[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4373":3,"related-4373":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":85},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。",null,"HSR (London)","https:\u002F\u002Fhoughtonstreetreview.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F2110","2026-10-05T00:00:00Z","论文",10,false,67,{"impact":18,"substance":19,"depth":20,"authority":14,"freshness":21,"relevant":22,"comment":23},18,16,15,8,1,"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。",[25],{"name":10,"url":11},[27,28,29,30,31],"智慧农业","无人机","农业人工智能","农业遥感","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":15,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58],{"code":46,"score":21,"supported":47,"title":48,"abstract":49,"tags":51},"I1",true,[],[31,50],"precision agriculture",[27,31],{"code":33,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},4,[],[56],"artificial intelligence",[29],{"code":59,"score":22,"supported":15,"title":60,"abstract":61,"tags":62},"I3",[],[],[30],[64,65],"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":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":76,"oa_url":11,"card":77,"direction":83,"ingested_from":84},"W7220406405",[72,74],{"name":73,"orcid":9},"Somitov Abdukadir Abdurayimovich",{"name":75,"orcid":9},"Worldly Knowledge Publishing Centre",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"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","openalex","2026-10-07T23:30:46.084251Z",{"total":87,"page":22,"page_size":87,"items":88},6,[89,173,235,276,379,455],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":97,"score_detail":98,"sources":101,"tags":103,"primary_topic":105,"secondary_topics":109,"classification":111,"search_phrases":150,"slug":153,"view_count":53,"doi":154,"paper":155,"created_at":172},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":99,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":100},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。",[102],{"name":95,"url":92},[27,28,29,30,104],"作物倒伏",{"code":59,"slug":106,"name":107,"path":108,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[110],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":15,"version":41,"method":42,"evidence":112},{"candidates":113,"agricultural_evidence":47},[114,122,130,136,143],{"code":59,"score":18,"supported":47,"title":115,"abstract":117,"tags":121},[116],"remote sensing",[118,116,119,120],"遥感","multispectral","hyperspectral",[30],{"code":33,"score":14,"supported":47,"title":123,"abstract":124,"tags":129},[],[125,126,127,128],"深度学习","机器学习","machine learning","deep learning",[29],{"code":46,"score":131,"supported":47,"title":132,"abstract":133,"tags":135},7,[],[27,134],"smart agriculture",[27],{"code":137,"score":87,"supported":47,"title":138,"abstract":139,"tags":142},"A8",[],[140,141],"粮食安全","food security",[],{"code":144,"score":87,"supported":47,"title":145,"abstract":146,"tags":149},"J3",[],[147,148],"全球农业","global agriculture",[],[151,152],"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":154,"openalex_id":156,"authors":157,"venue":95,"cited_by_count":76,"oa_url":92,"card":166,"direction":81,"ingested_from":84},"W7213542473",[158,161,163],{"name":159,"orcid":160},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":162,"orcid":9},"Changxing Geng",{"name":164,"orcid":165},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"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。","农业遥感与作物表型","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":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":181,"score_detail":182,"sources":187,"tags":189,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":209,"slug":212,"view_count":131,"doi":213,"paper":214,"created_at":234},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":183,"depth":18,"authority":184,"freshness":185,"relevant":22,"comment":186},20,13,9,"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。",[188],{"name":179,"url":176},[27,28,29,30,190],"目标检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":194},{"candidates":195,"agricultural_evidence":47},[196,201,205],{"code":33,"score":197,"supported":47,"title":198,"abstract":199,"tags":200},19,[128],[190,125,128],[29,190],{"code":46,"score":131,"supported":47,"title":202,"abstract":203,"tags":204},[],[31,50],[27],{"code":59,"score":53,"supported":47,"title":206,"abstract":207,"tags":208},[],[118],[30],[210,211],"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":213,"openalex_id":215,"authors":216,"venue":179,"cited_by_count":76,"oa_url":176,"card":229,"direction":170,"ingested_from":84},"W7213430256",[217,219,222,224,226],{"name":218,"orcid":9},"Ling Wen",{"name":220,"orcid":221},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":223,"orcid":9},"Caihong Ma",{"name":225,"orcid":9},"Shengzhou Ma",{"name":227,"orcid":228},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":230,"method":231,"finding":232,"direction":170,"opportunity":233},"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":236,"title":237,"url":238,"summary":239,"summary_zh":9,"content":240,"source_name":241,"source_url":9,"published_at":242,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":243,"score_detail":244,"sources":246,"tags":248,"primary_topic":249,"secondary_topics":250,"classification":251,"search_phrases":271,"slug":274,"view_count":266,"doi":9,"paper":9,"created_at":275},1195,"A Taxonomy of Machine Learning for UAV-Enabled Precision Agriculture: A Structured Survey (面向无人机赋能精准农业的机器学习方法分类:结构化综述)","https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249","MDPI AgriEngineering 2026, 8(6), 249。