KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT MENGGUNAKAN MODEL YOLOV8 BERBASIS DEEP LEARNING
Abstract
Determining the ripeness level of oil palm fruit is a crucial aspect in enhancing the efficiency and quality of palm oil production. To date, most ripeness classification processes are still manually conducted, leading to inconsistencies and human error. This study aims to develop an oil palm fruit ripeness classification model using YOLOv8, a state-of-the-art deep learning architecture known for its excellence in computer vision tasks. The dataset consists of six ripeness classes, divided into training, validation, and testing sets sourced from the Roboflow platform. The training process involved five YOLOv8 sub-models with optimized parameter configurations. Evaluation was carried out using MAPE and confidence score metrics to measure prediction accuracy. The results showed that all sub-models successfully classified fruit ripeness with high accuracy, with YOLOv8l-cls achieving the lowest MAPE value of 0.01167. These, confirm that the YOLOv8-based approach is highly effective in supporting automated classification of oil palm fruit ripeness, offering faster, more accurate, and consistent results, and holds strong potential for widespread application in the plantation industry.
References
Aji, W., & Hawari, K. (2019). A Study of Deep Learning Method Opportunity on Palm Oil FFB (Fresh Fruit Bunch) Grading Methods. Ahmad Dahlan International Conference Series on Engineering and Science (ADICS-ES 2019), 189, 35–38. https://doi.org/10.2991/adics-es-19.2019.9
Alfatni, M. S. M., Khairunniza-Bejo, S., Marhaban, M. H. B., Saaed, O. M. B., Mustapha, A., & Shariff, A. R. M. (2022). Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis. Agriculture (Switzerland), 12(9). https://doi.org/10.3390/agriculture12091461
Ashari, S., Yanris, G. J., & Purnama, I. (2022). Oil Palm Fruit Ripeness Detection using Deep Learning. Sinkron, 7(2), 649–656. https://doi.org/10.33395/sinkron.v7i2.11420
Azman, H., & Suriani, N. S. (2023). Grading Oil Palm Fruit Bunch using Convolution Neural Network. Evolution in Electrical and Electronic Engineering, 4(1), 185–194. http://publisher.uthm.edu.my/periodicals/index.php/eeee
Casas, E., Ramos, L., Romero, C., & Rivas-Echeverría, F. (2024). A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces. Array, 22(March), 100351. https://doi.org/10.1016/j.array.2024.100351
Daud, M. M., Kadim, Z., & Woon, H. H. (2023). Detection of Oil Palm Tree and Loose Fruitlets for Fresh Fruit Bunch’s Ready-to-Harvest Prediction via Deep Learning Approach. IAENG International Journal of Computer Science, 50(4).
Djerriri, K., Ghabi, M., Karoui, M. S., & Adjoudj, R. (2018). Palm trees counting in remote sensing imagery using regression convolutional neural network. International Geoscience and Remote Sensing Symposium (IGARSS), 2018-July, 2627–2630. https://doi.org/10.1109/IGARSS.2018.8519188
Farooq, J., Muaz, M., Khan Jadoon, K., Aafaq, N., & Khan, M. K. A. (2024). An improved YOLOv8 for foreign object debris detection with optimized architecture for small objects. Multimedia Tools and Applications, 83(21), 60921–60947. https://doi.org/10.1007/s11042-023-17838-w
Iskandar, M. J., Baharum, A., Anuar, F. H., & Othaman, R. (2018). Palm oil industry in South East Asia and the effluent treatment technology—A review. Environmental Technology and Innovation, 9(May 2017), 169–185. https://doi.org/10.1016/j.eti.2017.11.003
Jocher, G., Ayush Chaurasia, Alex Stoken, Jirka Borovec, NanoCode012, Yonghye Kwon, Kalen Michael, TaoXie, Jiacong Fang, imyhxy, Lorna, 曾逸夫(Zeng Yifu), Colin Wong, Abhiram V, Diego Montes, Zhiqiang Wang, Cristi Fati, Jebastin Nadar, Laughing, … Mrinal Jain. (2022). ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation (v7.0). Zenodo. https://doi.org/10.5281/zenodo.7347926
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
Li, W., Fu, H., Yu, L., & Cracknell, A. (2017). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010022
Malyala, R. (2024). Development of a Convolutional Neural Network Model for Automated Ripeness Classification of Palm Oil Fresh Fruit Bunches. 9(11), 1040–1046.
