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The conventional method of soil nitrate-nitrogen prediction based on ion-selective electrode had the problem of complex soil suspension components and the limited prediction accuracy and precision in single input variables. To improve the prediction accuracy and precision of soil NO - 3-N concentration employing ion-selective electrodes (ISEs), the support vector machine (SVM) model of soil NO - 3-N prediction based on sensor fusion was built. Grey relational analysis was applied to screen the major interference factors, which had a great impact on the soil NO - 3-N detection employing ISEs, and the support vector machine (SVM) model based on sensor fusion was built with the major factors. Then, the classical Nernst model and the SVM model with major factors and all considered factors were compared with the conventional method. According to the testing results, EC values, temperature and Cl - were the three major interference factors which had great influence on the prediction accuracy and precision of soil NO - 3-N concentration employing ISEs. With the optimized input parameters of NO - 3-N ISE potentials, EC, temperature and Cl - ISE potentials, the adjusted R 2 , average absolute error and root mean square error of the SVM model were 0.98, 3.38 mg/L and 4.51 mg/L, respectively. The SVM model based on sensor fusion showed more advantages than the Nernst model and it could successfully achieve the prediction purpose of NO - 3-N with high prediction accuracy and precision of the ISEs in soil extracted solution.