[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4649":3,"related-4649":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":67,"slug":70,"view_count":71,"doi":8,"paper":72,"created_at":82},4649,"[预印本]面向自主熟果采摘的智能语音引导UAV系统:基于视觉分类与自适应路径规划(IVG-UAV: An Intelligent Voice-Guided UAV System for Autonomous Ripe Fruit Harvesting with Vision-Based Classification and Adaptive Path Planning)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.09398","arXiv论文(2610.09398,2026年10月7日提交)提出IVG-UAV系统集成Whisper语音识别、LLM、计算机视觉成熟度分类与Gazebo仿真中的自适应路径规划,实现热带大型种植园自动采摘任务。系统在受控仿真环境验证,实际田间作业、复杂光照与多机协同性能待测。[预印本未经同行评议,仅仿真验证]。",null,"arXiv 2026-10-07 [预印本]","2026-10-07T00:00:00Z","论文",10,false,62,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":19,"relevant":20,"comment":21},12,18,16,8,1,"预印本提出语音引导无人机自主采摘系统，方法集成有新意但仅仿真验证，属细分领域技术进展。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","无人机","农业人工智能","采摘机器人","语音交互",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,57,62],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},4,true,[],[48],"计算机视觉",[27],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"A7",3,[],[55],"间作",[],{"code":58,"score":20,"supported":13,"title":59,"abstract":60,"tags":61},"F6",[],[],[28],{"code":63,"score":20,"supported":13,"title":64,"abstract":65,"tags":66},"I1",[],[],[25],[68,69],"IVG-UAV 采摘无人机","Whisper 语音识别 农业机器人","IVG-UAV采摘无人机-4649",2,{"doi":8,"openalex_id":8,"authors":73,"venue":8,"cited_by_count":74,"oa_url":8,"card":75,"direction":79,"ingested_from":81},[],0,{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"提出IVG-UAV系统，用语音、视觉与路径规划实现仿真中自主采摘熟果。","集成Whisper语音识别、LLM、视觉成熟度分类与Gazebo自适应路径规划。","仿真环境验证可行，但真实田间、复杂光照与多机协同性能待测。","农业人工智能与决策模型","可研究真实果园复杂光照下的鲁棒视觉分类与多机协同采摘调度。","agent","2026-10-11T00:09:47.894542Z",{"total":84,"page":20,"page_size":84,"items":85},6,[86,177,251,303,379,437],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":8,"source_name":92,"source_url":89,"published_at":93,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":94,"score_detail":95,"sources":100,"tags":102,"primary_topic":105,"secondary_topics":111,"classification":118,"search_phrases":148,"slug":151,"view_count":20,"doi":152,"paper":153,"created_at":176},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":18,"substance":96,"depth":17,"authority":97,"freshness":98,"relevant":20,"comment":99},21,13,9,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[101],{"name":92,"url":89},[25,26,27,103,104],"遥感监测","病害预警",{"code":106,"slug":107,"name":108,"path":109,"parent_code":110},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[112,113],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":114,"slug":115,"name":116,"path":117,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":38,"locked":13,"version":39,"method":40,"evidence":119},{"candidates":120,"agricultural_evidence":45},[121,129,136,141],{"code":106,"score":122,"supported":45,"title":123,"abstract":125,"tags":128},17,[124],"apple",[126,127,124],"苹果","葡萄",[],{"code":31,"score":130,"supported":45,"title":131,"abstract":133,"tags":135},15,[132],"deep learning",[134,132],"深度学习",[27],{"code":114,"score":16,"supported":45,"title":137,"abstract":139,"tags":140},[138],"multispectral",[138],[103],{"code":63,"score":142,"supported":45,"title":143,"abstract":144,"tags":147},7,[],[145,146],"精准农业","precision agriculture",[25],[149,150],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":152,"openalex_id":154,"authors":155,"venue":92,"cited_by_count":74,"oa_url":167,"card":168,"direction":174,"ingested_from":175},"W7221558726",[156,159,162,164],{"name":157,"orcid":158},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":160,"orcid":161},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":163,"orcid":8},"Fatih Sinan