[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2326":3,"related-2326":107},{"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,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":74,"slug":77,"view_count":63,"doi":78,"paper":79,"created_at":106},2326,"Drone-based leak detection and real-time monitoring of urban water in Harare, Zimbabwe","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwashdev.2026.227","ABSTRACT Increasing water scarcity resulting from inefficient urban water distribution systems across many African cities threatens the attainment of the Sustainable Development Goals. This is concerning given additional pressures on water resources such as climate change, pollution, increased demand from population growth and agriculture. Thus, there is a need to develop efficient, rapid and repeatable methods for tracking both urban water distribution and water use. This study applied drone-based thermal infra-red imaging for real-time leak detection and monitoring of Harare's aging water network by characterizing water distribution infrastructure with emphasis on spatial variations in pipe material and age. Very high-resolution thermal imagery was acquired using a DJI Mavic 3T drone and then processed before being integrated with water distribution infrastructure data within a GIS to identify temperature anomalies indicative of leaks. A scalable thermally based remote sensing algorithm was developed for detecting water leaks. The model achieved a precision of 82% and a recall of 90%, demonstrating the high accuracy of the model in detecting most leaks. An F1-Score of 0.86 demonstrated a strong balance between detection accuracy and false alarms, suitable for operational use. The integration of drone data within a GIS platform offers a scalable framework for a proactive approach in water-scarce urban contexts.","摘要 许多非洲城市因城市配水系统效率低下而加剧的水资源短缺，威胁着可持续发展目标的实现。考虑到气候变化、污染、人口增长和农业用水需求增加对水资源造成的额外压力，这一情况令人担忧。因此，有必要开发高效、快速且可重复的方法来追踪城市配水和水资源利用情况。本研究应用基于无人机的热红外成像技术，对哈拉雷老化的供水管网进行实时漏损检测与监测，通过刻画配水基础设施特征，重点关注管道材质和管龄的空间变化。研究使用DJI Mavic 3T无人机获取超高分辨率热红外影像，经处理后与GIS中的配水基础设施数据整合，以识别指示漏损的温度异常。研究开发了一种可扩展的基于热红外的遥感算法用于检测漏水。该模型达到了82%的精确率和90%的召回率，表明其在检测大多数漏损方面具有较高准确性。F1分数为0.86，表明检测准确性与误报之间取得了良好平衡，适合实际业务应用。将无人机数据整合至GIS平台，为水资源短缺的城市环境提供了一种可扩展的主动管理框架。",null,"Journal of Water Sanitation and Hygiene for Development","2026-09-11T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},14,21,18,13,8,1,"基于无人机热红外与GIS的城市供水漏损检测研究，方法可复用于农业灌溉管网监测，但场景偏城市水务，与三农直接关联有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"无人机","遥感监测","GIS","智慧水务","城市供水",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61,67],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},7,true,[],[50,51],"遥感","remote sensing",[28],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"H7",6,[],[58,59],"气候变化","climate change",[],{"code":62,"score":63,"supported":47,"title":64,"abstract":65,"tags":66},"I4",4,[],[29],[29],{"code":68,"score":69,"supported":47,"title":70,"abstract":71,"tags":73},"H3",3,[],[72],"水资源",[],[75,76],"城市供水 智慧水务 遥感监测 无人机","城市供水 智慧水务","城市供水智慧水务遥感监测无人机-2326","10.2166\u002Fwashdev.2026.227",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":97,"oa_url":6,"card":98,"direction":104,"ingested_from":105},"W7212404404",[82,85,88,91,94],{"name":83,"orcid":84},"Godknows Ignatius Junior Mugabe","https:\u002F\u002Forcid.org\u002F0009-0000-6557-3481",{"name":86,"orcid":87},"Webster Gumindoga","https:\u002F\u002Forcid.org\u002F0000-0001-8530-7383",{"name":89,"orcid":90},"Munyaradzi Davis Shekede","https:\u002F\u002Forcid.org\u002F0000-0002-6540-3316",{"name":92,"orcid":93},"Alexander