Fungal Disease Detection Using CNN Deep Learning Method

Dika Dika (Universitas Pembangunan Panca Budi Medan)
Muhammad Iqbal (Universitas Pembangunan Panca Budi)

The Journal of Algorithmic Digital Engineering and Networks (JADEN)
Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks

This study aims to detect mushroom diseases based on digital images using the Deep Learning Convolutional Neural Network (CNN) method. Fungal diseases are often the main cause of decreased quality and yield, so a fast and accurate detection method is needed. The dataset used consists of images of healthy mushrooms and diseased mushrooms obtained through direct image capture at the cultivation location. The research stages include image preprocessing, CNN model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the CNN model is able to detect mushroom diseases with a high level of accuracy, so this method has the potential to be used as a decision support system in mushroom cultivation.