Abstract: Mechanical bruising has been one of the main influencing factors of yellow peaches on consumer satisfaction and shelf life. The implicit bruises can often be suffered during picking, grading, and transportation. Early slight bruising can accelerate the spoilage and decay under the action of biological enzymes, due to the fungal infection. Therefore, it is crucial to timely and efficiently detect the implicit bruises in yellow peaches. However, the manual identification of surface bruises on fruits and vegetables cannot fully meet the large-scale production at present, due to the inefficiency, cost, and easy influence by subjective human factors. Common non-destructive testing techniques also share some limitations in detecting fruit and vegetable bruises, such as machine vision, multispectral/hyperspectral, X-ray/CT, thermal and fluorescence imaging. Structured illumination reflectance imaging (SIRI) is characterized by a non-contact, wide field of view and depth discrimination. The spatial frequency of structured light and the penetration depth of light in biological tissues can be regulated to overcome the limitation of conventional uniform illumination imaging that cannot obtain depth information. The SIRI can be combined the image processing and classification, in order to effectively detect the implicit defects of the fruits and vegetables. However, it is usually required to obtain three-phase shifted structured illumination images, which is limited to the imaging and detection speed. In this study, the SIRI technology was combined with the SPT fast demodulation and machine learning, and then rapidly detected the early implicit bruises in yellow peaches. The feasibility of hidden damage detection was verified using SIRI technology and two SPT demodulations. The detection efficiency of SIRI technology was improved for the online detection of implicit bruises in yellow peaches. Firstly, the SIRI system was used to collect the stripe-structured light reflection images at the spatial frequencies of 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30 mm⁻¹. Three-phase demodulation (TPD) was used to obtain the amplitude component (AC) and direct component (DC) images. The contrast index (CI) of the AC image was calculated to select the optimal spatial frequency. Three-phase stripe images were collected on all samples. The AC and DC images were obtained after capture. The ratio image (RT) of AC/DC was calculated to improve the uneven surface brightness of yellow peaches after SPT demodulation. According to the gray level co-occurrence matrix (GLCM) of DC, AC, and RT images, the local binary pattern (LBP) image texture features, and depth features extracted by ResNet-50, the inputs were taken as the GLCM-LBP, depth and mixed features of five images (DC, AC, RT, DC-AC, and DC-AC-RT). The healthy and bruised yellow peaches were classified using machine learning models, such as the support vector machine (SVM), K-Nearest Neighbor (KNN), XGBoost, and random forest (RF). The results showed that the highest average accuracies of the improved model were 92.6%, 95.0%, and 95.7%, respectively, for the detection of bruised peaches using GLCM-LBP, depth, and mixed features. The mixed feature model achieved the highest average accuracy. Therefore, the GMLC-LBP and depth features were combined to effectively improve the classification accuracy of the model. The XGBoost model with the DC-AC-RT image features also shared the highest accuracy of 97.6%. Furthermore, the overall classification accuracy of the SPT demodulated images was comparable to that of TPD ones, with the highest accuracy of 97.6%. The image acquisition time was also saved by one-third, with only any two-phase shifted images. In summary, the SIRI with the SPT and machine learning was achieved to detect the implicit bruises in peaches, with high accuracy and less detection time, thus improving the detection efficiency of SIRI technology. This finding can also provide a strong reference for the real-time detection of implicit bruises on fruits and vegetables.