Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO)
Elaeis guineensis is a tropical plant that originated in West Africa but is currently widely grown in many tropical places worldwide, including Malaysia. Malaysia is the world's second-largest palm oil exporter; hence, the government is willing to allocate RM100 million to the 2024 budget. With...
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45503/ |
| _version_ | 1848827435770970112 |
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| author | Lim, Bing Sern Abraham Nor Azuana, Ramli Wan Mohd Rozaimi, Wan Mustafa Arya, Suraj |
| author_facet | Lim, Bing Sern Abraham Nor Azuana, Ramli Wan Mohd Rozaimi, Wan Mustafa Arya, Suraj |
| author_sort | Lim, Bing Sern Abraham |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Elaeis guineensis is a tropical plant that originated in West Africa but is currently widely grown in many tropical places worldwide, including Malaysia. Malaysia is the world's second-largest palm oil exporter; hence, the government is willing to allocate RM100 million to the 2024 budget. With that budget allocation, palm oil production is expected to increase this year. However, the industry faces several problems and challenges involving palm oil, such as diseases caused by pests, the damage caused by rats, and the effects of climate change. It is necessary to overcome these problems and challenges to ensure the sustainability of palm oil production. One of the solutions is to apply remote sensing technology to monitor the health of oil palm trees. This study was conducted with the main objective of developing a model that is able to detect oil palm trees and classify their health by using You Only Look Once (YOLO). The dataset used for this study was collected from a site visit in Terengganu. Then, the dataset underwent pre-processing, such as auto-oriented, resizing, bounding boxes, and labelling. There are two categories for the health classification: healthy and unhealthy. The study was carried out by training a custom model with both YOLOv8 and YOLOv9 independently to consider which model performs better in precision, recall, mAP50, and mAP50-95. The results of YOLOv8 were a precision score of 58.7%, a recall value of 84.9%, a mAP50 of 71.3%, and a mAP50-95 of 48.9%, while YOLOv9 had a precision score of 64.1%, a recall value of 80.4%, a mAP50 of 72.6%, and a mAP50-95 of 50.2%. It was observed from the experiment conducted that YOLOv9 gave a better result than YOLOv8 in terms of precision, mAP50, and mAP50-95 overall, while YOLOv8 had a higher recall value during testing than YOLOv9. In addition, this study recommended implementing semi-automated systems, which combine automated processes with human oversight and apply augmentation techniques to enhance model resilience against variability. |
| first_indexed | 2025-11-15T04:00:40Z |
| format | Article |
| id | ump-45503 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:00:40Z |
| publishDate | 2025 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-455032025-08-28T01:31:21Z https://umpir.ump.edu.my/id/eprint/45503/ Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) Lim, Bing Sern Abraham Nor Azuana, Ramli Wan Mohd Rozaimi, Wan Mustafa Arya, Suraj QA75 Electronic computers. Computer science Elaeis guineensis is a tropical plant that originated in West Africa but is currently widely grown in many tropical places worldwide, including Malaysia. Malaysia is the world's second-largest palm oil exporter; hence, the government is willing to allocate RM100 million to the 2024 budget. With that budget allocation, palm oil production is expected to increase this year. However, the industry faces several problems and challenges involving palm oil, such as diseases caused by pests, the damage caused by rats, and the effects of climate change. It is necessary to overcome these problems and challenges to ensure the sustainability of palm oil production. One of the solutions is to apply remote sensing technology to monitor the health of oil palm trees. This study was conducted with the main objective of developing a model that is able to detect oil palm trees and classify their health by using You Only Look Once (YOLO). The dataset used for this study was collected from a site visit in Terengganu. Then, the dataset underwent pre-processing, such as auto-oriented, resizing, bounding boxes, and labelling. There are two categories for the health classification: healthy and unhealthy. The study was carried out by training a custom model with both YOLOv8 and YOLOv9 independently to consider which model performs better in precision, recall, mAP50, and mAP50-95. The results of YOLOv8 were a precision score of 58.7%, a recall value of 84.9%, a mAP50 of 71.3%, and a mAP50-95 of 48.9%, while YOLOv9 had a precision score of 64.1%, a recall value of 80.4%, a mAP50 of 72.6%, and a mAP50-95 of 50.2%. It was observed from the experiment conducted that YOLOv9 gave a better result than YOLOv8 in terms of precision, mAP50, and mAP50-95 overall, while YOLOv8 had a higher recall value during testing than YOLOv9. In addition, this study recommended implementing semi-automated systems, which combine automated processes with human oversight and apply augmentation techniques to enhance model resilience against variability. Semarak Ilmu Publishing 2025 Article PeerReviewed pdf en cc_by_nc_4 https://umpir.ump.edu.my/id/eprint/45503/1/SIJESE_V6_N1_PP19-37.pdf Lim, Bing Sern Abraham and Nor Azuana, Ramli and Wan Mohd Rozaimi, Wan Mustafa and Arya, Suraj (2025) Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO). Semarak International Journal of Electronic System Engineering, 6 (1). pp. 19 -37. ISSN 3030-5519. (Published) https://semarakilmu.my/index.php/sijese/article/view/539 |
| spellingShingle | QA75 Electronic computers. Computer science Lim, Bing Sern Abraham Nor Azuana, Ramli Wan Mohd Rozaimi, Wan Mustafa Arya, Suraj Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) |
| title | Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) |
| title_full | Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) |
| title_fullStr | Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) |
| title_full_unstemmed | Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) |
| title_short | Oil palm detection and health classification from UAV multispectral images using You Only Look Once (YOLO) |
| title_sort | oil palm detection and health classification from uav multispectral images using you only look once (yolo) |
| topic | QA75 Electronic computers. Computer science |
| url | https://umpir.ump.edu.my/id/eprint/45503/ https://umpir.ump.edu.my/id/eprint/45503/ |