Deep Learning Approaches to Banana Ripeness Detection

The study conducts a comparative analysis of five different deep learning models VGG16, VGG19, Xception, InceptionV3, and MobileNetV2 or banana ripeness classification. A dataset used in this study consists of four levels of ripeness: overripe, ripe, rotten, and unripe. Each deep learning model the model adjusted using pre-trained weights obtained from ImageNet. to adapt them for banana ripeness classification each model fine-tuned with pre-trained weights from ImageNet. The evaluation conducted using 5-Fold Cross-Validation The text discusses the importance of validation to confirm the reliability and strength of the results obtained shown, the VGG16 achieved the highest accurate performance of 93.7%, surpassing other models in all metrics. MobileNetV2 and Xception followed closely, demonstrating competitive results, while InceptionV3 had the lowest accuracy. Results indicate that VGG16 is best suited for banana ripeness. This research highlights the potential of deep learning in automating fruit ripeness detection and provides valuable insights for agricultural applications. Future work may explore the integration of additional datasets and real-time deployment for broader use cases.