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The dataset was constructed by collecting healthy and disease-infected apple and leaf images and expanded using data enhancement techniques. Using ResNet34 as the base model, multiple improved network models are obtained by combining migration learning, and finally the accuracy of apple surface scar classification is successfully improved to 99.7%. The experimental results show that migration learning improves the model accuracy in small samples, while data augmentation also significantly improves the model performance. 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