A Feasibility Study on Synthetic RGB-NIR Image Generation for Oil Palm Fresh Fruit Bunch Grading

Abstract: Accurate ripeness of grading oil palm fruit bunches (FFBs) is essential for optimizing oil quality and harvesting decisions. While near-infrared (NIR) imaging provides useful spectral cues for ripeness assessment, its adoption in field conditions is limited by sensor cost and system complexity. This study presents a low-cost alternative by generating synthetic NIR images from RGB inputs using a U-Net-based image translation model and integrating the generated NIR with RGB channels for ripeness classification. Five deep learning models, including a custom CNN, ResNet-50, EfficientNet-B0, DenseNet-201 and MobileNetV3, were evaluated under RGB-only and RGB + synthetic NIR configurations using identical training protocols. Experimental results demonstrate consistent performance improvements when synthetic NIR was incorporated. EfficientNet-B0 achieved the highest overall accuracy of 90.3%, while MobileNetV3 obtained the highest macro-averaged F1-score of 85.4%, indicating strong and balanced classification across ripeness classes. Confusion matrix analysis further revealed complementary strengths between the models, where EfficientNet-B0 showed stronger robustness in late-stage maturity detection, and MobileNetV3 provided improved discrimination of early-stage ripeness. The results demonstrate that synthetic NIR augmentation enhances classification performance and training stability without requiring specialized imaging hardware.

Nor Surayahani Suriani, Norzali Hj Mohd, Shaharil Mohd Shah, Siti Zarina Muji and Fadilla Atyka Nor Rashid. “A Feasibility Study on Synthetic RGB-NIR Image Generation for Oil Palm Fresh Fruit Bunch Grading”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170272

@article{Suriani2026,
title = {A Feasibility Study on Synthetic RGB-NIR Image Generation for Oil Palm Fresh Fruit Bunch Grading},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170272},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170272},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {Nor Surayahani Suriani and Norzali Hj Mohd and Shaharil Mohd Shah and Siti Zarina Muji and Fadilla Atyka Nor Rashid}
}