[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2906":3,"related-2906":57},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":56},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。",null,"Computers and Electronics in Agriculture","2026-09-18T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,15,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","农业人工智能","农业遥感","作物倒伏",[32,33],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906",0,"10.1016\u002Fj.compag.2026.112446",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":48,"direction":54,"ingested_from":55},"W7213542473",[40,43,45],{"name":41,"orcid":42},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":44,"orcid":9},"Changxing Geng",{"name":46,"orcid":47},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"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。","智慧农业 \u002F 农业物联网","openalex","2026-09-19T23:30:01.740139Z",{"total":58,"page":21,"page_size":58,"items":59},6,[60,105,148,174,207,235],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":9,"source_name":66,"source_url":63,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":75,"tags":77,"search_phrases":79,"slug":82,"view_count":35,"doi":83,"paper":84,"created_at":104},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":70,"substance":71,"depth":17,"authority":72,"freshness":73,"relevant":21,"comment":74},16,20,13,9,"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。",[76],{"name":66,"url":63},[26,27,28,29,78],"目标检测",[80,81],"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":83,"openalex_id":85,"authors":86,"venue":66,"cited_by_count":35,"oa_url":63,"card":99,"direction":52,"ingested_from":55},"W7213430256",[87,89,92,94,96],{"name":88,"orcid":9},"Ling Wen",{"name":90,"orcid":91},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":93,"orcid":9},"Caihong Ma",{"name":95,"orcid":9},"Shengzhou Ma",{"name":97,"orcid":98},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":100,"method":101,"finding":102,"direction":52,"opportunity":103},"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":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":10,"source_url":108,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"search_phrases":120,"slug":122,"view_count":35,"doi":123,"paper":124,"created_at":147},2614,"Boundary-aware dual-stream network for topology restoration and automated mapping of fragmented mulch films in UAV imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112428","Boundary-aware dual-stream network for topology restoration and automated mapping of fragmented mulch films in UAV imagery。Computers and Electronics in Agriculture","面向无人机影像中破碎地膜拓扑恢复与自动制图的边界感知双流网络。《农业计算机与电子》",77,{"impact":70,"substance":71,"depth":113,"authority":114,"freshness":13,"relevant":21,"comment":115},17,14,"发表于农业信息领域核心期刊的无人机遥感地膜碎片识别与制图新方法，方法新颖、结论可靠，对农田残膜监测与治理具有实用价值。",[117],{"name":10,"url":108},[26,27,28,29,119],"地膜回收",[121,81],"农业人工智能 农业遥感 地膜回收 智慧农业","农业人工智能农业遥感地膜回收智慧农业-2614","10.1016\u002Fj.compag.2026.112428",{"doi":123,"openalex_id":125,"authors":126,"venue":10,"cited_by_count":35,"oa_url":9,"card":142,"direction":52,"ingested_from":55},"W7213282871",[127,129,131,133,136,139],{"name":128,"orcid":9},"Penglei Yan",{"name":130,"orcid":9},"Sumin Lv",{"name":132,"orcid":9},"Xuan Li",{"name":134,"orcid":135},"Jianan Chi","https:\u002F\u002Forcid.org\u002F0009-0004-2943-1565",{"name":137,"orcid":138},"Xiao Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0111-8089",{"name":140,"orcid":141},"Nannan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-2956-8815",{"tldr":143,"method":144,"finding":145,"direction":52,"opportunity":146},"提出边界感知双流网络，从无人机影像中恢复破碎地膜拓扑并自动制图。","边界感知双流网络，基于无人机影像进行地膜分割与拓扑修复。","该方法能有效恢复破碎地膜的拓扑结构，实现自动化制图。","可探索破碎地膜拓扑恢复在残膜回收决策与农田污染评估中的应用。","2026-09-16T23:30:01.821423Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":9,"content":9,"source_name":153,"source_url":9,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":158,"tags":160,"search_phrases":162,"slug":164,"view_count":35,"doi":9,"paper":165,"created_at":173},2218,"《基于现役作物多模态无人机图像的大豆种子成分基因型感知预测》","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3121","Vasit Sagan等(圣路易斯大学、密苏里大学)开发端到端卷积神经网络框架,从基于无人机的多传感器图像及相关基因型和物候元数据估算大豆八种种子性状(蛋白质、油、蔗糖、纤维、淀粉、灰分、复合和简单碳水化合物)。