Nitrogen, phosphorus and potassium content are the three most important nutritional parameters for growing oilseed rape. We investigated visible and near infrared (Vis/NIR) spectroscopy combined with chemometrics for the fast and nondestructive determination of nutritional information in oilseed rape leaves. A total of 154 leaf samples were collected, with 104 randomly selected as the calibration set, and the remaining 50 samples used as the validation set. The performance of eight different preprocessing methods was compared in partial least squares (PLS) models. Some effective wavelengths selected by a successive projections algorithm (SPA) were also used to develop linear SPA-PLS, nonlinear back propagation neural network (BPNN), and nonlinear least squares-support vector machine (LS-SVM) models to determine nutritional information. The best prediction models were DOSC-PLS for nitrogen with r=0.9743 and RMSEP=0.1459, DOSC-SPA-BPNN for phosphorus with r=0.7054 and RMSEP=0.0594, and DOSC-SPA-BPNN for potassium with r=0.9380 and RMSEP=0.1788. The prediction precision for nitrogen and potassium determinations was acceptable for further practical applications, whereas further studies are needed to improve the prediction precision for phosphorus. The results indicated that Vis/NIR spectroscopy is feasible for nondestructive determination of nutritional information in oilseed rape leaves. It also provided an alternative technique for detecting other growth information about oilseed rape leaves. Nitrogen, phosphorus and potassium content are the three most important nutritional parameters for growing oilseed rape. We observed visible and near infrared (Vis / NIR) spectroscopy combined with chemometrics for the fast and nondestructive determination of nutritional information in oilseed rape leaves. A total of 154 leaf samples were collected, with 104 randomly selected as the calibration set, and the remaining 50 samples used as the validation set. The performance of eight different preprocessing methods was compared in partial least squares (PLS) models. Previous projections algorithms (SPA) were also used to develop linear SPA-PLS, nonlinear back propagation neural network (BPNN), and nonlinear least squares-support vector machine -PLS for nitrogen with r = 0.9743 and RMSEP = 0.1459, DOSC-SPA-BPNN for phosphorus with r = 0.7054 and RMSEP = 0.0594, and D OSC-SPA-BPNN for potassium with r = 0.9380 and RMSEP = 0.1788. The prediction precision for nitrogen and potassium determinations was acceptable for further practical applications, and further studies are needed to improve the prediction precision for phosphorus. The results indicated that Vis / NIR spectroscopy is feasible for nondestructive determination of nutritional information in oilseed rape leaves. It also provides an alternative technique for detecting other growth information about oilseed rape leaves.