Fiddin Yusfida A'la, Nurul Ngaeni, Akas Bagus Setiawan, Riza Akhsani Setyo Prayoga, Helmi Imaduddin, Anik Nur Hidayati
Rice leaf disease classification remains challenging due to limited dataset size, class imbalance, and overlapping visual symptoms, which reduce CNN generalization performance. This study investigates whether data augmentation can address these dataset limitations and improve CNN accuracy. The Rice Leaf Diseases Dataset from Kaggle, containing 1,886 images across eight classes, was divided into training, validation, and testing sets (80: 10: 10). Two training schemes were implemented: one with raw images and another with augmentation techniques, including random flipping, rotation, zoom, and contrast adjustment. Models were trained using the Adam optimizer and Sparse Categorical Cross entropy loss. Results showed that augmentation improved classification performance, with validation accuracy rising from 0.51 (non-augmented) to 0.69 (augmented). These findings confirm augmentation as an effective strategy to overcome dataset limitations, enhancing CNN robustness and supporting AI-based agricultural disease diagnosis. © 2025 IEEE.
Universitas Sebelas Maret, Department of Informatics Engineering, Surakarta, Indonesia; Politeknik Negeri Jember, Department of Information Technology, Jember, Indonesia; Universitas Negeri Surabaya, Department of Information Technology Education, Surabaya, Indonesia; Universitas Muhammadiyah Surakarta, Department of Informatics, Surakarta, Indonesia; Institut Pertanian Bogor, Department of Tropical Biodiversity Conservation, Bogor, Indonesia
