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Comparative Performance of Various Deep Learning based Models in Fruit Image Classification

Fruit type classification based on computer vision remains challenging because of shape, color and texture similarity among numerous species of fruits. Recent research results indicate that the Convolutional Neural Network based models have demonstrated substantial improvements in fruit image classification accuracy. This paper proposes fourteen different deep learning models for fruit type classification. Thirteen of these models exploit ImageNet pre-trained feature extractors. In addition to these thirteen models, a simple, lightweight 12-layer model called FruitNet has also been proposed. A large, publicly available fruit images dataset, called Fruits 360, has been used to train and evaluate the proposed models. Among all the models that exploited some pre-trained feature extractor, Xception based model has produced the best validation accuracy rate (i.e. 99.97%) while MobileNet based model has proved to be a very efficient model. Fine tuning has also been found to produce excellent results. In fact, the best validation accuracy of 99.98% is achieved when VGG16 based model is fine tuned. On the other hand, FruitNet has yielded accuracy rate of 99.73% which is excellent given its small size and simple structure. FruitNet is also the most efficient model among all the proposed models.