精准农业越来越依赖应用于无人机(UAV)采集的高分辨率数据的机器学习来支持作物监测、胁迫检测和产量预测。本综述提出了无人机赋能精准农业机器学习方法的结构化综述,在一个由感知模态、数据类型、模型家族和分析任务四个维度定义的统一分类法中组织100多项同行评议研究。该分类法能够在RGB、多光谱、高光谱、LiDAR和IoT数据源之间进行系统比较,并在经典机器学习、深度学习、混合顺序模型和新兴基于Transformer的架构之间进行系统比较。分析建模选择如何与数据特征相互作用以影响鲁棒性、跨环境泛化、计算效率和UAV及边缘平台上的部署可行性。识别新兴研究方向,包括数据高效学习、表示级多模态融合、域自适应、轻量级架构和不确定性感知决策支持。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: agriengineering-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagriengineering-logo.png?9e66d6162a2bcb52)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagriengineering)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Review\n\nby \nWan D. Bae\n\n 1,*[](mailto:baew@seattleu.edu)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0000-0002-4611-5546), \nShayma Alkobaisi\n\n 2[](mailto:shayma.alkobaisi@uaeu.ac.ae)[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0000-0003-4237-7976), \nMuhammad Farhan Safdar\n\n 3[](mailto:muhammad.safdar@pw.edu.pl)[![Image 4: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0000-0001-7818-4805) and \nPrachitee Chouhan\n\n 1[](mailto:pchouhan@seattleu.edu)\n\n1\n\nDepartment of Computer Science, Seattle University, Seattle, WA 98122, USA\n\n2\n\nCollege of Information Technology, United Arab Emirates University, Sheik Khalifa Bin Zayed Street, Al Ain P.O. Box 15551, Abu Dhabi, United Arab Emirates\n\n3\n\nFaculty of Electronics and Information Technology, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 21 February 2026 \u002F Revised: 4 June 2026 \u002F Accepted: 5 June 2026 \u002F Published: 18 June 2026\n\n## Abstract\n\nPrecision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture.\n\n[![Image 5](https:\u002F\u002Fpub.mdpi-res.com\u002Fagriengineering\u002Fagriengineering-08-00249\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fagriengineering-08-00249-ag-550.jpg?1782112396)](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#)\n\nGraphical Abstract\n\n## 1. Introduction\n\nPrecision agriculture (PA) increasingly depends on unmanned aerial vehicles (UAVs) and data-driven modeling systems to enable high-resolution crop monitoring, stress detection, and yield forecasting. Advances in sensing platforms, including RGB, multispectral, hyperspectral, thermal, and LiDAR (Light Detection and Ranging) sensors, combined with machine learning and deep learning techniques, have transformed how agricultural data are collected, processed, and interpreted. These technologies support scalable data-driven decision making leading to improved productivity, resource efficiency, and environmental sustainability.\n\nBeyond serving as an application domain, PA presents a set of characteristics that actively shape the development of artificial intelligence (AI) methods. Agricultural environments expose AI systems to strong non-stationarity across seasons, cultivars, and management practices; extreme intra class variability caused by growth stages and phenology; and severe data imbalance between healthy and stressed conditions. These properties challenge assumptions commonly made in benchmark-driven computer vision and time-series modeling, where data distributions are often static and labels are abundant.\n\nExisting AI approaches in PA are often limited in scope, focusing on specific sensing modalities, individual crops, or isolated analytical tasks such as segmentation, pest and disease detection, fruit and bloom count, and yield prediction. Many surveys emphasize either model architectures or sensing technologies but do not systematically connect data characteristics, model design, and downstream agricultural applications. As a result, it remains difficult to compare approaches across studies or to understand how methodological choices interact with real-world deployment constraints in UAV-enabled precision agriculture. In view of these limitations, the following research questions were formulated:\n\n*   How do different AI model families (e.g., convolutional neural networks (CNNs), transformers, recurrent neural networks (RNNs), and classical models) perform across UAV-based sensing modalities and agricultural tasks?\n\n*   What factors limit the reliability of AI-based predictions under varying real-world agricultural conditions?\n\n*   How can UAV-based AI systems be translated into actionable insights for crop monitoring and interventional management?\n\nUnlike previous surveys that primarily organize the literature around individual sensing platforms, crop types, or model architectures, this review introduces a unified four-dimensional taxonomy that jointly connects sensing modality, data type, model family, and analytical task within a single analytical framework. This structure enables systematic comparison across studies that would otherwise remain disconnected, including segmentation, detection, counting, and yield prediction pipelines operating under different sensing and deployment conditions. The proposed taxonomy also provides practical guidance for designing scalable and operationally feasible UAV-enabled agricultural analytics systems.