Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine

作     者：Shaomin Chen Lihui Ma Tiantian Hu Lihua Luo Qiong He Shaowu Zhang 

作者机构：Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of EducationNorthwest A&F UniversityYangling 712100ShaanxiChina Institute of Soil and Water ConservationNorthwest A&F University Yangling 712100ShaanxiChina 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2021年第14卷第3期

页      面：181-188页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：This work was supported by the National Key Research and Development Program of China(Grant No.2017YFD0201508) 

主　　题：partial least square variable extraction method extreme learning machine hyperspectral reflectance apple tree canopy nitrogen content 

摘      要：Nitrogen(N)is an important mineral element in apple *** estimation of apple tree N status is helpful for achieving precise N *** objective of this work was to explore partial least squares(PLS)regression in dimensional reduction of spectral data and build the diagnostic *** spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer,and leaf total N content was obtained at the same *** raw spectra were pretreated using Savitzky-Golay(SG)smoothing and a combination of SG and first-order derivative(SG_FD)or second-order derivative(SG_SD).The samples were divided into a calibration dataset and a prediction dataset using *** on 4 factors of PLS regression,including latent variables(LVs),X-loading,variable importance in projection(VIP)and regression coefficients(RC),the 6 methods(LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02)were derived and used for variable extraction,based on which PLS model and ELM model were *** results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent *** amounts of variables extracted by LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02 were 6,11,18,305,26 and 88,*** method of extracting variables with an RC threshold based on the minimum RMSEP(RC_02)could effectively avoid the omission of effective *** RC_02 method was recommended for related research which required accurate wavelength information as a *** variable extraction method based on LVs generated an ELM model with a simple *** prediction results showed that the ELM model outperformed the PLS *** PLS(LVs)_ELM model was the best;R2P,RMSEP and RPD were 0.837,2.393 and 2.220,respectively.