Mhizha","https:\u002F\u002Forcid.org\u002F0000-0002-6890-5143",{"name":95,"orcid":96},"Manuel Ndebele","https:\u002F\u002Forcid.org\u002F0000-0002-1432-6322",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"用无人机热红外成像检测津巴布韦哈拉雷城市供水管网泄漏并实时监测。","DJI Mavic 3T热红外影像结合GIS管网数据，开发热遥感泄漏检测算法。","模型精度82%、召回率90%、F1值0.86，可有效检测多数泄漏点。","智慧农业 \u002F 农业物联网","可将无人机热红外泄漏检测拓展至农业灌溉管网，结合物联网实现精准漏损预警。","农业遥感与作物表型","openalex","2026-09-13T23:30:23.478384Z",{"total":55,"page":22,"page_size":55,"items":108},[109,190,264,343,409,494],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":128,"secondary_topics":134,"classification":136,"search_phrases":161,"slug":164,"view_count":97,"doi":165,"paper":166,"created_at":189},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":119,"substance":120,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":121},16,22,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[123],{"name":115,"url":112},[125,27,28,126,127],"智慧农业","冬小麦","生物量估算",{"code":129,"slug":130,"name":131,"path":132,"parent_code":133},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[135],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":47},[139,145,149,156],{"code":129,"score":140,"supported":47,"title":141,"abstract":143,"tags":144},26,[142,130],"winter wheat",[131,142,130],[126],{"code":33,"score":119,"supported":47,"title":146,"abstract":147,"tags":148},[51],[50,51],[127,28],{"code":150,"score":55,"supported":47,"title":151,"abstract":152,"tags":155},"I2",[],[153,154],"机器学习","machine learning",[],{"code":157,"score":22,"supported":14,"title":158,"abstract":159,"tags":160},"I1",[],[],[125],[162,163],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":165,"openalex_id":167,"authors":168,"venue":115,"cited_by_count":97,"oa_url":112,"card":184,"direction":104,"ingested_from":105},"W7222806066",[169,171,173,175,178,180,182],{"name":170,"orcid":9},"Guangtao Wang",{"name":172,"orcid":9},"Sumei Yao",{"name":174,"orcid":9},"Yanzheng Xie",{"name":176,"orcid":177},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":179,"orcid":9},"Xiangdong Chen",{"name":181,"orcid":9},"Yuhao Zhang",{"name":183,"orcid":9},"Lina Jiang",{"tldr":185,"method":186,"finding":187,"direction":104,"opportunity":188},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":204,"tags":206,"primary_topic":209,"secondary_topics":210,"classification":211,"search_phrases":233,"slug":236,"view_count":22,"doi":237,"paper":238,"created_at":263},5162,"Detection of Eulecanium giganteum Infestation in Jujube Orchards Based on UAV Multispectral Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fhorticulturae12101286","The application of remote sensing technology to monitor agricultural pests and diseases provides an effective approach for rapid and non-destructive assessment of crop health. This study evaluated a UAV multispectral remote sensing framework integrating vegetation indices and SPAD information for assessing Eulecanium giganteum infestation in jujube orchards. Initially, 24 vegetation indices were extracted from canopy multispectral imagery, and Pearson correlation analysis was used for feature screening, resulting in a refined subset of 19 vegetation indices. Seven machine learning algorithms were evaluated during model development, with Grid Search combined with tree-level grouped 7-fold cross-validation for hyperparameter selection. Vegetation indices were used as predictor variables, while SPAD value and infestation severity were treated as response variables in the regression and classification tasks, respectively. During internal model development, the CatBoost-19opt model