[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2996":3,"related-2996":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},2996,"基于高光谱遥感和跨区域迁移学习的冬小麦叶绿素含量评估","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686128266574430720","甘肃农业大学李怡悦等与中国农业科学院作物科学研究所肖永贵、孟亚雄研究员团队合作，针对高光谱遥感跨区域叶绿素评估中因\"域偏移\"导致的模型泛化性能下降问题，提出一种稳健自适应迁移学习框架（RATL），通过自适应特征选择、特征权重调整与域适应训练3模块协同实现跨区域知识有效迁移。基于2023—2024年冬小麦灌浆后期新乡与周口两地共1491份冠层高光谱与叶片SPAD数据，设置4种场景比较RATL与直接迁移、迁移成分分析和相关对齐之间的精度。",null,"智慧农业(中英文)2026,8(4):47-59","2026-09-16T13:43:00Z","论文",10,false,76,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,14,6,1,"方法新颖、数据扎实的跨区域叶绿素遥感评估研究，对智慧农业遥感应用有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","遥感","冬小麦","迁移学习","叶绿素监测",[32,33],"冬小麦 叶绿素 高光谱遥感","跨区域迁移学习 冬小麦","冬小麦叶绿素高光谱遥感-2996",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"提出RATL迁移学习框架，解决高光谱遥感跨区域冬小麦叶绿素评估的域偏移问题。","基于新乡与周口1491份冠层高光谱与SPAD数据，对比RATL与直接迁移、TCA","RATL通过自适应特征选择、权重调整与域适应训练，有效提升跨区域叶绿素评估泛化精度。","农业遥感与作物表型","可探索多作物、多生育期及不同传感器间的迁移学习，构建通用跨域叶绿素评估模型。","agent","2026-09-20T00:03:07.685193Z",{"total":20,"page":21,"page_size":20,"items":47},[48,98,135,173,202,229],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":53,"content":8,"source_name":54,"source_url":51,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":60,"tags":62,"search_phrases":65,"slug":68,"view_count":35,"doi":69,"paper":70,"created_at":97},2527,"Extracting Summer-Harvested Crops in the Baojixia Irrigation District Using CycleGAN and Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183155","Remote sensing-based mapping of crop planting structures in irrigation districts plays a vital role in forecasting regional production and optimizing water resource allocation. However, optical satellite imagery is limited by insufficient spatial resolution when applied to fragmented farmland landscapes, while unmanned aerial vehicle (UAV) imagery is limited by spatial coverage and high data processing costs. To address these bottlenecks, this study proposed a cross-scale collaborative extraction framework utilizing the CycleGAN network and transfer learning to map summer harvest crops (winter wheat and rapeseed) in the Baojixia Irrigation District for the year 2023. First, multiple semantic segmentation models—including U-Net, DeepLabv3+, SegFormer, and HRNet—were evaluated on a joint satellite–UAV dataset, with U-Net selected as the optimal backbone. Next, CycleGAN was introduced to perform style translation from the UAV domain to the satellite domain. This step generated high-fidelity, satellite-like images that preserve UAV-derived high-resolution spatial details, which were subsequently used to pre-train the U-Net backbone, significantly reducing the labor of manual annotation. Finally, the model was fine-tuned with real satellite images to achieve precise crop extraction. Results indicated that this framework accelerates model convergence and improves segmentation accuracy. The proposed method achieved an mIoU of 85.09%, an mPA (Recall) of 91.63%, a Precision of 91.97%, an Accuracy of 93.57%, and an F1-Score of 91.80%, outperforming the baseline U-Net model by 2.98%, 2.00%, 1.66%, 1.52%, and 1.83%, respectively. By successfully transferring high-resolution prior knowledge into the satellite feature space, this study provides a cost-effective and highly accurate solution for crop identification in complex agricultural landscapes, breaking the spatial limitations of UAV remote sensing.","基于遥感的灌区作物种植结构制图对于区域产量预测与水资源优化配置具有重要作用。