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FRESHNETS: HIGHLY ACCURATE AND EFFICIENT FOOD FRESHNESS ASSESSMENT BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS
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  • Jorge Felix Martínez Pazos,
  • Jorge Gulín González,
  • David Batard Lorenzo,
  • Arturo Orellana García
Jorge Felix Martínez Pazos
Center for Computational Mathematics Studies (CEMC), University of Informatics Science. Carretera a San Antonio de los Baños Km

Corresponding Author:[email protected]

Author Profile
Jorge Gulín González
Center for Computational Mathematics Studies (CEMC). University of Informatics Science. Carretera a
David Batard Lorenzo
Center for Computational Mathematics Studies (CEMC). University of Informatics Science. Carretera a
Arturo Orellana García
Medical Informatic Center (CESIM). University of Informatics Science. Carretera a

Abstract

Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models' performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieve 97.6% and 94.0% respectively, while in terms of AUC score, both models achieve more than 99%, with the difference in inference time between each model over 844 images being 14 seconds.
03 Jan 2024Submitted to TechRxiv
10 Jan 2024Published in TechRxiv