Implementasi CNN untuk identifikasi penyakit daun jagung - Gumelar, Gilang; Tito Sugiharto; Iwan Lesmana (Universitas Muhammadiyah Riau, 2025)
Maize is an important commodity in Indonesia's agricultural sector. However, disease attacks on the leaves can reduce the quality and quantity of the harvest. At SMK Negeri 1 Kuningan, disease identification is still done manually, so there is a risk of errors. This research aims to design and build an Android application to automatically detect corn leaf diseases using the Convolutional Neural Network (CNN) algorithm. The development method used is Rapid Application Development (RAD), with a CNN model based on MobileNetV2 architecture trained using a dataset of diseased and healthy corn leaf images. Evaluation using test images resulted in an accuracy of 96.2%. The model was able to detect five categories: leaf spot, downy mildew, leaf blight, leaf rust, and healthy leaves. The F1-Score is 94% Leaf Spot, 96% Leaf Blight, 96% Healthy Leaf, 97% Leaf Blight, and 96% Leaf Rust, respectively. The precision and recall values of all classes are above 94%. These results show that the integration of CNN in mobile applications is effective in helping the automatic identification of corn leaf diseases in an educational environment.