Abstract
Ganoderma disease is one of the most destructive diseases affecting oil palm plants, causing basal stem rot, reduced productivity, plant mortality, and financial losses. This study applies Gamma Correction and CNN-Based Enhancement to improve the quality of oil palm images for Ganoderma disease identification. Gamma Correction improves image illumination and intensity, while CNN-Based Enhancement enhances structural details through a deep learning approach. The model achieved a test accuracy of 0.7714 and a test loss of 0.4165. The Healthy, Infected, and Initial Infection classes achieved F1-scores of 0.8571, 0.7500, and 0.7200, respectively. The results indicate that image transformation techniques can support Ganoderma disease identification in oil palm plants. The proposed enhancement process, which combines intensity correction with CNN-based enhancement, provides a potential approach for improving image quality and supporting the development of automated detection systems that are more accurate, adaptive, and suitable for field deployment.
How to Cite
| Issue | Vol. 4 No. 2 (2026) |
| Section | Articles |
| DOI | 10.58477/cj.v4i2.499 |
| Publication Date | 2026-08-30 |
| Pages | 257-268 |
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.