Transfer Learning with Convolutional Neural Networks for Cider Apple Varieties Classification

Abstract

Cider production requires detailed knowledge of the apple varieties used. Of the hundreds of varieties of cider and dessert apples in Spain, only a few are accepted for producing cider under the “Sidra de Asturias” protected designation of origin. The visual characteristics of many of these varieties are very similar, and only experts can distinguish them. In this study, an artificial intelligence system using Transfer Learning techniques was developed for classifying some Asturian apple varieties. The performance of several convolutional neural network architectures was compared for classifying an image database created by the authors that included nine of the most common apple varieties. The best overall accuracy (98.04%) was obtained with the InceptionV3 architecture, thus demonstrating the reliability of the classification system, which will be useful for all cider or apple producers.