2024
doi: 10.12720/jait.15.11.1295-1303
EdgeCutMix Augmentation: Enhancing the Leaf Disease Classification for the Minority Class
Derisma1,2
,
Nur Rokhman1,* ,
Dyah Aruming Tyas1
- 1Department of Computer Science, Universitas Gadjah Mada, Yogyakarta, Indonesia
- 2Faculty of Information Technology, Universitas Andalas, Padang, Indonesia
- Manuscript receivedApril 1, 2024
- revisedJune 6, 2024
- acceptedAugust 1, 2024
- publishedNovember 27, 2024
Abstract
Leaf disease classification faces significant challenges due to dataset imbalances, particularly within minority classes, leading to decreased model accuracy. This study addresses this problem by introducing EdgeCutMix, a novel image augmentation technique designed to enhance the representation of minority classes. EdgeCutMix integrates edge detection, using the Canny edge detection algorithm, and selective mixing strategies to generate realistic and informative augmented images. The Plant Pathology 2020 dataset, consisting of 3,642 apple leaf images, was used for evaluation. The experimental setup involved oversampling and comparison against existing techniques like MixUp, CutOut, CutMix, and Mosaic, and training on four CNN architectures: MobileNetV2, EfficientNetB7, ResNet50, and DenseNet201. Results showed that EdgeCutMix significantly improved classification accuracy for minority classes, achieving up to 98% accuracy with the EfficientNetB7 model. These findings suggest that EdgeCutMix provides a promising solution for improving model performance in imbalanced datasets, with potential applications in advancing deep learning in agricultural pathology.
Keywords
- leaf disease classification
- image augmentation
- minority class
- imbalanced dataset
- EdgeCutMix
How to Cite
Derisma, Nur Rokhman, and Dyah Aruming Tyas, "EdgeCutMix Augmentation: Enhancing the Leaf Disease Classification for the Minority Class," Journal of Advances in Information Technology, vol. 15, no. 11, pp. 1295-1303, 2024. https://doi.org/10.12720/jait.15.11.1295-1303
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
