Real-time Deep Learning Based Mobile Application for Detecting Edible Fungi: Mushapp

Real-time Deep Learning Based Mobile Application for Detecting Edible Fungi: Mushapp

Автор: Remzi Gürfidan, Zekeriya Akçay

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 5 vol.16, 2024 года.

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Mushroom consumption and wild mushroom gathering are increasing in our country and in the world. Mushroom poisoning has an important place in food poisoning cases. Mushroom poisoning accounts for approximately 7% of poisoning cases in adults. Mushroom collection and consumption is common in many regions of our country. In this study, a deep learning based mobile application was developed to reduce the incidence of mushroom poisoning by taking a photo of a mushroom and determining the type and toxicity of the mushroom from the photo. This mobile application is called MushAPP. In the first phase of the study, 5150 mushroom images of 20 mushroom species were used to create the dataset. The dataset was then pre-processed and converted into a format that can be used by the deep learning algorithm. The mobile application side of the project was developed in Android Studio IDE environment. An artificial intelligence model was integrated into the designed mobile application. In the application, the type and toxicity status of the mushroom viewed from the mobile device camera are determined and presented to the user. The research findings were analyzed and it was determined that the accuracy rate of the application in detecting the mushroom species was 99.8%.

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Mushroom, Deep Learning, CNN, Real-time Detection

Короткий адрес: https://sciup.org/15019514

IDR: 15019514   |   DOI: 10.5815/ijisa.2024.05.01

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