[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1326":3,"related-1326":101},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":70,"slug":73,"view_count":20,"doi":74,"paper":75,"created_at":100},1326,"Intelligent multi-agent systems for UAV-UGV path optimization via reflective evolution","https:\u002F\u002Fdoi.org\u002F10.1080\u002F02533839.2026.2714091","With the in-depth integration of artificial intelligence and agriculture, efficiently identifying fruit tree locations and optimizing operational paths have become increasingly important in the field of precision agriculture.To promote development, we developed an integrated path automation system that leverages collaboration between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to effectively address these challenges.The Multi-minigpt-v2 model constructed in this paper is a multi-task Vision language model (VLM) based on the collection of the multi-intelligent body framework MetaGPT and the large vision model Minigpt-v2, which performs tasks including image recognition and target localization on images captured by UAVs.The Adaptive Reflective Evolution model (AREvo) embeds the large language model (LLM) and the Chain-of-Thought (CoT) prompting strategy in an automated algorithm design (AAD) framework to plan paths for UAVs based on the coordinates provided intelligently. The method proposed in this paper can quickly plan paths for UGVs to perform tasks, effectively improving the efficiency of agricultural operations and reducing labor costs.","随着人工智能与农业的深度融合，高效识别果树位置并优化作业路径在精准农业领域日益重要。为促进发展，我们构建了一套集成路径自动化系统，利用无人机与无人地面车辆之间的协同作业，有效应对上述挑战。本文构建的Multi-minigpt-v2模型是一种多任务视觉语言模型，基于多智能体框架MetaGPT与大型视觉模型Minigpt-v2的集合，执行包括图像识别和目标定位在内的任务，这些任务基于无人机捕获的图像。自适应反射进化模型将大型语言模型和思维链提示策略嵌入自动化算法设计框架中，根据智能提供的坐标规划无人机路径。本文提出的方法能够快速规划无人地面车辆的作业路径，有效提升农业作业效率并降低劳动力成本。",null,"Journal of the Chinese Institute of Engineers","2026-08-30T00:00:00Z","论文",10,false,59,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,18,16,3,1,"提出UAV-UGV协同路径优化新方法，结合多模态大模型，对精准农业有实质技术贡献，但时效性稍差。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","无人机","农业人工智能","路径规划",{"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":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,51,59,65],{"code":31,"score":13,"supported":44,"title":45,"abstract":46,"tags":50},true,[],[47,48,49],"图像识别","artificial intelligence","large language model",[28],{"code":52,"score":53,"supported":44,"title":54,"abstract":55,"tags":58},"I1",7,[],[56,57],"精准农业","precision agriculture",[26],{"code":60,"score":20,"supported":44,"title":61,"abstract":62,"tags":64},"B2",[],[63],"果树",[],{"code":66,"score":21,"supported":14,"title":67,"abstract":68,"tags":69},"F5",[],[],[29],[71,72],"农业人工智能 智慧农业 路径规划 无人机","农业人工智能 智慧农业","农业人工智能智慧农业路径规划无人机-1326","10.1080\u002F02533839.2026.2714091",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":92,"oa_url":9,"card":93,"direction":97,"ingested_from":99},"W7204696964",[78,80,83,85,87,90],{"name":79,"orcid":9},"Yijie Chen",{"name":81,"orcid":82},"Kexin Zhu","https:\u002F\u002Forcid.org\u002F0009-0006-2744-4711",{"name":84,"orcid":9},"Yong Zhang",{"name":86,"orcid":9},"Z. Chen",{"name":88,"orcid":89},"Chen Feng","https:\u002F\u002Forcid.org\u002F0000-0003-3211-1576",{"name":91,"orcid":9},"Zhengjie Zhuang",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"构建无人机与无人车协作的智能路径自动化系统，提升精准农业效率。","结合MetaGPT与Minigpt-v2的多任务视觉语言模型，及嵌入LLM和Co","系统能快速规划路径，提高农业作业效率并降低劳动成本。","农业人工智能与决策模型","可探索多智能体协作在复杂农田环境下的鲁棒性，以及实时动态路径重规划与能耗优化。","openalex","2026-09-01T23:30:36.111005Z",{"total":102,"page":21,"page_size":102,"items":103},6,[104,189,245,319,370,444],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":118,"tags":120,"primary_topic":123,"secondary_topics":127,"classification":134,"search_phrases":161,"slug":164,"view_count":21,"doi":165,"paper":166,"created_at":188},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":19,"substance":114,"depth":18,"authority":115,"freshness":116,"relevant":21,"comment":117},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。",[119],{"name":110,"url":107},[26,27,28,121,122],"遥感监测","病害预警",{"code":60,"slug":124,"name":63,"path":125,"parent_code":126},"fruit","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[128,129],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":130,"slug":131,"name":132,"path":133,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":38,"locked":14,"version":39,"method":40,"evidence":135},{"candidates":136,"agricultural_evidence":44},[137,145,152,157],{"code":60,"score":138,"supported":44,"title":139,"abstract":141,"tags":144},17,[140],"apple",[142,143,140],"苹果","葡萄",[],{"code":31,"score":146,"supported":44,"title":147,"abstract":149,"tags":151},15,[148],"deep