Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms

IEEE BASE PAPER ABSTRACT:

Of the millions of mushroom species growing all around the world, one type is edible, while the other is poisonous. It is not easy to distinguish edible and poisonous mushrooms from each other and it is a condition that requires expertise. The classification of poisonous and edible mushrooms is therefore important. Machine learning algorithms are an alternative method for classifying poisonous and edible mushrooms using morphological or physical features of fungi. The dataset used in this study is the Mushroom dataset available in the UC Irvine Machine Learning Repository. Based on 22 features in the Mushroom dataset and four different machine learning algorithms, models have been created for the classification of edible and poisonous fungi. The classification success rates of these models were obtained from Naive Bayes, Decision Tree, Support Vector Machine and AdaBoost algorithms with 90.99%, 98.82%, 99.98% and 100%, respectively. When these results were examined, taking into account the physical appearance features of the mushrooms, it was determined whether the mushrooms were edible and poisonous by 100% with the AdaBoost model.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Decision Tree Classifier.

OUR PROPOSED ABSTRACT:

One of the best nutritional foods, mushrooms are rich in proteins, vitamins, and minerals. Antioxidants included in it shield consumers against cancer and heart disease. There are over 45000 species of mushrooms known worldwide. Only a few of these mushroom types were discovered to be edible. Some of these are extremely risky to eat.

It is really challenging for average people to recognize it. Hallucinations, illness, and even death are common outcomes of hazardous mushroom exposure and consumption situations all around the world. One contributing cause is that it can be challenging for general public collectors to tell some dangerous mushrooms apart from their edible equivalents. The classification of poisonous and edible mushrooms is therefore important.

Machine learning algorithms are an alternative method for classifying poisonous and edible mushrooms using morphological or physical features of fungi. The project “Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms” endeavors to develop a machine learning-based system that can effectively differentiate between edible and poisonous mushrooms using 22 features in the Mushroom dataset.

The proposed model for classification is the Decision Tree Classifier, which has shown to be a promising algorithm for such classification tasks. The performance evaluation is based on the training and testing accuracy of the Decision Tree Classifier. The results show that the proposed model achieves a 100% training and testing accuracy, which suggests its superiority over the existing system model AdaBoost.

This study contributes to the mushroom classification literature by providing a novel approach that can outperform existing models in terms of accuracy. This can have significant implications for the food industry, health care, and environmental studies, where accurate classification of mushrooms is essential.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Kemal Tutuncu; Ilkay Cinar; Ramazan Kursun; Murat Koklu, “Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms”, 2022 11th Mediterranean Conference on Embedded Computing (MECO), IEEE Conference 2022.

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Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms