[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2614":3,"related-2614":88},{"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":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":87},2614,"Boundary-aware dual-stream network for topology restoration and automated mapping of fragmented mulch films in UAV imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112428","Boundary-aware dual-stream network for topology restoration and automated mapping of fragmented mulch films in UAV imagery。Computers and Electronics in Agriculture","面向无人机影像中破碎地膜拓扑恢复与自动制图的边界感知双流网络。《农业计算机与电子》",null,"Computers and Electronics in Agriculture","2026-09-16T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},16,20,17,14,1,"发表于农业信息领域核心期刊的无人机遥感地膜碎片识别与制图新方法，方法新颖、结论可靠，对农田残膜监测与治理具有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","农业人工智能","农业遥感","地膜回收",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":53,"reason":54},[38,43,48],{"code":39,"score":21,"supported":14,"title":40,"abstract":41,"tags":42},"I1",[],[],[26],{"code":44,"score":21,"supported":14,"title":45,"abstract":46,"tags":47},"I2",[],[],[28],{"code":49,"score":21,"supported":14,"title":50,"abstract":51,"tags":52},"I3",[],[],[29],true,"缺少足够的主题证据",[56,57],"农业人工智能 农业遥感 地膜回收 智慧农业","农业人工智能 农业遥感","农业人工智能农业遥感地膜回收智慧农业-2614",5,"10.1016\u002Fj.compag.2026.112428",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":79,"oa_url":9,"card":80,"direction":84,"ingested_from":86},"W7213282871",[64,66,68,70,73,76],{"name":65,"orcid":9},"Penglei Yan",{"name":67,"orcid":9},"Sumin Lv",{"name":69,"orcid":9},"Xuan Li",{"name":71,"orcid":72},"Jianan Chi","https:\u002F\u002Forcid.org\u002F0009-0004-2943-1565",{"name":74,"orcid":75},"Xiao Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0111-8089",{"name":77,"orcid":78},"Nannan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-2956-8815",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"提出边界感知双流网络，从无人机影像中恢复破碎地膜拓扑并自动制图。","边界感知双流网络，基于无人机影像进行地膜分割与拓扑修复。","该方法能有效恢复破碎地膜的拓扑结构，实现自动化制图。","农业遥感与作物表型","可探索破碎地膜拓扑恢复在残膜回收决策与农田污染评估中的应用。","openalex","2026-09-16T23:30:01.821423Z",{"total":89,"page":21,"page_size":89,"items":90},6,[91,156,238,297,352,441],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":98,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":106,"tags":108,"primary_topic":110,"secondary_topics":115,"classification":116,"search_phrases":135,"slug":138,"view_count":139,"doi":140,"paper":141,"created_at":155},4373,"APPLICATION POTENTIAL OF UNMANNED AERIAL VEHICLES AND TRENDS IN THEIR DEVELOPMENT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23168044","This article examines the application potential and principal development trends of unmanned aerial vehicles across civilian, public-service, industrial, scientific, and defense-related domains. Particular attention is given to platform diversification, sensor and payload integration, artificial intelligence, autonomy, communications, energy efficiency, airspace integration, safety, cybersecurity, regulation, and human capital. The study applies a structured qualitative review of open official documents and peer-reviewed or professionally reviewed publications, combining international evidence with official sources from the Republic of Uzbekistan. The findings show that unmanned aerial vehicles are evolving from remotely piloted, task-specific platforms toward increasingly autonomous, networked, modular, data-driven