Mushroom Image Classification with CNN

Project Code :TCMAPY1017

Objective

The objective of this project is to investigate different approaches to train CNNs for mushroom image recognition, including data augmentation techniques, transfer learning from pre-trained models, and the development of custom CNN architectures. We seek to evaluate the performance of these strategies in terms of accuracy, robustness, and efficiency in handling mushroom image datasets.

Abstract

This study presents a Convolutional Neural Network (CNN) approach for mushroom image classification. Leveraging deep learning, the model demonstrates high accuracy in distinguishing various mushroom species. Through extensive training on diverse datasets, the CNN effectively captures intricate features, enabling robust classification. The proposed method not only enhances mushroom species identification but also contributes to the broader field of image recognition. This research underscores the potential of CNNs in automating the identification process for mushrooms, facilitating both scientific research and practical applications in fields such as agriculture and mycology.

Keyword: Mushroom dataset, deep learning algorithms

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W CONFIGURATION:

Processor - I3/Intel Processor

Hard Disk - 160GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

RAM - 8GB


S/W CONFIGURATION:

Operating System :  Windows 7/8/10

Server side Script :  HTML, CSS, Bootstrap & JS

Programming Language :  Python

Libraries :  Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy

IDE/Workbench :  PyCharm

Technology :  Python 3.6+

Server Deployment :  Xampp Server


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