\n\nThe key contributions of this survey are as follows:\n\n*   We propose a unified taxonomy for AI-driven precision agriculture that integrates four complementary dimensions: sensing modality, data type, model family, and analytical task, enabling systematic cross-study comparison.\n\n*   We identify structural challenges including data scarcity, annotation cost, domain shift, limited cross-modal integration, and deployment constraints that limit robustness and scalability.\n\n*   We outline emerging research directions, including data-efficient learning, domain adaptation, representation-level multimodal fusion, synthetic data generation, and lightweight architectures for real-time deployment.\n\nThe literature reviewed in this survey was collected primarily from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, ScienceDirect, and MDPI databases using combinations of keywords related to UAVs, precision agriculture, machine learning, deep learning, crop monitoring, disease detection, segmentation, fruit counting, and yield prediction. The survey focuses primarily on peer-reviewed journal and conference papers published between 2015 and 2025, with emphasis on studies involving UAV-enabled sensing and AI-driven analytical pipelines. Papers were selected based on their relevance to at least one of the four taxonomy dimensions introduced in this survey: sensing modality, data type, model family, or analytical task. More than 100 studies were analyzed across segmentation, pest and disease detection, bloom and fruit counting, and yield prediction tasks, spanning RGB, multispectral, hyperspectral, thermal, LiDAR, satellite, and IoT-based sensing modalities.\n\nThe remainder of this paper is organized as follows. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec2-agriengineering-08-00249) provides background on precision agriculture, discusses key challenges, and introduces our taxonomy. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec3-agriengineering-08-00249) reviews sensing modalities, data-acquisition strategies, and data-preparation techniques. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec4-agriengineering-08-00249) summarizes segmentation methods. [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec5-agriengineering-08-00249) reviews pest and disease detection. [Section 6](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec6-agriengineering-08-00249) covers bloom detection, fruit counting, and yield prediction. [Section 8](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec8-agriengineering-08-00249) synthesizes cross-cutting challenges and emerging research directions, and [Section 9](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec9-agriengineering-08-00249) concludes the survey.\n\n## 2. Background, and Taxonomy\n\n### 2.1. Background and Research Gap\n\nThe evolution of information and communication technologies, remote sensing, and internet of things (IoT) has dramatically expanded the range of data available for precision agriculture [[1](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B1-agriengineering-08-00249),[2](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B2-agriengineering-08-00249)]. UAVs and ground-based sensors provide high-resolution measurements of crop and soil conditions, while satellite platforms offer broad spatial and temporal coverage. Although these technologies enable data-driven decision making, they also introduce several challenges that directly impact the design and deployment of deep learning models.\n\nData acquisition is central to both deep learning (DL) models and IoT systems in agriculture. Sensors, UAVs, and other devices collect diverse data on pests, soil properties, canopy structure, crop yields, and environmental variables such as temperature, light, and humidity [[2](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B2-agriengineering-08-00249),[3](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B3-agriengineering-08-00249)]. Studies on smart agriculture systems [[4](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B4-agriengineering-08-00249),[5](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B5-agriengineering-08-00249),[6](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B6-agriengineering-08-00249)] emphasize the importance of reliable sensing infrastructures and communication networks for real-time monitoring and control.\n\nHigh-quality annotation is equally critical. Accurate labels enable supervised DL models to learn robust representations for tasks such as pest detection, disease diagnosis, and crop health monitoring [[7](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B7-agriengineering-08-00249),[8](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B8-agriengineering-08-00249)]. For UAV imagery, the annotation process is often labor intensive and requires domain expertise, particularly when dealing with subtle symptoms or occluded plant parts.