aerial systems. Their strongest growth areas include precision agriculture, geospatial mapping, infrastructure inspection, environmental monitoring, emergency response, logistics, communications support, and selected security and defense functions. The article remains at the strategic, technological, regulatory, and systems-engineering level and does not address tactical employment procedures or operational vulnerabilities.","本文考察了无人机在民用、公共服务、工业、科学及国防相关领域的应用潜力与主要发展趋势，特别关注平台多样化、传感器与载荷集成、人工智能、自主性、通信、能效、空域整合、安全、网络安全、监管及人力资本等方面。研究采用结构化定性综述方法，对公开官方文件及经同行评审或专业评审的出版物进行分析，并将国际证据与乌兹别克斯坦共和国的官方来源相结合。研究结果表明，无人机正从远程驾驶的专用任务平台，向日益自主化、网络化、模块化、数据驱动的空中系统演进。其增长最为强劲的领域包括精准农业、地理空间测绘、基础设施巡检、环境监测、应急响应、物流、通信支持以及特定的安全与国防功能。本文保持在战略、技术、监管和系统工程层面，不涉及战术运用程序或作战脆弱性。","HSR (London)","https:\u002F\u002Fhoughtonstreetreview.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F2110","2026-10-05T00:00:00Z",67,{"impact":102,"substance":17,"depth":103,"authority":13,"freshness":104,"relevant":21,"comment":105},18,15,8,"综述性论文，将无人机与精准农业、农业遥感等智慧农业场景关联，有战略参考价值但非突破性成果。",[107],{"name":97,"url":98},[26,27,28,29,109],"精准农业",{"code":44,"slug":111,"name":112,"path":113,"parent_code":114},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":117,"locked":14,"version":34,"method":35,"evidence":118},"classified",{"candidates":119,"agricultural_evidence":53},[120,125,131],{"code":39,"score":104,"supported":53,"title":121,"abstract":122,"tags":124},[],[109,123],"precision agriculture",[26,109],{"code":44,"score":126,"supported":53,"title":127,"abstract":128,"tags":130},4,[],[129],"artificial intelligence",[28],{"code":49,"score":21,"supported":14,"title":132,"abstract":133,"tags":134},[],[],[29],[136,137],"无人机 精准农业 应用","农业人工智能 农业遥感 智慧农业 精准农业","无人机精准农业应用-4373",2,"10.5281\u002Fzenodo.23168044",{"doi":140,"openalex_id":142,"authors":143,"venue":97,"cited_by_count":79,"oa_url":98,"card":148,"direction":154,"ingested_from":86},"W7220406405",[144,146],{"name":145,"orcid":9},"Somitov Abdukadir Abdurayimovich",{"name":147,"orcid":9},"Worldly Knowledge Publishing Centre",{"tldr":149,"method":150,"finding":151,"direction":152,"opportunity":153},"综述无人机应用潜力与发展趋势，强调其向自主化、网络化、数据驱动系统演进。","结构化定性综述官方文件与同行评议文献，结合国际与乌兹别克斯坦证据。","无人机最强增长领域包括精准农业、地理空间测绘、基础设施巡检与环境监测等。","智慧农业 \u002F 农业物联网","可聚焦精准农业中无人机多源数据融合与自主决策的落地瓶颈及监管适配研究。","农业人工智能与决策模型","2026-10-07T23:30:46.084251Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":161,"content":9,"source_name":10,"source_url":159,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":167,"tags":169,"primary_topic":171,"secondary_topics":175,"classification":177,"search_phrases":216,"slug":219,"view_count":126,"doi":220,"paper":221,"created_at":237},2906,"UAV remote sensing for crop lodging monitoring: A comprehensive review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112446","Global agriculture is facing increasing pressure to improve productivity while minimizing losses caused by extreme climate events. Crop lodging poses a major threat to food security by reducing crop yield, deteriorating grain quality, and increasing harvesting costs. As a flexible, high-resolution remote sensing platform, unmanned aerial vehicles (UAVs) have emerged as an effective solution for eliminating the gap between labor-intensive ground surveys and low-resolution satellite observations. This review systematically synthesizes the current state of research on UAV-based crop lodging monitoring