Detection of the storage time of light bruises in yellow peaches based on spectrum and texture features of hyperspectral image

Yellow peaches are soft, and they bruise easily; the bruised areas of them are prone to breed bacteria and molds, so the consumption and the safety of related products of yellow peaches are affected by the difference in the storage time of light bruises in them. In order to accurately distinguish of the storage time of light bruises in yellow peaches, the spectra of the sample bruised region were combined with texture features extracted based on gray-level co-occurrence matrix (GLCM), and the deep learning algorithm was used for modeling. A total of 80 samples were prepared in the experiment, and the hyperspectral images of them were acquired at four time periods (2, 8, 24, and 48 h), and the reflection spectral data as well as the texture features of the bruised samples were extracted from the hyperspectral images. First, the random forest (RF) and extreme gradient boosting (XGBoost) models were built based on spectral, texture, and spectral features combined with texture features (Feature Fusion 1), respectively, and the best model discrimination was the RF model under Feature Fusion 1, with an overall accuracy of 98.33%. In order to remove the redundant information of spectrum, the UVE and CARS algorithms were used to screen the normalized spectral feature data, and then, the texture features were combined again (Feature Fusion 2), and the RF and XGBoost models were built. The results show that the optimal model for distinguishing the storage time of yellow peaches after bruising is the RF model under Feature Fusion 2 (CARS), with an overall accuracy of 98.33%. In summary, this study shows that spectral features combined with texture features can be used to effectively improve the model's discrimination of storage time after bruising of yellow peaches, and it also provides a certain theoretical basis for hyperspectral imaging technology to discriminate storage time after bruising of fruits.