[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2538":3,"related-2538":86},{"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":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":85},2538,"Multitemporal UAV-Based Estimation of Kenaf (Hibiscus cannabinus L.) Plant Height Under Nitrogen and Compost Treatments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181805","This study aimed to evaluate the growth responses of kenaf (Hibiscus cannabinus L.) under nitrogen and compost treatments and to develop a UAV-based plant height estimation model. Ground-measured plant height differences were not significant at harvest (110 days after sowing, DAS), highlighting the need for multitemporal monitoring. Multispectral drone imagery was acquired at five growth stages (20–110 DAS). Object-based image segmentation was applied to extract pure vegetation areas, and digital surface model differencing (ΔDSMt) was used to reduce micro-topographic effects. A UAV-based multiple linear regression (UAV-MLR) model was developed using ΔDSMt, NDVI, GNDVI, and NGRDI to integrate complementary structural and spectral information. Evaluated on the calibration dataset, the UAV-MLR model demonstrated high fitting performance (adjusted R2 = 0.9858, RMSE = 14.95 cm, MAE = 11.31 cm), outperforming the ground-based simple linear regression (G-SLR) model based on stem diameter (adjusted R2 = 0.9615, RMSE = 39.68 cm, MAE = 29.16 cm). By integrating structural and spectral information, the proposed approach reduced RMSE by 62.3%, offering a highly accurate, non-destructive tool for crop monitoring and precision agriculture.","本研究旨在评估在氮肥和堆肥处理下洋麻（Hibiscus cannabinus L.）的生长响应，并构建基于无人机（UAV）的株高估测模型。在收获期（播种后110天，DAS），地面实测株高差异不显著，凸显了多时相监测的必要性。在5个生长阶段（20–110 DAS）获取了多光谱无人机影像。采用基于对象的图像分割提取纯植被区域，并利用数字表面模型差值（ΔDSMt）以降低微地形效应。利用ΔDSMt、NDVI、GNDVI和NGRDI构建了基于无人机的多元线性回归（UAV-MLR）模型，以整合互补的结构与光谱信息。在标定数据集上评估，UAV-MLR模型表现出较高的拟合性能（调整R² = 0.9858，RMSE = 14.95 cm，MAE = 11.31 cm），优于基于茎粗的地面简单线性回归（G-SLR）模型（调整R² = 0.9615，RMSE = 39.68 cm，MAE = 29.16 cm）。通过整合结构与光谱信息，所提方法将RMSE降低了62.3%，为作物监测和精准农业提供了一种高精度、非破坏性工具。",null,"Agronomy","2026-09-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,13,8,1,"该研究提出融合结构与光谱信息的无人机多时相株高估测模型，精度显著优于地面模型，对作物无损监测与精准农业具有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","无人机","精准农业","遥感监测","作物长势",{"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},[45,52],{"code":46,"score":21,"supported":47,"title":48,"abstract":49,"tags":51},"I1",true,[],[29,50],"precision agriculture",[27,29],{"code":33,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},4,[],[56],"multispectral",[30],[59,60],"作物长势 智慧农业 精准农业 遥感监测","作物长势 智慧农业","作物长势智慧农业精准农业遥感监测-2538",2,"10.3390\u002Fagronomy16181805",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":76,"oa_url":6,"card":77,"direction":83,"ingested_from":84},"W7213116925",[67,70,73],{"name":68,"orcid":69},"TaekJin Yoon","https:\u002F\u002Forcid.org\u002F0009-0003-6507-8415",{"name":71,"orcid":72},"Tae Wan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1742-1982",{"name":74,"orcid":75},"Sung Yung Yoo","https:\u002F\u002Forcid.org\u002F0000-0002-7889-3924",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"用多时相无人机影像估算氮肥与堆肥处理下红麻株高，构建高精度回归模型。","五期多光谱无人机影像，对象分割提取植被，ΔDSMt结合NDVI等建多元线性回归。","UAV-MLR模型拟合优度达0.9858，RMSE比地面模型降低62.3%。","农业遥感与作物表型","可推广至其他纤维\u002F能源作物，并融合机器学习与多源遥感提升跨生育期泛化能力。","数字乡村与农业信息化","openalex","2026-09-15T23:30:26.678242Z",{"total":87,"page":22,"page_size":87,"items":88},6,[89,183,257,329,408,473],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":10,"source_url":92,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":105,"secondary_topics":106,"classification":113,"search_phrases":147,"slug":150,"view_count":22,"doi":151,"paper":152,"created_at":182},2664,"Soil Salinity Mapping from UAV-Borne Hyperspectral Imagery with Soil Moisture Correction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181812","Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with Rp2 = 0.87, RMSEP = 0.67 mS·cm−1, and RPD = 2.93. Compared with the Ref–Stacking model, Rp2 increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm−1 (from 1.25 to 0.67 mS·cm−1). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture.","土壤盐渍化对全球农业可持续发展和生态安全构成重大威胁，每年造成大量作物损失。