[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3135":3,"related-3135":50},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"search_phrases":29,"slug":32,"view_count":33,"doi":34,"paper":35,"created_at":49},3135,"Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10453-3","Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework。Precision Agriculture",null,"Precision Agriculture","2026-09-22T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,14,1,"方法新颖、数据可靠，对草地精准施肥有参考价值，但属细分领域研究，未达重大突破层级。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","无人机","农业遥感","机器学习","草地氮素",[30,31],"UAV 多光谱 草地 氮素","Precision Agriculture 氮素估算","UAV多光谱草地氮素-3135",0,"10.1007\u002Fs11119-026-10453-3",{"doi":34,"openalex_id":36,"authors":37,"venue":9,"cited_by_count":33,"oa_url":8,"card":8,"direction":8,"ingested_from":48},"W7213942551",[38,40,43,46],{"name":39,"orcid":8},"Antônio de Oliveira Costa Neto",{"name":41,"orcid":42},"Yiannis Ampatzidis","https:\u002F\u002Forcid.org\u002F0000-0002-3660-3298",{"name":44,"orcid":45},"Andrea Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-1521-6942",{"name":47,"orcid":8},"Jim Fletcher","openalex","2026-09-22T23:30:03.351447Z",{"total":51,"page":19,"page_size":51,"items":52},6,[53,95,127,170,214,264],{"id":54,"title":55,"url":56,"summary":57,"summary_zh":58,"content":8,"source_name":59,"source_url":56,"published_at":60,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":61,"score_detail":62,"sources":66,"tags":68,"search_phrases":71,"slug":74,"view_count":19,"doi":75,"paper":76,"created_at":94},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":16,"substance":17,"depth":16,"authority":63,"freshness":64,"relevant":19,"comment":65},15,8,"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。",[67],{"name":59,"url":56},[24,25,69,26,70],"农业人工智能","作物倒伏",[72,73],"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":75,"openalex_id":77,"authors":78,"venue":59,"cited_by_count":33,"oa_url":56,"card":87,"direction":93,"ingested_from":48},"W7213542473",[79,82,84],{"name":80,"orcid":81},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":83,"orcid":8},"Changxing Geng",{"name":85,"orcid":86},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"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","2026-09-19T23:30:01.740139Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":8,"content":8,"source_name":100,"source_url":8,"published_at":101,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":102,"score_detail":103,"sources":109,"tags":111,"search_phrases":114,"slug":117,"view_count":33,"doi":8,"paper":118,"created_at":126},2855,"UAV无人机高光谱图像土壤盐度制图(湿度校正)——MDPI Agronomy","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1812","研究评估了6种光谱变换方法(原始反射率Ref、一阶导数FDR、PDS、OSC、FDR+PDS、FDR+OSC),结合3种机器学习算法(KNN、SVR、MLP)。进一步开发了集成这些基础学习者的Stacking集成模型,以提高湿度干扰下土壤盐度反演的精度。结果表明,Stacking模型在评估模型中达到最高的精度和稳定性。FDR+OSC-Stacking组合实现最佳验证性能,R²p=0.87,RMSEP=0.67 mS·cm⁻¹,RPD=2.93。FDR+OSC-Stacking组合成功应用于UAV高光谱图像,用于EC1:5的空间制图。来自吉林大学。","MDPI Agronomy","2026-09-15T00:00:00Z",75,{"impact":104,"substance":105,"depth":106,"authority":107,"freshness":64,"relevant":19,"comment":108},16,21,17,13,"方法组合新颖、验证指标扎实的无人机高光谱盐分制图研究，属细分领域实质进展，值得精选。",[110],{"name":100,"url":98},[24,25,27,112,113],"遥感","土壤盐渍化",[115,116],"土壤盐渍化 智慧农业 机器学习 无人机","土壤盐渍化 智慧农业","土壤盐渍化智慧农业机器学习无人机-2855",{"doi":8,"openalex_id":8,"authors":119,"venue":8,"cited_by_count":33,"oa_url":8,"card":120,"direction":91,"ingested_from":125},[],{"tldr":121,"method":122,"finding":123,"direction":91,"opportunity":124},"用无人机高光谱结合Stacking集成模型实现湿度干扰下的土壤盐度制图。","6种光谱变换与KNN、SVR、MLP及Stacking集成，基于UAV高光谱数据","FDR+OSC-Stacking最优，R²p=0.87、RMSEP=0.67 