Mamat, N., Othman, M. F., Abdulghafor, R., Alwan, A. A., & Gulzar, Y. (2023). Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach. Sustainability (Switzerland), 15(2). https://doi.org/10.3390/su15020901
Muna, M. S., Nugroho, A. P., Syarovy, M., Wiratmoko, A., Suwardi, & Sutiarso, L. (2023). Development of Automatic Counting System for Palm Oil Tree Based on Remote Sensing Imagery. Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022), 26(Dl), 503–508. https://doi.org/10.2991/978-94-6463-086-2_68
Putra, J. W. G. (2019). Pengenalan konsep pembelajaran mesin dan deep learning. Computational Linguistics and Natural Language Processing Laboratory, 4(August), 1–235. https://www.researchgate.net/publication/323700644
Saleh, A. Y., & Liansitim, E. (2020). Palm oil classification using deep learning. Science in Information Technology Letters, 1(1), 1–8. https://doi.org/10.31763/sitech.v1i1.1
Salim, E., & Suharjito. (2023). Hyperparameter optimization of YOLOv4 tiny for palm oil fresh fruit bunches maturity detection using genetics algorithms. Smart Agricultural Technology, 6(November), 100364. https://doi.org/10.1016/j.atech.2023.100364
Shabdin, M. K., Shariff, A. R. M., Johari, M. N. A., Saat, N. K., & Abbas, Z. (2016). A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach. IOP Conference Series: Earth and Environmental Science, 37(1). https://doi.org/10.1088/1755-1315/37/1/012039
Shiddiq, M. (2022). Counting of Oil Palm Fresh Fruit Bunches Using Computer Vision. Journal of Oil Palm Research. https://doi.org/10.21894/jopr.2022.0029
Sohan, M., Sai Ram, T., & Rami Reddy, C. V. (2024). A Review on YOLOv8 and Its Advancements. January, 529–545. https://doi.org/10.1007/978-981-99-7962-2_39
Zheng, J., Li, W., Xia, M., Dong, R., Fu, H., & Yuan, S. (2019). LARGE-SCALE OIL PALM TREE DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES USING FASTER-RCNN. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 1422–1425.
Alfatni, M. S. M., Khairunniza-Bejo, S., Marhaban, M. H. B., Saaed, O. M. B., Mustapha, A., & Shariff, A. R. M. (2022). Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis. Agriculture (Switzerland), 12(9). https://doi.org/10.3390/agriculture12091461
Ashari, S., Yanris, G. J., & Purnama, I. (2022). Oil Palm Fruit Ripeness Detection using Deep Learning. Sinkron, 7(2), 649–656. https://doi.org/10.33395/sinkron.v7i2.11420
Azman, H., & Suriani, N. S. (2023). Grading Oil Palm Fruit Bunch using Convolution Neural Network. Evolution in Electrical and Electronic Engineering, 4(1), 185–194. http://publisher.uthm.edu.my/periodicals/index.php/eeee
Casas, E., Ramos, L., Romero, C., & Rivas-Echeverría, F. (2024). A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces. Array, 22(March), 100351. https://doi.org/10.1016/j.array.2024.100351
Daud, M. M., Kadim, Z., & Woon, H. H. (2023). Detection of Oil Palm Tree and Loose Fruitlets for Fresh Fruit Bunch’s Ready-to-Harvest Prediction via Deep Learning Approach. IAENG International Journal of Computer Science, 50(4).
