[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3779":3,"related-3779":113},{"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":112},3779,"Quantifying the impact of grasshoppers: an integrated approach using dynamic vegetation indices, red edge shift and biophysical changes for remote sensing assessment in typical steppe of China","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fps.71312","Abstract BACKGROUND In conventional remote sensing monitoring, the quantitative estimation of vegetation damage caused by locust feeding has long been challenged by the spatiotemporal resolution limits of satellites, as well as a lack of understanding regarding the physiological characteristics of different locust species. To address this, cage experiments were conducted in a typical steppe region of northern China, with the primary grassland pest in the area, Oedaleus decorus asiaticus , as the study subject. RESULTS Field experiments in Xilinhot revealed that vegetation indices were significantly more sensitive to biomass loss at certain stages of locust development than at others. During the fourth instar stage, the ratio vegetation index (RVI) and green normalized difference vegetation index (GNDVI) exhibited high sensitivity to forage loss ( R 2 > 0.79). In the adult stage, the modified soil‐adjusted vegetation index (MSAVI) demonstrated greater predictive accuracy for locust feeding intensity ( R 2 > 0.95). The incorporation of natural pasture growth dynamics through the introduction of a vegetation index loss component (ΔVI′) effectively decouples environmental background interference, thereby enhancing both the accuracy and robustness of the model. Furthermore, with increasing pest population density, a decline in near‐infrared reflectance is observed, accompanied by a shift of the red‐edge position toward shorter wavelengths, a phenomenon commonly referred to as ‘blue shift.’ CONCLUSION Integrating the developmental phenology of locusts with multi‐temporal vegetation indices and red‐edge position dynamics enables real‐time monitoring of grassland locust infestations, quantitative damage assessment, and early warning capabilities. © 2026 Society of Chemical Industry.","摘要 背景 在传统的遥感监测中，蝗虫取食导致的植被损害定量估算长期受到卫星时空分辨率限制的制约，同时也缺乏对不同蝗虫物种生理特性的了解。为解决这一问题，在中国北方典型草原地区开展了笼罩实验，以该地区主要草地害虫——亚洲小车蝗（Oedaleus decorus asiaticus）为研究对象。结果 在锡林浩特进行的野外实验表明，植被指数在蝗虫发育的某些阶段对生物量损失的敏感性显著高于其他阶段。在四龄期，比值植被指数（RVI）和绿色归一化差异植被指数（GNDVI）对牧草损失表现出高敏感性（R² > 0.79）。在成虫期，修正土壤调节植被指数（MSAVI）对蝗虫取食强度的预测精度更高（R² > 0.95）。通过引入植被指数损失分量（ΔVI′）纳入天然牧草生长动态，有效解耦了环境背景干扰，从而提高了模型的准确性和稳健性。此外，随着害虫种群密度增加，近红外反射率下降，同时红边位置向短波方向移动，这一现象通常被称为“蓝移”。结论 将蝗虫发育物候与多时相植被指数及红边位置动态相结合，可实现草地蝗虫侵害的实时监测、损害定量评估和预警能力。© 2026 化学工业学会。",null,"Pest Management Science","2026-09-27T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"基于笼养实验与多时相植被指数、红边位移的草原蝗虫定量遥感监测新方法，方法新颖、结论可靠，对草地虫害预警有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感监测","植被指数","草原蝗虫","虫害预警",{"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":46},[45,52,60,67],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":51},true,[48],"remote sensing",[50,48],"遥感",[29,28],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":59},"H2",6,[],[57,58],"土壤","soil",[],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":66},"C4",3,[],[65],"forage",[],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"I1",[],[],[27],[73,74],"锡林浩特 草原蝗虫 遥感","红边位移 植被指数 蝗虫","锡林浩特草原蝗虫遥感-3779",2,"10.1002\u002Fps.71312",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":102,"oa_url":103,"card":104,"direction":110,"ingested_from":111},"W7214563841",[81,84,87,90,92,94,96,98,100],{"name":82,"orcid":83},"Qu HaiJuan","https:\u002F\u002Forcid.org\u002F0009-0007-4530-906X",{"name":85,"orcid":86},"Kuanyan Tang","https:\u002F\u002Forcid.org\u002F0000-0003-2080-9088",{"name":88,"orcid":89},"Peng Qi","https:\u002F\u002Forcid.org\u002F0000-0002-5048-9604",{"name":91,"orcid":9},"Jinming Zhao",{"name":93,"orcid":9},"Runze Chen",{"name":95,"orcid":9},"Narisu",{"name":97,"orcid":9},"Minna Pang",{"name":99,"orcid":9},"Xiaolong Liu",{"name":101,"orcid":9},"Ning