achieved an R2 of 0.81 and an RMSE of 1.84 for SPAD estimation. On the independent test set consisting of 58 jujube trees, the previously selected model achieved an R2 of 0.84 and an RMSE of 1.56. During internal model development, incorporating measured SPAD values improved pest classification performance, with XGBoost-19sopt achieving an OA of 0.89 and a Kappa coefficient of 0.84. On the independent test set, the measured-SPAD configuration achieved an OA of 0.88, whereas both the VI-only and VI combined with predicted-SPAD configurations achieved an OA of 0.71. Thus, incorporating predicted SPAD did not improve overall classification accuracy relative to using vegetation indices alone. Further model optimization and additional validation across different orchards, growing seasons, and phenological stages are required before the approach can be considered for operational large-scale monitoring.","遥感技术在农业病虫害监测中的应用，为作物健康的快速、无损评估提供了有效途径。本研究评估了一种集成植被指数与SPAD信息的无人机多光谱遥感框架，用于评估枣园中枣球蜡蚧（Eulecanium giganteum）的危害情况。首先，从冠层多光谱影像中提取了24个植被指数，并采用Pearson相关分析进行特征筛选，最终得到包含19个植被指数的精简子集。在模型开发过程中评估了7种机器学习算法，采用网格搜索结合树级分组7折交叉验证进行超参数选择。以植被指数作为预测变量，分别将SPAD值和危害程度作为回归任务和分类任务的响应变量。在内部模型开发中，CatBoost-19opt模型在SPAD估算中取得了R²为0.81、RMSE为1.84的结果。在由58株枣树构成的独立测试集上，先前选定的模型取得了R²为0.84、RMSE为1.56的结果。在内部模型开发中，引入实测SPAD值提升了病虫害分类性能，XGBoost-19sopt取得了OA为0.89、Kappa系数为0.84的结果。在独立测试集上，实测SPAD配置取得了OA为0.88的结果，而仅使用植被指数以及植被指数结合预测SPAD的配置均取得了OA为0.71的结果。因此，相对于仅使用植被指数，引入预测SPAD并未提升整体分类精度。在该方法可被视为适用于业务化大规模监测之前，仍需进一步的模型优化，并在不同枣园、生长季和物候期开展额外验证。","Horticulturae","2026-10-16T00:00:00Z",73,{"impact":200,"substance":120,"depth":201,"authority":20,"freshness":202,"relevant":22,"comment":203},12,17,9,"方法扎实、数据完整的单果园研究，但作者自述尚未达到大规模业务化监测水平，属细分领域进展，可入精选但非重大突破。",[205],{"name":196,"url":193},[125,27,28,207,208],"病虫害防治","枣树",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":212},{"candidates":213,"agricultural_evidence":47},[214,220,224,229],{"code":33,"score":140,"supported":47,"title":215,"abstract":217,"tags":219},[51,216],"multispectral",[50,218,51,216],"无人机多光谱",[28],{"code":150,"score":55,"supported":47,"title":221,"abstract":222,"tags":223},[],[153,154],[],{"code":225,"score":69,"supported":47,"title":226,"abstract":227,"tags":228},"B2",[],[208],[],{"code":157,"score":22,"supported":14,"title":230,"abstract":231,"tags":232},[],[],[125],[234,235],"无人机 多光谱 枣园 虫害","Eulecanium giganteum 遥感 监测","无人机多光谱枣园虫害-5162","10.3390\u002Fhorticulturae12101286",{"doi":237,"openalex_id":239,"authors":240,"venue":196,"cited_by_count":97,"oa_url":193,"card":258,"direction":104,"ingested_from":105},"W7222575708",[241,243,245,248,251,253,255],{"name":242,"orcid":9},"Yueteng Chao",{"name":244,"orcid":9},"Yulong Wang",{"name":246,"orcid":247},"Zhuoyang Xu","https:\u002F\u002Forcid.org\u002F0000-0003-0344-3424",{"name":249,"orcid":250},"Ruohong He","https:\u002F\u002Forcid.org\u002F0009-0000-7404-9112",{"name":252,"orcid":9},"Rina Yangdao",{"name":254,"orcid":9},"Hongqiang Dong",{"name":256,"orcid":257},"Ping Li","https:\u002F\u002Forcid.org\u002F0009-0007-4465-1023",{"tldr":259,"method":260,"finding":261,"direction":104,"opportunity":262},"用无人机多光谱植被指数与SPAD估算枣园大球蚧发生程度。","24个植被指数筛选19个，7种机器学习+网格搜索与分组交叉验证。","CatBoost估SPAD