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Estimation model between soil moisture content and the near infrared reflectance was established by the linear regression method and the models between soil total nitrogen content and the near infrared reflectance were also established by the BP neural network method and Support Vector Machine (SVM) method. Forty-eight soil samples were collected from China Agricultural University Experimental Farm. After the soil samples were taken into the laboratory, NIR absorbance spectra were rapidly measured under the original conditions by the FT-NIR (Fourier Transform Near Infrared Spectrum) analyzer. At the same time the soil moisture (SM) and soil total nitrogen (TN) were measured by the laboratory analysis methods. The results of the study showed that a linear regression method achieved an excellent regress effect for soil moisture. The correlation coefficient of the calibration (RC) was 0.88, and the correlation coefficient of the validation (RV) was 0.85. The model was passed F test and t test. For soil total nitrogen, the model effect of BP neural network was better than that of SVM method, and the correlation coefficient of the calibration (RC) coefficient and the validation (RV) was 0.92 and 0.88, respectively. Both RMSE and PMSE were low. The results provided an important reference for the development of a portable detector.