Performance Analysis of the YOLOv8 Algorithm for Detecting of Stacked Defective Oil Palm Fresh Fruit Bunches on a Moving Conveyor
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Crude palm oil (CPO) is the leading export commodity for countries such as Indonesia and Malaysia. The quality of CPO depends on the raw material and oil palm fresh fruit bunches (FFB). Various sorting and grading methods based on computer vision and machine learning have been developed to assess FFB quality automatically. However, most research has focused on fruit ripeness. In fact, empty bunches, rotten fruit, long stalks, and thorny or spiky bunches are also sorting parameters and are categorized as defective FFBs. This study aims to evaluate the performance of the YOLOv8l-Seg and YOLOv8x-Seg models in detecting and segmenting normal and defective FFBs stacked on a moving conveyor. Stacked FFBs mean there is more than one FFB in a camera field of view (FOV), which is easily found during the real-time sorting process. The dataset consists of five classes: normal, long stalks, thorny, empty, and rotten bunches. Evaluation was conducted using the mean Average Precision (mAP), accuracy, precision, recall, and F1-score metrics. The results show that YOLOv8x-Seg obtained 93% accuracy and 95% mAP. The YOLOv8l-Seg reached 92% accuracy and 93.4% mAP. Therefore, both models have the potential to be applied in real-time automated oil palm FFB sorting and grading systems.
##Keywords:##
Computer Vision, Deep Learning, Oil Palm, Stacked Fresh Fruit Bunches, YOLOv8 Algorithms.
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Published
Jul 30, 2026
How to Cite
SHIDDIQ, Minarni et al.
Performance Analysis of the YOLOv8 Algorithm for Detecting of Stacked Defective Oil Palm Fresh Fruit Bunches on a Moving Conveyor.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 70, n. 2, p. 193-206, july 2026.
ISSN 2527-6085.
Available at: <https://www.isomase.org/Journals/index.php/jomase/article/view/638>. Date accessed: 01 sep. 2026.
doi: http://dx.doi.org/10.36842/jomase.v70i2.638.
References
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[19] Arief, D. S, Shiddiq, M., Jahrizal, J., Dalil, M., Dalil, Salambue, R., Yuhendra, A., Dasta, V. V., Hamid, M.I., Anjarwati, D. & Harmailil, M. I. O. (2026). Machine vision for detection of defective oil palm fresh fruit bunches using YOLO Algorithm, J. Phys.: Conf. Ser. 3186 012026.
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[23] Zou, Z., Chen, K., Shi, Z., Guo, Y. & Ye, J. (2023). Object Detection in 20 Years: A Survey. Proceedings of the IEEE, 111(3), 257–276. https://doi.org/10.1109/JPROC.2023.3238524.
[2] Supriyatin, W. (2021). Palm oil extraction rate prediction based on the fruit ripeness levels using C4.5 algorithm. ILKOM Jurnal Ilmiah, 13(2), 92–100. https://doi.org/10.33096/ilkom.v13i2.714.92-100.
[3] Mansour, M. Y. M. A., Dambuli, K. D. & Yeep1, C. K. (2022). A review of non-destructive ripeness classification techniques for oil palm fresh fruit bunches. Journal of Oil Palm Research, 35(4), 543–554. https://doi.org/10.21894/jopr.2022.0063.
[4] Tian, H., Wang, T., Liu, Y., Qiao, X. & Li, Y. (2020). Computer vision technology in agricultural automation —A review. In Information Processing in Agriculture (Vol. 7, Nomor 1). https://doi.org/10.1016/j.inpa.2019.09.006.
[5] Ireri, D., Belal, E., Okinda, C., Makange, N. & Ji, C. (2019). A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture. https://doi.org/10.1016/j.aiia.2019.06.001.
[6] Ismail, N. & Malik, O. A. (2022). Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture, 9(1), 24–37. https://doi.org/10.1016/j.inpa.2021.01.005.
[7] Wang, C., Liu, S., Wang, Y., Xiong, J., Zhang, Z., Zhao, B., Luo, L., Lin, G. & He, P. (2022). Application of convolutional neural network-based detection methods in fresh fruit production: a comprehensive review. In Frontiers in Plant Science (Vol. 13). https://doi.org/10.3389/fpls.2022.868745.
