[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3179":3,"related-3179":56},{"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":24,"tags":26,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":55},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》",null,"Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,14,15,12,9,1,"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[25],{"name":10,"url":6},[27,28,29,30],"农业人工智能","棉花","遥感","作物分类",[32,33],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179",0,"10.1007\u002Fs12524-026-02541-8",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":48,"direction":52,"ingested_from":54},"W7213944040",[40,42,44,46],{"name":41,"orcid":9},"G. Suresh",{"name":43,"orcid":9},"G. Bhuvaneswari",{"name":45,"orcid":9},"G. Manikandan",{"name":47,"orcid":9},"M. Robinson Joel",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","农业遥感与作物表型","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","openalex","2026-09-22T23:30:23.811658Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,121,172,201,226,272],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":72,"tags":74,"search_phrases":77,"slug":80,"view_count":35,"doi":81,"paper":82,"created_at":120},2786,"SAF-CropNet: Spatially adaptive fusion of SAR and optical imagery for semantic segmentation in operational cropland mapping across regions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isprsjprs.2026.08.029","Accurate cropland mapping from remote sensing imagery is essential for agricultural monitoring, food security assessment, and land resource management. Although optical and synthetic aperture radar (SAR) observations provide complementary information for cropland extraction, their local contributions vary across imaging conditions, land-cover backgrounds, parcel morphologies, and regional agricultural systems. This variability limits direct concatenation and static fusion, as modality-specific errors may propagate into segmentation outputs in the absence of spatially adaptive weighting. This study proposes SAF-CropNet, a spatially adaptive SAR–optical fusion framework for binary cropland semantic segmentation. Specifically, SAF-CropNet first extracts modality-specific SAR structural features and optical spectral-textural features through separate encoder branches. The Paired Reciprocal Inter-modal Selective Modulator enhances complementary SAR–optical interactions, while the Contextual State-space Modeling module captures long-range parcel organization. A spatially adaptive fusion head then estimates per-pixel contribution weights for SAR, optical, and contextual features before decoding. Evaluation was conducted on a seven-area SAR–optical benchmark spanning China, Germany, and France. The benchmark integrates GF1\u002FGF3 and Sentinel-1\u002F2 imagery with reference labels derived from field surveys and RapidCrops products and covers fragmented smallholder systems, water-rich and peri-urban mosaics, and large mechanized cropland. Within-region five-fold validation shows that SAF-CropNet achieves an average F1 score of 0.8550 and an mIoU of 0.8068, outperforming the evaluated segmentation baselines while remaining lightweight, with 3.38 M parameters and 6.21 GFLOPs