Comparative Study of Machine Learning Techniques to Classify Edible and Poisonous Mushrooms

Abstract

Mushrooms are members of the fungi kingdom,
but they are classified as vegetables in cooking. Mushrooms
come in a variety of shapes and sizes, and they can be both
edible and poisonous. Each mushroom has its own
appearance and flavour. However, the nutritional content
of mushrooms varies depending on the type of mushroom
used. Proteins, vitamins, minerals, amino acids, antibiotics,
and antioxidants are among the essential nutrients found in
them. Mushrooms are nutritionally beneficial to our bodies.
However, not all mushroom species are edible; others are
toxic, causing health issues and even death. As a result, it is
necessary to verify if it is edible before consuming. The only
way to eat mushrooms in a healthy manner is to determine
and properly identify them. This paper compares the
output of various machine learning techniques like OneR,
k-Nearest Neighbors (KNN), J48, Random Forest on
mushroom dataset in order to classify edible and poisonous
mushrooms correctly.