Transfer Learning with Convolutional Neural Networks for Cider Apple Varieties Classification

  1. García Cortés, Silverio 1
  2. Menéndez Díaz, Agustín 1
  3. Oliveira Prendes, José Alberto 1
  4. Bello García, Antonio 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Agronomy

ISSN: 2073-4395

Año de publicación: 2022

Volumen: 12

Número: 11

Páginas: 2856

Tipo: Artículo

DOI: 10.3390/AGRONOMY12112856 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Agronomy

Resumen

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.

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Información de financiación

This study was funded by Project FUO-469-19 (Fundación de la Universidad de Oviedo) and co-financed by ENRG GESTIÓN EFICIENTE.

Financiadores

  • Fundación Universidad de Oviedo and ENRG Gestión Eficiente
    • FUO-469-19

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