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To deduce the process of bruise and reduce the number of bruised fruits from the source, the storage time of yellow peaches after bruise should be identified. In order to distinguish the different storage times of mild bruise’s yellow peaches more effectively than current detection methods, the combined hyperspectral imaging and machine learning method was proposed. Firstly, the sample bruise region spectrum was extracted as spectral features, and then, the hyperspectral images were processed by Principal Component Analysis (PCA), and eight single-wavelength images were selected according to the weight coefficient curve of PC1 images, and the gray values of the selected images were calculated as image features. Finally, in order to find the optimal discriminative model, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were built based on spectral features, image features, and spectral features combined with image features, respectively. The results show that the XGBoost models based on spectral features, image features, and spectral features combined with image features are the optimal models with the overall accuracy of 77.50%, 87.50% and 90.00%, respectively. To simplify the model, Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to screen the wavelength of the normalized spectral data, and then they were fused with the image feature data again and the XGBoost model with an overall model accuracy of 95.00% was built. To sum up, the combined hyperspectral imaging and machine learning method can be used to distinguish the different storage times (2 h, 8 h, 24 h and 48 h) of mild bruise’s yellow peaches effectively. It provides a certain theoretical basis for hyperspectral imaging technology in fruit bruise detection.

Crown pears are an important economic crop, but their quality and economy are seriously affected by the different levels of damage. To improve the overall quality of crown pears, sorting of crown pears with different levels of damage is required. However, there are some shortcomings in the traditional detection methods, such as low efficiency and large error. Therefore, the hyperspectral technology was used to discriminate between sound and 3 different levels of damage (defined as level I, II, and III damage, respectively) of crown pears in this study. To improve the discriminatory accuracy of the model, absorbance (A) spectra and Kubelka–Munk (K-M) spectra were added to reflectance (R) spectra. The three spectra were pretreated; then, the partial least squares discriminant analysis (PLS-DA) model and the support vector machine (SVM) model were established to discriminate the crown pears with different levels of damage. The results of the discriminant model show that the discrimination accuracy of the SVM based on R, A, and K-M spectra is higher than that of PLS-DA of them; the A-RAW-SVM model has the best discrimination performance with an overall discrimination accuracy of 100% for the test and 98.98% for calibration sets, respectively. Finally, the spectra were selected by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) to obtain the characteristic wavelengths, and the SVM models were built based on the filtered R, A, and K-M. Their discrimination results show that the A-RAW-CARS-SVM model has the best discrimination ability, and the discrimination accuracies of the test and calibration sets of the model are 96.88% and 100%, respectively. The results show that the best discrimination of different levels of damage of crown pears is the SVM model based on a spectra. This study provides a theoretical basis and experimental basis for detecting the damage of crown pears using hyperspectral.