for 256 × 256 SAR–optical inputs. In leave-one-area-out transfer experiments, stratified few-shot adaptation increased the average F1 score from 0.4986 under zero-shot transfer to 0.7698. Grouped cross-continental and cross-sensor experiments further showed that direct transfer remains constrained under compound shifts in sensor characteristics, spatial resolution, landscape structure, and reference-label conventions. These findings indicate that SAF-CropNet combines strong within-region segmentation accuracy and computational efficiency with substantial gains from limited target-domain adaptation, while direct zero-shot generalization under severe compound domain shifts remains limited.","从遥感影像中准确提取耕地信息对农业监测、粮食安全评估和土地资源管理至关重要。尽管光学与合成孔径雷达（SAR）观测为耕地提取提供了互补信息，但其局部贡献会随成像条件、地表覆盖背景、地块形态和区域农业系统的不同而变化。这种变异性限制了直接拼接和静态融合的效果，因为在缺乏空间自适应加权的情况下，模态特有的误差可能传播至分割输出中。本研究提出SAF-CropNet，一种用于二分类耕地语义分割的空间自适应SAR–光学融合框架。具体而言，SAF-CropNet首先通过独立的编码器分支提取模态特有的SAR结构特征和光学光谱-纹理特征。成对互反模态间选择性调制器增强SAR与光学之间的互补交互，而上下文状态空间建模模块则捕获长距离地块组织信息。随后，空间自适应融合头在解码前估计SAR、光学和上下文特征的逐像素贡献权重。评估在一个涵盖中国、德国和法国的七区域SAR–光学基准上进行。该基准整合了GF1\u002FGF3和Sentinel-1\u002F2影像，参考标签来源于实地调查和RapidCrops产品，覆盖破碎化小农系统、富水与城郊镶嵌景观以及大规模机械化耕地。区域内五折验证表明，SAF-CropNet平均F1分数达到0.8550，mIoU为0.8068，优于所评估的分割基线，同时保持轻量级，对于256 × 256的SAR–光学输入仅需3.38 M参数和6.21 GFLOPs。在留一区域迁移实验中，分层少样本自适应将平均F1分数从零样本迁移下的0.4986提升至0.7698。分组跨大陆和跨传感器实验进一步表明，在传感器特性、空间分辨率、景观结构和参考标签惯例的复合变化下，直接迁移仍然受到限制。这些发现表明，SAF-CropNet兼具较强的区域内分割精度和计算效率，并能从有限的目标域自适应中获得显著增益，而在严重复合域偏移下的直接零样本泛化能力仍然有限。","ISPRS Journal of Photogrammetry and Remote Sensing","2026-09-17T00:00:00Z",82,{"impact":69,"substance":70,"depth":69,"authority":18,"freshness":13,"relevant":22,"comment":71},18,22,"提出空间自适应SAR-光学融合分割框架，跨七区域基准验证，兼顾精度与轻量化，对耕地遥感制图有实质方法贡献。",[73],{"name":65,"url":62},[27,29,75,30,76],"耕地监测","SAR",[78,79],"农业人工智能 作物分类 耕地监测 遥感","农业人工智能 作物分类","农业人工智能作物分类耕地监测遥感-2786","10.1016\u002Fj.isprsjprs.2026.08.029",{"doi":81,"openalex_id":83,"authors":84,"venue":65,"cited_by_count":35,"oa_url":62,"card":115,"direction":52,"ingested_from":54},"W7213463520",[85,88,90,93,96,99,102,104,106,108,110,112],{"name":86,"orcid":87},"minghui chang","https:\u002F\u002Forcid.org\u002F0009-0008-7954-5308",{"name":89,"orcid":9},"Shuaifeng Peng",{"name":91,"orcid":92},"Tao Xu","https:\u002F\u002Forcid.org\u002F0000-0002-7855-4199",{"name":94,"orcid":95},"Yi Yuan","https:\u002F\u002Forcid.org\u002F0009-0006-9481-4938",{"name":97,"orcid":98},"洋一 馬目","https:\u002F\u002Forcid.org\u002F0000-0003-0226-5558",{"name":100,"orcid":101},"Jie Bai","https:\u002F\u002Forcid.org\u002F0000-0003-2426-2358",{"name":103,"orcid":9},"Fugui Luo",{"name":105,"orcid":9},"Jingyu Zhang",{"name":107,"orcid":9},"Xiaoyu Xiao",{"name":109,"orcid":9},"Yu Mu",{"name":111,"orcid":9},"Yong Wang",{"name":113,"orcid":114},"Shihua