2020-2021年密苏里州两个农场共采集372份大豆样本,在四个时间点捕获多光谱、热成像和LiDAR数据。蔗糖预测精度最高(R²=0.80),其次为简单碳水化合物(R²=0.70)和淀粉(R²=0.55)。多光谱图像在所有模态中提供最稳健的估计。","Remote Sensing 2026年9月11日","2026-09-11T00:00:00Z",82,{"impact":17,"substance":18,"depth":17,"authority":114,"freshness":13,"relevant":21,"comment":157},"多模态无人机遥感结合深度学习预测大豆种子成分，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[159],{"name":153,"url":151},[26,27,28,29,161],"大豆育种",[163,81],"农业人工智能 农业遥感 大豆育种 智慧农业","农业人工智能农业遥感大豆育种智慧农业-2218",{"doi":9,"openalex_id":9,"authors":166,"venue":9,"cited_by_count":35,"oa_url":9,"card":167,"direction":52,"ingested_from":172},[],{"tldr":168,"method":169,"finding":170,"direction":52,"opportunity":171},"用无人机多模态图像和CNN预测大豆八种种子成分，蔗糖精度最高。","端到端CNN，融合多光谱、热成像、LiDAR及基因型物候元数据，372份样本。","多光谱最稳健，蔗糖R²=0.80，简单碳水化合物0.70，淀粉0.55。","可探索多模态融合与基因型-表型关联，提升低精度性状预测及跨年份泛化。","agent","2026-09-12T00:06:40.739496Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":181,"sources":183,"tags":185,"search_phrases":188,"slug":191,"view_count":35,"doi":192,"paper":193,"created_at":206},2944,"Rule-driven functional zoning index prediction for sustainable agricultural landscapes design based on multimodal fusion deep learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72129-2","Abstract This study develops a rule-based functional zoning index system for agricultural landscapes that integrates multi-source variables, including remote sensing spectral information, landscape structure, topographic conditions, crop configuration, and land cover. To enable automated prediction of the proposed index system, a Multi-Modal Landscape Fusion Network (MMLF-Net) model is developed. By integrating multi-source data, including remote sensing images, topographic features, crop structures, and land cover, the model constructs an end-to-end AL functional zoning system. Experimental verification is carried out in the Fresno region of California, the United States of America. The results show that MMLF-Net effectively improves the accuracy of functional identification and the stability of zoning, achieving an overall accuracy of 87.34% and a Kappa coefficient of 0.84. Among all functional types, the F1 scores for intensive production and natural fallow zones exceed 0.90, demonstrating the advantages of multimodal feature fusion in functional identification. Further landscape pattern analysis reveals several key structural characteristics, including overextended production-ecology interfaces, fragmented ecological patches, and complex morphologies within composite management zones. These findings provide a quantitative basis for ecological restoration layout and agricultural pollution prevention and control. This study aims to offer a practical spatial decision-making tool for the sustainable development of regional agriculture.","摘要 本研究构建了一套基于规则的农业景观功能分区指标体系，该体系整合了多源变量，包括遥感光谱信息、景观结构、地形条件、作物配置和土地覆盖。为实现对所提指标体系的自动化预测，本研究开发了多模态景观融合网络（MMLF-Net）模型。通过整合遥感影像、地形特征、作物结构和土地覆盖等多源数据，该模型构建了端到端的农业景观功能分区系统。实验验证在美国加利福尼亚州弗雷斯诺地区开展。结果表明，MMLF-Net有效提升了功能识别的精度和分区的稳定性，总体精度达到87.34%，Kappa系数为0.84。在所有功能类型中，集约生产区和自然休耕区的F1分数均超过0.90，证明了多模态特征融合在功能识别中的优势。进一步的景观格局分析揭示了若干关键结构特征，包括生产-生态界面过度延伸、生态斑块破碎化以及复合管理区内形态复杂等。这些发现为生态修复布局和农业污染防治提供了定量依据。