Wang",0,"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fps.71312",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"通过笼养实验量化蝗虫取食对典型草原植被的损害，结合植被指数与红边位移实现遥感评估。","笼养实验、多时相植被指数（RVI\u002FGNDVI\u002FMSAVI）、红边位置动态与生物物","蝗虫四龄期RVI\u002FGNDVI敏感，成虫期MSAVI预测精度高；虫口密度增加导致红边蓝移。","农业遥感与作物表型","可探索多物种蝗虫生理差异与多源遥感融合，构建跨区域实时预警模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:12.207296Z",{"total":54,"page":22,"page_size":54,"items":114},[115,194,254,324,401,470],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":127,"tags":129,"primary_topic":133,"secondary_topics":139,"classification":141,"search_phrases":165,"slug":168,"view_count":102,"doi":169,"paper":170,"created_at":193},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":17,"substance":18,"depth":19,"authority":125,"freshness":21,"relevant":22,"comment":126},13,"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。",[128],{"name":121,"url":118},[27,130,28,131,132],"无人机","冬小麦","生物量估算",{"code":134,"slug":135,"name":136,"path":137,"parent_code":138},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[140],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":142},{"candidates":143,"agricultural_evidence":46},[144,150,154,161],{"code":134,"score":145,"supported":46,"title":146,"abstract":148,"tags":149},26,[147,135],"winter wheat",[136,147,135],[131],{"code":33,"score":17,"supported":46,"title":151,"abstract":152,"tags":153},[48],[50,48],[132,28],{"code":155,"score":54,"supported":46,"title":156,"abstract":157,"tags":160},"I2",[],[158,159],"机器学习","machine learning",[],{"code":68,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[27],[166,167],"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":169,"openalex_id":171,"authors":172,"venue":121,"cited_by_count":102,"oa_url":118,"card":188,"direction":108,"ingested_from":111},"W7222806066",[173,175,177,179,182,184,186],{"name":174,"orcid":9},"Guangtao Wang",{"name":176,"orcid":9},"Sumei Yao",{"name":178,"orcid":9},"Yanzheng Xie",{"name":180,"orcid":181},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":183,"orcid":9},"Xiangdong Chen",{"name":185,"orcid":9},"Yuhao Zhang",{"name":187,"orcid":9},"Lina Jiang",{"tldr":189,"method":190,"finding":191,"direction":108,"opportunity":192},"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":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":208,"tags":210,"primary_topic":213,"secondary_topics":214,"classification":215,"search_phrases":227,"slug":230,"view_count":102,"doi":231,"paper":232,"created_at":253},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha","2026-10-16T00:00:00Z",62,{"impact":21,"substance":19,"depth":204,"authority":205,"freshness":206,"relevant":22,"comment":207},15,12,9,"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[209],{"name":200,"url":197},[27,211,50,29,212],"产量预测","菜豆",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":216},{"candidates":217,"agricultural_evidence":46},[218,223],{"code":33,"score":205,"supported":46,"title":219,"abstract":220,"tags":222},[],[50,48,221],"multispectral",[211,29,50],{"code":68,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[27],[228,229],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":231,"openalex_id":233,"authors":234,"venue":200,"cited_by_count":102,"oa_url":247,"card":248,"direction":108,"ingested_from":111},"W7222720635",[235,238,241,244],{"name":236,"orcid":237},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":239,"orcid":240},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":242,"orcid":243},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":245,"orcid":246},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":249,"method":250,"finding":251,"direction":108,"opportunity":252},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":270,"secondary_topics":271,"classification":272,"search_phrases":293,"slug":296,"view_count":22,"doi":297,"paper":298,"created_at":323},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",73,{"impact":205,"substance":18,"depth":263,"authority":125,"freshness":206,"relevant":22,"comment":264},17,"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。",[266],{"name":260,"url":257},[27,130,28,268,269],"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":273},{"candidates":274,"agricultural_evidence":46},[275,280,284,289],{"code":33,"score":145,"supported":46,"title":276,"abstract":277,"tags":279},[48,221],[50,278,48,221],"无人机多光谱",[28],{"code":155,"score":54,"supported":46,"title":281,"abstract":282,"tags":283},[],[158,159],[],{"code":285,"score":62,"supported":46,"title":286,"abstract":287,"tags":288},"B2",[],[269],[],{"code":68,"score":22,"supported":14,"title":290,"abstract":291,"tags":292},[],[],[27],[294,295],"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":297,"openalex_id":299,"authors":300,"venue":260,"cited_by_count":102,"oa_url":257,"card":318,"direction":108,"ingested_from":111},"W7222575708",[301,303,305,308,311,313,315],{"name":302,"orcid":9},"Yueteng