Bir",{"name":165,"orcid":166},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:10.242467Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":8,"source_name":183,"source_url":180,"published_at":184,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":185,"score_detail":186,"sources":188,"tags":190,"primary_topic":193,"secondary_topics":194,"classification":196,"search_phrases":224,"slug":227,"view_count":74,"doi":228,"paper":229,"created_at":250},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":16,"substance":17,"depth":18,"authority":97,"freshness":98,"relevant":20,"comment":187},"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。",[189],{"name":183,"url":180},[25,26,27,191,192],"遥感","葡萄园管理",{"code":114,"slug":115,"name":116,"path":117,"parent_code":35},[195],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":197},{"candidates":198,"agricultural_evidence":45},[199,204,208,212,217],{"code":114,"score":17,"supported":45,"title":200,"abstract":201,"tags":203},[138],[191,202,138],"remote sensing",[191],{"code":31,"score":130,"supported":45,"title":205,"abstract":206,"tags":207},[132],[134,132],[27],{"code":63,"score":142,"supported":45,"title":209,"abstract":210,"tags":211},[],[145,146],[25],{"code":106,"score":84,"supported":45,"title":213,"abstract":214,"tags":216},[],[127,215],"grape",[],{"code":218,"score":84,"supported":45,"title":219,"abstract":220,"tags":223},"I4",[],[221,222],"空间分析","GIS",[],[225,226],"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":228,"openalex_id":230,"authors":231,"venue":183,"cited_by_count":74,"oa_url":180,"card":245,"direction":172,"ingested_from":175},"W7220993218",[232,234,237,239,242],{"name":233,"orcid":8},"Dimitra Passa",{"name":235,"orcid":236},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":238,"orcid":8},"Georgios Nektarios Tselos",{"name":240,"orcid":241},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":243,"orcid":244},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":246,"method":247,"finding":248,"direction":172,"opportunity":249},"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":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":8,"source_name":257,"source_url":258,"published_at":259,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":260,"score_detail":261,"sources":263,"tags":265,"primary_topic":267,"secondary_topics":268,"classification":269,"search_phrases":285,"slug":288,"view_count":71,"doi":289,"paper":290,"created_at":302},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":17,"substance":18,"depth":130,"authority":12,"freshness":19,"relevant":20,"comment":262},"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。",[264],{"name":257,"url":258},[25,26,27,266,145],"农业遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":270},{"candidates":271,"agricultural_evidence":45},[272,276,281],{"code":63,"score":19,"supported":45,"title":273,"abstract":274,"tags":275},[],[145,146],[25,145],{"code":31,"score":44,"supported":45,"title":277,"abstract":278,"tags":280},[],[279],"artificial intelligence",[27],{"code":114,"score":20,"supported":13,"title":282,"abstract":283,"tags":284},[],[],[266],[286,287],"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","10.5281\u002Fzenodo.23168044",{"doi":289,"openalex_id":291,"authors":292,"venue":257,"cited_by_count":74,"oa_url":258,"card":297,"direction":79,"ingested_from":175},"W7220406405",[293,295],{"name":294,"orcid":8},"Somitov Abdukadir Abdurayimovich",{"name":296,"orcid":8},"Worldly Knowledge Publishing Centre",{"tldr":298,"method":299,"finding":300,"direction":174,"opportunity":301},"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。","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。","2026-10-07T23:30:46.084251Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":8,"source_name":309,"source_url":306,"published_at":310,"category":11,"cover_url":8,"hotness":12,"is_selected":45,"score":311,"score_detail":312,"sources":316,"tags":318,"primary_topic":321,"secondary_topics":322,"classification":323,"search_phrases":347,"slug":350,"view_count":44,"doi":351,"paper":352,"created_at":378},4055,"A collaborative perception framework for green jujube: from lightweight key point detection to posture-aware harvest analysis","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09842-7","Abstract Automated harvesting is pivotal to smart agriculture, yet precisely localizing picking points in complex orchard environments remains a key challenge for robotic harvesting systems. To this end, this study proposes a collaborative perception framework integrating a lightweight pose estimation model