本研究旨在为区域农业可持续发展提供实用的空间决策工具。","Scientific Reports",{"impact":17,"substance":18,"depth":17,"authority":114,"freshness":73,"relevant":21,"comment":182},"多模态深度学习用于农业景观功能分区，方法新颖、指标可靠，对农业空间决策有参考价值。",[184],{"name":180,"url":177},[26,28,29,186,187],"多模态融合","功能分区",[189,190],"MMLF-Net 农业景观 功能分区","Fresno 农业景观 多模态","MMLF-Net农业景观功能分区-2944","10.1038\u002Fs41598-026-72129-2",{"doi":192,"openalex_id":194,"authors":195,"venue":180,"cited_by_count":35,"oa_url":177,"card":201,"direction":52,"ingested_from":55},"W7213550895",[196,198],{"name":197,"orcid":9},"Ruifen Wen",{"name":199,"orcid":200},"Juan Du","https:\u002F\u002Forcid.org\u002F0000-0002-7422-8767",{"tldr":202,"method":203,"finding":204,"direction":52,"opportunity":205},"构建规则驱动农业景观功能分区指数，并用多模态融合深度学习实现自动预测。","多模态融合网络MMLF-Net，融合遥感、地形、作物结构与土地覆盖数据。","MMLF-Net总体精度87.34%、Kappa 0.84，集约生产与自然休耕区F1超0.90。","可探索规则驱动指数与可解释深度学习的耦合，并迁移到不同农业景观区验证泛化性。","2026-09-19T23:30:32.908587Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":9,"content":9,"source_name":212,"source_url":9,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":217,"tags":219,"search_phrases":222,"slug":225,"view_count":35,"doi":9,"paper":226,"created_at":234},2901,"AgriScope:面向农业图像的像素级多模态理解统一框架,arXiv 2609.20325(预印本)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20325","Boudiaf、Alanssari、Hussain、Javed提出AgriScope,一个统一的像素级多模态农业图像理解框架,联合支持图像级、区域级、像素级理解,可实现接地描述生成、指代表达分割、多轮多模态交互等任务。集成生物专用语义表征、密集空间表征与像素解码;引入大规模像素级农业多模态指令调优数据集AgriGround,包含50万+图像和1100万+指令跟随样本,涵盖植物病害分析、作物与杂草识别、昆虫识别、细粒度植物理解。实验表明AgriScope在多项农业视觉语言任务上有效。","arXiv (preprint)","2026-09-17T00:00:00Z",75,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":73,"relevant":21,"comment":216},"提出统一像素级农业多模态理解框架并开源50万图像、1100万指令样本的大规模数据集，方法新颖、数据规模突出，但为arXiv预印本、未经同行评审，权威性有限，值得作为前沿技术动态精选。",[218],{"name":212,"url":210},[26,28,29,220,221],"植物病害识别","多模态大模型",[223,224],"AgriScope 农业图像 多模态","AgriGround 像素级 农业数据集","AgriScope农业图像多模态-2901",{"doi":9,"openalex_id":9,"authors":227,"venue":9,"cited_by_count":35,"oa_url":9,"card":228,"direction":232,"ingested_from":172},[],{"tldr":229,"method":230,"finding":231,"direction":232,"opportunity":233},"提出AgriScope统一框架，实现农业图像像素级多模态理解与多任务交互。","构建AgriGround数据集（50万+图像、1100万+指令样本），融合语义与","AgriScope在接地描述、指代分割、多轮交互等农业视觉语言任务上有效。","农业人工智能与决策模型","可探索像素级多模态模型在田间实时病害诊断与精准施药决策中的落地与轻量化。","2026-09-19T00:06:08.678379Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":9,"content":9,"source_name":240,"source_url":9,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":244,"tags":246,"search_phrases":249,"slug":252,"view_count":35,"doi":9,"paper":253,"created_at":261},2858,"农业视觉感知综述:基于4D混沌和Mamba网络的新颖隐私保护框架——Frontiers in Plant Science 9月17日","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260917000338095.htm","基于冬小麦-夏玉米双季种植系统的长期定位田间试验,氮肥后效对土壤-作物协调及微生物功能的调控机制尚不明确。试验设置四个处理:CK(小麦季和玉米季均不施氮)、W1M0(仅在冬小麦季施氮)、W0M1(仅在夏玉米季施氮)、W1M1(小麦季和玉米季均施氮)。与不施氮对照(CK)相比,W1M0和W0M1分别使平均年产量提高45.7%和42.2%,而W1M1提高58.6%。PLS-SEM揭示,前茬小麦季土壤氮含量解释了后茬玉米土壤理化性质97.5%的方差。","Frontiers in Plant Science \u002F 生物通",78,{"impact":17,"substance":71,"depth":113,"authority":72,"freshness":13,"relevant":21,"comment":243},"该文提出基于4D混沌与Mamba网络的农业视觉感知隐私保护框架，方法新颖且属农业人工智能前沿交叉方向，信源为核心期刊，时效性强，具备进入每日精选的价值。",[245],{"name":240,"url":238},[26,28,29,247,248],"作物表型","隐私保护",[250,251],"农业人工智能 作物表型 农业遥感 智慧农业","农业人工智能 作物表型","农业人工智能作物表型农业遥感智慧农业-2858",{"doi":9,"openalex_id":9,"authors":254,"venue":9,"cited_by_count":35,"oa_url":9,"card":255,"direction":259,"ingested_from":172},[],{"tldr":256,"method":257,"finding":258,"direction":259,"opportunity":260},"通过长期定位试验研究氮肥后效对冬小麦-夏玉米轮作系统土壤-作物协调及微生物功能的调控机制。","长期定位田间试验，设置四种施氮处理，结合PLS-SEM分析。","双季施氮年产量提高58.6%，前茬小麦季土壤氮解释后茬玉米土壤理化性质97.5%的方差。","农业绿色发展与碳","可探究氮肥后效对土壤微生物功能及碳氮循环的长期影响，优化轮作施氮策略。","2026-09-18T00:03:31.115969Z"]