Mushroom Classification Using Deep Learning

Abstract

This project focuses on developing a deep learning-based system for the classification of mushrooms into six categories: Blue Oyster, Oyster, Phoenix Oyster, Pink Oyster, Poisonous, and Agaricus. A dataset of 2,134 images was prepared, combining 1,500 manually collected images and 634 sourced online. Advanced preprocessing techniques such as resizing, normalization, and augmentation were applied to enhance data quality. Several pre-trained models, including VGG16, MobileNetV2, ResNet50, and InceptionV3, were evaluated, with InceptionV3 achieving the highest accuracy of 98% after fine-tuning. The system was deployed using a Streamlet-based web interface, enabling real-time predictions with minimal latency. Evaluation metrics, including precision, recall, and F1-score, validated the model’s performance. The project highlights the practical application of deep learning in mushroom classification and offers a scalable, efficient solution with potential for further enhancements in transparency and dataset expansion