Djerriri, K., Ghabi, M., Karoui, M. S., & Adjoudj, R. (2018). Palm trees counting in remote sensing imagery using regression convolutional neural network. International Geoscience and Remote Sensing Symposium (IGARSS), 2018-July, 2627–2630. https://doi.org/10.1109/IGARSS.2018.8519188
Farooq, J., Muaz, M., Khan Jadoon, K., Aafaq, N., & Khan, M. K. A. (2024). An improved YOLOv8 for foreign object debris detection with optimized architecture for small objects. Multimedia Tools and Applications, 83(21), 60921–60947. https://doi.org/10.1007/s11042-023-17838-w
Iskandar, M. J., Baharum, A., Anuar, F. H., & Othaman, R. (2018). Palm oil industry in South East Asia and the effluent treatment technology—A review. Environmental Technology and Innovation, 9(May 2017), 169–185. https://doi.org/10.1016/j.eti.2017.11.003
Jocher, G., Ayush Chaurasia, Alex Stoken, Jirka Borovec, NanoCode012, Yonghye Kwon, Kalen Michael, TaoXie, Jiacong Fang, imyhxy, Lorna, 曾逸夫(Zeng Yifu), Colin Wong, Abhiram V, Diego Montes, Zhiqiang Wang, Cristi Fati, Jebastin Nadar, Laughing, … Mrinal Jain. (2022). ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation (v7.0). Zenodo. https://doi.org/10.5281/zenodo.7347926
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
Li, W., Fu, H., Yu, L., & Cracknell, A. (2017). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010022
Malyala, R. (2024). Development of a Convolutional Neural Network Model for Automated Ripeness Classification of Palm Oil Fresh Fruit Bunches. 9(11), 1040–1046.
Mamat, N., Othman, M. F., Abdulghafor, R., Alwan, A. A., & Gulzar, Y. (2023). Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach. Sustainability (Switzerland), 15(2). https://doi.org/10.3390/su15020901
Muna, M. S., Nugroho, A. P., Syarovy, M., Wiratmoko, A., Suwardi, & Sutiarso, L. (2023). Development of Automatic Counting System for Palm Oil Tree Based on Remote Sensing Imagery. Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022), 26(Dl), 503–508. https://doi.org/10.2991/978-94-6463-086-2_68
Putra, J. W. G. (2019). Pengenalan konsep pembelajaran mesin dan deep learning. Computational Linguistics and Natural Language Processing Laboratory, 4(August), 1–235. https://www.researchgate.net/publication/323700644
Saleh, A. Y., & Liansitim, E. (2020). Palm oil classification using deep learning. Science in Information Technology Letters, 1(1), 1–8. https://doi.org/10.31763/sitech.v1i1.1
Salim, E., & Suharjito. (2023). Hyperparameter optimization of YOLOv4 tiny for palm oil fresh fruit bunches maturity detection using genetics algorithms. Smart Agricultural Technology, 6(November), 100364. https://doi.org/10.1016/j.atech.2023.100364
Shabdin, M. K., Shariff, A. R. M., Johari, M. N. A., Saat, N. K., & Abbas, Z. (2016). A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach. IOP Conference Series: Earth and Environmental Science, 37(1). https://doi.org/10.1088/1755-1315/37/1/012039
Shiddiq, M. (2022). Counting of Oil Palm Fresh Fruit Bunches Using Computer Vision. Journal of Oil Palm Research. https://doi.org/10.21894/jopr.2022.0029
Sohan, M., Sai Ram, T., & Rami Reddy, C. V. (2024). A Review on YOLOv8 and Its Advancements. January, 529–545. https://doi.org/10.1007/978-981-99-7962-2_39
Zheng, J., Li, W., Xia, M., Dong, R., Fu, H., & Yuan, S. (2019). LARGE-SCALE OIL PALM TREE DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES USING FASTER-RCNN. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 1422–1425.
Published
2025-06-16
How to Cite
MUNA, Mukhes Sri et al.
KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT MENGGUNAKAN MODEL YOLOV8 BERBASIS DEEP LEARNING.
Journal of Agricultural and Biosystem Engineering Research, [S.l.], v. 6, n. 1, p. 50-62, june 2025.
ISSN 2776-821X.
Available at: <https://jos.unsoed.ac.id/index.php/jaber/article/view/15953>. Date accessed: 18 july 2025.
doi: https://doi.org/10.20884/1.jaber.2025.6.1.15953.
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Articles
