Un enfoque de algoritmos clásicos de aprendizaje automático para la identificación del estado de maduración del banano utilizando características de color RGB en imágenes
DOI:
https://doi.org/10.14482/inde.44.01.444.327Palabras clave:
análisis sensorial, aprendizaje de máquinas, cavendish, ciencia del color, escala de maduración de Von Loesecke, Musa sp. L, visión computacionalResumen
Debido a su valor nutricional, características sensoriales y disponibilidad durante todo el año, hacen que el banano sea una de las frutas más consumidas a nivel mundial, con una alta demanda en diferentes sectores poblacionales. Durante su periodo de maduración, presenta cambios en sus características organolépticas como el color, sabor, aroma y composición nutricional. El momento oportuno de cosecha es un aspecto clave para su adecuada conservación y comercialización, pero frecuentemente se determina usando criterios subjetivos por parte del agricultor. Los métodos tradicionales basados en la observación visual o el uso de equipos especializados, son costosos, imprecisos o invasivos. En este trabajo, se propone el uso de herramientas computacionales basadas en algoritmos de aprendizaje automático para la clasificación del estado de maduración de bananos durante el proceso de cosecha, usando imágenes digitales de la fruta. Se evaluaron seis algoritmos clásicos de aprendizaje automático, con el fin de clasificar el estado de maduración utilizando características de color RGB de las imágenes mencionadas. Las mejores precisiones (exactitud 0.82) se obtuvieron para los algoritmos de k-vecinos cercanos, máquinas de soporte vectorial y bosques aleatorios. Los resultados permiten abrir una agenda futura que incluya además de las características espectrales, otras variables como tamaño, forma y textura.
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