Mushroom Diagnosis Assistance System Based on Machine Learning by Using Mobile Devices

Abstract

Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices.  Part of the proposed system is a mobile application that easily used by a user - mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users.

This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the mushroom types.  The proposed approach selects the most effective of the already known mushroom attributes, and then specify the mushroom type. The use of specific features in mushroom determination process achieved very accurate results.