Detecting BSR-infected oil palm seedlings using thermal imaging technique
Basal Stem Rot (BSR) is the most destructive disease instigated by a white wood rotting fungus called Ganoderma boninense, which cause great economic setback in oil palm productivity. It attacks the basal stem of oil palm trees, causing them to slowly rot. It also affects the xylem tissues that even...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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University of Basrah, College of Agriculture
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/96630/1/ABSTRACT.pdf |
| _version_ | 1796983270625247232 |
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| author | Mohd Johari, Siti Nurul Afiah Abdol Lajis, Ghaibulna Keat, Neoh B. Ithnin, Nalisha Bejo, Siti Khairunniza Daim, Leona Daniela Jeffery Yap, Yun Ci |
| author_facet | Mohd Johari, Siti Nurul Afiah Abdol Lajis, Ghaibulna Keat, Neoh B. Ithnin, Nalisha Bejo, Siti Khairunniza Daim, Leona Daniela Jeffery Yap, Yun Ci |
| author_sort | Mohd Johari, Siti Nurul Afiah |
| collection | UPM |
| description | Basal Stem Rot (BSR) is the most destructive disease instigated by a white wood rotting fungus called Ganoderma boninense, which cause great economic setback in oil palm productivity. It attacks the basal stem of oil palm trees, causing them to slowly rot. It also affects the xylem tissues that eventually interrupt water transportation to the upper part of the oil palm, turning the leaves at the frond become yellow. This problem should be prevented during nursery stage by separating between healthy and BSR-infected seedling. Therefore, this study focuses on the potential use of thermal imaging for detecting BSR in oil palm at seedling. Thermal images of oil palm seedling from healthy and BSR-infected were captured and processed to extract several thermal properties of the seedling, i.e., maximum, minimum, mean, and standard deviation of pixel intensity value. These values were then undergone statistical analysis to identify its significant different in differentiating healthy and BSR-infected seedling. Several classification models were tested including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM) and k-Nearest Neighbour (kNN). Principal Component Analysis (PCA) was used to reduce the dimensionality of the dataset. The results demonstrated that the highest accuracy achieved at 80.0 % using SVM (fine gaussian) classification model with PC1 and PC3 as the input parameter. This summarizes the potential of thermal imaging in detecting BSR-infected oil palm trees at seedling stage. |
| first_indexed | 2024-03-06T11:04:09Z |
| format | Article |
| id | upm.eprints-96630 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T11:04:09Z |
| publishDate | 2021 |
| publisher | University of Basrah, College of Agriculture |
| record_format | dspace |
| spelling | upm.eprints-966302023-01-11T07:02:47Z http://psasir.upm.edu.my/id/eprint/96630/ Detecting BSR-infected oil palm seedlings using thermal imaging technique Mohd Johari, Siti Nurul Afiah Abdol Lajis, Ghaibulna Keat, Neoh B. Ithnin, Nalisha Bejo, Siti Khairunniza Daim, Leona Daniela Jeffery Yap, Yun Ci Basal Stem Rot (BSR) is the most destructive disease instigated by a white wood rotting fungus called Ganoderma boninense, which cause great economic setback in oil palm productivity. It attacks the basal stem of oil palm trees, causing them to slowly rot. It also affects the xylem tissues that eventually interrupt water transportation to the upper part of the oil palm, turning the leaves at the frond become yellow. This problem should be prevented during nursery stage by separating between healthy and BSR-infected seedling. Therefore, this study focuses on the potential use of thermal imaging for detecting BSR in oil palm at seedling. Thermal images of oil palm seedling from healthy and BSR-infected were captured and processed to extract several thermal properties of the seedling, i.e., maximum, minimum, mean, and standard deviation of pixel intensity value. These values were then undergone statistical analysis to identify its significant different in differentiating healthy and BSR-infected seedling. Several classification models were tested including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM) and k-Nearest Neighbour (kNN). Principal Component Analysis (PCA) was used to reduce the dimensionality of the dataset. The results demonstrated that the highest accuracy achieved at 80.0 % using SVM (fine gaussian) classification model with PC1 and PC3 as the input parameter. This summarizes the potential of thermal imaging in detecting BSR-infected oil palm trees at seedling stage. University of Basrah, College of Agriculture 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/96630/1/ABSTRACT.pdf Mohd Johari, Siti Nurul Afiah and Abdol Lajis, Ghaibulna and Keat, Neoh B. and Ithnin, Nalisha and Bejo, Siti Khairunniza and Daim, Leona Daniela Jeffery and Yap, Yun Ci (2021) Detecting BSR-infected oil palm seedlings using thermal imaging technique. Basrah Journal of Agricultural Sciences, 34 (spec.1). 73 - 80. ISSN 1814-5868; ESSN: 2520-0860 https://bjas.bajas.edu.iq/index.php/bjas/article/view/417 10.37077/25200860.2021.34.sp1.8 |
| spellingShingle | Mohd Johari, Siti Nurul Afiah Abdol Lajis, Ghaibulna Keat, Neoh B. Ithnin, Nalisha Bejo, Siti Khairunniza Daim, Leona Daniela Jeffery Yap, Yun Ci Detecting BSR-infected oil palm seedlings using thermal imaging technique |
| title | Detecting BSR-infected oil palm seedlings using thermal imaging technique |
| title_full | Detecting BSR-infected oil palm seedlings using thermal imaging technique |
| title_fullStr | Detecting BSR-infected oil palm seedlings using thermal imaging technique |
| title_full_unstemmed | Detecting BSR-infected oil palm seedlings using thermal imaging technique |
| title_short | Detecting BSR-infected oil palm seedlings using thermal imaging technique |
| title_sort | detecting bsr infected oil palm seedlings using thermal imaging technique |
| url | http://psasir.upm.edu.my/id/eprint/96630/1/ABSTRACT.pdf |
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