This study developed a deep learning-based software module for classifying the ripeness of bananas in real time as they move along a conveyor belt. A total of 5,286 images annotated with three ripeness stages, namely unripe, ripe, and overripe, were divided into training, validation, and test datasets at a ratio of 88:8:4. The datasets were used to train YOLOv5s and YOLOv5l object detection models over 50 epochs. The model performance was evaluated using box loss, object loss, class loss, and mean average precision (mAP). Both models exhibited decreasing loss values approaching zero and achieved mAP, precision, and recall scores exceeding 90%, thus indicating a robust classification performance without overfitting. The software module integrated with the trained YOLOv5l model accurately identified the ripeness stage of bananas in motion on the conveyor system without misclassification. Collectively, these findings indicate that the proposed system can be effectively applied to banana-processing lines for automated and accurate ripeness-based sorting.