Chao",{"name":304,"orcid":9},"Yulong Wang",{"name":306,"orcid":307},"Zhuoyang Xu","https:\u002F\u002Forcid.org\u002F0000-0003-0344-3424",{"name":309,"orcid":310},"Ruohong He","https:\u002F\u002Forcid.org\u002F0009-0000-7404-9112",{"name":312,"orcid":9},"Rina Yangdao",{"name":314,"orcid":9},"Hongqiang Dong",{"name":316,"orcid":317},"Ping Li","https:\u002F\u002Forcid.org\u002F0009-0007-4465-1023",{"tldr":319,"method":320,"finding":321,"direction":108,"opportunity":322},"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":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":332,"sources":334,"tags":336,"primary_topic":340,"secondary_topics":345,"classification":347,"search_phrases":371,"slug":374,"view_count":375,"doi":376,"paper":377,"created_at":400},4998,"Unmanned aerial vehicle time‐series phenotyping illuminates genotype‐specific responses to off‐target dicamba exposure in soybean","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70118","Abstract Dicamba, a synthetic auxin herbicide, is widely used for postemergence weed control. However, the high potential for volatility and spray drift raises concerns about damage to nontarget dicot crops lacking the dicamba monooxygenase transgene, which provides bioengineered resistance. Our objective was to evaluate the practical application of unmanned aerial vehicle (UAV)‐based multispectral imaging for quantifying off‐target dicamba injury, using non‐genetically modified (GM) soybean ( Glycine max (L.) Merr.) genotypes with known tolerant (T) and sensitive (S) responses to provide a reference framework. We conducted field experiments in 2021 and 2022 using varying dicamba rates. Temporal visual injury estimates (0–5 scale) were recorded approximately every 5 days for 6–8 weeks. Significant differences were detected between T and S lines, especially with 1\u002F500 single and weekly repeated treatments, which provided the largest separation. To augment the precision of visual injury assessment, UAV‐based multispectral imagery from each of multiple flights was used to calculate four vegetation indices (VIs): green leaf index (GLI), normalized excess green index (ExG2), normalized green red difference index (NGRDI), and red edge normalized difference vegetation index (RENDVI). These VIs captured differences between T and S lines, indicating dicamba injury can be quantified through remote sensing across contrasting soybean genotypes. Strong negative correlations were observed between visual injury scores and VI scores across treatments at each flight date, with overall correlations for GLI, ExG2, NGRDI, and RENDVI being −0.43, −0.43, −0.44, and −0.39 in 2021 and −0.54, −0.54, −0.64, and −0.57 in 2022. These results support the utility of UAV‐derived VIs for detecting and quantifying off‐target herbicide injury in soybean, facilitating scientific research efforts to develop non‐GM‐tolerant genotypes.","摘要 麦草畏（dicamba）是一种合成生长素类除草剂，广泛用于苗后杂草防除。然而，其高挥发性和喷雾飘移潜力引发了人们对非靶标双子叶作物的担忧，这些作物缺乏提供生物工程抗性的麦草畏单加氧酶（dicamba monooxygenase）转基因。本研究旨在评估基于无人机（UAV）的多光谱成像在量化非靶标麦草畏药害中的实际应用，利用对麦草畏具有已知耐受（T）和敏感（S）响应的非转基因（GM）大豆（Glycine max (L.) Merr.）基因型作为参考框架。我们于2021年和2022年开展了田间试验，使用不同的麦草畏剂量。在约6–8周内，每约5天记录一次时序视觉药害评估（0–5级）。在T和S品系之间检测到显著差异，尤其是在1\u002F500单次和每周重复处理下，差异最为明显。为提高视觉药害评估的精度，利用多次飞行获取的无人机多光谱影像计算了4种植被指数（VIs）：绿叶指数（GLI）、归一化超绿指数（ExG2）、归一化绿红差异指数（NGRDI）和红边归一化差异植被指数（RENDVI）。这些植被指数捕捉到了T和S品系之间的差异，表明麦草畏药害可通过遥感在不同大豆基因型间进行量化。在各飞行日期的所有处理中，视觉药害评分与植被指数评分之间均观察到强负相关，2021年GLI、ExG2、NGRDI和RENDVI的总体相关系数分别为−0.43、−0.43、−0.44和−0.39，2022年分别为−0.54、−0.54、−0.64和−0.57。这些结果支持无人机衍生植被指数在大豆非靶标除草剂药害检测和量化中的实用性，有助于推动开发非转基因耐受基因型的科学研究工作。","The Plant Phenome Journal","2026-10-14T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":125,"freshness":21,"relevant":22,"comment":333},"该研究利用无人机多光谱时序表型定量评估大豆非靶标除草剂药害，方法新颖、数据扎实，对智慧农业遥感监测具有参考价值。",[335],{"name":330,"url":327},[27,337,338,29,339],"大豆","无人机遥感","除草剂药害",{"code":341,"slug":342,"name":343,"path":344,"parent_code":138},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[346],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,355,360,367],{"code":341,"score":204,"supported":46,"title":351,"abstract":353,"tags":354},[352],"soybean",[337,352],[337],{"code":33,"score":356,"supported":46,"title":357,"abstract":358,"tags":359},11,[],[50,278,48,221],[338,29],{"code":361,"score":54,"supported":46,"title":362,"abstract":363,"tags":366},"F4",[],[364,365],"除草","weed control",[],{"code":68,"score":22,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[27],[372,373],"无人机 多光谱 大豆 药害","麦草畏 飘移 大豆 