Modeling Chlorophyll Content of Korean Pine Needles with NIR and SVM

Model for predicting chlorophyll content of Korean pine needles was developed using near-infrared spectroscopy (NIR) combined with support vector machines (SVM). A hundred and forty-four Korean pine needle samples were collected in the study. Chlorophyll content of needle samples was measured with chlorophyll tester of SPAD502. Support vector machines for regression (SVR) was applied to model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting chlorophyll content of Korean pine needles. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8342 and 0.3104, respectively. The R2 and MSE were 0.8207 and 0.4618, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance for rapid and accurate prediction of chlorophyll content of Korean pine needles.