Nitrogen (N), phosphorus (P) and potassium (K) are important biochemical components of plant organic matters, and estimating their contents are useful for monitoring plant metabolism processes and health. Visible and near-infrared (VNIR) spectroscopy has been applied to monitor plant biochemical parameters with many modeling methods, in which support vector machine (SVM) has been proved to be a potential approach for modeling the nonlinear relationships between the reflectance spectra and biochemical parameters of plant organic matters, and the successful application of SVM relies on the proper selection of kernels. This study aimed to compare the performances of radial basis function (RBF), polynomial and sigmoid kernels based support vector machine regression (SVR) models in estimating the contents of nitrogen (cN), phosphorus (cP) and potassium (cK) of diverse plant leaves using laboratory-based VNIR spectroscopy. The cN, cP, cK and VNIR reflectance of leaf samples in eight plant species(rice, corn, sesame, soybean, tea, grass, shrub and arbor) were measured in laboratory. Three transformation methods, namely the first derivative(FD), standard normal variate (SNV) and logrithmic reciprocal transformation (Log(1/R)) were used for spectral transformation. The SVR models using three aforementioned kernels were calibrated and validated with 1 000 bootstrap sample datasets. The average determination coefficients (R2) as well as ratio of performance to standard deviate (RPD) were calculated to compare the performances of three different kernels. The results showed that, the RBF kernel based SVR model with FD and absorbance transformation obtained the best accuracy for cN and cK estimations (cN: mean R2=0.64, mean RPD=1.67; cK: mean R2=0.56, mean RPD=1.48), and the RBF kernel based SVR model with FD transformation obtained the best accuracy for cP estimations (cP: mean R2=0.68, mean RPD=1.73). The study indicated that RBF kernel based SVR model has great potential in estimating biochemical component contents of diverse plant leaves with VNIR spectroscopy.