International Journal of Scientific Research and Engineering Development

Mushrooms are highly nutritious foods rich in proteins, vitamins, minerals, and antioxidants that support human health. However, among the thousands of mushroom species worldwide, only a limited number are safe for consumption, while many are poisonous and may cause serious illness or even death. Identifying edible and poisonous mushrooms is difficult for non-experts because many harmful species closely resemble edible ones. Therefore, an accurate classification system is essential to ensure public safety. This project proposes a machine learning–based approach to classify mushrooms using morphological features from a dataset containing 22 attributes. A Decision Tree Classifier is implemented to distinguish between edible and poisonous varieties. The model is trained and tested using structured data to evaluate its performance. Experimental results show that the proposed model achieves 100% accuracy, outperforming traditional approaches such as AdaBoost. The system demonstrates that machine learning can provide a reliable, efficient, and practical solution for mushroom classification.

Dineshkumar B, Ms.N.Vaishnavi, "Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms" International Journal of Scientific Research and Engineering Development, V9(2): Page(105-113) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.