R²达0.84；实测SPAD提升分类，预测SPAD无增益。","可探索预测SPAD失效原因及跨果园、物候期泛化与多源数据融合监测。","2026-10-17T23:30:28.025984Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":272,"sources":274,"tags":276,"primary_topic":279,"secondary_topics":284,"classification":290,"search_phrases":317,"slug":320,"view_count":22,"doi":321,"paper":322,"created_at":342},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",{"impact":119,"substance":18,"depth":19,"authority":20,"freshness":202,"relevant":22,"comment":273},"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[275],{"name":270,"url":267},[125,27,277,28,278],"农业人工智能","病害预警",{"code":225,"slug":280,"name":281,"path":282,"parent_code":283},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[285,289],{"code":150,"slug":286,"name":287,"path":288,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":291},{"candidates":292,"agricultural_evidence":47},[293,300,307,311],{"code":225,"score":201,"supported":47,"title":294,"abstract":296,"tags":299},[295],"apple",[297,298,295],"苹果","葡萄",[],{"code":150,"score":301,"supported":47,"title":302,"abstract":304,"tags":306},15,[303],"deep learning",[305,303],"深度学习",[277],{"code":33,"score":200,"supported":47,"title":308,"abstract":309,"tags":310},[216],[216],[28],{"code":157,"score":46,"supported":47,"title":312,"abstract":313,"tags":316},[],[314,315],"精准农业","precision agriculture",[125],[318,319],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":321,"openalex_id":323,"authors":324,"venue":270,"cited_by_count":97,"oa_url":336,"card":337,"direction":102,"ingested_from":105},"W7221558726",[325,328,331,333],{"name":326,"orcid":327},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":329,"orcid":330},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":332,"orcid":9},"Fatih Sinan Bir",{"name":334,"orcid":335},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":338,"method":339,"finding":340,"direction":104,"opportunity":341},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":348,"content":9,"source_name":349,"source_url":346,"published_at":350,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":354,"tags":356,"primary_topic":359,"secondary_topics":365,"classification":367,"search_phrases":383,"slug":386,"view_count":387,"doi":388,"paper":389,"created_at":408},4354,"Monitoring growth and ground cover of turfgrass germplasm using a UAV-mounted multispectral sensor in a ground-based system","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74076-4","Efficient selection of turfgrass germplasm with persistent green coverage requires evaluation across multiple observation periods rather than at a single time point. This study developed an integrated framework for temporal evaluation and multi-criteria selection of 526 turfgrass accessions monitored monthly from August to December 2021 during one growing season in a single experimental environment. Green cover percentage (GCP) quantified visible canopy coverage, whereas the normalized difference vegetation index (NDVI) provided complementary spectral information on canopy greenness. Four multi-month summary traits GCP area under the curve (AUC), late-season GCP, NDVI AUC, and late-season NDVI formed a common trait space examined through complementary analytical steps: K-means clustering characterized broad temporal-performance groups, Pareto screening retained non-dominated trade-offs, and an a priori equal-weight composite score ranked the resulting candidates. The three-cluster solution separated a high-performance group of 97 accessions, and the global Pareto set comprised 14 accessions. The ranking identified five promising candidates, sensitivity analysis indicated a stable core of four, whereas the fifth position depended on moderate changes in trait weights. Within the limits of a single season and environment, the results show that integrating multi-month traits, clustering, Pareto screening, composite ranking, and sensitivity analysis provides a transparent framework for prioritizing turfgrass germplasm for further evaluation.","高效选育具有持久绿色覆盖的草坪草种质，需要在多个观测时期进行评估，而非仅依据单一时间点。