Clasificación de maduración del banano usando Modelación Neuronal Artificial Aplicada al espacio RGB en imágenes
Resumen
Mundialmente, el banano es una de las frutas más consumidas, representando un renglón importante de la economía colombiana. Su estado de maduración determina su calidad, vida útil y valor comercial; por ello, es esencial identificar el momento óptimo de cosecha y comercialización. Convencionalmente, su evaluación es manual y visual, lo que implica subjetividad y errores humanos. Este estudio propone una alternativa basada en la modelación neuronal artificial aplicada a imágenes digitales en el espacio de color RGB, capturadas con teléfonos móviles. Dos modelos neuronales artificiales se elaboraron y compararon: una red perceptrón multicapa (MLP) y una red neuronal convolucional (CNN). Ambas redes clasificaron cuatro estados de maduración en banano variedad Cavendish. La red MLP alcanzó una exactitud general del 0.77, mostrando un desempeño adecuado para entornos limitados en recursos. Por su parte, la red CNN logró una exactitud del 0.99, con métricas de precisión (0.98-1.00) y sensibilidad (0.97-1.00) sobresalientes, evidenciando su capacidad para extraer características relevantes directamente de las imágenes. Los resultados confirman la viabilidad del uso de dispositivos móviles como herramientas de captura confiables y el potencial de los modelos neuronales para su integración en aplicaciones de clasificación automática en tiempo real. Este trabajo abre una agenda futura orientada al desarrollo de soluciones accesibles que impulsen la transformación digital del sector bananero colombiano.
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