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Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm

Basal stem rot (BSR), caused by Ganoderma boninense is known as the most destructive disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoder...

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Main Authors: Liaghat, Shohreh, Mansor, Shattri, Ehsani, Reza, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja
Format: Article
Published: Elsevier 2014
Online Access:http://psasir.upm.edu.my/id/eprint/34527/
http://www.sciencedirect.com/science/article/pii/S0168169913003098
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spelling my.upm.eprints.345272015-12-15T08:31:44Z http://psasir.upm.edu.my/id/eprint/34527/ Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm Liaghat, Shohreh Mansor, Shattri Ehsani, Reza Mohd Shafri, Helmi Zulhaidi Meon, Sariah Sankaran, Sindhuja Basal stem rot (BSR), caused by Ganoderma boninense is known as the most destructive disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the use of chemicals. This study focuses on the development of a statistical model for the discrimination of Ganoderma infestation on oil palm trees at different stages using a Fourier transform infrared (FT-IR) spectroscopic technique. Leaf samples of healthy, mild, moderately, and severely-infected trees were measured using FT-IR spectrometers to obtain absorbance data from the range of 2.55–25.05 μm s (3921–399 cm−1). The samples were analyzed with and without dilution with KBr. After pre-processing (baseline correction and normalization), the Savitzky–Golay method was used to calculate first and second derivatives. Then, for the preprocessed raw, first derivatives and second derivatives datasets, principal component analysis was performed to reduce the dimensionality of the data. The selected principal component scores were used in classification using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN) and Naive-Bayes (NB) multivariate classification algorithms. The algorithms were tested to classify the leaf samples into four levels of disease severity. The results demonstrated that when samples were prepared with KBr, the LDA-based model resulted in the highest average overall classification accuracy of 92%, with individual classification accuracies greater than about 90% using the pre-processed raw dataset. This verifies the potential of mid-infrared spectroscopy for Ganoderma detection in early symptomless stages of infection in oil palm. Elsevier 2014-02 Article PeerReviewed Liaghat, Shohreh and Mansor, Shattri and Ehsani, Reza and Mohd Shafri, Helmi Zulhaidi and Meon, Sariah and Sankaran, Sindhuja (2014) Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm. Computers and Electronics in Agriculture, 101. pp. 48-54. ISSN 0168-1699 http://www.sciencedirect.com/science/article/pii/S0168169913003098 10.1016/j.compag.2013.12.012
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/
description Basal stem rot (BSR), caused by Ganoderma boninense is known as the most destructive disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the use of chemicals. This study focuses on the development of a statistical model for the discrimination of Ganoderma infestation on oil palm trees at different stages using a Fourier transform infrared (FT-IR) spectroscopic technique. Leaf samples of healthy, mild, moderately, and severely-infected trees were measured using FT-IR spectrometers to obtain absorbance data from the range of 2.55–25.05 μm s (3921–399 cm−1). The samples were analyzed with and without dilution with KBr. After pre-processing (baseline correction and normalization), the Savitzky–Golay method was used to calculate first and second derivatives. Then, for the preprocessed raw, first derivatives and second derivatives datasets, principal component analysis was performed to reduce the dimensionality of the data. The selected principal component scores were used in classification using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN) and Naive-Bayes (NB) multivariate classification algorithms. The algorithms were tested to classify the leaf samples into four levels of disease severity. The results demonstrated that when samples were prepared with KBr, the LDA-based model resulted in the highest average overall classification accuracy of 92%, with individual classification accuracies greater than about 90% using the pre-processed raw dataset. This verifies the potential of mid-infrared spectroscopy for Ganoderma detection in early symptomless stages of infection in oil palm.
format Article
author Liaghat, Shohreh
Mansor, Shattri
Ehsani, Reza
Mohd Shafri, Helmi Zulhaidi
Meon, Sariah
Sankaran, Sindhuja
spellingShingle Liaghat, Shohreh
Mansor, Shattri
Ehsani, Reza
Mohd Shafri, Helmi Zulhaidi
Meon, Sariah
Sankaran, Sindhuja
Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
author_facet Liaghat, Shohreh
Mansor, Shattri
Ehsani, Reza
Mohd Shafri, Helmi Zulhaidi
Meon, Sariah
Sankaran, Sindhuja
author_sort Liaghat, Shohreh
title Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
title_short Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
title_full Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
title_fullStr Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
title_full_unstemmed Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
title_sort mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
publisher Elsevier
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/34527/
http://www.sciencedirect.com/science/article/pii/S0168169913003098
_version_ 1643831180769886208
score 13.211869

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