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...

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Autores principales:	Siti Khairunniza-Bejo, Muhamad Syahir Shahibullah, Aiman Nabilah Noor Azmi, Mahirah Jahari
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	<i>Ganoderma boninense</i> hyperspectral data near infrared support vector machine vegetation index non-destructive detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/328737fada684e688a35b6aa2bd0893a
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spelling	oai:doaj.org-article:328737fada684e688a35b6aa2bd0893a2021-11-25T16:39:43ZNon-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine10.3390/app1122108782076-3417https://doaj.org/article/328737fada684e688a35b6aa2bd0893a2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10878https://doaj.org/toc/2076-3417Breeding 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).Siti Khairunniza-BejoMuhamad Syahir ShahibullahAiman Nabilah Noor AzmiMahirah JahariMDPI AGarticle<i>Ganoderma boninense</i>hyperspectral datanear infraredsupport vector machinevegetation indexnon-destructive detectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10878, p 10878 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	<i>Ganoderma boninense</i> hyperspectral data near infrared support vector machine vegetation index non-destructive detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	<i>Ganoderma boninense</i> hyperspectral data near infrared support vector machine vegetation index non-destructive detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 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
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).
format	article
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
title	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_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_sort	non-destructive detection of asymptomatic <i>ganoderma boninense</i> infection of oil palm seedlings using nir-hyperspectral data and support vector machine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/328737fada684e688a35b6aa2bd0893a
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Non-Destructive Detection of Asymptomatic <i>Ganoderma boninense</i> Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine

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