Machine Learning-Based Classification of Mushrooms Using a Smartphone Application

Worldwide, a large number of cases of harmful mushroom exposure and consumption result in hallucinations, sickness, and death. One contributing factor is that certain poisonous mushrooms closely resemble their edible counterparts, making it difficult for general public collectors to distinguish one from the other. We propose a method to classify mushroom types from field-collection images using a smartphone application based on a convolutional neural network. The application helps people without proper mycology background or training to distinguish poisonous mushrooms from edible ones with which they may be confused. We showed three case studies to classify two-, three-, and five-class models by optimizing their training steps by cross-validation. An android app was developed by transferring the server-based trained model and allowing users to obtain probability scores for the correct genus classification. Our experiments showed that this method could provide sensitivity and specificity of two-, three-, and five-class mushroom models ranging from 89% to 100% using an image from the field with diverse backgrounds and objects.