Li","https:\u002F\u002Forcid.org\u002F0000-0003-4807-5012",{"tldr":116,"method":117,"finding":118,"direction":52,"opportunity":119},"提出SAF-CropNet，用空间自适应融合SAR与光学影像做跨区域耕地语义分割。","双分支编码器提取SAR结构\u002F光学纹理特征，结合互模态调制与状态空间建模，在七区域","区域内F1达0.8550且轻量；零样本迁移F1仅0.4986，少样本适配可升至0.7698。","复合域偏移下零样本泛化差，可研究跨传感器、跨区域的无监督域自适应融合分割方法。","2026-09-17T23:30:34.424579Z",{"id":122,"title":123,"url":124,"summary":125,"summary_zh":9,"content":9,"source_name":126,"source_url":124,"published_at":127,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":133,"tags":135,"search_phrases":138,"slug":141,"view_count":35,"doi":142,"paper":143,"created_at":171},1208,"Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112335","Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-08-25T00:00:00Z",62,{"impact":20,"substance":69,"depth":130,"authority":18,"freshness":131,"relevant":22,"comment":132},16,2,"基于无人机影像的残膜污染检测与卷曲度预测研究，方法新颖，对农业环保有实际价值，但时效性较低。",[134],{"name":126,"url":124},[136,27,28,29,137],"智慧农业","残膜污染",[139,140],"农业人工智能 智慧农业 残膜污染 棉花","农业人工智能 智慧农业","农业人工智能智慧农业残膜污染棉花-1208","10.1016\u002Fj.compag.2026.112335",{"doi":142,"openalex_id":144,"authors":145,"venue":126,"cited_by_count":35,"oa_url":9,"card":166,"direction":52,"ingested_from":54},"W7204210197",[146,149,151,154,157,160,163],{"name":147,"orcid":148},"Zhiqiang Zhai","https:\u002F\u002Forcid.org\u002F0000-0002-5242-9779",{"name":150,"orcid":9},"Lupeng Miao",{"name":152,"orcid":153},"Huting Wang","https:\u002F\u002Forcid.org\u002F0009-0008-3605-2685",{"name":155,"orcid":156},"Jiangbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7856-9131",{"name":158,"orcid":159},"Yue Chen","https:\u002F\u002Forcid.org\u002F0009-0004-5110-6400",{"name":161,"orcid":162},"Songxin Ye","https:\u002F\u002Forcid.org\u002F0000-0002-8895-8525",{"name":164,"orcid":165},"Ruoyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0370-3249",{"tldr":167,"method":168,"finding":169,"direction":52,"opportunity":170},"用无人机图像修复残膜遮挡并预测卷曲程度，评估棉田残膜污染。","无人机图像、遮挡修复、卷曲度预测模型。","成功修复遮挡并预测卷曲度，支持残膜污染评估。","可扩展至不同作物、不同残膜类型，结合深度学习提升修复精度和预测鲁棒性。","2026-09-01T04:03:02.336224Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":9,"content":9,"source_name":177,"source_url":9,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":183,"tags":185,"search_phrases":188,"slug":191,"view_count":35,"doi":9,"paper":192,"created_at":200},3250,"农机化研究2026(11)：基于多源感知与语义分割的果树行识别与路径跟踪控制——郑州工业应用技术学院许洋洋等 DeepLabv3+ F1达0.94","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F166268.html","农机化研究2026年第11期发表郑州工业应用技术学院许洋洋、柴亚珂、王莹、牛瑞利团队论文：针对果园中裸露土壤、遮蔽物干扰和冠层阴影等因素导致导航路径难以稳定提取的问题，提出融合多源感知与语义分割的自走式植保机果树识别与路径控制方法。首先利用无人机获取多光谱影像并生成正射影像（DOM）与数字表面模型（DSM），计算归一化差异绿度指数（NDGI），增强果树冠层与背景的可分性；然后采用U-Net\u002FDeepLabv3+语义分割网络在保留空间上下文信息的同时实现果树行\u002F背景端到端分割。田间对比试验结果表明，DeepLabv3+整体分割F1可达0.94，每样区生成导航线断裂1.2次，且转向工况下横向误差RMSE为0.11 m，显著优于传统阈值分割和SVM方法。","农机化研究","2026-09-22T00:00:00Z",79,{"impact":130,"substance":70,"depth":69,"authority":181,"freshness":13,"relevant":22,"comment":182},13,"多源遥感与语义分割结合的果园导航研究，F1达0.94且横向误差0.11米，方法新颖、数据扎实，具备行业参考价值。",[184],{"name":177,"url":175},[136,27,186,29,187],"农机导航","果园植保",[189,190],"郑州工业应用技术学院 果树行识别","DeepLabv3 果园导航 路径跟踪","郑州工业应用技术学院果树行识别-3250",{"doi":9,"openalex_id":9,"authors":193,"venue":9,"cited_by_count":35,"oa_url":9,"card":194,"direction":52,"ingested_from":199},[],{"tldr":195,"method":196,"finding":197,"direction":52,"opportunity":198},"提出融合无人机多光谱与语义分割的果树行识别与路径跟踪方法，实现植保机自主导航。","无人机多光谱DOM\u002FDSM与NDGI，U-Net\u002FDeepLabv3+语义分割，","DeepLabv3+分割F1达0.94，导航线断裂1.2次，转向横向误差RMSE 0.11 m，优于","可探索多光谱与DSM特征融合的轻量化分割模型，并迁移至多树种、多季节果园导航。","agent","2026-09-23T00:04:33.496233Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":9,"content":9,"source_name":206,"source_url":9,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":207,"sources":209,"tags":211,"search_phrases":214,"slug":217,"view_count":35,"doi":9,"paper":218,"created_at":225},3246,"Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）","https:\u002F\u002Fwww.mdpi.com\u002F3043-1204\u002F1\u002F1\u002F4","MDPI AI Precis. Agric. 