基于无人机的高光谱遥感凭借其高空间分辨率和灵活的数据获取能力，为土壤盐分监测提供了有前景的解决方案。然而，土壤水分会改变电磁辐射的散射和吸收特性，从而改变土壤光谱反射率并掩盖与盐分相关的诊断性光谱特征，这可能降低传统盐分估算模型的精度。本研究评估了六种光谱变换方法——原始反射率数据（Ref）、一阶导数（FDR）、分段直接标准化（PDS）、正交信号校正（OSC）、FDR + PDS和FDR + OSC——并结合三种机器学习算法：K近邻（KNN）、支持向量回归（SVR）和多层感知机（MLP）。进一步构建了集成这些基学习器的Stacking集成模型，以提高水分干扰下的土壤盐分反演精度。结果表明，Stacking模型在所评估的模型中达到了最高的精度和稳定性。与XGBoost和随机森林（RF）的额外比较进一步证实了所提出的Stacking框架的竞争性性能。FDR + OSC–Stacking组合取得了最佳的验证表现，Rp2 = 0.87，RMSEP = 0.67 mS·cm−1，RPD = 2.93。与Ref–Stacking模型相比，Rp2提高了0.32（从0.55增至0.87），而RMSEP降低了0.58 mS·cm−1（从1.25降至0.67 mS·cm−1）。结果表明，PDS在校正水分相关光谱变异方面效果有限，而OSC在保留与盐分估算相关的光谱信息的同时，更有效地减轻了水分干扰。在所评估的机器学习模型中，Stacking集成模型取得了优于MLP、SVR和KNN的预测性能。此外，FDR + OSC–Stacking组合在所评估的建模框架中表现最佳，并成功应用于无人机高光谱影像进行EC1:5的空间制图。这些发现证明了将适当的光谱校正与Stacking相结合用于基于无人机的土壤盐分评估的潜力，并为精准农业中的针对性盐分管理提供了有用的技术支持。","2026-09-15T00:00:00Z",78,{"impact":98,"substance":18,"depth":19,"authority":20,"freshness":99,"relevant":22,"comment":100},16,9,"该研究提出FDR+OSC光谱校正与Stacking集成模型相结合的无人机高光谱土壤盐分反演方法，验证精度显著提升（Rp²=0.87），为盐渍化农田精准管理提供了可落地的技术方案，专业深度与信息增量均较突出。",[102],{"name":10,"url":92},[27,28,29,30,104],"土壤盐渍化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[107],{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":114},{"candidates":115,"agricultural_evidence":47},[116,123,129,133,140],{"code":33,"score":19,"supported":47,"title":117,"abstract":119,"tags":122},[118],"hyperspectral",[120,121,118],"遥感","remote sensing",[30],{"code":108,"score":124,"supported":47,"title":125,"abstract":126,"tags":128},14,[109],[127,109],"土壤",[],{"code":46,"score":21,"supported":47,"title":130,"abstract":131,"tags":132},[],[29,50],[27,29],{"code":134,"score":87,"supported":47,"title":135,"abstract":136,"tags":139},"I2",[],[137,138],"机器学习","machine learning",[],{"code":141,"score":142,"supported":47,"title":143,"abstract":144,"tags":146},"J3",3,[],[145],"全球农业",[],[148,149],"土壤盐渍化 智慧农业 精准农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业精准农业遥感监测-2664","10.3390\u002Fagronomy16181812",{"doi":151,"openalex_id":153,"authors":154,"venue":10,"cited_by_count":76,"oa_url":92,"card":177,"direction":81,"ingested_from":84},"W7213244904",[155,157,159,161,163,165,167,169,172,174],{"name":156,"orcid":9},"Haiye Yu",{"name":158,"orcid":9},"Muyan Yu",{"name":160,"orcid":9},"Ranzhe Jiang",{"name":162,"orcid":9},"Xin Zhang",{"name":164,"orcid":9},"Zhu Guo",{"name":166,"orcid":9},"Yaohui Fu",{"name":168,"orcid":9},"Xingbang Liu",{"name":170,"orcid":171},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":173,"orcid":9},"Bingze Li",{"name":175,"orcid":176},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":178,"method":179,"finding":180,"direction":81,"opportunity":181},"用无人机高光谱结合水分校正与Stacking集成模型反演土壤盐分。","六种光谱变换与KNN\u002FSVR\u002FMLP及Stacking，对比XGBoost、RF","FDR+OSC-Stacking最优，Rp²=0.87，OSC比PDS更能消除水分干扰。","可探索水分校正与深度学习结合、多时相无人机盐分动态监测及跨区域迁移。","2026-09-16T23:30:29.016721Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":194,"tags":196,"primary_topic":199,"secondary_topics":205,"classification":207,"search_phrases":228,"slug":231,"view_count":76,"doi":232,"paper":233,"created_at":256},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":98,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":193},"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。",[195],{"name":189,"url":186},[27,28,30,197,198],"冬小麦","生物量估算",{"code":200,"slug":201,"name":202,"path":203,"parent_code":204},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[206],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":208},{"candidates":209,"agricultural_evidence":47},[210,216,220,224],{"code":200,"score":211,"supported":47,"title":212,"abstract":214,"tags":215},26,[213,201],"winter wheat",[202,213,201],[197],{"code":33,"score":98,"supported":47,"title":217,"abstract":218,"tags":219},[121],[120,121],[198,30],{"code":134,"score":87,"supported":47,"title":221,"abstract":222,"tags":223},[],[137,138],[],{"code":46,"score":22,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[27],[229,230],"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":232,"openalex_id":234,"authors":235,"venue":189,"cited_by_count":76,"oa_url":186,"card":251,"direction":81,"ingested_from":84},"W7222806066",[236,238,240,242,245,247,249],{"name":237,"orcid":9},"Guangtao