然而，光学卫星影像在应用于破碎化农田景观时受限于空间分辨率不足，而无人机（UAV）影像则受限于空间覆盖范围和数据获取成本高。为解决这些瓶颈问题，本研究提出了一种利用CycleGAN网络和迁移学习的跨尺度协同提取框架，用于绘制2023年宝鸡峡灌区夏收作物（冬小麦和油菜）分布图。首先，在卫星—无人机联合数据集上评估了多种语义分割模型——包括U-Net、DeepLabv3+、SegFormer和HRNet——并选择U-Net作为最优主干网络。其次，引入CycleGAN进行从无人机域到卫星域的风格转换。该步骤生成了高保真、类卫星影像，同时保留了无人机来源的高分辨率空间细节，随后用于预训练U-Net主干网络，显著减少了人工标注的工作量。最后，利用真实卫星影像对模型进行微调，以实现精确的作物提取。结果表明，该框架加速了模型收敛并提高了分割精度。所提方法达到了85.09%的mIoU、91.63%的mPA（召回率）、91.97%的精确率、93.57%的总体精度和91.80%的F1分数，分别优于基线U-Net模型2.98%、2.00%、1.66%、1.52%和1.83%。通过成功将高分辨率先验知识迁移至卫星特征空间，本研究为复杂农业景观中的作物识别提供了一种经济高效且高精度的解决方案，突破了无人机遥感的空间局限性。","Remote Sensing","2026-09-14T00:00:00Z",78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":58,"relevant":21,"comment":59},8,"提出CycleGAN跨尺度协同框架，将无人机高分辨率先验迁移至卫星影像，实现灌溉区夏收作物高精度提取，方法新颖、指标扎实，对农业遥感监测有实用价值。",[61],{"name":54,"url":51},[26,27,63,29,64],"作物识别","灌溉区",[66,67],"作物识别 智慧农业 迁移学习 灌溉区","作物识别 智慧农业","作物识别智慧农业迁移学习灌溉区-2527","10.3390\u002Frs18183155",{"doi":69,"openalex_id":71,"authors":72,"venue":54,"cited_by_count":35,"oa_url":51,"card":91,"direction":42,"ingested_from":96},"W7212518427",[73,75,77,79,81,83,86,88],{"name":74,"orcid":8},"Zili Chen",{"name":76,"orcid":8},"Zhilong Gao",{"name":78,"orcid":8},"Zefeng Jia",{"name":80,"orcid":8},"Pengjie Pan",{"name":82,"orcid":8},"Wen Gao",{"name":84,"orcid":85},"Jun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6972-6828",{"name":87,"orcid":8},"Zijie Niu",{"name":89,"orcid":90},"Dongyan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3509-7482",{"tldr":92,"method":93,"finding":94,"direction":42,"opportunity":95},"用CycleGAN和迁移学习融合无人机与卫星影像，提取宝鸡峡灌区夏收作物。","CycleGAN风格迁移+U-Net迁移学习，联合卫星-无人机数据集。","框架提升分割精度，mIoU达85.09%，优于基线U-Net。","可探索跨尺度迁移学习在更多作物和区域的应用，降低标注成本。","openalex","2026-09-15T23:30:20.433643Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":104,"source_url":101,"published_at":105,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":106,"score_detail":107,"sources":110,"tags":112,"search_phrases":115,"slug":118,"view_count":35,"doi":119,"paper":120,"created_at":134},2163,"A Hybrid Transfer Learning Framework for Seasonal Classification of Satellite Images","https:\u002F\u002Fdoi.org\u002F10.29109\u002Fgujsc.1958797","Seasonal classification from satellite imagery is an important remote sensing task for monitoring vegetation dynamics, agricultural processes, environmental change, and climate-related spatial patterns. However, developing robust deep learning models for this task is challenging due to limited labeled data, regional variability, and the computational cost of training large-scale networks from scratch. This study proposes a hybrid transfer learning-based framework for seasonal classification using satellite images collected from 81 provinces of Türkiye. A custom dataset was constructed from monthly satellite images, and eight pretrained deep learning architectures were evaluated as feature extractors. The extracted deep representations were classified