Severity of Ganoderma boninense disease classification using SAR data
Basal stem rot disease (BSR) in oil palm plantations is caused by Ganoderma boninense fungus. BSR is a major disease attacking oil palm plantations in Malaysia and Indonesia. But for now, the only available treatment is to prolong the life of oil palm trees as there is no effective treatment for BSR...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/67019/1/39TH%20ACRS-6.pdf |
| _version_ | 1796978424930107392 |
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| author | Che Hashim, Izrahayu Mohamed Shariff, Abdul Rashid Bejo, Siti Khairunniza Muharam, Farrah Melissa Ahmad, Khairulmazmi |
| author_facet | Che Hashim, Izrahayu Mohamed Shariff, Abdul Rashid Bejo, Siti Khairunniza Muharam, Farrah Melissa Ahmad, Khairulmazmi |
| author_sort | Che Hashim, Izrahayu |
| collection | UPM |
| description | Basal stem rot disease (BSR) in oil palm plantations is caused by Ganoderma boninense fungus. BSR is a major disease attacking oil palm plantations in Malaysia and Indonesia. But for now, the only available treatment is to prolong the life of oil palm trees as there is no effective treatment for BSR. To control this disease, early detection of G. Boninense infection is a decent strategy. Many researchers have used remote sensing techniques for early detection and mapping of BSR disease in oil palm plantations based on G. boninense infection symptoms. The main objective of this project is to study the potential of radar backscattering for classifying severity level of G. boninense disease in oil palm plantation. The processing stage involved the usage of two different machine learning algorithms to classify severity level of G. boninense in oil palm plantation. This study uses Alos Palsar 2 image with dual polarization; HH (Horizontal - transmit and Horizontal - receive) and HV (Horizontal - transmit and Vertical - receive) archived on March 20, 2017. Two classifier models: Multilayer Perceptron (MP) and Kstar are tested by using Weka open source software. The MP classifier model for HV polarization is the best for predicting and classifying severity level of G. boninense in oil palm plantation in terms of correctly classified. Model MP classifier and HV polarization reach 77.17% correctly classified. In addition, this study can separate oil palm by severity of each T0 (92.73%), T1 (0%), T2 (93.33%) and T3 (54.55%). |
| first_indexed | 2024-03-06T09:54:23Z |
| format | Conference or Workshop Item |
| id | upm.eprints-67019 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:54:23Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | upm.eprints-670192019-03-06T05:38:38Z http://psasir.upm.edu.my/id/eprint/67019/ Severity of Ganoderma boninense disease classification using SAR data Che Hashim, Izrahayu Mohamed Shariff, Abdul Rashid Bejo, Siti Khairunniza Muharam, Farrah Melissa Ahmad, Khairulmazmi Basal stem rot disease (BSR) in oil palm plantations is caused by Ganoderma boninense fungus. BSR is a major disease attacking oil palm plantations in Malaysia and Indonesia. But for now, the only available treatment is to prolong the life of oil palm trees as there is no effective treatment for BSR. To control this disease, early detection of G. Boninense infection is a decent strategy. Many researchers have used remote sensing techniques for early detection and mapping of BSR disease in oil palm plantations based on G. boninense infection symptoms. The main objective of this project is to study the potential of radar backscattering for classifying severity level of G. boninense disease in oil palm plantation. The processing stage involved the usage of two different machine learning algorithms to classify severity level of G. boninense in oil palm plantation. This study uses Alos Palsar 2 image with dual polarization; HH (Horizontal - transmit and Horizontal - receive) and HV (Horizontal - transmit and Vertical - receive) archived on March 20, 2017. Two classifier models: Multilayer Perceptron (MP) and Kstar are tested by using Weka open source software. The MP classifier model for HV polarization is the best for predicting and classifying severity level of G. boninense in oil palm plantation in terms of correctly classified. Model MP classifier and HV polarization reach 77.17% correctly classified. In addition, this study can separate oil palm by severity of each T0 (92.73%), T1 (0%), T2 (93.33%) and T3 (54.55%). 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/67019/1/39TH%20ACRS-6.pdf Che Hashim, Izrahayu and Mohamed Shariff, Abdul Rashid and Bejo, Siti Khairunniza and Muharam, Farrah Melissa and Ahmad, Khairulmazmi (2018) Severity of Ganoderma boninense disease classification using SAR data. In: 39th Asian Conference on Remote Sensing (ACRS 2018), 15-19 Oct. 2018, Renaissance Kuala Lumpur Hotel, Malaysia. (pp. 2492-2499). |
| spellingShingle | Che Hashim, Izrahayu Mohamed Shariff, Abdul Rashid Bejo, Siti Khairunniza Muharam, Farrah Melissa Ahmad, Khairulmazmi Severity of Ganoderma boninense disease classification using SAR data |
| title | Severity of Ganoderma boninense disease classification using SAR data |
| title_full | Severity of Ganoderma boninense disease classification using SAR data |
| title_fullStr | Severity of Ganoderma boninense disease classification using SAR data |
| title_full_unstemmed | Severity of Ganoderma boninense disease classification using SAR data |
| title_short | Severity of Ganoderma boninense disease classification using SAR data |
| title_sort | severity of ganoderma boninense disease classification using sar data |
| url | http://psasir.upm.edu.my/id/eprint/67019/1/39TH%20ACRS-6.pdf |
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