondary_topics":252,"classification":253,"search_phrases":283,"slug":286,"view_count":17,"doi":287,"paper":288,"created_at":316},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。","Computers and Electronics in Agriculture","2026-09-14T00:00:00Z",81,{"impact":157,"substance":245,"depth":157,"authority":94,"freshness":159,"relevant":21,"comment":246},22,"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。",[248],{"name":241,"url":238},[26,27,28,29,250],"作物氮素监测",{"code":48,"slug":166,"name":167,"path":168,"parent_code":104},[],{"status":107,"locked":14,"version":34,"method":35,"evidence":254},{"candidates":255,"agricultural_evidence":41},[256,261,265,272,279],{"code":48,"score":157,"supported":41,"title":257,"abstract":259,"tags":260},[258],"hyperspectral",[29,180,258],[29],{"code":39,"score":189,"supported":41,"title":262,"abstract":263,"tags":264},[],[186,184],[28],{"code":266,"score":82,"supported":41,"title":267,"abstract":268,"tags":271},"A2",[],[269,270],"小麦","wheat",[],{"code":273,"score":82,"supported":41,"title":274,"abstract":275,"tags":278},"A3",[],[276,277],"玉米","maize",[],{"code":54,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[26],[284,285],"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":287,"openalex_id":289,"authors":290,"venue":241,"cited_by_count":70,"oa_url":238,"card":311,"direction":76,"ingested_from":79},"W7212964562",[291,293,295,297,299,302,304,306,309],{"name":292,"orcid":9},"Jing Zhao",{"name":294,"orcid":9},"Hong Li",{"name":296,"orcid":9},"Junping Liu",{"name":298,"orcid":9},"Wei Chen",{"name":300,"orcid":301},"Xin Guo","https:\u002F\u002Forcid.org\u002F0000-0002-1583-5364",{"name":303,"orcid":9},"Yunlong Wu",{"name":305,"orcid":9},"Menglong Zhao",{"name":307,"orcid":308},"Junaid Nawaz Chauhdary","https:\u002F\u002Forcid.org\u002F0000-0001-7398-5646",{"name":310,"orcid":9},"Zhaoxia Yan",{"tldr":312,"method":313,"finding":314,"direction":76,"opportunity":315},"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":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":332,"secondary_topics":338,"classification":341,"search_phrases":363,"slug":366,"view_count":111,"doi":367,"paper":368,"created_at":400},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":157,"substance":245,"depth":157,"authority":94,"freshness":17,"relevant":21,"comment":327},"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。",[329],{"name":323,"url":320},[26,27,28,29,331],"大豆育种",{"code":333,"slug":334,"name":335,"path":336,"parent_code":337},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[339,340],{"code":39,"slug":171,"name":172,"path":173,"parent_code":104},{"code":48,"slug":166,"name":167,"path":168,"parent_code":104},{"status":107,"locked":14,"version":34,"method":35,"evidence":342},{"candidates":343,"agricultural_evidence":41},[344,350,355,359],{"code":333,"score":40,"supported":41,"title":345,"abstract":347,"tags":349},[346],"soybean",[348,346],"大豆",[331],{"code":39,"score":13,"supported":41,"title":351,"abstract":352,"tags":354},[],[186,184,353],"artificial intelligence",[28],{"code":48,"score":13,"supported":41,"title":356,"abstract":357,"tags":358},[],[29,180,50],[29],{"code":54,"score":189,"supported":41,"title":360,"abstract":361,"tags":362},[],[192,114],[26],[364,365],"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":367,"openalex_id":369,"authors":370,"venue":323,"cited_by_count":70,"oa_url":320,"card":395,"direction":76,"ingested_from":79},"W7212223520",[371,374,377,380,383,385,387,390,393],{"name":372,"orcid":373},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":375,"orcid":376},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":378,"orcid":379},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":381,"orcid":382},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":384,"orcid":9},"Aviskar Giri",{"name":386,"orcid":9},"Ashutosh Pawar",{"name":388,"orcid":389},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":391,"orcid":392},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":394,"orcid":9},"Felix