Comparison of Plain Network and ResNet Classifiers for Fruit Image Classification

As deep learning techniques continue to advance, image recognition and classification have become research hotspots in the field of computer vision. This study aims to investigate the application of deep learning classifier in fruit image recognition problems, and we have compared two distinct deep neural network architectures: Plain Network and ResNet. In addition, this study primarily focuses on the practical application of deep learning neural networks in classification problems. Therefore, we use fruit classification as an example and build a deep learning classifier for fruits. The introduction and popularization of deep learning neural networks hold significant meaning. The contributions of this study are as follows: 1) Popularization of deep learning neural networks through educational efforts. 2) Experimental evidence demonstrates that the deep network learning architecture ResNet outperforms the traditional Plain Network in terms of learning efficiency and accuracy in classification problems.