Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification

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Resumen

This paper presents a performance comparison between 8 deep learning models trained to classify Mexican lemons by their visual appearance. The models were trained using 913 lemon images. These images were divided into two classes: faulty and healthy. Half of the models were designed to take color images as input. The other half will take grayscale images. Also, two distributions were used for the training stage. The models were tested against new data, and their performance was acceptable. The best model achieved an accuracy of 92% for the training stage and, for the new data, it was able to classify all the new images correctly.

Idioma originalInglés
Título de la publicación alojadaTelematics and Computing - 10th International Congress, WITCOM 2021, Proceedings
EditoresMiguel Félix Mata-Rivera, Roberto Zagal-Flores
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas62-73
Número de páginas12
ISBN (versión impresa)9783030895853
DOI
EstadoPublicada - 2021
Evento10th International Congress on Telematics and Computing, WITCOM 2021 - Virtual, Online
Duración: 8 nov 202112 nov 2021

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1430 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia10th International Congress on Telematics and Computing, WITCOM 2021
CiudadVirtual, Online
Período8/11/2112/11/21

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