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Quantification of Nitrogen Status in Oilseed Rape by Least-Squares Support Vector Machines and Reflectance Spectroscopy
He Yong * #,Cen Haiyan ,Bao Yidan ,Huang Min
Zhejiang University
*Correspondence author
#Submitted by
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Funding: 教育部博士点基金（No.20040335034 ）
Opened online:14 December 2007
Accepted by: none
Citation: He Yong,Cen Haiyan ,Bao Yidan .Quantification of Nitrogen Status in Oilseed Rape by Least-Squares Support Vector Machines and Reflectance Spectroscopy[OL]. [14 December 2007] http://en.paper.edu.cn/en_releasepaper/content/16936
 
 
The estimation of nitrogen status non-destructively in oilseed rape in a crop-growing period was performed using reflectance spectroscopy with least-squares support vector (LS-SVM). This study was conducted at the experiment farm of Zhejiang University, Hangzhou, China. The SPAD value was used as a reference data that reflects nitrogen status in oilseed rape. A total of 159 oilseed rape leaf samples were used for visible and near infrared reflectance spectroscopy at 325-1075 nm using a field spectroradiometer. The reflectance data processed by median filter was applied for LS-SVM regression model to predict SPAD values. The performance of LS-SVM with RBF kernel function and five input variables derived from scores of partial least squares (PLS) latent variables (LVs) was investigated. To serve this purpose, the grid-search technique using 5-fold cross-validation was used to find out the optimal values of two important parameters in LS-SVM regression model. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BPNN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD values of oilseed rape leaves. It is concluded that LS-SVM regression method is a promising technique for chemometrics in the field of quantitative prediction.
Keywords:oilseed rape; nitrogen; least-squares support vector; partial least squares
 
 
 

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