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A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm

Basal stem rot (BSR) disease in oil palm is caused by Ganoderma boninense fungus. This plant disease is deemed highly destructive and would cause substantial economic loss. The use of spectroscopy technique with the capacity to deal with a large amount of spectral data has gained growing attention a...

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Main Authors: Khaled, Alfadhl Yahya, Abd Aziz, Samsuzana, Bejo, Siti Khairunniza, Mat Nawi, Nazmi, Jamaludin, Diyana, Ibrahim, Nur Ul Atikah
Format: Article
Language:English
Published: Elsevier 2020
Online Access:http://psasir.upm.edu.my/id/eprint/87581/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87581/
https://www.sciencedirect.com/science/article/pii/S0168169919324500
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spelling my.upm.eprints.875812022-07-06T08:38:40Z http://psasir.upm.edu.my/id/eprint/87581/ A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm Khaled, Alfadhl Yahya Abd Aziz, Samsuzana Bejo, Siti Khairunniza Mat Nawi, Nazmi Jamaludin, Diyana Ibrahim, Nur Ul Atikah Basal stem rot (BSR) disease in oil palm is caused by Ganoderma boninense fungus. This plant disease is deemed highly destructive and would cause substantial economic loss. The use of spectroscopy technique with the capacity to deal with a large amount of spectral data has gained growing attention as a robust method, particularly to identify the symptoms of plant disease in its initial stage. The dimensionality reduction is pivotal in the use of spectroscopy technique due to its improved prediction performance and optimum processing. Considering that, this study assessed the feasibility of utilising dielectric spectral properties to classify the severity levels of BSR disease in oil palm across a frequency range of 100 kHz–30 MHz. The support vector machine-feature selection (SVM-FS) and principal component analysis (PCA) were applied as data reduction methods. After selecting the optimum number of significant frequencies, this study proceeded to assess the effectiveness of the support vector machine (SVM) and quadratic discriminant analysis (QDA) classifiers in identifying the four different levels of BSR disease. The performance of both classifiers with and without data reduction methods was subsequently compared in terms of the classification accuracy, while the whole spectrum data served as part of the control method. The resultant outcomes revealed that the use of QDA classifier with PCA recorded the highest classification accuracy (up to 96.36%). As for the case of without using data reduction methods, the SVM classifier recorded the highest classification accuracy at only 79.55%. Conclusively, this study proved the significance of dimensionality reduction of dielectric spectral data for the classification of BSR disease in oil palm. Elsevier 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87581/1/ABSTRACT.pdf Khaled, Alfadhl Yahya and Abd Aziz, Samsuzana and Bejo, Siti Khairunniza and Mat Nawi, Nazmi and Jamaludin, Diyana and Ibrahim, Nur Ul Atikah (2020) A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm. Computers and Electronics in Agriculture, 170. art. no. 105288. pp. 1-9. ISSN 0168-1699 https://www.sciencedirect.com/science/article/pii/S0168169919324500 10.1016/j.compag.2020.105288
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Basal stem rot (BSR) disease in oil palm is caused by Ganoderma boninense fungus. This plant disease is deemed highly destructive and would cause substantial economic loss. The use of spectroscopy technique with the capacity to deal with a large amount of spectral data has gained growing attention as a robust method, particularly to identify the symptoms of plant disease in its initial stage. The dimensionality reduction is pivotal in the use of spectroscopy technique due to its improved prediction performance and optimum processing. Considering that, this study assessed the feasibility of utilising dielectric spectral properties to classify the severity levels of BSR disease in oil palm across a frequency range of 100 kHz–30 MHz. The support vector machine-feature selection (SVM-FS) and principal component analysis (PCA) were applied as data reduction methods. After selecting the optimum number of significant frequencies, this study proceeded to assess the effectiveness of the support vector machine (SVM) and quadratic discriminant analysis (QDA) classifiers in identifying the four different levels of BSR disease. The performance of both classifiers with and without data reduction methods was subsequently compared in terms of the classification accuracy, while the whole spectrum data served as part of the control method. The resultant outcomes revealed that the use of QDA classifier with PCA recorded the highest classification accuracy (up to 96.36%). As for the case of without using data reduction methods, the SVM classifier recorded the highest classification accuracy at only 79.55%. Conclusively, this study proved the significance of dimensionality reduction of dielectric spectral data for the classification of BSR disease in oil palm.
format Article
author Khaled, Alfadhl Yahya
Abd Aziz, Samsuzana
Bejo, Siti Khairunniza
Mat Nawi, Nazmi
Jamaludin, Diyana
Ibrahim, Nur Ul Atikah
spellingShingle Khaled, Alfadhl Yahya
Abd Aziz, Samsuzana
Bejo, Siti Khairunniza
Mat Nawi, Nazmi
Jamaludin, Diyana
Ibrahim, Nur Ul Atikah
A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
author_facet Khaled, Alfadhl Yahya
Abd Aziz, Samsuzana
Bejo, Siti Khairunniza
Mat Nawi, Nazmi
Jamaludin, Diyana
Ibrahim, Nur Ul Atikah
author_sort Khaled, Alfadhl Yahya
title A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
title_short A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
title_full A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
title_fullStr A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
title_full_unstemmed A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
title_sort comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (bsr) disease in oil palm
publisher Elsevier
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/87581/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87581/
https://www.sciencedirect.com/science/article/pii/S0168169919324500
_version_ 1738511957796847616
score 13.211869

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