Development of a Computer Vision System for the Non-Destructive Detection of Bananas
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Abstract
Quality assessment in fresh fruits is essential to ensure their commercial value and reduce losses throughout the supply chain. This paper presents the development of a computer vision–based system for the non-destructive detection of bananas, aiming to automate the classification process according to ripeness. The YOLOv11 algorithm was trained with a dataset consisting of 824 banana images in different states (green, ripe, and overripe). The system was implemented on a conveyor belt, incorporating 3D-printed components and an automatic segregation mechanism. Experimental tests achieved a classification accuracy of 97.2%, validating the applicability of the proposed system in agro-industrial environments.
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