The Alexnet-ResNet-Inception Network for Classifying Fruit Images

Fruit classification contributes to improving the self-checkout and packaging systems in supermarkets. The convolutional neural networks can automatically extract features through directly processing the original images, which has thus attracted wide attention from researchers in terms of fruit classification. However, it is difficult to achieve more accurate recognition due to the complexity of category similarity. In this study, the Alexnet, ResNet, and Inception networks were integrated to construct a deep convolutional neural network named Interfruit, which was then utilized in identifying various types of fruit images. Afterwards, a fruit dataset involving 40 categories was also constructed to train the network model and to assessits performance. According to the evaluation results, the overall accuracy of Interfruit reached 92.74% in the test set, which was superior to several state-of-the-art methods. To sum up, findings in this study indicate that the classification system Interfruitr ecognizes fruits with high accuracy and has a broad application prospect. All data sets and codes used in this study are available at https://pan.baidu.com/s/19LywxsGuMC9laDiou03fxg , code: 35d3.