Fruit 360 classification based on the convolutional neural network

Abstract

This research paper focuses on the Fruit360 Classification challenge, a task aimed at developing a fruit classification model capable of accurately identifying various fruits and distinguishing them from each other. In this study, the Fruit360 dataset is used, consisting of 90380 images of 131 fruits and vegetable classes. Prior to training the CNN model, the images are preprocessed by resizing, normalizing, and augmenting them. The authors employ a pre-trained CNN model called ResNet-50 using the PyTorch deep learning framework and add a custom fully connected layer on top to adapt the model to the specific classification task. The authors conclude that the proposed model achieved excellent performance on the Fruit360 dataset. The study highlights the importance of the Fruit360 Classification challenge in advancing the field of computer vision, specifically in the development of deep learning algorithms for image classification tasks. The proposed model has the potential to improve the efficiency and accuracy of fruit classification, which can benefit the fruit industry in terms of enhanced productivity and cost-effectiveness.