Wang",{"name":239,"orcid":9},"Sumei Yao",{"name":241,"orcid":9},"Yanzheng Xie",{"name":243,"orcid":244},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":246,"orcid":9},"Xiangdong Chen",{"name":248,"orcid":9},"Yuhao Zhang",{"name":250,"orcid":9},"Lina Jiang",{"tldr":252,"method":253,"finding":254,"direction":81,"opportunity":255},"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":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":270,"tags":272,"primary_topic":275,"secondary_topics":276,"classification":277,"search_phrases":298,"slug":301,"view_count":22,"doi":302,"paper":303,"created_at":328},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":267,"substance":18,"depth":268,"authority":20,"freshness":99,"relevant":22,"comment":269},12,17,"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。",[271],{"name":263,"url":260},[27,28,30,273,274],"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":278},{"candidates":279,"agricultural_evidence":47},[280,285,289,294],{"code":33,"score":211,"supported":47,"title":281,"abstract":282,"tags":284},[121,56],[120,283,121,56],"无人机多光谱",[30],{"code":134,"score":87,"supported":47,"title":286,"abstract":287,"tags":288},[],[137,138],[],{"code":290,"score":142,"supported":47,"title":291,"abstract":292,"tags":293},"B2",[],[274],[],{"code":46,"score":22,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[27],[299,300],"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":302,"openalex_id":304,"authors":305,"venue":263,"cited_by_count":76,"oa_url":260,"card":323,"direction":81,"ingested_from":84},"W7222575708",[306,308,310,313,316,318,320],{"name":307,"orcid":9},"Yueteng Chao",{"name":309,"orcid":9},"Yulong Wang",{"name":311,"orcid":312},"Zhuoyang Xu","https:\u002F\u002Forcid.org\u002F0000-0003-0344-3424",{"name":314,"orcid":315},"Ruohong He","https:\u002F\u002Forcid.org\u002F0009-0000-7404-9112",{"name":317,"orcid":9},"Rina Yangdao",{"name":319,"orcid":9},"Hongqiang Dong",{"name":321,"orcid":322},"Ping Li","https:\u002F\u002Forcid.org\u002F0009-0007-4465-1023",{"tldr":324,"method":325,"finding":326,"direction":81,"opportunity":327},"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":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":9,"source_name":335,"source_url":332,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":337,"sources":340,"tags":342,"primary_topic":345,"secondary_topics":350,"classification":356,"search_phrases":381,"slug":384,"view_count":22,"doi":385,"paper":386,"created_at":407},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":98,"substance":338,"depth":19,"authority":20,"freshness":99,"relevant":22,"comment":339},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。",[341],{"name":335,"url":332},[27,28,343,30,344],"农业人工智能","病害预警",{"code":290,"slug":346,"name":347,"path":348,"parent_code":349},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[351,355],{"code":134,"slug":352,"name":353,"path":354,"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":357},{"candidates":358,"agricultural_evidence":47},[359,366,372,376],{"code":290,"score":268,"supported":47,"title":360,"abstract":362,"tags":365},[361],"apple",[363,364,361],"苹果","葡萄",[],{"code":134,"score":17,"supported":47,"title":367,"abstract":369,"tags":371},[368],"deep learning",[370,368],"深度学习",[343],{"code":33,"score":267,"supported":47,"title":373,"abstract":374,"tags":375},[56],[56],[30],{"code":46,"score":377,"supported":47,"title":378,"abstract":379,"tags":380},7,[],[29,50],[27],[382,383],"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":385,"openalex_id":387,"authors":388,"venue":335,"cited_by_count":76,"oa_url":400,"card":401,"direction":406,"ingested_from":84},"W7221558726",[389,392,395,397],{"name":390,"orcid":391},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":393,"orcid":394},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":396,"orcid":9},"Fatih Sinan Bir",{"name":398,"orcid":399},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":402,"method":403,"finding":404,"direction":81,"opportunity":405},"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。