Determinación no destructiva de la calidad en fresas empacadas en PET mediante imágenes RGB e IR
DOI:
https://doi.org/10.26507/paper.4589Palabras clave:
Poscosecha, Procesamiento de imagenes, Artificial Intelligence (AI), EpidermisResumen
Se desarrolló un método para identificar, segmentar y analizar la variación de color, la presencia de moho (Botrytis cinerea) y la temperatura de la epidermis en fresas empacadas en PET, sin comprometer su integridad. El procesamiento de imágenes se llevó a cabo mediante un modelo YOLO V8 de visión por computadora, entrenado con un conjunto de 1,100 imágenes RGB. En estas imágenes, se etiquetaron características fisiológicas asociadas con la satisfacción del consumidor. Las evaluaciones realizadas mediante métricas de clasificación demostraron la efectividad del método propuesto, con un accuracy mínimo del 80%, una precision del 100% en todos los casos, un recall mínimo del 91.84% y valores de F1-score superiores al 95%. Este enfoque aporta al conocimiento científico, dado que solo se ha identificado un estudio previo que emplea YOLO V8 en el análisis de fresas. En comparación con otros modelos usados en literatura en la poscosecha de fresa tales como CNN, Mask R-CNN, discriminación lineal y no lineal, y AlexNet, el método propuesto cuenta con un desempeño igual o superior.
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Derechos de autor 2025 Asociación Colombiana de Facultades de Ingeniería - ACOFI
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
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