本研究构建了一个整合框架，用于对526份草坪草种质进行时间序列评价和多标准选择，这些种质在2021年8月至12月的一个生长季内、单一试验环境中每月监测一次。绿色覆盖百分比（GCP）量化了可见冠层覆盖度，而归一化差异植被指数（NDVI）则提供了冠层绿色度的补充光谱信息。四个多月份汇总性状——GCP曲线下面积（AUC）、后期GCP、NDVI AUC和后期NDVI——构成了一个共同性状空间，并通过互补的分析步骤加以检验：K均值聚类刻画了宽泛的时间表现分组，帕累托筛选保留了非支配权衡，先验等权综合评分对所得候选材料进行排序。三聚类方案分离出一个由97份种质组成的高表现组，全局帕累托集包含14份种质。排序确定了5份有前景的候选材料，敏感性分析表明其中4份构成稳定核心，而第5个位置取决于性状权重的适度变化。在单一生长季和环境的限制范围内，结果表明，整合多月份性状、聚类、帕累托筛选、综合排序和敏感性分析，为优先选择草坪草种质以开展进一步评价提供了一个透明的框架。","Scientific Reports","2026-10-05T00:00:00Z",72,{"impact":200,"substance":18,"depth":201,"authority":17,"freshness":21,"relevant":22,"comment":353},"将无人机多光谱遥感与聚类、帕累托筛选结合用于草坪草种质多期评价，方法新颖、数据规模可观，对智慧育种与表型监测有参考价值。",[355],{"name":349,"url":346},[125,27,357,28,358],"种质资源","草坪草",{"code":360,"slug":361,"name":362,"path":363,"parent_code":364},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[366],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":368},{"candidates":369,"agricultural_evidence":47},[370,375,379],{"code":360,"score":200,"supported":47,"title":371,"abstract":373,"tags":374},[372],"germplasm",[372],[357],{"code":33,"score":202,"supported":47,"title":376,"abstract":377,"tags":378},[216],[],[28],{"code":157,"score":22,"supported":14,"title":380,"abstract":381,"tags":382},[],[],[125],[384,385],"无人机 多光谱 草坪草 种质","NDVI 草坪草 绿度","无人机多光谱草坪草种质-4354",2,"10.1038\u002Fs41598-026-74076-4",{"doi":388,"openalex_id":390,"authors":391,"venue":349,"cited_by_count":97,"oa_url":402,"card":403,"direction":104,"ingested_from":105},"W7220416490",[392,395,398,400],{"name":393,"orcid":394},"E. M. B. M. Karunathilake","https:\u002F\u002Forcid.org\u002F0009-0009-8841-1480",{"name":396,"orcid":397},"Sheikh Mansoor","https:\u002F\u002Forcid.org\u002F0000-0003-1902-4611",{"name":399,"orcid":9},"Yong Suk Chung",{"name":401,"orcid":9},"Gyung Deok Han","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74076-4_reference.pdf",{"tldr":404,"method":405,"finding":406,"direction":104,"opportunity":407},"用无人机多光谱监测526份草坪草种质，结合多时相性状与多准则筛选优良品系。","无人机多光谱获取GCP和NDVI，K-means聚类、Pareto筛选与加权评分","三聚类分出97份高性能种质，Pareto集14份，最终5个候选且4个稳健。","可扩展到多环境多年份验证，并将无人机表型与基因组数据结合做育种决策。","2026-10-07T23:30:19.253986Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":417,"score_detail":418,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":430,"classification":432,"search_phrases":478,"slug":481,"view_count":22,"doi":482,"paper":483,"created_at":493},3364,"Geographical Analysis of Desertification and Environmental Degradation and the Spatial Variation of Their Manifestations in Parts of the Jifara Plain","https:\u002F\u002Fdoi.org\u002F10.65405\u002Fsjh.2.3.85","This research aims to analyze the spatial variation of desertification and environmental degradation manifestations in the Al-Jfara Plain, identify the degrees, intensity, and geographical distribution of desertification, and determine the main manifestations associated with land degradation. It also seeks to identify the effects of natural and human factors on the spread of desertification and determine the areas most vulnerable to it, thereby contributing to efforts to conserve natural resources and reduce land degradation. The research adopted the analytical approach as the main method for analyzing data related to degraded lands, vegetation cover, rangelands, rainfall, erosion, and agricultural and livestock production. It also employed the descriptive approach to present the natural and human characteristics of the research area, as well as the comparative approach to compare different spatial units and time periods. The research further relied on geographical references, scientific