Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine
Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses on the early dete...
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| Format: | Article |
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MDPI
2021
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| _version_ | 1796982971172913152 |
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| author | Bejo, Siti Khairunniza Shahibullah, Muhamad Syahir Noor Azmi, Aiman Nabilah Jahari, Mahirah |
| author_facet | Bejo, Siti Khairunniza Shahibullah, Muhamad Syahir Noor Azmi, Aiman Nabilah Jahari, Mahirah |
| author_sort | Bejo, Siti Khairunniza |
| collection | UPM |
| description | Breeding programs to develop planting materials resistant to G. boninense involve a manual
census to monitor the progress of the disease development associated with various treatments. It is
prone to error due to a lack of experience and subjective judgements. This study focuses on the early
detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral
data and a support vector machine (SVM). The study aims to use a small number of wavelengths by
using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results
of detection obtained from the vegetation indices developed using spectral reflectance taken from the
same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two
classes would be more suitable for G. boninense detection compared to the others, both for single-band
reflectance and vegetation index datasets. A linear SVM which was developed using a single-band
reflectance at 934 nm was identified as the best model of detection since it was not only economical,
but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and
area under the receiver operating characteristic curve (AUC) (0.95). |
| first_indexed | 2024-03-06T10:59:41Z |
| format | Article |
| id | upm.eprints-94368 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T10:59:41Z |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | upm.eprints-943682023-04-04T02:27:13Z http://psasir.upm.edu.my/id/eprint/94368/ Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine Bejo, Siti Khairunniza Shahibullah, Muhamad Syahir Noor Azmi, Aiman Nabilah Jahari, Mahirah Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses on the early detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral data and a support vector machine (SVM). The study aims to use a small number of wavelengths by using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results of detection obtained from the vegetation indices developed using spectral reflectance taken from the same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two classes would be more suitable for G. boninense detection compared to the others, both for single-band reflectance and vegetation index datasets. A linear SVM which was developed using a single-band reflectance at 934 nm was identified as the best model of detection since it was not only economical, but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and area under the receiver operating characteristic curve (AUC) (0.95). MDPI 2021-11-17 Article PeerReviewed Bejo, Siti Khairunniza and Shahibullah, Muhamad Syahir and Noor Azmi, Aiman Nabilah and Jahari, Mahirah (2021) Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine. Applied Sciences, 11 (22). art. no. 10878. pp. 1-17. ISSN 2076-3417 https://www.mdpi.com/2076-3417/11/22/10878 10.3390/app112210878 |
| spellingShingle | Bejo, Siti Khairunniza Shahibullah, Muhamad Syahir Noor Azmi, Aiman Nabilah Jahari, Mahirah Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine |
| title | Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine |
| title_full | Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine |
| title_fullStr | Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine |
| title_full_unstemmed | Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine |
| title_short | Non-destructive detection of asymptomatic Ganoderma boninense infection of oil palm seedlings using NIR-hyperspectral data and support vector machine |
| title_sort | non destructive detection of asymptomatic ganoderma boninense infection of oil palm seedlings using nir hyperspectral data and support vector machine |
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