Abstract
This study presents an automated system for classifying the fermentation levels of cocoa beans using convolutional neural networks, specifically employing YOLO-based object detection models. RGB images of cocoa beans, which were cut using a guillotine to expose their internal structure, were analyzed and manually labeled by experts according to the NTC 1252:2021 standard. A dataset of 19 high-resolution images, containing approximately 1,850 annotated beans, was used for both training and evaluation. Four versions of YOLOv8 (n, s, m, l) were tested, with YOLOv8m demonstrating the best overall performance, achieving an Intersection over Union (IoU) of 0.6522, accuracy of 0.6817, recall of 0.6558, and an F1-score of 0.6685. Comparative tests with earlier YOLO versions (YOLOv5 to YOLOv7) confirmed YOLOv8m as the most efficient model for this task. In addition, it achieved a competitive inference time of 89.87 ms per image. These results highlight the potential of deep learning and computer vision techniques to automate the classification of cocoa bean fermentation levels, providing a faster, more objective alternative to traditional manual inspection methods.