Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitati...

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Main Authors: Liaghat, Shohreh, Ehsani, Reza, Mansor, Shattri, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja, Azam, Siti H. M. N.
Format: Article
Language:English
Published: Taylor & Francis 2014
Online Access:http://psasir.upm.edu.my/id/eprint/37181/1/Early%20detection%20of%20basal%20stem%20rot%20disease.pdf
http://psasir.upm.edu.my/id/eprint/37181/
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spelling my.upm.eprints.371812015-10-01T02:33:16Z http://psasir.upm.edu.my/id/eprint/37181/ Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms Liaghat, Shohreh Ehsani, Reza Mansor, Shattri Mohd Shafri, Helmi Zulhaidi Meon, Sariah Sankaran, Sindhuja Azam, Siti H. M. N. Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm. Taylor & Francis 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/37181/1/Early%20detection%20of%20basal%20stem%20rot%20disease.pdf Liaghat, Shohreh and Ehsani, Reza and Mansor, Shattri and Mohd Shafri, Helmi Zulhaidi and Meon, Sariah and Sankaran, Sindhuja and Azam, Siti H. M. N. (2014) Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. International Journal of Remote Sensing, 35 (10). pp. 3427-3439. ISSN 0143-1161; ESSN: 1366-5901 10.1080/01431161.2014.903353
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) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.
format Article
author Liaghat, Shohreh
Ehsani, Reza
Mansor, Shattri
Mohd Shafri, Helmi Zulhaidi
Meon, Sariah
Sankaran, Sindhuja
Azam, Siti H. M. N.
spellingShingle Liaghat, Shohreh
Ehsani, Reza
Mansor, Shattri
Mohd Shafri, Helmi Zulhaidi
Meon, Sariah
Sankaran, Sindhuja
Azam, Siti H. M. N.
Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
author_facet Liaghat, Shohreh
Ehsani, Reza
Mansor, Shattri
Mohd Shafri, Helmi Zulhaidi
Meon, Sariah
Sankaran, Sindhuja
Azam, Siti H. M. N.
author_sort Liaghat, Shohreh
title Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
title_short Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
title_full Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
title_fullStr Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
title_full_unstemmed Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
title_sort early detection of basal stem rot disease (ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms
publisher Taylor & Francis
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/37181/1/Early%20detection%20of%20basal%20stem%20rot%20disease.pdf
http://psasir.upm.edu.my/id/eprint/37181/
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