[8] Gao, F., Yang, T. & Fu, L. (2021). Apple fruit detection and counting based on deep learning and trunk tracking. American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021, 1. https://doi.org/10.13031/aim.202100193.
[9] Xiao, B., Nguyen, M. & Yan, W. Q. (2023). Fruit ripeness identification using YOLOv8 model. Multimedia Tools and Applications, 83(9), 28039–28056. https://doi.org/10.1007/s11042-023-16570-9.
[10] Junos, M. H., Mohd Khairuddin, A. S., Thannirmalai, S. & Dahari, M. (2021). An optimized YOLO?based object detection model for crop harvesting system. IET Image Processing, 15(9), 2112–2125. https://doi.org/10.1049/ipr2.12181.
[11] Mansour, M. Y. M. A., D. Dambul, K. & Choo, K. Y. (2022). Object detection algorithms for ripeness classification of oil palm fresh fruit bunch. International Journal of Technology, 13(6), 1326. https://doi.org/10.14716/ijtech.v13i6.5932.
[12] Lai, J. W., Ramli, H. R., Ismail, L. I. & Hasan, W. Z. W. (2022). Real-time detection of ripe oil palm fresh fruit bunch based on YOLOv4. IEEE Access, 10, 95763–95770. https://doi.org/10.1109/ACCESS.2022.3204762
[13] Shiddiq, M., Saktioto, Salambue, R., Wardana, F., Dasta, V. V., Harmailil, I. O., Rabin, M. F., Arpyanti, N. & Wahyudi, D. (2024). Multispectral imaging and deep learning for oil palm fruit bunch ripeness detection. Bulletin of Electrical Engineering and Informatics, 13(6).https://doi.org/10.11591/eei.v13i6.8120
[14] Kiet, V. M., Chang, F. S. & Tsung, C. K. (2025). A study of oil palm fruit ripeness using YOLOv8 for smart agriculture applications. IS3C 2025 - International Symposium on Computer, Consumer and Control. https://doi.org/10.1109/IS3C65361.2025.11131089.
[15] Gunawan, T. S., Kartiwi, M., Mansor, H. & Yusoff, N. Md. (2023). Palm fruit ripeness detection and classification using various YOLOv8 models. IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 193–198. https://doi.org/10.1109/ICSIMA59853.2023.10373435.
[16] Sapkota, R., Ahmed, D., Churuvija, M. & Karkee, M. (2024). immature green apple detection and sizing in commercial orchards using YOLOv8 and shape fitting techniques. IEEE Access, 12, 43436–43452. https://doi.org/10.1109/ACCESS.2024.3378261.
[17] Li, P., Zheng, J., Li, P., Long, H., Li, M. & Gao, L. (2023). Tomato maturity detection and counting model based on MHSA-YOLOv8. Sensors, 23(15), 6701. https://doi.org/10.3390/s23156701.
[18] Sapkota, R., Ahmed, D. & Karkee, M. (2024). Artificial intelligence in agriculture comparing YOLOv8 and mask R-CNN for instance segmentation in complex orchard environments. Artificial Intelligence in Agriculture, 13, 84–99. https://doi.org/10.1016/j.aiia.2024.07.001.
[19] Arief, D. S, Shiddiq, M., Jahrizal, J., Dalil, M., Dalil, Salambue, R., Yuhendra, A., Dasta, V. V., Hamid, M.I., Anjarwati, D. & Harmailil, M. I. O. (2026). Machine vision for detection of defective oil palm fresh fruit bunches using YOLO Algorithm, J. Phys.: Conf. Ser. 3186 012026.
[20] Kainec, K. A., Caccavaro, J., Barnes, M., Hoff, C., Berlin, A. & Spencer, R. M. C. (2024). Compared to research-grade actigraphy and polysomnography. Sensors, 24, 635.
[21] Chicco, D. & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. 1–13.
[22] Suharjito, Asrol, M., Utama, D. N., Junior, F. A. & Marimin. (2023). Real-time oil palm fruit grading system using smartphone and modified YOLOv4. IEEE Access, 11, 59758–59773. https://doi.org/10.1109/ACCESS.2023.3285537.
[23] Zou, Z., Chen, K., Shi, Z., Guo, Y. & Ye, J. (2023). Object Detection in 20 Years: A Survey. Proceedings of the IEEE, 111(3), 257–276. https://doi.org/10.1109/JPROC.2023.3238524.
