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DEEP LEARNING FOR APPLE AND PEAR RECOGNITION

Vitālijs Žukovs, Ilmārs Apeināns, Sergejs Kodors

Abstract


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.

Keywords


AlexNet; apple; CNN; Fruits360; Food2030; neural network; pear

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References


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DOI: https://doi.org/10.17770/het2021.25.6791

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