1(1) 4 发表系统综述：综述2019-2026年Scopus、Web of Science及补充检索文献，考察精准农业中的应用、多模态数据集成与决策支持。综述表明研究范式从孤立的制图与预测转向集成工作流，整合卫星与无人机影像、田间传感器、气象、土壤与管理记录。报告效益包括产量预测改善、压力与病害更早检测、土壤制图更精细、灌溉与投入品更精准。然而，产量、环境和经济效益仍依赖当地条件和运行验证。核心发现是：地理参考数据本身不能确保真正的地理空间AI；可靠工作流必须解决空间依赖性、尺度不匹配、可迁移性和不确定性。可解释性和融入农场运营对可执行推荐同样重要。采用仍受数据互操作性、连接性、可负担性、隐私和技术能力限制。","MDPI AI in Precision Agriculture",{"impact":69,"substance":70,"depth":69,"authority":18,"freshness":13,"relevant":22,"comment":208},"系统综述梳理2019-2026年GeoAI在精准农业的集成工作流与落地瓶颈，结论扎实、时效性强，对智慧农业技术路线有参考价值。",[210],{"name":206,"url":204},[136,27,29,212,213],"决策支持","多模态数据",[215,216],"GeoAI 精准农业 系统综述","地理空间人工智能 精准农业","GeoAI精准农业系统综述-3246",{"doi":9,"openalex_id":9,"authors":219,"venue":9,"cited_by_count":35,"oa_url":9,"card":220,"direction":52,"ingested_from":199},[],{"tldr":221,"method":222,"finding":223,"direction":52,"opportunity":224},"系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。","系统综述Scopus、Web of Science等文献，分析多模态数据集成与决","地理参考数据不等于地理空间AI，可靠工作流须解决空间依赖、尺度、可迁移性与不确定性。","可研究跨尺度空间依赖建模与可迁移性评估，提升模型在不同农场条件下的泛化能力。","2026-09-23T00:04:33.172821Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":237,"tags":239,"search_phrases":242,"slug":245,"view_count":35,"doi":246,"paper":247,"created_at":271},3176,"Relaxing the clear-sky assumption: Cloud-tolerant spatiotemporal fusion via mask-guided feature modulation and temporal-memory collaboration","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jag.2026.105571","The precise characterization of dynamic Earth surface processes relies on time-series remote sensing imagery with both high spatial fidelity and frequent temporal coverage. While spatiotemporal fusion (STF) bridges this resolution trade-off by integrating complementary observations, its practical application remains constrained by the idealized ”clear-sky assumption”. This requirement often favors the selection of temporally distant cloud-free acquisitions over temporally closer but cloud-contaminated observations, potentially reducing the relevance of reference information. To address this limitation, we propose CloudSTF, a robust framework designed to achieve high-fidelity reconstruction by effectively exploiting partially occluded observations. Our approach employs a Mask-guided Multi-scale Swin Transformer (M 2 ST) encoder to capture cloud spatial patterns and suppress noise propagation during feature extraction. This is coupled with a Cross-temporal Memory-guided Fusion (CTMF) module that adaptively integrates temporal trends with textural details retrieved from a spatio-temporal memory bank. To validate this paradigm, we introduce the Global Cloud-shrouded Regions (GCR-STF) benchmark, a geographically distributed dataset comprising 24 representative agricultural and urban sites across six continents. Extensive experiments demonstrate that CloudSTF consistently outperforms state-of-the-art methods. Additional assessments across varying cloud densities and diverse landscapes further demonstrate the robustness and applicability of CloudSTF under realistic cloud-contaminated conditions. These results suggest that CloudSTF provides a reliable solution for continuous Earth monitoring in realistic, cloud-prone environments. Source code and the GCR-STF benchmark are available at https:\u002F\u002Fgithub.com\u002FSichen-Lu\u002FCloudSTF .","精确刻画动态地球表面过程依赖于兼具高空间保真度与高频时间覆盖的时间序列遥感影像。