Citation

Naghipour Khanaposhtani, Mohammadmahdi and Lew, Sook Ling and Connie, Tee (2024) YOLO-Based Oil Palm FFB Ripeness Detection. In: 5th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2024, 30-31 October 2024, Kuala Lumpur.

Text YOLO-Based Oil Palm FFB Ripeness Detection.pdf - Published Version Restricted to Repository staff only Download (975kB)

Abstract

The palm oil industry is a huge contributor to Malaysia’s economic growth. Adopting new technologies such as AI to optimize the different processes involved in the palm oil industry and minimizing the problems such as high time consumption and labor intensiveness associated with the traditional ways of handling fruits in the industry will further assist the economic growth of Malaysia and will help reduce negative environmental effects. This research offers insights on the related literature and employs a YOLOv10-S model with mAP (mean average precision) of 0.95 for detecting ripe FFB (fresh fruit bunch) from unripe FFB. After the completion of the training the model was exported and used in an interface.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Agriculture, artificial intelligence, computer vision
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science S Agriculture > S Agriculture (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Feb 2025 08:20
Last Modified:	20 Feb 2025 08:20
URII:	http://shdl.mmu.edu.my/id/eprint/13532

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