Fritschi",{"tldr":396,"method":397,"finding":398,"direction":76,"opportunity":399},"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",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":241,"source_url":404,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":407,"score_detail":408,"sources":411,"tags":413,"primary_topic":415,"secondary_topics":420,"classification":421,"search_phrases":441,"slug":444,"view_count":445,"doi":446,"paper":447,"created_at":468},2284,"Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112378","Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet。Computers and Electronics in Agriculture","基于增强型NightNet的无人机图像轻量化夜间柑橘果实检测。《计算机与电子农业》",77,{"impact":19,"substance":409,"depth":93,"authority":94,"freshness":159,"relevant":21,"comment":410},21,"核心期刊论文，提出轻量化夜间柑橘检测方法，方法新颖且面向无人机夜间作业场景，具备细分领域参考价值。",[412],{"name":241,"url":404},[26,27,28,29,414],"柑橘",{"code":195,"slug":416,"name":417,"path":418,"parent_code":419},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":107,"locked":14,"version":34,"method":35,"evidence":422},{"candidates":423,"agricultural_evidence":41},[424,429,433,437],{"code":195,"score":40,"supported":41,"title":425,"abstract":427,"tags":428},[426],"citrus",[414,426],[414],{"code":54,"score":21,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[26],{"code":39,"score":21,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[28],{"code":48,"score":21,"supported":14,"title":438,"abstract":439,"tags":440},[],[],[29],[442,443],"农业人工智能 智慧农业 无人机 柑橘","农业人工智能 智慧农业","农业人工智能智慧农业无人机柑橘-2284",2,"10.1016\u002Fj.compag.2026.112378",{"doi":446,"openalex_id":448,"authors":449,"venue":241,"cited_by_count":70,"oa_url":9,"card":463,"direction":76,"ingested_from":79},"W7212284586",[450,452,455,457,459,461],{"name":451,"orcid":9},"Zhixian Wu",{"name":453,"orcid":454},"Juntao Xiong","https:\u002F\u002Forcid.org\u002F0000-0002-1095-812X",{"name":456,"orcid":9},"Zhen Liu",{"name":458,"orcid":9},"Sitong Li",{"name":460,"orcid":9},"Jingru Peng",{"name":462,"orcid":9},"Sidi Chen",{"tldr":464,"method":465,"finding":466,"direction":76,"opportunity":467},"提出增强NightNet实现无人机夜间柑橘果实轻量检测。","基于NightNet改进的轻量网络，用于无人机夜间图像。","增强NightNet在夜间无人机图像中实现轻量且准确的柑橘检测。","夜间无人机果实检测的轻量化与低照度增强可进一步结合多光谱或时序信息。","2026-09-13T23:30:02.020275Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":474,"content":9,"source_name":153,"source_url":472,"published_at":475,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":484,"secondary_topics":485,"classification":486,"search_phrases":513,"slug":516,"view_count":70,"doi":517,"paper":518,"created_at":529},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",74,{"impact":20,"substance":245,"depth":157,"authority":20,"freshness":13,"relevant":21,"comment":478},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[480],{"name":153,"url":472},[26,28,482,29,483],"粮食安全","气候分析",{"code":39,"slug":171,"name":172,"path":173,"parent_code":104},[],{"status":107,"locked":14,"version":34,"method":35,"evidence":487},{"candidates":488,"agricultural_evidence":41},[489,493,500,505,509],{"code":39,"score":159,"supported":41,"title":490,"abstract":491,"tags":492},[353],[],[28],{"code":494,"score":495,"supported":41,"title":496,"abstract":497,"tags":499},"I5",3,[],[498],"农业传感",[],{"code":501,"score":21,"supported":14,"title":502,"abstract":503,"tags":504},"A8",[],[],[482],{"code":54,"score":21,"supported":14,"title":506,"abstract":507,"tags":508},[],[],[26],{"code":48,"score":21,"supported":14,"title":510,"abstract":511,"tags":512},[],[],[29],[514,515],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":517,"openalex_id":519,"authors":520,"venue":153,"cited_by_count":70,"oa_url":472,"card":523,"direction":528,"ingested_from":79},"W7223111766",[521],{"name":522,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":524,"method":525,"finding":526,"direction":76,"opportunity":527},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z"]