Near infrared (NIR) spectroscopy combined with successive projections algorithm (SPA) was investigated for the fast and nondestructive determination of total amino acids (TAA) in oilseed rape leaves. Total amino acids are important indices of the growing status of oilseed rape. A total of 150 leave samples were scanned, the calibration set was composed of 80 samples, the validation set was composed of 40 samples and the prediction set was composed of 30 samples. The optimal partial least squares (PLS) model was developed for the prediction of total amino acids in oilseed rape leaves after the performance comparison of different pretreatments, including smoothing method, standard normal variate (SNV), the first derivative and second derivative. Simultaneously, successive projections algorithm was applied for the extraction of effective wavelengths (EWs), which were thought to have least collinearity and redundancies in the spectral data. The selected effective wavelengths were used as the inputs of multiple linear regression (MLR), partial least squares (PLS) and least square-support vector machine (LS-SVM). Then the SPA-MLR, SPA-PLS and SPA-LS-SVM models were developed for performance comparison. The determination coefficient (R2) and root mean square error (RMSE) were used as the model evaluation indices. The results indicated that both SPA-MLR and SPA-PLS models were better than full-spectrum PLS model, and the best performance was achieved by SPA-LS-SVM model with R2=0.983 0 and RMSEP=0.396 4. An excellent prediction precision was achieved. In conclusion, successive projections algorithm is a powerful way for effective wavelength selection, and it is feasible to determine the total amino acids in oilseed rape leaves using near infrared spectroscopy and SPA-LS-SVM, and an excellent prediction precision was obtained. This study supplied a new and alternative approach to the further application of near infrared spectroscopy in the response of stress and on-field monitoring of the growing oilseed rape.