\n\nSeveral works highlight the difficulties of vegetation stress detection using UAVs, where models must capture both intra field variability and temporal dynamics [[9](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B9-agriengineering-08-00249),[10](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B10-agriengineering-08-00249)]. Large-scale, well-annotated datasets are rarely available, and models trained on small datasets tend to overfit or fail to generalize to new fields. To mitigate these issues, researchers employ data augmentation and regularization strategies such as geometric transformations and dropout layers [[11](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B11-agriengineering-08-00249)]. The use of high-resolution imagery and appropriate preprocessing steps is particularly important for building reliable prediction models [[12](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B12-agriengineering-08-00249)].\n\nData quality issues remain a recurring challenge. Agricultural data vary widely across crop types, regions, and seasons, leading to covariate shifts that degrade model performance. Incremental or phased adoption of advanced sensing and annotation technologies has been proposed as a practical strategy to improve data quality over time and support the sustainable deployment of PA systems [[13](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B13-agriengineering-08-00249)].\n\nIn summary, the following research gap continues to shape the development of AI-based solutions in UAV-enabled precision agriculture:\n\n*   Data variability and generalization: Although UAV sensing enables high-resolution monitoring, significant variability across environments, crop varieties, and management practices limits the generalizability of machine learning (ML) models.\n\n*   Data quality and limited annotated datasets: Many deep learning approaches rely on large, consistently annotated datasets, which remain scarce and costly to obtain, particularly for fine-grained agricultural tasks.\n\n*   Operational deployment and scalability: Practical deployment requires the robust integration of sensing platforms, data infrastructures, and analytical models, which is still evolving in many precision agriculture systems.\n\nThis preliminary information about the research gap motivates the methodological choices discussed in subsequent sections and underscores the need for a structured taxonomy that connects sensing modalities, data types, model families, and analytical tasks.\n\n### 2.2. Taxonomy of UAV-Based Sensing and Machine Learning in Precision Agriculture\n\nThe diversity of sensing platforms, data types, model architectures, and agricultural applications makes it challenging to compare existing approaches in a systematic manner. To provide a unifying perspective, we introduce a taxonomy that organizes UAV-based sensing and machine learning methods along four complementary dimensions: (1) sensing modality, (2) data type, (3) model family, and (4) analytical task. This taxonomy links upstream sensing and data preparation steps in [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#sec3-agriengineering-08-00249) with downstream segmentation, detection, counting, and prediction methods discussed in later sections. It also provides the structural foundation for the survey, with each subsequent section mapped explicitly to one or more of these four dimensions.\n\nSeveral recent surveys review deep learning applications in agriculture or remote sensing independently; however, they typically organize the literature along a single dimension, such as model architecture, crop type, or sensing platform. In contrast, the taxonomy proposed in this survey integrates four complementary dimensions including sensing modality, data type, model family, and analytical task into a unified framework. This multidimensional structure enables systematic comparison across studies that would otherwise appear disconnected, such as UAV-based object detection and satellite-driven yield forecasting.\n\nUnlike model centric surveys that emphasize architectural trends, or remote sensing reviews that focus primarily on sensor characteristics, our taxonomy explicitly links upstream sensing infrastructure and data preparation choices to downstream learning models and decision support objectives. This integrated perspective reveals underexplored combinations of sensing modalities and model families and provides a structured foundation for analyzing and designing scalable, end-to-end data-driven systems in precision agriculture.\n\nCompared to previous reviews that focus primarily on algorithms or crop-specific case studies, this survey integrates sensing infrastructure, data structure, modeling approaches, and analytical objectives within a unified engineering framework. By bridging sensing technologies and machine learning methodologies, the proposed taxonomy supports the design and evaluation of deployable, robust, and scalable agricultural systems.\n\n1.\nSensing modality: AI-enabled precision agriculture relies on heterogeneous sensing platforms that vary widely in spatial, spectral, and temporal resolution. UAV-mounted RGB, multispectral, hyperspectral, thermal, and LiDAR sensors provide high spatial detail for canopy structure, plant health assessment, and fine-scale monitoring [[3](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B3-agriengineering-08-00249),[14](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B14-agriengineering-08-00249)]. Satellite platforms extend temporal and regional coverage, offering multi-temporal vegetation indices and spectral diagnostics used extensively for crop health and yield modeling [[2](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B2-agriengineering-08-00249)]. Ground-based IoT sensors, including soil moisture probes, weather stations, and in-field cameras provide micro-environmental and crop-level observations that complement airborne and satellite sensing [[15](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B15-agriengineering-08-00249)]. Together, these modalities form the multi-resolution sensing backbone of modern agricultural analytics.