Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease
Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has be...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI AG
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/94065/ |
| _version_ | 1848861907693338624 |
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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 |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy. |
| first_indexed | 2025-11-15T13:08:35Z |
| format | Article |
| id | upm-94065 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:08:35Z |
| publishDate | 2021 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-940652023-04-06T03:24:19Z http://psasir.upm.edu.my/id/eprint/94065/ Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease Che Hashim, Izrahayu Mohamed Shariff, Abdul Rashid Bejo, Siti Khairunniza Muharam, Farrah Melissa Ahmad, Khairulmazmi Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy. MDPI AG 2021-03-12 Article PeerReviewed Che Hashim, Izrahayu and Mohamed Shariff, Abdul Rashid and Bejo, Siti Khairunniza and Muharam, Farrah Melissa and Ahmad, Khairulmazmi (2021) Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease. Agronomy, 11 (3). art. no. 532. pp. 1-17. ISSN 2073-4395 https://www.mdpi.com/2073-4395/11/3/532 10.3390/agronomy11030532 |
| spellingShingle | Che Hashim, Izrahayu Mohamed Shariff, Abdul Rashid Bejo, Siti Khairunniza Muharam, Farrah Melissa Ahmad, Khairulmazmi Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| title | Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| title_full | Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| title_fullStr | Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| title_full_unstemmed | Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| title_short | Machine-learning approach using SAR data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| title_sort | machine-learning approach using sar data for the classification of oil palm trees that are non-infected and infected with the basal stem rot disease |
| url | http://psasir.upm.edu.my/id/eprint/94065/ http://psasir.upm.edu.my/id/eprint/94065/ http://psasir.upm.edu.my/id/eprint/94065/ |