mS·cm⁻¹、RPD","可探索多时相\u002F多传感器融合与迁移学习，提升不同湿度与区域下盐度反演泛化性。","agent","2026-09-18T00:03:30.822732Z",{"id":128,"title":129,"url":130,"summary":131,"summary_zh":132,"content":8,"source_name":133,"source_url":130,"published_at":134,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":135,"score_detail":136,"sources":140,"tags":142,"search_phrases":144,"slug":147,"view_count":33,"doi":148,"paper":149,"created_at":169},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":104,"substance":137,"depth":16,"authority":107,"freshness":138,"relevant":19,"comment":139},20,9,"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。",[141],{"name":133,"url":130},[24,25,69,26,143],"目标检测",[145,146],"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":148,"openalex_id":150,"authors":151,"venue":133,"cited_by_count":33,"oa_url":130,"card":164,"direction":91,"ingested_from":48},"W7213430256",[152,154,157,159,161],{"name":153,"orcid":8},"Ling Wen",{"name":155,"orcid":156},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":158,"orcid":8},"Caihong Ma",{"name":160,"orcid":8},"Shengzhou Ma",{"name":162,"orcid":163},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":165,"method":166,"finding":167,"direction":91,"opportunity":168},"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":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":101,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":177,"score_detail":178,"sources":180,"tags":182,"search_phrases":185,"slug":188,"view_count":33,"doi":189,"paper":190,"created_at":213},2663,"Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2061","Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining \"SAM + MobileNetV3-Small classification\" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.","准确监测水稻地上生物量(AGB)对指导农业生产管理至关重要。本研究聚焦水稻AGB的高精度估算与自动化制图。田间试验在浙江省湖州市南浔区开展，采集了无人机RGB和多光谱影像、水稻AGB及株高数据。通过融合植被指数、纹理特征和株高信息，利用Stacking等算法构建AGB估算模型。提出了一种“SAM+MobileNetV3-Small分类”框架，以实现稻田自动化提取和物候识别。结果表明，基于Stacking集成算法的水稻AGB预测模型表现优异，株高的引入显著提高了模型精度。例如，在抽穗期，R2从0.421提升至0.739，RPIQ从1.995提升至3.015。本研究开发的稻田自动化提取与物候识别框架在本研究数据集上实现了0.968的分割精度和0.993的分类精度，且无需人工标注。本研究为水稻AGB的自动化高精度制图提供了技术参考。","Journal of Agricultural Engineering",78,{"impact":104,"substance":17,"depth":16,"authority":18,"freshness":64,"relevant":19,"comment":179},"基于无人机遥感与Stacking集成模型实现水稻地上生物量自动化制图，方法新颖、数据扎实，对精准农业管理有参考价值。",[181],{"name":176,"url":173},[24,25,26,183,184],"水稻","作物表型",[186,187],"作物表型 农业遥感 智慧农业 无人机","作物表型 农业遥感","作物表型农业遥感智慧农业无人机-2663","10.4081\u002Fjae.2026.2061",{"doi":189,"openalex_id":191,"authors":192,"venue":176,"cited_by_count":33,"oa_url":173,"card":208,"direction":91,"ingested_from":48},"W7213229220",[193,195,197,199,201,204,206],{"name":194,"orcid":8},"Honggang Xu",{"name":196,"orcid":8},"Xuehan Li",{"name":198,"orcid":8},"Jia Shen",{"name":200,"orcid":8},"Ziyi Li",{"name":202,"orcid":203},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":205,"orcid":8},"Yiming Li",{"name":207,"orcid":8},"Pengcheng Nie",{"tldr":209,"method":210,"finding":211,"direction":91,"opportunity":212},"基于无人机RGB与多光谱影像，结合株高与Stacking集成算法，实现水稻地上生物量高精度自动制图。","无人机RGB\u002F多光谱影像、植被指数、纹理与株高，Stacking集成及SAM+M","引入株高显著提升精度，抽穗期R²从0.421升至0.739；自动稻田提取与物候识别精度达0.968和","可探索多生育期、多品种下株高与纹理特征的迁移性，并耦合深度学习实现全自动生物量时空制图。","2026-09-16T23:30:28.929376Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":8,"source_name":59,"source_url":217,"published_at":101,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":220,"score_detail":221,"sources":223,"tags":225,"search_phrases":228,"slug":231,"view_count":33,"doi":232,"paper":233,"created_at":263},2617,"Dynamic ROI-constrained UAV multispectral estimation of single-plant LAI in trellis-trained kiwifruit","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112437","Leaf area index (LAI) is a key biophysical parameter for characterizing orchard canopy structure and growth status, and it is closely associated with crop productivity. In trellis-trained orchards, severe branch overlap and persistent background interference make it difficult to delineate single-plant observation boundaries, while radiometric drift across multi-temporal observations further reduces feature comparability. To address these issues, this study proposes a dynamic ROI-constrained framework for single-plant LAI estimation from UAV multispectral imagery. First, relative radiometric calibration based on pseudo-invariant features was applied to reduce inter-date spectral bias and establish a unified radiometric baseline for multi-temporal analysis. Second, a dynamic ROI extraction algorithm was developed using dual constraints