Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine
Breeding programs to develop planting materials resistant to <i>G. boninense</i> 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 o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/22/10878 |
| _version_ | 1797511274091773952 |
|---|---|
| author | Siti Khairunniza-Bejo Muhamad Syahir Shahibullah Aiman Nabilah Noor Azmi Mahirah Jahari |
| author_facet | Siti Khairunniza-Bejo Muhamad Syahir Shahibullah Aiman Nabilah Noor Azmi Mahirah Jahari |
| author_sort | Siti Khairunniza-Bejo |
| collection | DOAJ |
| description | Breeding programs to develop planting materials resistant to <i>G. boninense</i> 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 <i>G. boninense</i> 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 <i>G. boninense</i> 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 | 2024-03-10T05:43:03Z |
| format | Article |
| id | doaj.art-328737fada684e688a35b6aa2bd0893a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:43:03Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-328737fada684e688a35b6aa2bd0893a2023-11-22T22:20:19ZengMDPI AGApplied Sciences2076-34172021-11-0111221087810.3390/app112210878Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector MachineSiti Khairunniza-Bejo0Muhamad Syahir Shahibullah1Aiman Nabilah Noor Azmi2Mahirah Jahari3Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaBreeding programs to develop planting materials resistant to <i>G. boninense</i> 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 <i>G. boninense</i> 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 <i>G. boninense</i> 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).https://www.mdpi.com/2076-3417/11/22/10878<i>Ganoderma boninense</i>hyperspectral datanear infraredsupport vector machinevegetation indexnon-destructive detection |
| spellingShingle | Siti Khairunniza-Bejo Muhamad Syahir Shahibullah Aiman Nabilah Noor Azmi Mahirah Jahari Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine Applied Sciences <i>Ganoderma boninense</i> hyperspectral data near infrared support vector machine vegetation index non-destructive detection |
| title | Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine |
| title_full | Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine |
| title_fullStr | Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine |
| title_full_unstemmed | Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine |
| title_short | Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine |
| title_sort | non destructive detection of asymptomatic i ganoderma boninense i infection of oil palm seedlings using nir hyperspectral data and support vector machine |
| topic | <i>Ganoderma boninense</i> hyperspectral data near infrared support vector machine vegetation index non-destructive detection |
| url | https://www.mdpi.com/2076-3417/11/22/10878 |
| work_keys_str_mv | AT sitikhairunnizabejo nondestructivedetectionofasymptomaticiganodermaboninenseiinfectionofoilpalmseedlingsusingnirhyperspectraldataandsupportvectormachine AT muhamadsyahirshahibullah nondestructivedetectionofasymptomaticiganodermaboninenseiinfectionofoilpalmseedlingsusingnirhyperspectraldataandsupportvectormachine AT aimannabilahnoorazmi nondestructivedetectionofasymptomaticiganodermaboninenseiinfectionofoilpalmseedlingsusingnirhyperspectraldataandsupportvectormachine AT mahirahjahari nondestructivedetectionofasymptomaticiganodermaboninenseiinfectionofoilpalmseedlingsusingnirhyperspectraldataandsupportvectormachine |