Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy
Basal stem rot (BSR) is the most destructive disease in oil palm plantations caused by Ganoderma boninense fungus, leading to a major economic setback in palm oil production. In order to reduce the losses caused by this disease, an effective early detection method is needed. Early detection not only...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/72256/1/Development%20of%20classification%20models%20for%20basal%20stem%20rot.pdf |
| _version_ | 1825950054617186304 |
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| author | Al-Khaled, Al-Fadhl Yahya Khaled Abd Aziz, Samsuzana Bejo, Siti Khairunniza Mat Nawi, Nazmi Abu Seman, Idris Anuar, Mohamad Izzuddin |
| author_facet | Al-Khaled, Al-Fadhl Yahya Khaled Abd Aziz, Samsuzana Bejo, Siti Khairunniza Mat Nawi, Nazmi Abu Seman, Idris Anuar, Mohamad Izzuddin |
| author_sort | Al-Khaled, Al-Fadhl Yahya Khaled |
| collection | UPM |
| description | Basal stem rot (BSR) is the most destructive disease in oil palm plantations caused by Ganoderma boninense fungus, leading to a major economic setback in palm oil production. In order to reduce the losses caused by this disease, an effective early detection method is needed. Early detection not only prevents production losses, but it also reduces the use of chemicals. Therefore, this paper aims at investigating an early detection method utilizing dielectric properties (impedance, capacitance, dielectric constant, and dissipation factor) of oil palm trees. Leaf samples of healthy, mild, moderate, and severely-infected trees were collected and leaves’ dielectric properties were measured at a frequency range of 100 kHz–30 MHz with 100 kHz intervals. These spectral data were then reduced by principal component analysis (PCA) method. Following that, the reduced spectral data were tested to classify the leaf samples into four levels of disease severity. The classifiers used are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), and Naïve Bayes (NB). The results showed that the dielectric spectra of oil palm leaves of diffident BSR severity levels were statistically different (p < 0.0004). In addition, despite the slight better performance of QDA classifier, ANOVA test revealed that there was no significant difference in accuracy between all other classifier models (p = 0.7169). Amongst the tested dielectric properties, impedance is considered the best parameter to assess the severity of BSR disease in oil palm with overall accuracy ranging from 81.82% to 100%. These results verify the potential of dielectric spectroscopy for detecting BSR disease in oil palm. |
| first_indexed | 2024-03-06T10:08:45Z |
| format | Article |
| id | upm.eprints-72256 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:08:45Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-722562020-04-26T15:29:17Z http://psasir.upm.edu.my/id/eprint/72256/ Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy Al-Khaled, Al-Fadhl Yahya Khaled Abd Aziz, Samsuzana Bejo, Siti Khairunniza Mat Nawi, Nazmi Abu Seman, Idris Anuar, Mohamad Izzuddin Basal stem rot (BSR) is the most destructive disease in oil palm plantations caused by Ganoderma boninense fungus, leading to a major economic setback in palm oil production. In order to reduce the losses caused by this disease, an effective early detection method is needed. Early detection not only prevents production losses, but it also reduces the use of chemicals. Therefore, this paper aims at investigating an early detection method utilizing dielectric properties (impedance, capacitance, dielectric constant, and dissipation factor) of oil palm trees. Leaf samples of healthy, mild, moderate, and severely-infected trees were collected and leaves’ dielectric properties were measured at a frequency range of 100 kHz–30 MHz with 100 kHz intervals. These spectral data were then reduced by principal component analysis (PCA) method. Following that, the reduced spectral data were tested to classify the leaf samples into four levels of disease severity. The classifiers used are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), and Naïve Bayes (NB). The results showed that the dielectric spectra of oil palm leaves of diffident BSR severity levels were statistically different (p < 0.0004). In addition, despite the slight better performance of QDA classifier, ANOVA test revealed that there was no significant difference in accuracy between all other classifier models (p = 0.7169). Amongst the tested dielectric properties, impedance is considered the best parameter to assess the severity of BSR disease in oil palm with overall accuracy ranging from 81.82% to 100%. These results verify the potential of dielectric spectroscopy for detecting BSR disease in oil palm. Elsevier 2018-11 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72256/1/Development%20of%20classification%20models%20for%20basal%20stem%20rot.pdf Al-Khaled, Al-Fadhl Yahya Khaled and Abd Aziz, Samsuzana and Bejo, Siti Khairunniza and Mat Nawi, Nazmi and Abu Seman, Idris and Anuar, Mohamad Izzuddin (2018) Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy. Industrial Crops and Products, 124. 99 - 107. ISSN 0926-6690 https://www.sciencedirect.com/science/article/abs/pii/S0926669018306514 10.1016/j.indcrop.2018.07.050 |
| spellingShingle | Al-Khaled, Al-Fadhl Yahya Khaled Abd Aziz, Samsuzana Bejo, Siti Khairunniza Mat Nawi, Nazmi Abu Seman, Idris Anuar, Mohamad Izzuddin Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy |
| title | Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy |
| title_full | Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy |
| title_fullStr | Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy |
| title_full_unstemmed | Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy |
| title_short | Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy |
| title_sort | development of classification models for basal stem rot bsr disease in oil palm using dielectric spectroscopy |
| url | http://psasir.upm.edu.my/id/eprint/72256/1/Development%20of%20classification%20models%20for%20basal%20stem%20rot.pdf |
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