西北农林科技大学学报（自然科学版）2026，Vol.54Issue(9)：27-37,11.DOI:10.13207/j.jnwafu.2026.09.003
基于无人机多光谱影像的油茶冠层氮磷钾含量估算模型
Estimation model of the canopy N,P,K content for Camellia oleifera based on UAV multispectral images
摘要
Abstract
[Objective]Based on UAV multispectral images,this study aims to build the diagnostic models for estimating canopy nitrogen(N),phosphorus(P),and potassium(K)content in Camellia oleifera,which provides references for fertilization management.[Method]This study was conducted in a C.oleifera plantation in Meilinhu,Heyuan city,Guangdong province.The characteristic vegetation indices were selected based on competitive adaptive reweighted sampling(CARS)method and 39 vegetation indices were calculated from UAV multispectral images(Yunsense MS600V2 sensor).Extreme Learning Machine(ELM),Partial Least Square Regression Model(PLSR)and ridge regression(RR)models were built to estimate canopy N,P,K contents based on the field collected mature leaves and CARS selected vegetation indices.The leave-one-out cross validation method was used for accuracy assessment,with performance metrics including RMSE,RRMSE,and R2,supplemented by an analysis of the spatial and numerical distribution of the estimates.[Result]The CARS method selected 9(EXB,TGI,BNDVI,ENDVI,DATT750,NDREI,RECI,SCCCI750,TVI),7(EXB,TGI,ENDVI,GNDVI,NDVI,MCARI750,TVI)and 6(IKAW,TGI,VARI,NDVI,NDRE,RECI750)characteristic vegetation indices for estimating canopy N,P,K contents,respectively.For canopy N content esti-mation,the R2 values of the RR(0.528)and PLSR(0.669)models were slightly lower than that of the ELM model(0.679),but both models had lower RRMSE(RR:5.08％;PLSR:5.23％)than the ELM model(5.53％).Furthermore,the mean,stand deviation,and range of the RR model's estimates were closer to the field-measured data.For estimating canopy P and K contents,the RR model demonstrated superior performance over the ELM and PLSR models,as its estimates showed the closest agreement with the field-measured data in terms of mean,standard deviation,and range.[Conclusion]This study provides a non-destructive,low-cost,high-frequency,and high-efficiency method for rapid canopy N,P,K estimation of C.oleifera.By integrating UAV multispectral images with vegetation indices selected via CARS and the RR model,it provides a practical tool to support precision fertilization and yield enhancement.关键词
油茶/冠层氮磷钾含量/无人机/多光谱影像/营养诊断Key words
Camellia oleifera/canopy N,P,K content/UAV/multispectral images/nutrition diagnosis分类
农业科技引用本文复制引用
徐丹丹,段丹丹,陈龙跃,赵春江..基于无人机多光谱影像的油茶冠层氮磷钾含量估算模型[J].西北农林科技大学学报（自然科学版）,2026,54(9):27-37,11.基金项目
岭南现代农业科学与技术广东省实验室河源分中心项目(DT20220001) （DT20220001）
国家重点研发计划项目(2022YFD2202102) （2022YFD2202102）
广东省科技专项资金项目(210909114530725) （210909114530725）
广东省科技计划项目(2023B0208010002) （2023B0208010002）
