Abstract
Ganoderma disease is one of the most destructive diseases affecting oil palm plants, causing basal stem rot, reduced productivity, plant mortality, and substantial financial losses. This study proposes an image enhancement pipeline for Ganoderma disease identification by combining Gamma Correction and CNN-Based Enhancement. Gamma Correction is applied to improve illumination and pixel intensity, while CNN-Based Enhancement is used to enhance structural and textural details through a deep learning approach. After enhancement, the processed images are classified into three categories: Healthy, Infected, and Initial Infection. The model achieved a test accuracy of 0.7714 with a test loss of 0.4165. For each class, the F1-scores of Healthy, Infected, and Initial Infection were 0.8571, 0.7500, and 0.7200, respectively. The results indicate that image transformation techniques can effectively support Ganoderma disease identification in oil palm plants. By improving image quality prior to classification, the model becomes more stable in emphasizing disease-relevant visual features under diverse lighting conditions. Overall, the proposed enhancement process, which integrates intensity correction with CNN-based reconstruction, offers a promising direction for developing 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 (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.