尽管时空融合（STF）通过整合互补观测弥合了这一分辨率之间的权衡，但其实际应用仍受限于理想化的“晴空假设”。该要求往往倾向于选择时间上相距较远但无云的影像，而非时间上更近但受云污染的观测，这可能降低参考信息的相关性。为解决这一局限，我们提出了CloudSTF，一个旨在通过有效利用部分被遮挡的观测来实现高保真重建的稳健框架。我们的方法采用掩膜引导的多尺度Swin Transformer（M²ST）编码器来捕捉云的空间模式并抑制特征提取过程中的噪声传播。该编码器与跨时间记忆引导融合（CTMF）模块相结合，后者自适应地将时间趋势与从时空记忆库中检索到的纹理细节进行整合。为验证这一范式，我们引入了全球云覆盖区域（GCR-STF）基准数据集，这是一个地理分布广泛的数据集，涵盖六大洲24个代表性农业和城市站点。大量实验表明，CloudSTF始终优于现有最先进方法。在不同云密度和多样景观下的额外评估进一步证明了CloudSTF在真实云污染条件下的稳健性和适用性。这些结果表明，CloudSTF为真实且多云环境中的连续地球监测提供了可靠解决方案。源代码和GCR-STF基准数据集可在https:\u002F\u002Fgithub.com\u002FSichen-Lu\u002FCloudSTF获取。","International Journal of Applied Earth Observation and Geoinformation",80,{"impact":130,"substance":70,"depth":235,"authority":18,"freshness":21,"relevant":22,"comment":236},19,"提出抗云时空融合框架CloudSTF并发布全球云覆盖基准数据集，方法新颖、数据规模大，对多云地区农业遥感监测有实用价值。",[238],{"name":232,"url":229},[136,27,29,240,241],"时空融合","云污染",[243,244],"CloudSTF 时空融合","GCR-STF 云覆盖基准数据集","CloudSTF时空融合-3176","10.1016\u002Fj.jag.2026.105571",{"doi":246,"openalex_id":248,"authors":249,"venue":232,"cited_by_count":35,"oa_url":229,"card":266,"direction":52,"ingested_from":54},"W7213903535",[250,253,256,258,261,263],{"name":251,"orcid":252},"Sichen Lu","https:\u002F\u002Forcid.org\u002F0009-0009-3215-6521",{"name":254,"orcid":255},"Juanjuan Jing","https:\u002F\u002Forcid.org\u002F0009-0002-0371-7245",{"name":257,"orcid":9},"Junhua Yu",{"name":259,"orcid":260},"Lei Yang","https:\u002F\u002Forcid.org\u002F0000-0001-8297-0868",{"name":262,"orcid":9},"Boyang Nie",{"name":264,"orcid":265},"Jinsong Zhou","https:\u002F\u002Forcid.org\u002F0009-0006-4704-0685",{"tldr":267,"method":268,"finding":269,"direction":52,"opportunity":270},"提出CloudSTF框架，利用含云影像实现高保真时空融合，突破晴空假设。","掩膜引导多尺度Swin Transformer编码器与跨时记忆融合模块，构建GC","在六个大洲24个站点上优于现有方法，不同云密度下均鲁棒。","可探索云污染下融合结果对作物长势监测与产量估测的精度影响。","2026-09-22T23:30:23.475701Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":233,"score_detail":279,"sources":281,"tags":283,"search_phrases":286,"slug":289,"view_count":35,"doi":290,"paper":291,"created_at":307},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture",{"impact":69,"substance":70,"depth":69,"authority":181,"freshness":21,"relevant":22,"comment":280},"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[282],{"name":278,"url":275},[136,27,284,29,285],"水稻","作物表型",[287,288],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":290,"openalex_id":292,"authors":293,"venue":278,"cited_by_count":35,"oa_url":275,"card":302,"direction":52,"ingested_from":54},"W7213887432",[294,297,299],{"name":295,"orcid":296},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":298,"orcid":9},"Noboru Noguchi",{"name":300,"orcid":301},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":303,"method":304,"finding":305,"direction":52,"opportunity":306},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z"]