A Predictive Model for Recognizing Banana Ripeness

The classification of banana ripeness remains an important task in the food industry, as it directly affects the quality of the product and its shelf life. This paper presents an automated ripeness assessment system implemented using a comparative analysis of machine learning and deep learning algorithms. We tested the effectiveness of Random Forest, a custom CNN model, as well as the pre-trained ResNet50, EfficientNetB0, and VGG16 models, based on a dataset of 9960 images categorized into 3 ripeness stages (overripe, ripe, unripe). The results show the superiority of deep neural networks over classical methods: the ResNet50 architecture demonstrated 98% accuracy with a macro-averaged F1-score of 96%. The implementation of the proposed solution in the retail sector can automate ripeness monitoring and significantly reduce food waste.