through a comprehensive analysis of peer-reviewed literature. It covers the complete technical workflow, from lodging mechanisms to lodging quantification. The capabilities of diverse sensing modalities, including RGB, multispectral imagery (MSI), hyperspectral imagery (HSI), thermal infrared (TIR), light detection and ranging (LiDAR), and synthetic aperture radar (SAR), are systematically evaluated for detecting lodging-induced variations in color, texture, spectral characteristics, canopy temperature, structural attributes, and radar backscatter. In addition, recent advances in analytical approaches are reviewed, encompassing traditional statistical analyses, machine learning techniques, and state-of-the-art deep learning models. Furthermore, the review summarizes UAV-based lodging assessments across a wide range of crop species and examines methods for quantifying key lodging parameters, including lodging severity, lodging type, and affected area. By identifying current research gaps and emerging opportunities, this review outlines future research directions and provides valuable guidance for advancing accurate, efficient, and real-time crop lodging monitoring in smart agriculture.","全球农业正面临日益增大的压力，既要提高生产力，又要尽量减少极端气候事件造成的损失。作物倒伏通过降低作物产量、恶化籽粒品质并增加收获成本，对粮食安全构成重大威胁。作为一种灵活的高分辨率遥感平台，无人机（UAV）已成为弥合劳动密集型地面调查与低分辨率卫星观测之间差距的有效解决方案。本文通过系统分析同行评议文献，全面综述了基于无人机的作物倒伏监测研究现状，涵盖从倒伏机理到倒伏量化的完整技术流程。系统评估了多种传感模态的能力，包括RGB影像、多光谱影像（MSI）、高光谱影像（HSI）、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR），用于检测倒伏引起的颜色、纹理、光谱特征、冠层温度、结构属性和雷达后向散射变化。此外，综述了分析方法的最新进展，涵盖传统统计分析、机器学习技术和前沿深度学习模型。同时，本文总结了针对多种作物类型的基于无人机的倒伏评估研究，并探讨了关键倒伏参数的量化方法，包括倒伏严重程度、倒伏类型和受影响面积。通过识别当前研究空白和新兴机遇，本文展望了未来研究方向，为推进智慧农业中准确、高效、实时的作物倒伏监测提供了有价值的指导。","2026-09-18T00:00:00Z",81,{"impact":102,"substance":165,"depth":102,"authority":103,"freshness":104,"relevant":21,"comment":166},22,"核心期刊发表的无人机遥感作物倒伏监测综述，系统梳理多模态传感与深度学习分析路径，对智慧农业精准监测有实质参考价值。",[168],{"name":10,"url":159},[26,27,28,29,170],"作物倒伏",{"code":49,"slug":172,"name":173,"path":174,"parent_code":114},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[176],{"code":44,"slug":111,"name":112,"path":113,"parent_code":114},{"status":117,"locked":14,"version":34,"method":35,"evidence":178},{"candidates":179,"agricultural_evidence":53},[180,188,196,202,209],{"code":49,"score":102,"supported":53,"title":181,"abstract":183,"tags":187},[182],"remote sensing",[184,182,185,186],"遥感","multispectral","hyperspectral",[29],{"code":44,"score":13,"supported":53,"title":189,"abstract":190,"tags":195},[],[191,192,193,194],"深度学习","机器学习","machine learning","deep learning",[28],{"code":39,"score":197,"supported":53,"title":198,"abstract":199,"tags":201},7,[],[26,200],"smart agriculture",[26],{"code":203,"score":89,"supported":53,"title":204,"abstract":205,"tags":208},"A8",[],[206,207],"粮食安全","food security",[],{"code":210,"score":89,"supported":53,"title":211,"abstract":212,"tags":215},"J3",[],[213,214],"全球农业","global agriculture",[],[217,218],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906","10.1016\u002Fj.compag.2026.112446",{"doi":220,"openalex_id":222,"authors":223,"venue":10,"cited_by_count":79,"oa_url":159,"card":232,"direction":152,"ingested_from":86},"W7213542473",[224,227,229],{"name":225,"orcid":226},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":228,"orcid":9},"Changxing