Fresh and Rotten Bangladeshi Fruits Classification Using Deep Learning Techniques

Abstract

This thesis presents a deep learning–based approach for automating the classification of fruits as fresh or rotten, focusing on apples, bananas, and oranges. A dataset of over 13,000 images was preprocessed through resizing, normalization, and augmentation before being used to train and evaluate multiple architectures, including VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a hybrid model. The results revealed that the Trained Hybrid Model and InceptionV3 outperformed others, with validation accuracies of 96.52% and 91.89%, respectively, demonstrating strong generalization ability. Results showed reliable detection of rotten fruits but weaker performance for fresh fruits, highlighting the importance of balanced datasets and improved optimization. The study demonstrates the potential of deep learning for smart agriculture, offering insights into model performance and practical deployment, and suggests future improvements such as larger datasets, advanced architectures, mobile integration, and explainable AI for trustworthy decision making.