using seven machine learning algorithms. The experimental results showed that both the choice of pretrained feature extractor and the classifier affect seasonal classification performance. Among models, ConvNeXt combined with the Multi-Layer Perceptron achieved the best performance. Based on the comparative analysis, ConvNeXt, Vision Transformer, and Swin Transformer were selected as the top three feature extractors, while the Multi-Layer Perceptron was selected as the final classifier. The proposed framework provides an effective and computationally practical approach for seasonal classification and offers a promising basis for future environmental monitoring and agricultural remote sensing applications.","基于卫星影像的季节分类是一项重要的遥感任务，可用于监测植被动态、农业过程、环境变化以及与气候相关的空间格局。然而，由于标注数据有限、区域差异以及从零开始训练大规模网络的计算成本，开发用于该任务的稳健深度学习模型具有挑战性。本研究提出了一种基于混合迁移学习的框架，利用从土耳其81个省份收集的卫星影像进行季节分类。研究构建了一个由月度卫星影像组成的自定义数据集，并评估了八种预训练深度学习架构作为特征提取器的效果。提取出的深层表示使用七种机器学习算法进行分类。实验结果表明，预训练特征提取器和分类器的选择均会影响季节分类性能。在各类模型中，ConvNeXt结合多层感知机取得了最佳性能。基于对比分析，ConvNeXt、Vision Transformer和Swin Transformer被选为排名前三的特征提取器，而多层感知机被选为最终分类器。所提出的框架为季节分类提供了一种有效且计算上实用的方法，并为未来环境监测和农业遥感应用提供了有前景的基础。","Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji","2026-09-10T00:00:00Z",66,{"impact":108,"substance":18,"depth":16,"authority":108,"freshness":58,"relevant":21,"comment":109},12,"基于土耳其81省卫星影像的迁移学习季节分类框架，方法对比扎实、结论可靠，对农业遥感监测有参考价值，但属学术论文且非国内应用，影响力有限。",[111],{"name":104,"url":101},[26,113,27,114,29],"农业人工智能","作物监测",[116,117],"农业人工智能 作物监测 智慧农业 迁移学习","农业人工智能 作物监测","农业人工智能作物监测智慧农业迁移学习-2163","10.29109\u002Fgujsc.1958797",{"doi":119,"openalex_id":121,"authors":122,"venue":104,"cited_by_count":35,"oa_url":101,"card":129,"direction":42,"ingested_from":96},"W7212161719",[123,126],{"name":124,"orcid":125},"Eyyüp YILDIZ","https:\u002F\u002Forcid.org\u002F0000-0002-7051-3368",{"name":127,"orcid":128},"Özge Aslan Yıldız","https:\u002F\u002Forcid.org\u002F0000-0001-7688-9326",{"tldr":130,"method":131,"finding":132,"direction":42,"opportunity":133},"提出混合迁移学习框架，用预训练模型提取特征并结合机器学习分类器实现卫星图像季节分类。","基于土耳其81省月度卫星图像构建数据集，评估8种预训练模型和7种分类器。","ConvNeXt结合多层感知机表现最佳，特征提取器和分类器选择均影响性能。","可探索该框架在作物物候监测、跨区域迁移及多时相农业遥感中的泛化能力。","2026-09-11T23:30:29.840009Z",{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":8,"source_name":54,"source_url":138,"published_at":141,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":142,"score_detail":143,"sources":147,"tags":149,"search_phrases":151,"slug":154,"view_count":35,"doi":155,"paper":156,"created_at":172},1996,"An Interpretable BO-TCBDA Deep Learning Framework for Winter Wheat Yield Estimation Using Multi-Source Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183061","Reliable crop yield estimation is fundamental to food security and efficient agricultural management. However, current deep learning models still face limitations in selecting and integrating multi-source features, and their high predictive accuracy is often accompanied by limited interpretability. This study introduces a Bayesian Optimization–Temporal Convolutional Network–Bidirectional Long Short-Term Memory–Dual Attention (BO-TCBDA) deep learning framework for winter wheat yield estimation. Using Henan Province, China, as the study