studies, and available data, using percentages and rates of change to compare levels of degradation. The research concluded that there is clear spatial variation in the manifestations of desertification and environmental degradation across the Al-Jfara Plain. Wind erosion, declining vegetation cover, drought, and rainfall variability emerged as the most prominent manifestations of degradation. In addition, overgrazing, excessive exploitation of soil and groundwater resources, and urban expansion contributed to increasing pressure on natural resources. The research recommends reducing overgrazing, rationalizing groundwater use, combating erosion, increasing vegetation cover, regulating land use, and enhancing environmental awareness. It also proposes periodic monitoring of desertification using remote sensing and Geographic Information Systems (GIS), preparing updated maps of land degradation, and implementing projects for the rehabilitation of degraded lands.","本研究旨在分析贾法拉平原荒漠化与环境退化表现的空间差异，识别荒漠化的程度、强度及地理分布，并确定与土地退化相关的主要表现。研究还力求查明自然因素和人为因素对荒漠化蔓延的影响，确定最易受荒漠化影响的区域，从而为保护自然资源和减少土地退化的努力作出贡献。研究采用分析方法作为分析与退化土地、植被覆盖、牧场、降雨、侵蚀以及农牧业生产相关数据的主要方法，同时运用描述性方法呈现研究区域的自然和人文特征，并采用比较方法对不同空间单元和时间段进行比较。研究还依托地理文献、科学研究和现有数据，利用百分比和变化率来比较退化程度。研究得出结论：贾法拉平原各地荒漠化与环境退化表现在明显的空间差异。风蚀、植被覆盖下降、干旱和降雨变率是最突出的退化表现。此外，过度放牧、土壤和地下水资源的过程开发以及城市扩张也加剧了对自然资源的压力。研究建议减少过度放牧、合理利用地下水、防治侵蚀、增加植被覆盖、规范土地利用并增强环境意识。研究还提出利用遥感和地理信息系统（GIS）对荒漠化进行定期监测，编制更新的土地退化地图，并实施退化土地恢复项目。","Shihab Journal of Humanities","2026-09-23T00:00:00Z",55,{"impact":21,"substance":19,"depth":17,"authority":55,"freshness":202,"relevant":22,"comment":419},"区域荒漠化空间分析研究，方法常规、结论有参考价值，但属地方性学术成果，公共影响有限。",[421],{"name":415,"url":412},[423,28,29,424,425],"土地退化","植被覆盖","荒漠化防治",{"code":62,"slug":427,"name":428,"path":429,"parent_code":37},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[431],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":433},{"candidates":434,"agricultural_evidence":47},[435,441,445,452,458,464,468,473],{"code":62,"score":13,"supported":47,"title":436,"abstract":437,"tags":440},[],[438,29,439],"地理信息","geographic information",[29],{"code":33,"score":21,"supported":47,"title":442,"abstract":443,"tags":444},[],[50,51],[424,28],{"code":446,"score":55,"supported":47,"title":447,"abstract":448,"tags":451},"H2",[],[449,450],"土壤","soil",[],{"code":54,"score":55,"supported":47,"title":453,"abstract":454,"tags":457},[],[455,456],"干旱","drought",[],{"code":459,"score":69,"supported":47,"title":460,"abstract":461,"tags":463},"C",[],[462],"livestock",[],{"code":68,"score":69,"supported":47,"title":465,"abstract":466,"tags":467},[],[72],[],{"code":469,"score":22,"supported":14,"title":470,"abstract":471,"tags":472},"H1",[],[],[423],{"code":474,"score":22,"supported":14,"title":475,"abstract":476,"tags":477},"H6",[],[],[425],[479,480],"GIS 荒漠化 遥感监测","荒漠化防治 土地退化 植被覆盖 遥感监测","GIS荒漠化遥感监测-3364","10.65405\u002Fsjh.2.3.85",{"doi":482,"openalex_id":484,"authors":485,"venue":415,"cited_by_count":97,"oa_url":412,"card":488,"direction":104,"ingested_from":105},"W7214127171",[486],{"name":487,"orcid":9},"حسنية السني رجب البكوري",{"tldr":489,"method":490,"finding":491,"direction":104,"opportunity":492},"分析吉法拉平原荒漠化与环境退化的空间差异及主要表现，并提出防治建议。","采用分析法、描述法与比较法，结合地理文献和统计数据评估退化程度。","风蚀、植被减少、干旱和降水变率是主要退化表现，过度放牧与地下水超采加剧压力。","可结合遥感与GIS开展荒漠化动态监测，构建多因子退化风险评估模型。","2026-09-24T23:30:21.130938Z",{"id":495,"title":496,"url":497,"summary":498,"summary_zh":499,"content":9,"source_name":500,"source_url":497,"published_at":501,"category":12,"cover_url":9,"hotness":502,"is_selected":14,"score":503,"score_detail":504,"sources":506,"tags":510,"primary_topic":513,"secondary_topics":514,"classification":520,"search_phrases":544,"slug":547,"view_count":63,"doi":548,"paper":549,"created_at":563},3279,"ASSESSMENT