Quantification of Nitrogen Status in Oilseed Rape by Least-Squares Support Vector Machines and Reflectance Spectroscopy

• 1、Zhejiang University

Abstract：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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