To monitor tea tree growth and nitrogen nutrition in tea leaves, visible-near infrared spectroscopy was used to determine total nitrogen content. One hundred eleven fresh tea leaves of different nitrogen levels were sampled according to different tea type, plant age, leaf age, leaf position and soil nutrients, which covered a wide range of nitrogen content. Visible-near infrared reflectance spectra were scanned under the sunlight with a portable spectroradiometer (ASD FieldSpec 3) in field. The software of NIRSA developed by Jiangsu University was used to establish the calibration models and prediction models, which included spectra data editing, preprocessing, sample analysis, spectrogram comparison, calibration model and prediction model, analysis reporting and system configuration Eighty six samples were used to establish the calibration model with the preprocessing of first/second-order derivative plus moving average filter and the algorithm of PLS regression, stepwise regression, principal component regression, PLS regression plus artificial neural network and so on The result shows that the PLS regression calibration model with 7 principal component factors after the preprocessing of first-order derivative plus moving average filter is the best and correspondingly the root mean square error of calibration is 0. 973. Twenty five unknown samples were used to establish the prediction model and the correlation coefficient between predicted values and real values is 0.8881, while the root mean square error of prediction is 0. 130 4 with the mean relative error of 4.339%. Therefore, visible-near infrared spectroscopy has a huge potential for the determination of total nitrogen content in fresh tea leaves in a rapid and nondestructive way. Consequently, the technique can be significant to monitoring the tea tree growth and fertilization management.