DEEP LEARNING FOR APPLE AND PEAR RECOGNITION - Žukovs, Vitālijs; Apeināns, Ilmārs; Kodors, Sergejs (HUMAN. ENVIRONMENT. TECHNOLOGY. Proceedings of the Students International Scientific and Practical Conference, 2021)
The aim of this work is to develop a neural network, which can recognize apples and pears. To achieve the goal, the authors applied AlexNet architecture and the open dataset “Fruits360”. The 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 training the neural network model using the MobileNet architecture and verify it using the Cohen`s Kappa coefficient.