The soluble sugar to nitrogen ratio reflects the coordination degree of carbon (C) and nitrogen (N) metabolism. Precise and realtime monitoring of soluble sugar to nitrogen ratio is of significant importance for nitrogen diagnosis and management regulation in wheat production. In this study, timecourse near infrared spectroscopy and soluble sugar to nitrogen ratio of fresh and dry leaves were obtained under different field experiments with varied years and cultivar and N rates. The methods of partial least squares (PLS), backpropagation neural network (BPNN) and wavelet neural network (WNN) were used to develop the calibration models with the preprocessed spectra, respectively, and the dataset selected randomly was used to evaluate the constructed models. The results showed that the performance of the models for freshleaves was not satisfied, but good for dryleaves with the root mean square errors of prediction (RMSEP) by PLS, BPNN and WNN models based on 1655-2378 nm less than 0.3% and with the coefficients of determination (R²) over than 0.9, respectively. In comparison, the model based on WNN was the best one. All these indicated that near infrared spectrometry could be applied to estimating the soluble sugar to nitrogen ratio in plant. The results provided the theoretical basis and technological approach for diagnosing crop C/N.