Fungus Classification Based on CNN Deep Learning Model

Abstract

Artificial intelligence has been developing day by day and has started to take a more prominent place in human life. As computer technologies advance, research on artificial intelligence has also increased in this direction. One of the main goals of this research is to examine how real problems in human life can be solved using artificial intelligence-based deep learning, and to present a case study. Poisoning from the consumption of poisonous fungi is a common problem worldwide. To prevent these poisonings, a mobile application has been developed using Convolutional Neural Networks (CNNs) and transfer learning to detect the species of fungus. The application informs the user about the type of fungus, whether it is poisonous or non-toxic, and whether it is safe to eat. The aim of this study is to reduce poisoning events caused by incorrect fungus detection and to facilitate the identification of fungus species. The developed deep learning model is integrated into a mobile application developed by Flutter that is a mobile application development framework, which enable the detection of fungus species from images taken from the camera or selected from the gallery. CNNs and the EfficientNetV2 model, a transfer learning method, were used. By using these two methods together, the classification accuracy rate for 77 fungus species was obtained as 97%.