Algoritmo de Clasificación Visual de Tomates Chonto Según Norma NTC-1103-1 (Parámetros de Color, Tamaño y Forma)

Eduar Andrés Correa Cantillo; Leonel Felipe Sotelo Jiménez; Eugenio Yime Rodríguez; Javier Agustín Roldán Mckinley

doi:10.24054/rcta.v1i45.2894

Authors

Eduar Andrés Correa Cantillo Universidad del Atlántico https://orcid.org/0009-0002-3732-2348
Leonel Felipe Sotelo Jiménez Universidad del Atlántico https://orcid.org/0009-0004-4199-2146
Eugenio Yime Rodríguez Unviersidad del Atlántico https://orcid.org/0000-0003-3576-7095
Javier Agustín Roldán Mckinley Universidad del Atlántico https://orcid.org/0000-0003-1444-6460

DOI:

https://doi.org/10.24054/rcta.v1i45.2894

Keywords:

automation, computer vision, tomatoes classification

Abstract

This article introduces the development and implementation a low-cost system for the classification of Chonto tomatoes according to their color, shape and size, in accordance with the Colombian technical standard NTC 1103-1. To achieve the proposed objective, a classification algorithm is developed using Python and OpenCV software. The obtained results show that the classification for color and maturity had an accuracy of 93%. In the classification by size the precision was 98%. Regarding the evaluation of eccentricity for determining the shape, an accuracy of 80% was obtained. The precision values mentioned above are comparisons with the results obtained manually by a trained individual, which are considered the ideal classification. However, the response time of the algorithm is 0.48 sec, in average, which is much less than the time required for human inspection and classification. In addition to precision achieved, it can be said that the developed software responds to the need to detect and classify chonto tomatoes according to their color, size and shape established in the Colombian technical standard NTC 1103- 1.

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