area, county-level winter wheat yield from 2013 to 2022 was estimated using the Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and climate data. The proposed model was compared with five commonly used machine learning and deep learning models. BO-TCBDA achieved the best performance, with an R2 of 0.823 and an RMSE of 561.26 kg\u002Fha. SIF improved the predictive performance of all models, with statistically significant gains observed in the deep learning models. The dual-attention mechanism provided interpretable insights by revealing relatively balanced contributions among the input features and highlighting the grain-filling stage through temporal attention. Furthermore, SHAP-based cross-validation analysis identified T12, corresponding to the latter part of the jointing stage, as the period with the highest contribution to yield prediction. The model also achieved an R2 of approximately 0.80 about 25 days before harvest. Overall, BO-TCBDA provides an accurate and interpretable approach for county-level winter wheat yield estimation and supports regional food security assessments and precision agriculture.","可靠的作物产量估算对于粮食安全和高效农业管理至关重要。然而，当前的深度学习模型在多源特征的选择与整合方面仍存在局限，且高预测精度往往伴随着有限的可解释性。本研究提出了一种基于贝叶斯优化–时间卷积网络–双向长短期记忆–双重注意力（BO-TCBDA）的深度学习框架，用于冬小麦产量估算。以中国河南省为研究区域，利用增强型植被指数（EVI）、叶面积指数（LAI）、太阳诱导叶绿素荧光（SIF）及气候数据，对2013至2022年县级冬小麦产量进行了估算。将所提模型与五种常用的机器学习和深度学习模型进行了比较。BO-TCBDA取得了最佳性能，其决定系数（R²）为0.823，均方根误差（RMSE）为561.26千克\u002F公顷。SIF提升了所有模型的预测性能，其中在深度学习模型中观察到了统计学上显著的增益。双重注意力机制通过揭示输入特征间相对均衡的贡献，并借助时间注意力突出灌浆期，提供了可解释性的见解。此外，基于SHAP的交叉验证分析识别出T12时段（对应拔节期后期）对产量预测的贡献最大。该模型在收获前约25天时，R²亦达到约0.80。总体而言，BO-TCBDA为县级冬小麦产量估算提供了一种准确且可解释的方法，并支持区域粮食安全评估和精准农业实践。","2026-09-08T00:00:00Z",73,{"impact":18,"substance":17,"depth":18,"authority":144,"freshness":145,"relevant":21,"comment":146},13,2,"提出可解释的深度学习框架，结合多源遥感数据提升冬小麦估产精度与可解释性，对精准农业有实质贡献。",[148],{"name":54,"url":138},[26,113,27,150,28],"产量估算",[152,153],"农业人工智能 产量估算 智慧农业 冬小麦","农业人工智能 产量估算","农业人工智能产量估算智慧农业冬小麦-1996","10.3390\u002Frs18183061",{"doi":155,"openalex_id":157,"authors":158,"venue":54,"cited_by_count":35,"oa_url":138,"card":166,"direction":42,"ingested_from":96},"W7211938917",[159,161,163],{"name":160,"orcid":8},"Anqi Xue",{"name":162,"orcid":8},"Shufang Tian",{"name":164,"orcid":165},"Tingyan Fu","https:\u002F\u002Forcid.org\u002F0000-0003-4207-4211",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"提出BO-TCBDA深度学习框架，融合多源遥感数据估算冬小麦产量，兼具高精度与可解释性。","贝叶斯优化、TCN、BiLSTM、双注意力机制，结合EVI、LAI、SIF及气候","BO-TCBDA性能最优（R²=0.823），SIF提升预测，拔节后期贡献最大，收获前25天可预测。","农业人工智能与决策模型","可探索将双注意力与SHAP结合用于其他作物或区域，或开发实时预警系统，提升模型泛化与实用性。","2026-09-09T23:30:22.411579Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":8,"content":8,"source_name":178,"source_url":8,"published_at":179,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":180,"score_detail":181,"sources":184,"tags":186,"search_phrases":190,"slug":193,"view_count":35,"doi":8,"paper":194,"created_at":201},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。","MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z",74,{"impact":182,"substance":17,"depth":18,"authority":144,"freshness":20,"relevant":21,"comment":183},15,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[185],{"name":178,"url":176