Research on the improved apple classification method of AlexNet

Abstract

To address the issue of high cost and low efficiency in the manual sorting of apples, we proposed an improved apple classification method based on the AlexNet architecture. The algorithm added a batch normalization layer after each convolutional layer in the network structure to speed up the model's training process. Furthermore, we replaced the fully connected layer with a global average pooling layer to reduce the number of training parameters and save model training time. To improve the algorithm's robustness, we also performed data augmentation on the training samples before validating the algorithm to obtain an expanded dataset. Experimental results showed that the improved AlexNet network shortened the training time by 0.54%, increased the testing speed by 2.5%, and improved the accuracy by 1.12% compared to the original AlexNet network. Moreover, the training time of the improved AlexNet network was lower than that of other networks (AlexNet, ResNet50, Vgg16). The improved AlexNet network can efficiently and quickly classify apples and promote the automation of apple classification.