Abstract: Abstract: In this study, we developed a fast and non-destructive technology for the prediction of the nitrogen content in lettuce leaves based on hyperspectral imaging technology which contains abundant spectral and spatial information in an object. First, hyperspectral images of lettuce leaves in the visible and near infrared (390-1050 nm) regions were acquired by the hyperspectral imaging system, and then the corresponding nitrogen content in the lettuce leaves were obtained by Kjeldahl method successively. The binary mask image was successfully determined by the method of dividing the image of very high reflectance intensity by the image of very low reflectance intensity with a certain threshold, and ROI (Region of Interest) in the sample of lettuce leaf was determined by removing the regions of noise using the acquired binary mask image. As the hyperspectral imaging technology provided much more information including spectral and spatial information for all the samples of lettuce leaves, and in which some information is noisy and redundant. In fact, this leads to the difficulty of meeting the needs for fast and efficient detection of some objects. So it is very hard to be directly used for on-line industrial application in our daily life. Therefore, effective selection of several characteristic wavelengths is necessary for the hyperspectral images. In this paper, the initial investigation was carried out by using a principal component analysis (PCA) to identify a number of potential characteristic wavelengths (662.9 nm, 711.7 nm, 735.0 nm, 934.6 nm) according to the weight coefficient distribution curve of the first three principal component images (PC1, PC2, PC3) under the full wavelengths. Both spectral data and texture data based on a co-occurrence matrix were extracted from the four characteristic wavelength images on the ROI, and the texture data of the first three principal component images were also extracted simultaneously. Then spectral data from four characteristic wavelength images, texture data (from four characteristic wavelength images, from the first three principle component images) and the combined data were utilized to develop different SVR (support vector machine regression) models to predict the nitrogen content in the lettuce leaves respectively. According to the performance of all the SVR models in the calibration set and the prediction set, the experiment results show that, from the calibration performance index, the model based on spectral data combined texture data from four characteristic wavelength images is the best with a coefficient of determination (R2C =0.996) and the root-mean-square error (RMSEC) of 0.034. From the prediction performance index, however the model based merely on spectral data is the best with a coefficient of determination (R2P=0.86) and the root-mean-square error (RMSEP) of 0.22. This study provides valuable information for rapid and non-destructive nitrogen detection in crops.