termed RepViT-DSConv-ARC-PoseHead enhanced YOLO (RDA-YOLO) with Segment Anything Model 2 (SAM2). RDA-YOLO is the lightweight component responsible for real-time fruit detection and keypoint localization, while SAM2 is adopted for fruit contour segmentation and occlusion assessment. Based on the YOLOv11n-POSE architecture, we introduce Distribution Shift Convolution (DSConv) to reduce computational overhead and design the C3k2_RepViT module to enhance multi-scale feature fusion. Crucially, a specialized Adaptive Regression Coordination Pose Head (ARC-PoseHead) is developed to replace traditional prediction structures, employing log-scale polar regression and subpixel refinement to significantly improve coordinate perception of jujubes and key points under complex backgrounds. RDA-YOLO performs real-time detection and locates key points such as the pedicel-fruit transition zone to calculate optimal harvesting angles, while SAM2 performs fine-grained contour segmentation based on detection boxes to determine occlusion status and establish harvesting priority. Experimental results show that RDA-YOLO achieves 93.9% precision and 94.9% mAP@50 for green jujube detection, while for key point detection, it attains a superior precision of 90.4% and a mAP@50 of 83.6%. Notably, the model achieves a lightweight design by reducing parameters to 2.490 million and computational load to 5.7 GFLOPs. Deployment feasibility was verified on the field robot control computer and a robotic platform using jujube fruit models, demonstrating the potential of the proposed framework for practical harvesting applications.","自动采摘是智慧农业的关键环节，但在复杂果园环境中精准定位采摘点仍是机器人采摘系统面临的核心挑战。为此，本研究提出了一种协同感知框架，将轻量级姿态估计模型RDA-YOLO（RepViT-DSConv-ARC-PoseHead增强型YOLO）与Segment Anything Model 2（SAM2）相结合。RDA-YOLO作为轻量级组件，负责实时果实检测与关键点定位；SAM2则用于果实轮廓分割与遮挡评估。基于YOLOv11n-POSE架构，我们引入分布偏移卷积（DSConv）以降低计算开销，并设计C3k2_RepViT模块以增强多尺度特征融合。关键在于，我们开发了专用的自适应回归协调姿态头（ARC-PoseHead）以替代传统预测结构，采用对数尺度极坐标回归和亚像素细化，显著提升了复杂背景下枣果及关键点的坐标感知能力。RDA-YOLO执行实时检测并定位果柄-果实过渡区等关键点以计算最优采摘角度，而SAM2基于检测框进行细粒度轮廓分割，以判定遮挡状态并建立采摘优先级。实验结果表明，RDA-YOLO对青枣检测的精确率达到93.9%，mAP@50为94.9%；在关键点检测方面，精确率达到90.4%，mAP@50为83.6%。值得注意的是，该模型通过将参数量降至249万、计算量降至5.7 GFLOPs，实现了轻量化设计。在田间机器人控制计算机及使用枣果模型的机器人平台上验证了部署可行性，展示了所提框架在实际采摘应用中的潜力。","BMC Plant Biology","2026-10-03T00:00:00Z",82,{"impact":17,"substance":313,"depth":17,"authority":314,"freshness":12,"relevant":20,"comment":315},22,14,"提出轻量化姿态估计与SAM2协同感知框架，实现青枣采摘点定位与遮挡评估，方法新颖、指标扎实，对智能采收有实用价值。",[317],{"name":309,"url":306},[25,27,319,28,320],"目标检测","青枣",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":324},{"candidates":325,"agricultural_evidence":45},[326,331,337,342],{"code":63,"score":142,"supported":45,"title":327,"abstract":328,"tags":330},[],[25,329],"smart agriculture",[25],{"code":31,"score":332,"supported":45,"title":333,"abstract":334,"tags":336},5,[],[335],"YOLO",[27,319],{"code":58,"score":44,"supported":45,"title":338,"abstract":339,"tags":341},[],[340],"robotic harvesting",[28],{"code":106,"score":52,"supported":45,"title":343,"abstract":344,"tags":346},[],[345],"orchard",[],[348,349],"RDA-YOLO SAM2 青枣 采摘","青枣 关键点检测 采摘机器人","RDA-YOLOSAM2青枣采摘-4055","10.1186\u002Fs12870-026-09842-7",{"doi":351,"openalex_id":353,"authors":354,"venue":309,"cited_by_count":74,"oa_url":306,"card":373,"direction":174,"ingested_from":175},"W7216260984",[355,358,361,363,365,367,369,371],{"name":356,"orcid":357},"Hongyan Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8703-6289",{"name":359,"orcid":360},"Runlong Cao","https:\u002F\u002Forcid.org\u002F0009-0002-9535-4946",{"name":362,"orcid":8},"Shuai Qin",{"name":364,"orcid":8},"Chengzhi Lin",{"name":366,"orcid":8},"Weizhe Sun",{"name":368,"orcid":8},"Yanli Zou",{"name":370,"orcid":8},"Zhixian Liao",{"name":372,"orcid":8},"Yong He",{"tldr":374,"method":375,"finding":376,"direction":79,"opportunity":377},"提出RDA-YOLO与SAM2协同感知框架，实现青枣采摘点定位与姿态分析。","改进YOLOv11n-POSE，引入DSConv、RepViT与ARC-Pose","检测精度93.9%、关键点精度90.4%，模型仅2.49M参数、5.7 GFLOPs。","可探索多作物通用轻量姿态估计与遮挡感知采摘决策的迁移研究。","2026-10-03T23:30:09.616335Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":8,"content":8,"source_name":384,"source_url":8,"published_at":385,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":311,"score_detail":386,"sources":388,"tags":390,"primary_topic":393,"secondary_topics":398,"classification":405,"search_phrases":425,"slug":428,"view_count":332,"doi":8,"paper":429,"created_at":436},3249,"设施番茄采摘机器人识别定位与采摘方法研究——江苏大学雷志龙等 基于改进YOLO v5-HSV融合算法识别准确率95.01%","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455758.html","智慧农业(中英文)期刊发表江苏大学雷志龙、刘畅、王权团队研究：针对设施单果番茄采摘需求，设计一款设施番茄智能采摘平台，主要由升降机构、采摘机构、识别与定位系统等部分组成。