Paper Key : IRJ************895
Author: Anagha Bhoopalam T R
Date Published: 09 Jul 2024
Abstract
This paper introduces a novel approach to classify mushrooms as either edible or poisonous by using a deep learning framework rooted in the Residual Network (ResNet) architecture. By utilizing convolutional neural networks (CNNs) along with a specially labeled dataset, Our approach is able to precisely determin mushroom species. The ResNet framework, renowned for managing deep networks effectively, significantly boosts prediction accuracy and performance. We've designed the application with a user-friendly graphical user interface (GUI) created in Tkinter, ensuring it's accessible even to those without technical backgrounds. This tool is geared towards helping foragers, hobbyists, and consumers safely determine the edibility of mushrooms they encounter.Keywords: Mushroom classification, Deep learning, ResNet architecture, Edible mushrooms, Poisonous mushrooms, Image processing, Tkinter GUI