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:10.242467Z",{"id":409,"title":410,"url":411,"summary":412,"summary_zh":413,"content":9,"source_name":414,"source_url":411,"published_at":415,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":416,"score_detail":417,"sources":419,"tags":421,"primary_topic":424,"secondary_topics":430,"classification":432,"search_phrases":448,"slug":451,"view_count":62,"doi":452,"paper":453,"created_at":472},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":267,"substance":338,"depth":268,"authority":124,"freshness":21,"relevant":22,"comment":418},"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。",[420],{"name":414,"url":411},[27,28,422,30,423],"种质资源","草坪草",{"code":425,"slug":426,"name":427,"path":428,"parent_code":429},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[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},[435,440,444],{"code":425,"score":267,"supported":47,"title":436,"abstract":438,"tags":439},[437],"germplasm",[437],[422],{"code":33,"score":99,"supported":47,"title":441,"abstract":442,"tags":443},[56],[],[30],{"code":46,"score":22,"supported":14,"title":445,"abstract":446,"tags":447},[],[],[27],[449,450],"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","10.1038\u002Fs41598-026-74076-4",{"doi":452,"openalex_id":454,"authors":455,"venue":414,"cited_by_count":76,"oa_url":466,"card":467,"direction":81,"ingested_from":84},"W7220416490",[456,459,462,464],{"name":457,"orcid":458},"E. M. B. M. Karunathilake","https:\u002F\u002Forcid.org\u002F0009-0009-8841-1480",{"name":460,"orcid":461},"Sheikh Mansoor","https:\u002F\u002Forcid.org\u002F0000-0003-1902-4611",{"name":463,"orcid":9},"Yong Suk Chung",{"name":465,"orcid":9},"Gyung Deok Han","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74076-4_reference.pdf",{"tldr":468,"method":469,"finding":470,"direction":81,"opportunity":471},"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":474,"title":475,"url":476,"summary":477,"summary_zh":478,"content":9,"source_name":479,"source_url":476,"published_at":480,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":481,"score_detail":482,"sources":484,"tags":486,"primary_topic":489,"secondary_topics":494,"classification":496,"search_phrases":518,"slug":521,"view_count":76,"doi":522,"paper":523,"created_at":565},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":267,"freshness":99,"relevant":22,"comment":483},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[485],{"name":479,"url":476},[27,28,487,120,488],"育种","紫花苜蓿",{"code":490,"slug":491,"name":492,"path":493,"parent_code":204},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[495],{"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":47},[499,505,509,513],{"code":490,"score":17,"supported":47,"title":500,"abstract":502,"tags":504},[501],"alfalfa",[503,501],"苜蓿",[488],{"code":33,"score":267,"supported":47,"title":506,"abstract":507,"tags":508},[56],[56],[120],{"code":46,"score":377,"supported":47,"title":510,"abstract":511,"tags":512},[],[29,50],[27],{"code":514,"score":53,"supported":47,"title":515,"abstract":516,"tags":517},"C4",[],[503],[488],[519,520],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":522,"openalex_id":524,"authors":525,"venue":479,"cited_by_count":76,"oa_url":476,"card":560,"direction":81,"ingested_from":84},"W7223083966",[526,529,532,535,538,540,542,545,548,551,554,557],{"name":527,"orcid":528},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":530,"orcid":531},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":533,"orcid":534},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":536,"orcid":537},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":539,"orcid":9},"Julie Hansen",{"name":541,"orcid":9},"Christopher Pierce",{"name":543,"orcid":544},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":546,"orcid":547},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":549,"orcid":550},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":552,"orcid":553},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":555,"orcid":556},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":558,"orcid":559},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":561,"method":562,"finding":563,"direction":81,"opportunity":564},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]