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Apple processing has been one of the most important aspects in the field of fruit and vegetable processing for a long time, and how to screen out the defects of apple with high precision and low cost has been one of the key research directions at home and abroad. In view of the current situation of fruit sorting which mainly completed by manual operation in China, the deep transfer model GoogLeNet based on deep convolutional neural network was used to detect the defects of apple, and the results showed that the accuracy rates of GoogLeNet could reach up to 100% and 91.91% based on 1932 expanded training samples and 235 testing samples, respectively. At the same time, through assessing the performance of common machine learning algorithms in the field of apple defects detection, the results of GoogLeNet were compared with the shallow convolutional neural network (AlexNet and the improved LeNet-5) and traditional machine learning algorithms (K-nearest neighbor, K-NN;random forest, RF;support vector machine, SVM) in order to further verify the superiority of GoogLeNet.The results indicated that deep convolutional neural network had better generalization ability and robustness when compared with other conventional algorithms in the field of apple defects detection, which supported its broad application prospects.