from the spatial distance percentile (P) and the local spectral purity threshold (Q). Parameter ablation results showed that the P90_Q70 combination achieved the best balance between retaining core canopy information and suppressing background noise. Compared with the best fixed-radius strategy (3.00 m), this scheme improved R 2 from 0.700 to 0.766 and reduced RMSE to 0.122. On this basis, spectral vegetation indices and near-infrared texture features were integrated to construct a multidimensional feature set. The gradient boosting decision tree (GBDT) model achieved the best predictive performance, with R 2 = 0.772, RMSE = 0.120, and RPD = 2.094, and was further used to generate high-resolution LAI maps of the orchard. These results indicate that the combination of dynamic boundary constraints and spectral purity screening can improve the robustness of single-plant feature extraction in trellis-trained kiwifruit orchards and provide a practical reference for quantitative remote sensing of other densely closed orchard systems.","叶面积指数（LAI）是表征果园冠层结构与生长状态的关键生物物理参数，与作物生产力密切相关。在棚架式栽培果园中，严重的枝条重叠和持续的背景干扰使得单株观测边界难以界定，而多时相观测间的辐射漂移进一步降低了特征可比性。为解决上述问题，本研究提出了一种基于无人机多光谱影像的单株LAI估算动态ROI约束框架。首先，采用基于伪不变特征的相对辐射校正以减小日期间的光谱偏差，为多时相分析建立统一的辐射基准。其次，利用空间距离百分位数（P）和局部光谱纯度阈值（Q）的双重约束，开发了动态ROI提取算法。参数消融结果表明，P90_Q70组合在保留核心冠层信息与抑制背景噪声之间取得了最佳平衡。与最优固定半径策略（3.00 m）相比，该方案将R²从0.700提升至0.766，并将RMSE降至0.122。在此基础上，融合光谱植被指数与近红外纹理特征构建多维特征集。梯度提升决策树（GBDT）模型取得了最佳预测性能，R²=0.772，RMSE=0.120，RPD=2.094，并进一步用于生成果园高分辨率LAI分布图。上述结果表明，动态边界约束与光谱纯度筛选相结合可提高棚架式猕猴桃果园单株特征提取的鲁棒性，并为其他密集郁闭果园系统的定量遥感提供实践参考。",79,{"impact":104,"substance":17,"depth":16,"authority":18,"freshness":138,"relevant":19,"comment":222},"提出动态ROI约束与辐射校正结合的无人机多光谱单株LAI估测框架，方法新颖、指标提升明确，对密植果园定量遥感有实用参考价值。",[224],{"name":59,"url":217},[24,25,26,226,227],"猕猴桃","叶面积指数",[229,230],"叶面积指数 农业遥感 智慧农业 无人机","叶面积指数 农业遥感","叶面积指数农业遥感智慧农业无人机-2617","10.1016\u002Fj.compag.2026.112437",{"doi":232,"openalex_id":234,"authors":235,"venue":59,"cited_by_count":33,"oa_url":217,"card":258,"direction":91,"ingested_from":48},"W7213243341",[236,238,240,242,245,247,249,251,253,256],{"name":237,"orcid":8},"Wenjie Li",{"name":239,"orcid":8},"Hongen Liu",{"name":241,"orcid":8},"Linghuan Ouyang",{"name":243,"orcid":244},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":246,"orcid":8},"Tianqi Lv",{"name":248,"orcid":8},"Jiali Li",{"name":250,"orcid":8},"Xintao Lin",{"name":252,"orcid":8},"Chen Yao",{"name":254,"orcid":255},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":257,"orcid":8},"Jianping Qian",{"tldr":259,"method":260,"finding":261,"direction":91,"opportunity":262},"提出动态ROI约束框架，用无人机多光谱影像估算棚架猕猴桃单株LAI。","伪不变特征辐射校正、动态ROI提取（P90_Q70）、植被指数与纹理特征、GBD","动态ROI优于固定半径，GBDT最优（R²=0.772，RMSE=0.120，RPD=2.094）。","可迁移至其他密植果园，探索多时相辐射校正与动态边界约束的普适性及轻量化模型。","2026-09-16T23:30:02.136941Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":8,"source_name":59,"source_url":267,"published_at":134,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":270,"score_detail":271,"sources":273,"tags":275,"search_phrases":277,"slug":279,"view_count":33,"doi":280,"paper":281,"created_at":304},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":104,"substance":137,"depth":106,"authority":18,"freshness":12,"relevant":19,"comment":272},"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。",[274],{"name":59,"url":267},[24,25,69,26,276],"地膜回收",[278,146],"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":280,"openalex_id":282,"authors":283,"venue":59,"cited_by_count":33,"oa_url":8,"card":299,"direction":91,"ingested_from":48},"W7213282871",[284,286,288,290,293,296],{"name":285,"orcid":8},"Penglei Yan",{"name":287,"orcid":8},"Sumin Lv",{"name":289,"orcid":8},"Xuan Li",{"name":291,"orcid":292},"Jianan Chi","https:\u002F\u002Forcid.org\u002F0009-0004-2943-1565",{"name":294,"orcid":295},"Xiao Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0111-8089",{"name":297,"orcid":298},"Nannan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-2956-8815",{"tldr":300,"method":301,"finding":302,"direction":91,"opportunity":303},"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"]