Abstract: Mechanical damage is one of the most important factors to affect the banana quality and sales. The collision damage of green bananas can also be early identified by naked eye. However, manual sorting of green bananas cannot fully meet the large-scale production in recent years, due to the time-consuming, low efficiency and easy to produce the deviation. In this study, the nondestructive detection and classification of mechanical damage of green bananas were realized to utilize the spectral and image information using hyperspectral technique. Firstly, a simulation device of collision damage was built and designed, according to the structural characteristics of green bananas. The same batch of healthy green Brazilian bananas were subjected to group impact experiments with different impact energies during simulation of collision device. Secondly, the samples were placed in the hyperspectral imager within 48h after the impact to scan and collect the sample data. The sample data after black/white and lens correction were imported into the software ENVI5.3. The region of interest (ROI) of the image was selected to obtain the average spectral reflectance data and image information on the surface of the healthy green banana and the wounds. Thirdly, savitzky golay (SG) and multiplicative scattering correction (MSC) were combined to preprocess the original spectral data, and then Monte Carlo algorithm and principal component analysis (PCA) combined with Mahalanlet distance were used to remove the abnormal samples. A series of experiments were carried out to verify the classification and grouping accuracy of samples with different damage degrees. Support vector machine (SVM), least square support vector machine (LSSVM), and particle swarm optimization-least square support vector machine (PSO-LSSVM) were utilized to process the spectral reflectance data after removing abnormal samples. The successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) were used to extract the characteristic wavelengths of green bananas, whereas, the interval combination optimization algorithm (ICO) was used to verify the accuracy of the extracted characteristic wavelengths. Finally, the low-dimensional image was obtained under the characteristic wavelength. The wound area and the pixel distribution data were identified using the binarization processing, Canny edge detection and image segmentation. The training and test set data of BP neural network were used to combine with the spectral reflectance data under the full pixel points derived from ENVI5.3. A nondestructive testing model was established for the classification and grouping of mechanical damage degree of green bananas. The test results show that the invisible minor collision damage was identified by hyperspectral technology. The recognition accuracy of the BP neural network detection model was 97.53%,92.59%, 93.82%, and 96.29% for the test set of healthy, mild collision, moderate collision, and severe collision samples. The accuracy of judging the overall damage degree was 95.06%. The visual image of damage level was output for the later use. This finding can provide the theoretical support to arrange the shelf life of green banana, particularly for the instrument of hyperspectral technology to identify the damage of green banana in real time.