Palm fruit ripeness detection and classification using various YOLOv8 models

Gunawan, Teddy Surya and Mansor, Hasmah and Kartiwi, Mira and Md Yusoff, Nelidya (2024) Palm fruit ripeness detection and classification using various YOLOv8 models. In: IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2023), 17th - 18th October 2023, Kuala Lumpur, Malaysia.

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Abstract

The significance of palm oil, which contributes 30 % of the world's total vegetable oil production, cannot be overstated. Its numerous applications, ranging from soap to cosmetics, have increased demand, thereby increasing the importance of yield management. Human graders have traditionally been responsible for determining the ripeness of oil palm fresh fruit bunches (FFBs), a task upon which the oil extraction rate (OER) relies heavily. This rate has significant economic implications: a 0.13 % drop in OER due to unripe fruits can result in a staggering RM 340 million loss. Precision is stressed, prompting automated detection research. Computer vision and Artificial Intelligence are becoming more effective at assessing oil palm fruit ripeness. However, many methods require complex operations, controlled settings, or manual calibrations. Although innovative, microwave sensors and inductive techniques have drawbacks like sample preparation and equipment dependence. This study investigates the potential of the YOLOv8 framework, particularly its YOLOv8m variant, for ripeness classification and detection. This model's mAP50-95 of 0.927 balances computational efficiency and accuracy, indicating its potential to revolutionize the palm oil industry's fruit assessment procedures. The findings here shed light on the model's efficacy and highlight its potential as an industry-standard solution, bridging gaps in ripeness detection methodologies.

Item Type:	Proceeding Paper (Invited Papers)
Additional Information:	External collaboration (national) with UTM
Uncontrolled Keywords:	palm oil ripeness, object detection, computer vision, deep learning, YOLOv8
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	15 Jan 2024 10:14
Last Modified:	28 Feb 2024 13:09
URI:	http://irep.iium.edu.my/id/eprint/110159

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