Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification

Angel Hernández; Francisco Javier Ornelas-Rodríguez; Juan B. Hurtado-Ramos; José Joel González-Barbosa

doi:10.1007/978-3-030-89586-0_5

Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification

Angel Hernández, Francisco Javier Ornelas-Rodríguez, Juan B. Hurtado-Ramos, José Joel González-Barbosa

Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada (CICATA), Unidad Querétaro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Telematics and Computing - 10th International Congress, WITCOM 2021, Proceedings
Editors	Miguel Félix Mata-Rivera, Roberto Zagal-Flores
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	62-73
Number of pages	12
ISBN (Print)	9783030895853
DOIs	https://doi.org/10.1007/978-3-030-89586-0_5
State	Published - 2021
Event	10th International Congress on Telematics and Computing, WITCOM 2021 - Virtual, Online Duration: 8 Nov 2021 → 12 Nov 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1430 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	10th International Congress on Telematics and Computing, WITCOM 2021
City	Virtual, Online
Period	8/11/21 → 12/11/21

Keywords

Citrus quality classification
Computer vision system
Deep learning
Mexican lemon quality

Access to Document

10.1007/978-3-030-89586-0_5

Cite this

Hernández, A., Ornelas-Rodríguez, F. J., Hurtado-Ramos, J. B., & González-Barbosa, J. J. (2021). Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification. In M. F. Mata-Rivera, & R. Zagal-Flores (Eds.), Telematics and Computing - 10th International Congress, WITCOM 2021, Proceedings (pp. 62-73). (Communications in Computer and Information Science; Vol. 1430 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-89586-0_5

Hernández, Angel ; Ornelas-Rodríguez, Francisco Javier ; Hurtado-Ramos, Juan B. et al. / Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification. Telematics and Computing - 10th International Congress, WITCOM 2021, Proceedings. editor / Miguel Félix Mata-Rivera ; Roberto Zagal-Flores. Springer Science and Business Media Deutschland GmbH, 2021. pp. 62-73 (Communications in Computer and Information Science).

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title = "Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification",

abstract = "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.",

keywords = "Citrus quality classification, Computer vision system, Deep learning, Mexican lemon quality",

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Hernández, A, Ornelas-Rodríguez, FJ , Hurtado-Ramos, JB & González-Barbosa, JJ 2021, Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification. in MF Mata-Rivera & R Zagal-Flores (eds), Telematics and Computing - 10th International Congress, WITCOM 2021, Proceedings. Communications in Computer and Information Science, vol. 1430 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 62-73, 10th International Congress on Telematics and Computing, WITCOM 2021, Virtual, Online, 8/11/21. https://doi.org/10.1007/978-3-030-89586-0_5

Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification. / Hernández, Angel; Ornelas-Rodríguez, Francisco Javier ; Hurtado-Ramos, Juan B. et al.
Telematics and Computing - 10th International Congress, WITCOM 2021, Proceedings. ed. / Miguel Félix Mata-Rivera; Roberto Zagal-Flores. Springer Science and Business Media Deutschland GmbH, 2021. p. 62-73 (Communications in Computer and Information Science; Vol. 1430 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

KW - Citrus quality classification

KW - Computer vision system

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Hernández A, Ornelas-Rodríguez FJ , Hurtado-Ramos JB , González-Barbosa JJ. Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification. In Mata-Rivera MF, Zagal-Flores R, editors, Telematics and Computing - 10th International Congress, WITCOM 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 62-73. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-89586-0_5

Accuracy Comparison Between Deep Learning Models for Mexican Lemon Classification

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