Geng",{"name":230,"orcid":231},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":233,"method":234,"finding":235,"direction":84,"opportunity":236},"综述无人机遥感在作物倒伏监测中的技术流程、传感器与分析模型。","系统综述RGB、多光谱、高光谱、热红外、LiDAR、SAR及机器学习深度学习方法","多模态遥感结合深度学习可实现倒伏程度、类型与面积的精准量化。","多源数据融合与实时轻量化模型是倒伏监测的潜在突破方向。","2026-09-19T23:30:01.740139Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":250,"tags":252,"primary_topic":254,"secondary_topics":255,"classification":256,"search_phrases":272,"slug":274,"view_count":197,"doi":275,"paper":276,"created_at":296},2798,"Deep Learning-Based Small-Object Detection in UAV Imagery: A Symmetry-Informed Review of Scale Variation and Scenario Constraints","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18091548","As UAV platforms are increasingly employed in military reconnaissance, disaster monitoring, precision agriculture, and other remote-sensing applications, small-object detection in UAV imagery is a critical yet challenging task. Existing reviews primarily address either general small-object detection or general object detection in UAV imagery, whereas reviews specifically devoted to small-object detection in UAV imagery remain scarce. This review examines existing deep learning-based research on small-object detection in UAV imagery from a problem-driven and deployment-oriented perspective. A symmetry-informed perspective was also adopted to analyze how variations in object-scale, viewpoint, imaging quality, and scene conditions affected feature representation and detection stability. Building on five major challenges in UAV small-object detection, a three-tier framework encompassing detection paradigms, key technical strategies, and scenario-specific constraints was established. Representative UAV datasets were further examined to characterize object-scale distributions, while existing methods were reviewed in terms of multiscale representation, feature enhancement, training optimization, and lightweight deployment. The applicability of different technical approaches was then discussed across five typical UAV scenarios. In addition, under relatively consistent experimental conditions, representative detection models based on two-stage, one-stage, and Transformer-based paradigms were analyzed in terms of detection accuracy, computational efficiency, and deployment feasibility. Finally, current limitations and future directions were summarized for scale-robust, scenario-adaptive, and resource-aware small-object detection in UAV imagery.","随着无人机平台在军事侦察、灾害监测、精准农业等遥感应用中的日益普及，无人机图像中的小目标检测成为一项关键但具有挑战性的任务。现有综述主要关注通用小目标检测或无人机图像中的通用目标检测，而专门针对无人机图像中小目标检测的综述仍然匮乏。本综述从问题驱动和部署导向的视角，审视了现有的基于深度学习的无人机图像小目标检测研究。同时采用对称性视角，分析了目标尺度、视角、成像质量和场景条件的变化如何影响特征表示和检测稳定性。基于无人机小目标检测中的五大挑战，构建了一个涵盖检测范式、关键技术策略和场景特定约束的三层框架。进一步考察了代表性无人机数据集以刻画目标尺度分布，同时从多尺度表示、特征增强、训练优化和轻量化部署等方面综述了现有方法。随后讨论了不同技术方法在五种典型无人机场景中的适用性。此外，在相对一致的实验条件下，对基于两阶段、单阶段和Transformer范式的代表性检测模型在检测精度、计算效率和部署可行性方面进行了分析。最后，总结了当前局限性及未来方向，以推动无人机图像中尺度鲁棒、场景自适应和资源感知的小目标检测研究。","Symmetry",76,{"impact":17,"substance":18,"depth":102,"authority":247,"freshness":248,"relevant":21,"comment":249},13,9,"无人机小目标检测的系统性综述，提出对称性视角与三层框架，对农业遥感与精准农业部署有实质参考价值。",[251],{"name":244,"url":241},[26,27,28,29,253],"目标检测",{"code":44,"slug":111,"name":112,"path":113,"parent_code":114},[],{"status":117,"locked":14,"version":34,"method":35,"evidence":257},{"candidates":258,"agricultural_evidence":53},[259,264,268],{"code":44,"score":260,"supported":53,"title":261,"abstract":262,"tags":263},19,[194],[253,191,194],[28,253],{"code":39,"score":197,"supported":53,"title":265,"abstract":266,"tags":267},[],[109,123],[26],{"code":49,"score":126,"supported":53,"title":269,"abstract":270,"tags":271},[],[184],[29],[273,57],"农业人工智能 