learning",[150,148],"深度学习",[28],{"code":130,"score":17,"supported":44,"title":153,"abstract":155,"tags":156},[154],"multispectral",[154],[121],{"code":52,"score":53,"supported":44,"title":158,"abstract":159,"tags":160},[],[56,57],[26],[162,163],"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":165,"openalex_id":167,"authors":168,"venue":110,"cited_by_count":92,"oa_url":180,"card":181,"direction":187,"ingested_from":99},"W7221558726",[169,172,175,177],{"name":170,"orcid":171},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":173,"orcid":174},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":176,"orcid":9},"Fatih Sinan Bir",{"name":178,"orcid":179},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":182,"method":183,"finding":184,"direction":185,"opportunity":186},"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","2026-10-13T23:30:10.242467Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":9,"source_name":194,"source_url":9,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":200,"tags":202,"primary_topic":205,"secondary_topics":206,"classification":207,"search_phrases":231,"slug":234,"view_count":235,"doi":9,"paper":236,"created_at":244},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]。","arXiv 2026-10-07 [预印本]","2026-10-07T00:00:00Z",62,{"impact":17,"substance":18,"depth":19,"authority":198,"freshness":198,"relevant":21,"comment":199},8,"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。",[201],{"name":194,"url":192},[26,27,28,203,204],"采摘机器人","语音交互",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":208},{"candidates":209,"agricultural_evidence":44},[210,216,222,227],{"code":31,"score":211,"supported":44,"title":212,"abstract":213,"tags":215},4,[],[214],"计算机视觉",[28],{"code":217,"score":20,"supported":44,"title":218,"abstract":219,"tags":221},"A7",[],[220],"间作",[],{"code":223,"score":21,"supported":14,"title":224,"abstract":225,"tags":226},"F6",[],[],[203],{"code":52,"score":21,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[26],[232,233],"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":9,"openalex_id":9,"authors":237,"venue":9,"cited_by_count":92,"oa_url":9,"card":238,"direction":97,"ingested_from":243},[],{"tldr":239,"method":240,"finding":241,"direction":97,"opportunity":242},"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。","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",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":264,"search_phrases":292,"slug":295,"view_count":92,"doi":296,"paper":297,"created_at":318},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":18,"depth":19,"authority":115,"freshness":116,"relevant":21,"comment":255},"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。",[257],{"name":251,"url":248},[26,27,28,259,260],"遥感","葡萄园管理",{"code":130,"slug":131,"name":132,"path":133,"parent_code":35},[263],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":265},{"candidates":266,"agricultural_evidence":44},[267,272,276,280,285],{"code":130,"score":18,"supported":44,"title":268,"abstract":269,"tags":271},[154],[259,270,154],"remote sensing",[259],{"code":31,"score":146,"supported":44,"title":273,"abstract":274,"tags":275},[148],[150,148],[28],{"code":52,"score":53,"supported":44,"title":277,"abstract":278,"tags":279},[],[56,57],[26],{"code":60,"score":102,"supported":44,"title":281,"abstract":282,"tags":284},[],[143,283],"grape",[],{"code":286,"score":102,"supported":44,"title":287,"abstract":288,"tags":291},"I4",[],[289,290],"空间分析","GIS",[],[293,294],"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":296,"openalex_id":298,"authors":299,"venue":251,"cited_by_count":92,"oa_url":248,"card":313,"direction":185,"ingested_from":99},"W7220993218",[300,302,305,307,310],{"name":301,"orcid":9},"Dimitra Passa",{"name":303,"orcid":304},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":306,"orcid":9},"Georgios Nektarios