DEEP LEARNING FOR APPLE AND PEAR RECOGNITION

Abstract

The aim of this work is to develop a neural network, which is able to recognize apples and pears. To achieve the goal, the authors of this work used the architecture of the neural network AlexNet and the open dataset “Fruits360”. A trained model showed a good result testing it on validation images: total accuracy 0.97 and latency 35ms/step. In the future research, authors consider to train the neural network model using the MobileNet architecture and verify it using the Cohen`s Kappa coefficient.