Abstract: Nitrogen is one of the most important elements for plant growth. Producers often use a lot of nitrogen fertilizer during plant growth process. However, excessive fertilizer often cause ground-water pollution. In this study, we proposed a nondestructive testing method for total nitrogen content in leaves of sandalwood (Santalum album) based on ST-PCA-BP neural network. The results showed that, due to the wide color range of L^*a^*b^* color system and its robustness in illumination change, images obtained from the field which were converted from RGB to L^*a^*b^* color system had a satisfying segmentation result. The proposed ST-PCA-BP neural network was characterized by choosing variables through significance test (ST) and using variance inflation factor (VIF) and conditional index (CI) to analyze collinearity of results, and further using principal component analysis (PCA) to eliminate it. Such a method reduced the probability of the chance that BP neural network fell into the local minimum. Compared with the result of original data, data after ST processing, and data after PCA processing, the results of proposed method had the highest coefficient of determination, while the mean residual error and the root mean square error were the smallest. It was the best way to detect the total nitrogen content of sandalwood leaves with real-time and non-destructive method.