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《Journal of Northwest A & F University(Natural Science Edition)》 2019-01
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Estimation of chlorophyll content in apple leaves based on red edge parameters and artificial neural network

LUO Dan;CHANG Qingrui;QI Yanbing;College of Resources and Environment,Northwest A&F University;  
【Objective】The nonlinear relationship between hyperspectral data and chlorophyll content of apple leaf was solved using artificial neural network(ANN)and the best model for estimating chlorophyll content of apple leaves was established to provide basis for rapid and non-destructive monitoring of chlorophyll content in apple leaves.【Method】A total of 15 apple trees at the age of 4-5years in Xinglin,Fufeng,Shaanxi were selected and leaf samples were collected in flowering(April 27),young fruit(May30),young fruit expansion(July 6),coloring(August 5),and fruit maturation(September 11)stages in2015.Hyperspectral reflectance was collected by spectrometer(SVC HR-1024i),and relative chlorophyll content was measured by chlorophyll meter SPAD-502.The corrections between original spectrum,first derivative spectrum and chlorophyll content were analyzed.Five red edge parameters were calculated as the first derivative.The traditional single variable empirical regression algorithms,back propagation(BP)and radial basis function(RBF)based on the well-related red edge parameters were applied to establish chlorophyll content estimated models.The accuracy of models was determined based on determination coefficient(R2),root mean square error(RMSE)and relative error(RE).【Result】Compared with the original spectrum,the first derivative spectra had higher relevance with chlorophyll content of apple leaves.The red edge position,kurtosis and skewness had higher correlation among the 5red edge parameters(P0.01).The correlation coefficients of models based on red edge position,kurtosis and skewness reached over 0.7.The skewness based polynomial model was the best with R2,RMSE and RE of 0.872,4.631 and 8.81%,respectively.Red edge position,kurtosis and skewness were selected as input variables of ANN to build model for estimating chlorophyll content.The node of hidden layer and network type in BP-ANN and the SPREAD value in ANN-RBF were optimized to improve precision.The R2 based on the BP neural network optimal model with 4node of double hidden layer was 0.891,and RMSE and RE were 3.844 and 7.55%.When SPREAD was 0.6,R2,RMSE and RE of the optimized RBF-ANN were 0.955,2.517 and 3.69%,respectively.【Conclusion】Compared to the models with single variable,ANN can improve estimation accuracy of chlorophyll content prediction.RBF-ANN had the advantages of rapid,accurate,and reliable.
【Fund】: 国家高新技术研究与发展计划项目(“863”计划项目)(2013AA102401)
【CateGory Index】: S661.1
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