平台整体结构由低压一体化伺服滚珠丝杠副升降机构、六轴协作机械臂和力控末端执行器组成。基于改进YOLO v5-HSV融合算法来识别检测，通过对H分量进行图像阈值分割，提高对成熟目标果实识别的准确率，有效排除未成熟番茄和枝叶背景的干扰；通过眼在手外的标定方法，使用ZED双目相机进行定位。搭建的设施番茄采摘机样机平台在现场采摘试验中识别准确率达到95.01%，采摘成功率为87.96%，单果平均采摘时间为14.56s。","智慧农业(中英文)·江苏大学","2026-09-22T00:00:00Z",{"impact":17,"substance":313,"depth":17,"authority":314,"freshness":12,"relevant":20,"comment":387},"核心期刊论文，方法有改进、数据完整，识别准确率与采摘成功率等指标明确，对设施农业智能装备研发有参考价值。",[389],{"name":384,"url":382},[25,27,28,391,392],"机器视觉","设施番茄",{"code":394,"slug":395,"name":396,"path":397,"parent_code":110},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",[399,400],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":58,"slug":401,"name":402,"path":403,"parent_code":404},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":38,"locked":13,"version":39,"method":40,"evidence":406},{"candidates":407,"agricultural_evidence":45},[408,412,417,421],{"code":31,"score":97,"supported":45,"title":409,"abstract":410,"tags":411},[335],[335],[27,391],{"code":394,"score":16,"supported":45,"title":413,"abstract":415,"tags":416},[414],"番茄",[414],[392],{"code":58,"score":98,"supported":45,"title":418,"abstract":419,"tags":420},[28],[],[28],{"code":63,"score":44,"supported":45,"title":422,"abstract":423,"tags":424},[],[25],[25],[426,427],"江苏大学 设施番茄 采摘机器人","YOLO v5 番茄 识别定位","江苏大学设施番茄采摘机器人-3249",{"doi":8,"openalex_id":8,"authors":430,"venue":8,"cited_by_count":74,"oa_url":8,"card":431,"direction":79,"ingested_from":81},[],{"tldr":432,"method":433,"finding":434,"direction":79,"opportunity":435},"设计设施番茄智能采摘平台，融合改进YOLO v5与HSV实现识别定位与采摘。","改进YOLO v5-HSV融合算法、ZED双目相机眼在手外标定、六轴机械臂力控末","识别准确率95.01%，采摘成功率87.96%，单果平均采摘时间14.56秒。","可探索多果簇、遮挡与弱光环境下识别定位鲁棒性，并优化采摘效率与末端力控。","2026-09-23T00:04:33.401768Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":8,"source_name":443,"source_url":440,"published_at":444,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":445,"score_detail":446,"sources":448,"tags":450,"primary_topic":452,"secondary_topics":453,"classification":455,"search_phrases":487,"slug":490,"view_count":44,"doi":491,"paper":492,"created_at":508},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。","Computers and Electronics in Agriculture","2026-09-18T00:00:00Z",81,{"impact":17,"substance":313,"depth":17,"authority":130,"freshness":19,"relevant":20,"comment":447},"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。",[449],{"name":443,"url":440},[25,26,27,266,451],"作物倒伏",{"code":114,"slug":115,"name":116,"path":117,"parent_code":35},[454],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":456},{"candidates":457,"agricultural_evidence":45},[458,463,469,473,480],{"code":114,"score":17,"supported":45,"title":459,"abstract":460,"tags":462},[202],[191,202,138,461],"hyperspectral",[266],{"code":31,"score":12,"supported":45,"title":464,"abstract":465,"tags":468},[],[134,466,467,132],"机器学习","machine learning",[27],{"code":63,"score":142,"supported":45,"title":470,"abstract":471,"tags":472},[],[25,329],[25],{"code":474,"score":84,"supported":45,"title":475,"abstract":476,"tags":479},"A8",[],[477,478],"粮食安全","food security",[],{"code":481,"score":84,"supported":45,"title":482,"abstract":483,"tags":486},"J3",[],[484,485],"全球农业","global agriculture",[],[488,489],"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":491,"openalex_id":493,"authors":494,"venue":443,"cited_by_count":74,"oa_url":440,"card":503,"direction":174,"ingested_from":175},"W7213542473",[495,498,500],{"name":496,"orcid":497},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":499,"orcid":8},"Changxing Geng",{"name":501,"orcid":502},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":504,"method":505,"finding":506,"direction":172,"opportunity":507},"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"]