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All Journal Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Cindi Melinda Malau
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Riau, Indonesia
Aerospace Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Performance Analysis of the YOLOv8 Algorithm for Detecting of Stacked Defective Oil Palm Fresh Fruit Bunches on a Moving Conveyor Minarni Shiddiq; Dodi Sofyan Arief; Roni Salambue; Cindi Melinda Malau; Yohana Christia Navili; Vicky Vernando Dasta; Muhammad Ikhsan Hamid; Nanda Syaputra
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 70 No 2 (2026): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v70i2.638

Abstract

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.
Co-Authors Dodi Sofyan Arief Minarni Shiddiq Muhammad Ikhsan Hamid Nanda Syaputra Roni Salambue Vicky Vernando Dasta Yohana Christia Navili