农业遥感 智慧农业 目标检测","农业人工智能农业遥感智慧农业目标检测-2798","10.3390\u002Fsym18091548",{"doi":275,"openalex_id":277,"authors":278,"venue":244,"cited_by_count":79,"oa_url":241,"card":291,"direction":84,"ingested_from":86},"W7213430256",[279,281,284,286,288],{"name":280,"orcid":9},"Ling Wen",{"name":282,"orcid":283},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":285,"orcid":9},"Caihong Ma",{"name":287,"orcid":9},"Shengzhou Ma",{"name":289,"orcid":290},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":292,"method":293,"finding":294,"direction":84,"opportunity":295},"综述无人机图像小目标检测的深度学习研究，提出对称性视角与三层框架。","文献综述，分析代表性数据集与两阶段、单阶段、Transformer模型。","尺度变化和场景约束是核心挑战，需尺度鲁棒、场景自适应和资源感知检测。","可探索农业场景下尺度自适应与轻量化部署的平衡，以及跨场景泛化的小目标检测。","2026-09-17T23:30:37.479759Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":9,"content":9,"source_name":302,"source_url":9,"published_at":303,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":304,"score_detail":305,"sources":307,"tags":309,"primary_topic":311,"secondary_topics":317,"classification":318,"search_phrases":339,"slug":341,"view_count":342,"doi":9,"paper":343,"created_at":351},2218,"《基于现役作物多模态无人机图像的大豆种子成分基因型感知预测》","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3121","Vasit Sagan等(圣路易斯大学、密苏里大学)开发端到端卷积神经网络框架,从基于无人机的多传感器图像及相关基因型和物候元数据估算大豆八种种子性状(蛋白质、油、蔗糖、纤维、淀粉、灰分、复合和简单碳水化合物)。2020-2021年密苏里州两个农场共采集372份大豆样本,在四个时间点捕获多光谱、热成像和LiDAR数据。蔗糖预测精度最高(R²=0.80),其次为简单碳水化合物(R²=0.70)和淀粉(R²=0.55)。多光谱图像在所有模态中提供最稳健的估计。","Remote Sensing 2026年9月11日","2026-09-11T00:00:00Z",82,{"impact":102,"substance":165,"depth":102,"authority":20,"freshness":13,"relevant":21,"comment":306},"多模态无人机遥感结合深度学习预测大豆种子成分，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[308],{"name":302,"url":300},[26,27,28,29,310],"大豆育种",{"code":312,"slug":313,"name":314,"path":315,"parent_code":316},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":117,"locked":14,"version":34,"method":35,"evidence":319},{"candidates":320,"agricultural_evidence":53},[321,327,331,335],{"code":312,"score":322,"supported":53,"title":323,"abstract":325,"tags":326},12,[324],"大豆",[324],[310],{"code":39,"score":21,"supported":14,"title":328,"abstract":329,"tags":330},[],[],[26],{"code":44,"score":21,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[28],{"code":49,"score":21,"supported":14,"title":336,"abstract":337,"tags":338},[],[],[29],[340,57],"农业人工智能 农业遥感 大豆育种 智慧农业","农业人工智能农业遥感大豆育种智慧农业-2218",3,{"doi":9,"openalex_id":9,"authors":344,"venue":9,"cited_by_count":79,"oa_url":9,"card":345,"direction":84,"ingested_from":350},[],{"tldr":346,"method":347,"finding":348,"direction":84,"opportunity":349},"用无人机多模态图像和CNN预测大豆八种种子成分，蔗糖精度最高。","端到端CNN，融合多光谱、热成像、LiDAR及基因型物候元数据，372份样本。","多光谱最稳健，蔗糖R²=0.80，简单碳水化合物0.70，淀粉0.55。","可探索多模态融合与基因型-表型关联，提升低精度性状预测及跨年份泛化。","agent","2026-09-12T00:06:40.739496Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":9,"source_name":358,"source_url":355,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":368,"secondary_topics":373,"classification":381,"search_phrases":409,"slug":412,"view_count":79,"doi":413,"paper":414,"created_at":440},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":17,"substance":165,"depth":102,"authority":20,"freshness":13,"relevant":21,"comment":362},