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.

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}
}