遥感","无人机多光谱大豆药害-4998",4,"10.1002\u002Fppj2.70118",{"doi":376,"openalex_id":378,"authors":379,"venue":330,"cited_by_count":102,"oa_url":327,"card":395,"direction":108,"ingested_from":111},"W7221759072",[380,383,386,389,392],{"name":381,"orcid":382},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":384,"orcid":385},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":387,"orcid":388},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":390,"orcid":391},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":393,"orcid":394},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":396,"method":397,"finding":398,"direction":108,"opportunity":399},"用无人机多光谱时序表型量化非转基因大豆受除草剂飘移药害的基因型差异。","2021-2022田间试验，无人机多光谱影像计算GLI、ExG2、NGRDI、R","植被指数与目测药害评分显著负相关，能区分耐性与敏感基因型，2022年相关性更强。","可拓展到多作物、多除草剂飘移的无人机早期诊断，并融合机器学习构建药害分级预警模型。","2026-10-15T23:30:20.537887Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":418,"secondary_topics":419,"classification":426,"search_phrases":441,"slug":444,"view_count":102,"doi":445,"paper":446,"created_at":469},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing","2026-10-12T00:00:00Z",75,{"impact":205,"substance":18,"depth":19,"authority":20,"freshness":206,"relevant":22,"comment":411},"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[413],{"name":407,"url":404},[28,29,415,416,417],"干旱风险","适应策略","农业气候",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[420],{"code":421,"slug":422,"name":423,"path":424,"parent_code":425},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":427},{"candidates":428,"agricultural_evidence":46},[429,433],{"code":33,"score":13,"supported":46,"title":430,"abstract":431,"tags":432},[48],[],[29,28],{"code":421,"score":206,"supported":46,"title":434,"abstract":435,"tags":440},[],[436,437,438,439],"气候变化","干旱","climate change","drought",[],[442,443],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":445,"openalex_id":447,"authors":448,"venue":407,"cited_by_count":102,"oa_url":404,"card":464,"direction":108,"ingested_from":111},"W7221570035",[449,452,455,458,461],{"name":450,"orcid":451},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":453,"orcid":454},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":456,"orcid":457},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":459,"orcid":460},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":462,"orcid":463},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":465,"method":466,"finding":467,"direction":108,"opportunity":468},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z",{"id":471,"title":472,"url":473,"summary":474,"summary_zh":475,"content":9,"source_name":476,"source_url":473,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":477,"sources":480,"tags":482,"primary_topic":485,"secondary_topics":490,"classification":496,"search_phrases":523,"slug":526,"view_count":22,"doi":527,"paper":528,"created_at":548},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",{"impact":17,"substance":478,"depth":19,"authority":125,"freshness":206,"relevant":22,"comment":479},21,"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。",[481],{"name":476,"url":473},[27,130,483,28,484],"农业人工智能","病害预警",{"code":285,"slug":486,"name":487,"path":488,"parent_code":489},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[491,495],{"code":155,"slug":492,"name":493,"path":494,"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":497},{"candidates":498,"agricultural_evidence":46},[499,506,512,516],{"code":285,"score":263,"supported":46,"title":500,"abstract":502,"tags":505},[501],"apple",[503,504,501],"苹果","葡萄",[],{"code":155,"score":204,"supported":46,"title":507,"abstract":509,"tags":511},[508],"deep learning",[510,508],"深度学习",[483],{"code":33,"score":205,"supported":46,"title":513,"abstract":514,"tags":515},[221],[221],[28],{"code":68,"score":517,"supported":46,"title":518,"abstract":519,"tags":522},7,[],[520,521],"精准农业","precision agriculture",[27],[524,525],"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":527,"openalex_id":529,"authors":530,"venue":476,"cited_by_count":102,"oa_url":542,"card":543,"direction":110,"ingested_from":111},"W7221558726",[531,534,537,539],{"name":532,"orcid":533},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":535,"orcid":536},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":538,"orcid":9},"Fatih Sinan Bir",{"name":540,"orcid":541},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":544,"method":545,"finding":546,"direction":108,"opportunity":547},"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"]