},[26,187,27,188,189],"精准农业","作物表型","春小麦",[191,192],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",{"doi":8,"openalex_id":8,"authors":195,"venue":8,"cited_by_count":35,"oa_url":8,"card":196,"direction":42,"ingested_from":44},[],{"tldr":197,"method":198,"finding":199,"direction":42,"opportunity":200},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","2026-09-20T00:03:08.168753Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":8,"content":8,"source_name":207,"source_url":8,"published_at":208,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":209,"score_detail":210,"sources":212,"tags":214,"search_phrases":217,"slug":220,"view_count":35,"doi":8,"paper":221,"created_at":228},2999,"基于改进DeepLabv3+的高标准农田田间道路提取与结构指标量化框架","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1986","沈阳农业大学刘永生等开发了基于MobileNetV2改进DeepLabv3+的高标准农田田间道路提取轻量化框架，集成Normalization-based Attention Module与Content-Aware ReAssembly of FEatures。三次随机种子训练平均mIoU 93.34%、mPA 96.75%、精度98.90%，模型参数6.14M、推理速度17.04 FPS；沥青、混凝土、砾石道路宽度预测R²分别为0.650、0.486、0.662，宽度MAE 0.130\u002F0.140\u002F0.100 m。第二验证区域连通性指数从0.4682提升至0.4795，支持高标准农田田间道路高效、可量化、可追溯的验收检查。","MDPI Agriculture 16(18):1986","2026-09-16T00:00:00Z",77,{"impact":16,"substance":17,"depth":18,"authority":144,"freshness":58,"relevant":21,"comment":211},"方法有创新、指标详实，对高标准农田道路验收有实用价值，但属细分技术论文，影响面有限。",[213],{"name":207,"url":205},[26,113,215,27,216],"高标准农田","田间道路",[218,219],"沈阳农业大学 高标准农田 道路提取","DeepLabv3 田间道路 遥感","沈阳农业大学高标准农田道路提取-2999",{"doi":8,"openalex_id":8,"authors":222,"venue":8,"cited_by_count":35,"oa_url":8,"card":223,"direction":42,"ingested_from":44},[],{"tldr":224,"method":225,"finding":226,"direction":42,"opportunity":227},"提出改进DeepLabv3+轻量框架，提取高标准农田田间道路并量化结构指标。","MobileNetV2+NAM+CARAFE改进DeepLabv3+，多区域遥感","mIoU 93.34%，道路宽度预测R²最高0.662，连通性指数提升至0.4795。","可拓展至多作物、多地形道路提取，并结合时序遥感实现道路损毁动态监测。","2026-09-20T00:03:08.023498Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":8,"content":8,"source_name":234,"source_url":8,"published_at":235,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":239,"tags":241,"search_phrases":244,"slug":247,"view_count":35,"doi":8,"paper":248,"created_at":255},2998,"物候阶段渐进式多源数据融合的县域冬小麦产量预测","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-09-18\u002Fdoc-inisheny8322115.shtml","西安财经大学王毅等联合江苏大学张立元副教授、西安理工大学西北旱区生态水利国家重点实验室、中国科学院重庆绿色智能技术研究院团队，以河南省100个冬小麦主产县为研究区融合2013—2022年遥感变量、气象变量和日光诱导叶绿素荧光光合变量，构建覆盖冬小麦分蘖期至成熟期的多源时序特征集。提出物候阶段渐进式多源数据融合方法明确不同物候阶段信息累积对县域冬小麦估产性能的影响。","智慧农业(中英文)2026,8(4):70-84","2026-09-18T00:00:00Z",80,{"impact":18,"substance":17,"depth":18,"authority":19,"freshness":58,"relevant":21,"comment":238},"以河南100个主产县2013—2022年遥感、气象与SIF数据构建物候阶段渐进式融合估产方法，数据规模与方法新颖性突出，对县域粮食产量预测有参考价值。",[240],{"name":234,"url":232},[26,113,242,28,243],"遥感估产","多源数据融合",[245,246],"河南 冬小麦 遥感估产","物候阶段 多源数据融合 产量预测","河南冬小麦遥感估产-2998",{"doi":8,"openalex_id":8,"authors":249,"venue":8,"cited_by_count":35,"oa_url":8,"card":250,"direction":42,"ingested_from":44},[],{"tldr":251,"method":252,"finding":253,"direction":42,"opportunity":254},"融合遥感、气象与SIF数据，提出物候阶段渐进式融合方法预测河南县域冬小麦产量。","2013—2022年河南100县遥感、气象、SIF多源时序特征，按物候阶段渐进融","明确不同物候阶段信息累积对县域冬小麦估产性能的影响，提升预测精度。","可探索物候自适应加权与深度学习融合，并迁移至其他作物及极端气候情景下的县域估产。","2026-09-20T00:03:07.932461Z"]