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[364],{"name":358,"url":355},[26,28,29,366,367],"氮素监测","紫花苜蓿",{"code":369,"slug":370,"name":371,"path":372,"parent_code":316},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[374,375],{"code":49,"slug":172,"name":173,"path":174,"parent_code":114},{"code":376,"slug":377,"name":378,"path":379,"parent_code":380},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":117,"locked":14,"version":34,"method":35,"evidence":382},{"candidates":383,"agricultural_evidence":53},[384,388,394,400,405],{"code":49,"score":102,"supported":53,"title":385,"abstract":386,"tags":387},[186],[184,182,186],[29],{"code":369,"score":103,"supported":53,"title":389,"abstract":391,"tags":393},[390],"alfalfa",[392,390],"苜蓿",[367],{"code":376,"score":13,"supported":53,"title":395,"abstract":396,"tags":399},[],[397,392,398],"饲草","forage",[367],{"code":44,"score":13,"supported":53,"title":401,"abstract":402,"tags":404},[],[191,192,193,194,403],"decision support",[28],{"code":39,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[26],[410,411],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":413,"openalex_id":415,"authors":416,"venue":358,"cited_by_count":79,"oa_url":355,"card":435,"direction":84,"ingested_from":86},"W7223154576",[417,419,421,423,425,428,430,433],{"name":418,"orcid":9},"Shuai Fu",{"name":420,"orcid":9},"Yunhao Li",{"name":422,"orcid":9},"Chunli Miao",{"name":424,"orcid":9},"Jie Liu",{"name":426,"orcid":427},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":429,"orcid":9},"Yongjia Hu",{"name":431,"orcid":432},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":434,"orcid":9},"吴彩霞",{"tldr":436,"method":437,"finding":438,"direction":84,"opportunity":439},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":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":457,"secondary_topics":461,"classification":462,"search_phrases":478,"slug":481,"view_count":79,"doi":482,"paper":483,"created_at":495},5079,"Lightweight Blockchain Based Authentication and Secure Data Sharing in UAV Networks","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23390977","Unmanned Aerial Vehicle (UAV) networks, oper- ating within Flying Ad-Hoc Networks (FANETs), play an in- dispensable role in contemporary surveillance, border patrol, smart agriculture, disaster recovery, and intelligent transporta- tion systems. Owing to their open wireless propagation channels, dynamic line-of-sight flight paths, and severe computational and battery resource constraints, UAVs are exceptionally susceptible to eavesdropping, message forgery, node impersonation, physical node capture, and denial-of-service (DoS) attacks. Conventional public key infrastructure (PKI) frameworks and centralized Certificate Authorities (CAs) exhibit heavy cryptographic over- head, substantial communication latency, and single-point-of- failure vulnerabilities that render them unsuitable for mission- critical aerial swarms. In this paper, we propose LBAS-UAV, a robust, lightweight, consortium blockchain-assisted mutual au- thentication, key agreement, and secure data sharing framework tailored specifically for resource-constrained UAV environments. LBAS-UAV integrates elliptic curve cryptography (ECC), physi- cal unclonable functions (PUFs), and keyed cryptographic hash functions to achieve mutual authentication without high-overhead bilinear pairings or certificate