\n\n2.\nData type: The raw data produced by these sensing modalities differ in structure, dimensionality, and temporal properties. We categorize data types into five groups: (i) single frame 2D imagery, (ii) multi-temporal image sequences used for phenological monitoring and yield prediction [[16](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B16-agriengineering-08-00249)], (iii) 3D point clouds derived from LiDAR or structure from motion photogrammetry [[14](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B14-agriengineering-08-00249)], (iv) tabular sensor and environmental streams from IoT systems, and (v) fused multimodal datasets combining imagery, spectral indices, and ground measurements [[17](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B17-agriengineering-08-00249)]. Each data type imposes different requirements on preprocessing pipelines, feature extraction, and model selection.\n\n3.\nModel family: A wide range of machine learning and deep learning models have been applied to agricultural imagery and sensor data. Classical methods such as support vector machines (SVMs), random forests, and fuzzy rule-based systems remain competitive when datasets are small or interpretability is required. Deep learning models dominate imagery-driven tasks, including convolutional neural networks (CNNs) [[18](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B18-agriengineering-08-00249)], residual networks (ResNet) [[19](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B19-agriengineering-08-00249)], encoder–decoder architectures such as U-Net [[20](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B20-agriengineering-08-00249)], region-based detectors including Faster region-CNN (Faster R-CNN) and Mask Region-CNN (Mask R-CNN) [[21](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B21-agriengineering-08-00249),[22](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B22-agriengineering-08-00249)], and one-stage detectors such as you only look once (YOLO) variants [[23](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B23-agriengineering-08-00249),[24](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B24-agriengineering-08-00249)]. Sequential models (long short-term memory (LSTM) [[25](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B25-agriengineering-08-00249)], Convolutional LSTM (ConvLSTM) [[26](https:\u002F\u002Fwww.mdpi.com\u002F2624-7402\u002F8\u002F6\u002F249#B26-agriengineering-08-00249)]) and hybrid architectures (CNN–SVM, CNN–LSTM, and stacking ensembles) further extend these capabilities. Recent advances in attention-based and transformer architectures further expand this model family dimension, offering alternatives to convolutional and recurrent networks for modeling long-range spatial and temporal dependencies in agricultural data.\n\n4.\nAnalytical task: The fi","AgriEngineering 2026, 8(6), 249","2026-08-25T08:00:00Z",73,{"impact":18,"substance":99,"depth":18,"authority":184,"freshness":67,"relevant":22,"comment":245},"结构化综述系统梳理无人机精准农业机器学习方法，提出四维分类法，对领域研究有重要参考价值。",[247],{"name":241,"url":238},[27,28,29,126,31],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":252},{"candidates":253,"agricultural_evidence":47},[254,259,264],{"code":33,"score":255,"supported":47,"title":256,"abstract":257,"tags":258},24,[126,127],[125,126],[29,126],{"code":46,"score":260,"supported":47,"title":261,"abstract":262,"tags":263},21,[31,50],[31],[27,31],{"code":265,"score":266,"supported":47,"title":267,"abstract":268,"tags":270},"I5",3,[],[269],"IoT",[],[272,273],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器学习精准农业-1195","2026-09-01T00:03:40.394459Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":283,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":284,"score_detail":285,"sources":287,"tags":289,"primary_topic":292,"secondary_topics":293,"classification":304,"search_phrases":355,"slug":358,"view_count":76,"doi":359,"paper":360,"created_at":378},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":18,"substance":99,"depth":18,"authority":184,"freshness":185,"relevant":22,"comment":286},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[288],{"name":282,"url":279},[27,29,290,31,291],"物联网","气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[294,298],{"code":265,"slug":295,"name":296,"path":297,"parent_code":37},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":299,"slug":300,"name":301,"path":302,"parent_code":303},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":15,"version":41,"method":42,"evidence":305},{"candidates":306,"agricultural_evidence":47},[307,313,317,323,329,336,343,349],{"code":33,"score":18,"supported":47,"title":308,"abstract":309,"tags":312},[56],[125,126,310,127,128,311],"计算机视觉","computer