A Deep Convolutional Neural Network Model for Multi-class Fruits Classification

Abstract

Fruits classification is a challenging task due to the several types of fruits. To classify fruits more effectively, we propose a new deep convolutional neural network model to classify 118 fruits classes. The proposed model combines two aspects of convolutional neural networks, which are traditional and parallel convolutional layers. The parallel convolutional layers have been employed with different filter sizes to have better feature extraction. It also helps with backpropagation since the error can backpropagate from multiple paths. To avoid gradient vanishing problem and to have better feature representation, we have used residual connections. We have trained and tested our model on Fruits-360 dataset. Our model achieved an accuracy of 100% on a divided image set from the training set and achieved 99.6% on the test set, which outperformed previous methods.