DEEP LEARNING FOR APPLE AND PEAR RECOGNITION

Authors

  • Vitālijs Žukovs Rezekne Academy of Technologies (LV)
  • Ilmārs Apeināns Rezekne Academy of Technologies (LV)
  • Sergejs Kodors Rezekne Academy of Technologies (LV)

DOI:

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

Keywords:

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

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.

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References

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Published

2021-04-23

Issue

Section

Information Technologies

How to Cite

[1]
V. Žukovs, I. Apeināns, and S. Kodors, “DEEP LEARNING FOR APPLE AND PEAR RECOGNITION”, HET, no. 25, pp. 119–124, Apr. 2021, doi: 10.17770/het2021.25.6791.