vision",[29],{"code":46,"score":19,"supported":47,"title":314,"abstract":315,"tags":316},[50],[31,50],[27,31],{"code":265,"score":14,"supported":47,"title":318,"abstract":319,"tags":322},[],[290,320,269,321],"边缘计算","edge computing",[290],{"code":299,"score":185,"supported":47,"title":324,"abstract":325,"tags":328},[],[326,327,300],"灌溉","水资源",[],{"code":330,"score":131,"supported":47,"title":331,"abstract":332,"tags":335},"H7",[],[333,334],"气候变化","climate change",[291],{"code":337,"score":87,"supported":47,"title":338,"abstract":339,"tags":342},"H2",[],[340,341],"土壤","soil",[],{"code":344,"score":266,"supported":47,"title":345,"abstract":346,"tags":348},"A7",[],[347],"间作",[],{"code":350,"score":266,"supported":47,"title":351,"abstract":352,"tags":354},"F3",[],[353],"养分管理",[],[356,357],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":359,"openalex_id":361,"authors":362,"venue":282,"cited_by_count":76,"oa_url":279,"card":373,"direction":81,"ingested_from":84},"W7223121456",[363,366,368,370],{"name":364,"orcid":365},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":367,"orcid":9},"Oluwadare OYEBODE",{"name":369,"orcid":9},"Iretiayo Adelaiye",{"name":371,"orcid":372},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":374,"method":375,"finding":376,"direction":83,"opportunity":377},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":9,"source_name":385,"source_url":382,"published_at":386,"category":13,"cover_url":9,"hotness":14,"is_selected":47,"score":387,"score_detail":388,"sources":391,"tags":393,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":423,"slug":426,"view_count":53,"doi":427,"paper":428,"created_at":454},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":389,"substance":255,"depth":197,"authority":20,"freshness":185,"relevant":22,"comment":390},26,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[392],{"name":385,"url":382},[27,29,31,118,394],"全球评估",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":398},{"candidates":399,"agricultural_evidence":47},[400,405,409,413,417],{"code":46,"score":401,"supported":47,"title":402,"abstract":403,"tags":404},11,[],[27,31,50],[27,31],{"code":33,"score":131,"supported":47,"title":406,"abstract":407,"tags":408},[],[126,127],[29],{"code":299,"score":87,"supported":47,"title":410,"abstract":411,"tags":412},[],[326,300],[],{"code":59,"score":53,"supported":47,"title":414,"abstract":415,"tags":416},[],[118],[118],{"code":418,"score":266,"supported":47,"title":419,"abstract":420,"tags":422},"F4",[],[421],"除草",[],[424,425],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":427,"openalex_id":429,"authors":430,"venue":385,"cited_by_count":76,"oa_url":382,"card":449,"direction":81,"ingested_from":84},"W7222974188",[431,434,437,440,443,446],{"name":432,"orcid":433},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":435,"orcid":436},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":438,"orcid":439},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":441,"orcid":442},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":444,"orcid":445},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":447,"orcid":448},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":450,"method":451,"finding":452,"direction":81,"opportunity":453},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":9,"source_name":461,"source_url":458,"published_at":462,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":463,"score_detail":464,"sources":468,"tags":470,"primary_topic":472,"secondary_topics":473,"classification":474,"search_phrases":489,"slug":492,"view_count":76,"doi":493,"paper":494,"created_at":531},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",77,{"impact":18,"substance":183,"depth":465,"authority":466,"freshness":21,"relevant":22,"comment":467},17,14,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[469],{"name":461,"url":458},[27,29,126,31,471],"农户认知",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":15,"version":41,"method":42,"evidence":475},{"candidates":476,"agricultural_evidence":47},[477,481,485],{"code":33,"score":401,"supported":47,"title":478,"abstract":479,"tags":480},[],[126,127,56],[29,126],{"code":337,"score":87,"supported":47,"title":482,"abstract":483,"tags":484},[],[340,341],[],{"code":46,"score":67,"supported":15,"title":486,"abstract":487,"tags":488},[],[],[27,31],[490,491],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":493,"openalex_id":495,"authors":496,"venue":461,"cited_by_count":76,"oa_url":458,"card":526,"direction":83,"ingested_from":84},"W7222866367",[497,500,503,506,509,512,515,518,520,523],{"name":498,"orcid":499},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":501,"orcid":502},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":504,"orcid":505},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":507,"orcid":508},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":510,"orcid":511},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":513,"orcid":514},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":516,"orcid":517},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":519,"orcid":9},"M. Z. Walkup",{"name":521,"orcid":522},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":524,"orcid":525},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":527,"method":528,"finding":529,"direction":83,"opportunity":530},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z"]