Deep learning-based Mushroom classification system

The accurate identification of wild mushroom species is a task of critical importance, as many edible species have poisonous look-alikes that can cause severe illness or death if consumed. Traditional identification relies on expert mycological knowledge, which is not widely available to the general public. This paper presents a deep learning-based system designed for the automated classification of mushrooms from digital images. The system utilizes a deep Convolutional Neural Network (CNN) to learn the fine-grained visual features that distinguish different mushroom species. We employ a transfer learning approach, fine- tuning a state-of-the-art CNN architecture (such as EfficientNet) pre-trained on the ImageNet dataset. The model is trained and validated on a large, public dataset of mushroom images. The resulting system achieves high classification accuracy, demonstrating its potential as a reliable, accessible, and rapid decision-support tool for foragers, nature enthusiasts, and emergency medical personnel.