Tselos",{"name":308,"orcid":309},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":311,"orcid":312},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":314,"method":315,"finding":316,"direction":185,"opportunity":317},"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":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":326,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":331,"tags":333,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":352,"slug":355,"view_count":235,"doi":356,"paper":357,"created_at":369},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":18,"substance":19,"depth":146,"authority":13,"freshness":198,"relevant":21,"comment":330},"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。",[332],{"name":325,"url":326},[26,27,28,334,56],"农业遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":338},{"candidates":339,"agricultural_evidence":44},[340,344,348],{"code":52,"score":198,"supported":44,"title":341,"abstract":342,"tags":343},[],[56,57],[26,56],{"code":31,"score":211,"supported":44,"title":345,"abstract":346,"tags":347},[],[48],[28],{"code":130,"score":21,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[334],[353,354],"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":356,"openalex_id":358,"authors":359,"venue":325,"cited_by_count":92,"oa_url":326,"card":364,"direction":97,"ingested_from":99},"W7220406405",[360,362],{"name":361,"orcid":9},"Somitov Abdukadir Abdurayimovich",{"name":363,"orcid":9},"Worldly Knowledge Publishing Centre",{"tldr":365,"method":366,"finding":367,"direction":187,"opportunity":368},"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":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":382,"tags":384,"primary_topic":386,"secondary_topics":387,"classification":389,"search_phrases":422,"slug":425,"view_count":211,"doi":426,"paper":427,"created_at":443},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":18,"substance":380,"depth":18,"authority":146,"freshness":198,"relevant":21,"comment":381},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。",[383],{"name":376,"url":373},[26,27,28,334,385],"作物倒伏",{"code":130,"slug":131,"name":132,"path":133,"parent_code":35},[388],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":390},{"candidates":391,"agricultural_evidence":44},[392,397,403,408,415],{"code":130,"score":18,"supported":44,"title":393,"abstract":394,"tags":396},[270],[259,270,154,395],"hyperspectral",[334],{"code":31,"score":13,"supported":44,"title":398,"abstract":399,"tags":402},[],[150,400,401,148],"机器学习","machine learning",[28],{"code":52,"score":53,"supported":44,"title":404,"abstract":405,"tags":407},[],[26,406],"smart agriculture",[26],{"code":409,"score":102,"supported":44,"title":410,"abstract":411,"tags":414},"A8",[],[412,413],"粮食安全","food security",[],{"code":416,"score":102,"supported":44,"title":417,"abstract":418,"tags":421},"J3",[],[419,420],"全球农业","global agriculture",[],[423,424],"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":426,"openalex_id":428,"authors":429,"venue":376,"cited_by_count":92,"oa_url":373,"card":438,"direction":187,"ingested_from":99},"W7213542473",[430,433,435],{"name":431,"orcid":432},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":434,"orcid":9},"Changxing Geng",{"name":436,"orcid":437},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":439,"method":440,"finding":441,"direction":185,"opportunity":442},"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":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":9,"source_name":450,"source_url":447,"published_at":451,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":452,"score_detail":453,"sources":456,"tags":458,"primary_topic":460,"secondary_topics":461,"classification":462,"search_phrases":478,"slug":481,"view_count":53,"doi":482,"paper":483,"created_at":503},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","2026-09-16T00:00:00Z",76,{"impact":19,"substance":454,"depth":18,"authority":115,"freshness":116,"relevant":21,"comment":455},20,"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。",[457],{"name":450,"url":447},[26,27,28,334,459],"目标检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":463},{"candidates":464,"agricultural_evidence":44},[465,470,474],{"code":31,"score":466,"supported":44,"title":467,"abstract":468,"tags":469},19,[148],[459,150,148],[28,459],{"code":52,"score":53,"supported":44,"title":471,"abstract":472,"tags":473},[],[56,57],[26],{"code":130,"score":211,"supported":44,"title":475,"abstract":476,"tags":477},[],[259],[334],[479,480],"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","农业人工智能农业遥感智慧农业目标检测-2798","10.3390\u002Fsym18091548",{"doi":482,"openalex_id":484,"authors":485,"venue":450,"cited_by_count":92,"oa_url":447,"card":498,"direction":185,"ingested_from":99},"W7213430256",[486,488,491,493,495],{"name":487,"orcid":9},"Ling Wen",{"name":489,"orcid":490},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":492,"orcid":9},"Caihong Ma",{"name":494,"orcid":9},"Shengzhou Ma",{"name":496,"orcid":497},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":499,"method":500,"finding":501,"direction":185,"opportunity":502},"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"]