OF SOIL SALINIZATION IN IRRIGATED LANDS BASED ON REMOTE SENSING AND GIS TECHNOLOGIES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900049","This thesis analyzes modern approaches to monitoring and assessing soil salinity in irrigated areas of Uzbekistan and Central Asia. Soil salinization is one of the major land degradation processes affecting the reclamation condition of irrigated lands, crop growth and agricultural productivity. Although conventional field and laboratory surveys provide detailed information, they have limitations in terms of regular monitoring of large areas. Therefore, remote sensing and geographic information systems provide promising tools for rapid and spatial assessment of soil salinity. The study considers the application of satellite imagery, particularly Sentinel-2 and Landsat data, for soil salinity assessment, including the use of spectral bands and salinity indices, GIS-based thematic mapping, and validation using field and laboratory observations. Recent studies demonstrate the potential of remote sensing data for spatial assessment and mapping of soil salinity under the conditions of Uzbekistan. Such an integrated approach can support rapid monitoring of the reclamation status of irrigated lands, identification of salt-affected areas and spatial planning of appropriate reclamation measures.","本论文分析了乌兹别克斯坦和中亚灌溉区土壤盐分监测与评估的现代方法。土壤盐渍化是影响灌溉土地改良状况、作物生长和农业生产力的主要土地退化过程之一。尽管传统的野外调查和实验室分析能够提供详细信息，但在大范围定期监测方面存在局限性。因此，遥感与地理信息系统为土壤盐分的快速空间评估提供了有前景的工具。本研究探讨了卫星影像，特别是Sentinel-2和Landsat数据在土壤盐分评估中的应用，包括光谱波段和盐分指数的使用、基于GIS的主题制图，以及利用野外和实验室观测数据进行验证。近期研究表明，遥感数据在乌兹别克斯坦条件下用于土壤盐分空间评估与制图具有潜力。这种综合方法可支持灌溉土地改良状况的快速监测、盐渍化区域的识别以及适当改良措施的空间规划。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,66,{"impact":200,"substance":19,"depth":301,"authority":200,"freshness":202,"relevant":22,"comment":505},"基于遥感与GIS的灌溉区土壤盐渍化监测论文，方法成熟、结论可靠，对盐碱地治理与农业信息化有参考价值，但属区域性研究，影响层级有限。",[507,508],{"name":500,"url":497},{"name":500,"url":509},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[423,28,29,511,512],"土壤盐渍化","灌溉农业",{"code":62,"slug":427,"name":428,"path":429,"parent_code":37},[515,516],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":446,"slug":450,"name":517,"path":518,"parent_code":519},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":521},{"candidates":522,"agricultural_evidence":47},[523,527,531,535,540],{"code":62,"score":19,"supported":47,"title":524,"abstract":525,"tags":526},[29],[438,29,439],[29],{"code":33,"score":301,"supported":47,"title":528,"abstract":529,"tags":530},[51],[50,51],[28],{"code":446,"score":17,"supported":47,"title":532,"abstract":533,"tags":534},[450],[449,450],[],{"code":68,"score":63,"supported":47,"title":536,"abstract":537,"tags":539},[],[538],"灌溉",[512],{"code":469,"score":22,"supported":14,"title":541,"abstract":542,"tags":543},[],[],[423],[545,546],"乌兹别克斯坦 土壤盐渍化 遥感","Sentinel-2 盐渍化 灌溉","乌兹别克斯坦土壤盐渍化遥感-3279","10.5281\u002Fzenodo.22900049",{"doi":548,"openalex_id":550,"authors":551,"venue":500,"cited_by_count":97,"oa_url":497,"card":558,"direction":104,"ingested_from":105},"W7214052773",[552,554,556],{"name":553,"orcid":9},"Mardiyev",{"name":555,"orcid":9},"Ergashaliyev",{"name":557,"orcid":9},"G'aniyev",{"tldr":559,"method":560,"finding":561,"direction":104,"opportunity":562},"综述遥感与GIS评估乌兹别克斯坦及中亚灌溉土壤盐渍化的方法。","Sentinel-2与Landsat影像、光谱盐分指数、GIS制图及野外验证。","遥感与GIS可快速空间评估盐渍化，支持灌溉地改良状态监测与规划。","可探索多源遥感与机器学习融合的盐分指数模型，提升干旱区盐渍化反演精度与时效。","2026-09-23T23:30:19.341486Z"]