[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2538":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":54},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",0,"10.3390\u002Fagronomy16181805",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":46,"direction":52,"ingested_from":53},"W7213116925",[37,40,43],{"name":38,"orcid":39},"TaekJin Yoon","https:\u002F\u002Forcid.org\u002F0009-0003-6507-8415",{"name":41,"orcid":42},"Tae Wan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1742-1982",{"name":44,"orcid":45},"Sung Yung Yoo","https:\u002F\u002Forcid.org\u002F0000-0002-7889-3924",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"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"]