management. Dynamic UAV pseudonyms protect flight-path privacy and user untraceability. A consortium blockchain, maintained jointly by Ground Control Stations (GCS) and mobile edge servers under Practical Byzan- tine Fault Tolerance (PBFT) consensus, provides decentralized public-key validation, non-repudiation, and tamper-proof access auditing. To eliminate on-chain storage bottlenecks, high-volume aerial sensory telemetry is encrypted via AES-128-GCM and stored off-chain in the InterPlanetary File System (IPFS), while only 32-byte cryptographic content addresses and fine-grained access policies are registered on-chain. Formal security verification using the Real-Or-Random (ROR) oracle model, Burrows-Abadi- Needham (BAN) logic, and the automated AVISPA tool confirms that LBAS-UAV guarantees session key confidentiality and re- sists physical capture, replay, man-in-the-middle, and ephemeral secret leakage attacks. Extensive hardware benchmarking and network simulations demonstrate that LBAS-UAV demands only 0.318 ms of cryptographic execution time and 206 bytes of communication overhead on the UAV, outperforming recent state- of-the-art schemes by up to 38.4% in computation and 25.3% in energy efficiency.","无人机网络在飞行自组网(FANETs)中运行，在当代监视、边境巡逻、智慧农业、灾难恢复和智能交通系统中发挥着不可或缺的作用。由于其开放的无线传播信道、动态的视距飞行路径以及严重的计算和电池资源限制，无人机极易受到窃听、消息伪造、节点冒充、物理节点捕获和拒绝服务(DoS)攻击。传统的公钥基础设施(PKI)框架和集中式证书颁发机构(CAs)存在沉重的密码学开销、显著的通信延迟和单点故障漏洞，使其不适用于任务关键的空中集群。本文提出了LBAS-UAV，一种鲁棒的、轻量级的、联盟区块链辅助的相互认证、密钥协商和安全数据共享框架，专为资源受限的无人机环境量身定制。LBAS-UAV集成了椭圆曲线密码学(ECC)、物理不可克隆函数(PUFs)和带密钥的密码学哈希函数，无需高开销的双线性配对或证书管理即可实现相互认证。动态无人机假名保护飞行路径隐私和用户不可追踪性。由地面控制站(GCS)和移动边缘服务器在实用拜占庭容错(PBFT)共识下共同维护的联盟区块链，提供去中心化的公钥验证、不可否认性和防篡改的访问审计。为消除链上存储瓶颈，大容量空中传感遥测数据通过AES-128-GCM加密并存储在星际文件系统(IPFS)的链下，而仅将32字节的密码学内容地址和细粒度访问策略注册在链上。使用Real-Or-Random(ROR)预言机模型、Burrows-Abadi-Needham(BAN)逻辑和自动化AVISPA工具进行的形式化安全验证确认，LBAS-UAV保证会话密钥机密性，并能抵抗物理捕获、重放、中间人和临时秘密泄露攻击。广泛的硬件基准测试和网络仿真表明，LBAS-UAV在无人机上仅需0.318毫秒的密码学执行时间和206字节的通信开销，在计算方面优于近期最先进的方案高达38.4%，在能效方面优于25.3%。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",74,{"impact":322,"substance":165,"depth":102,"authority":247,"freshness":248,"relevant":21,"comment":451},"面向资源受限无人机网络的轻量级区块链认证与安全数据共享方案，方法新颖、验证充分，对智慧农业无人机应用有参考价值，但属通用技术论文，农业场景关联较间接。",[453],{"name":447,"url":444},[26,27,29,455,456],"区块链","数据安全",{"code":39,"slug":458,"name":459,"path":460,"parent_code":114},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":117,"locked":14,"version":34,"method":35,"evidence":463},{"candidates":464,"agricultural_evidence":53},[465,469,473],{"code":39,"score":197,"supported":53,"title":466,"abstract":467,"tags":468},[],[26,200],[26],{"code":49,"score":21,"supported":14,"title":470,"abstract":471,"tags":472},[],[],[29],{"code":474,"score":21,"supported":14,"title":475,"abstract":476,"tags":477},"I6",[],[],[456],[479,480],"UAV 区块链 认证 数据共享","LBAS-UAV 无人机 网络安全","UAV区块链认证数据共享-5079","10.5281\u002Fzenodo.23390977",{"doi":482,"openalex_id":484,"authors":485,"venue":447,"cited_by_count":79,"oa_url":444,"card":490,"direction":152,"ingested_from":86},"W7222323013",[486,488],{"name":487,"orcid":9},"B.  Gnanasankari",{"name":489,"orcid":9},"R.  Padmanaban",{"tldr":491,"method":492,"finding":493,"direction":152,"opportunity":494},"提出轻量级联盟链认证与安全数据共享框架LBAS-UAV，用于资源受限无人机网络。","融合ECC、PUF与哈希函数，PBFT联盟链结合IPFS链下存储，AES-128","仅需0.318ms计算与206字节通信开销，计算性能提升38.4%，可抵御多种攻击。","可将该轻量认证框架迁移至农业无人机植保与农田监测场景，研究能耗与实时性权衡。","2026-10-16T23:30:08.237868Z"]