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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Mobile Computational Vision System in the Identification of White Quinoa Quality

Author 1: Percimil Lecca-Pino
Author 2: Daniel Tafur-Vera
Author 3: Michael Cabanillas-Carbonell
Author 4: José Luis Herrera Salazar
Author 5: Esteban Medina-Rafaile

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120850

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.

  • Abstract and Keywords
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Abstract: Quinoa is currently in high commercial demand due to its large benefits and vitamin components. The process of selecting this grain is mostly done manually, being prone to errors, because many times this work is subject to fatigue and to subjective criteria of those in charge, causing the quality to decrease due to not making an adequate selection subject to standards. For this reason, a study focused on determining the influence of the computer vision system for the identification of the quality of white quinoa, based on the standards and techniques for the development of a computer vision system through the phases of PDI. Managing to determine the influence of this, concluding that it is possible to ensure the implementation of robust systems to solve problems by applying computer vision thanks to technological advances for mobile devices.

Keywords: Computer vision system; quinoa quality; digital image processing

Percimil Lecca-Pino, Daniel Tafur-Vera, Michael Cabanillas-Carbonell, José Luis Herrera Salazar and Esteban Medina-Rafaile, “Mobile Computational Vision System in the Identification of White Quinoa Quality” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120850

@article{Lecca-Pino2021,
title = {Mobile Computational Vision System in the Identification of White Quinoa Quality},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120850},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120850},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Percimil Lecca-Pino and Daniel Tafur-Vera and Michael Cabanillas-Carbonell and José Luis Herrera Salazar and Esteban Medina-Rafaile}
}


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