The determination of moisture content in potato leaves using spectral technique was studied in this paper. Spectral signatures of one hundred and ten fresh potato leaves in the wavelengths of 900-2100 nm were acquired by the spectral device. Then, the moisture content was measured by the drying method.The near-infrared reflection spectrum information was corrected by the Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC) and standard normal variable (SNV) correction. The quantitative relationship between spectral information and moisture was built by partial least squares regression (PLSR) and BP neural network respectively. The effective wavelength was identified by regression coefficients (RC) and corrected by three pretreatment methods. Then the PLSR and BP neural network models were built respectively. The results showed that for full wavelengths-based models, MSC-BP model performed the best with the coefficient of determination (R²) of 0.9791 and the root mean square error (RMSE) of 0.3723 in the prediction. For selected wavelengths-based models, it was the SG-BP model that obtained the optimal result. The R² value was 0.965 8 and the RMSE value was 0.475 9 in the prediction. This experiment verified that the prediction results of the model established by combining the characteristic band with BP neural network were not different from those of the model established by the whole band, so it could greatly reduce the computation and improve the efficiency.