Machine Learning-based Classification of Edible and Poisonous Mushrooms: A Performance Comparison

Abstract

Abstract: In the disciplines of computer vision and machine learning, classifying mushrooms is a key problem. The objective of this study is to create a machine learning model that can correctly categorise different species of mushrooms as either edible or harmful based on their visual characteristics. The Field Guide to North American Mushrooms of the Audubon Society served as the study's data source, and it includes descriptions of hypothetical samples of 23 species of gilled mushrooms from the Agaricus and Lepiota families. Each sample is categorised as either definitely edible, definitely poisonous. To create our machine learning model for this study, we combined five different algorithms: SVM, logistic regression, decision tree, random forest, and KNN. Metrics such as accuracy, precision, recall, and F1 score were used to assess each algorithm's performance. Our findings show that the random forest algorithm outperformed the other algorithms with a 95% accuracy rate. However, by employing feature selection strategies and hyperparameter tuning, we further improved our model. We were able to raise the accuracy of our model to 97% by choosing the most pertinent features and fine-tuning its hyperparameters. The outcomes of this study should offer a practical method for precisely categorising mushrooms and lowering the danger of mushroom poisoning. This paper proposes an ensemble approach utilizing five different machine learning classifiers to detect mushroom classification. The study analyzes various features, such as cap-shape, gill-colour, stem texture and ring-type & stalk features, and the finding