Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since...

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Main Authors: Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Fatimah, Dzaharudin, Chalermwisutkul, Suramate, Akkaraekthalin, Prayoot
Format: Article
Language:English
Published: MDPI 2021
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Online Access:http://umpir.ump.edu.my/id/eprint/31641/1/Ganoderma%20boninense%20disease%20detection%20by%20near%E2%80%90infrared%20spectroscopy%20classification.pdf
http://umpir.ump.edu.my/id/eprint/31641/
https://doi.org/10.3390/ s21093052
https://doi.org/10.3390/ s21093052
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spelling my.ump.umpir.316412021-07-13T07:57:29Z http://umpir.ump.edu.my/id/eprint/31641/ Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review Mas Ira Syafila, Mohd Hilmi Tan Mohd Faizal, Jamlos Ahmad Fairuz, Omar Fatimah, Dzaharudin Chalermwisutkul, Suramate Akkaraekthalin, Prayoot TP Chemical technology Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future. MDPI 2021-04-27 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/31641/1/Ganoderma%20boninense%20disease%20detection%20by%20near%E2%80%90infrared%20spectroscopy%20classification.pdf Mas Ira Syafila, Mohd Hilmi Tan and Mohd Faizal, Jamlos and Ahmad Fairuz, Omar and Fatimah, Dzaharudin and Chalermwisutkul, Suramate and Akkaraekthalin, Prayoot (2021) Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review. Sensors, 21 (9). pp. 1-21. ISSN 1424-8220 https://doi.org/10.3390/ s21093052 https://doi.org/10.3390/ s21093052
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
Mas Ira Syafila, Mohd Hilmi Tan
Mohd Faizal, Jamlos
Ahmad Fairuz, Omar
Fatimah, Dzaharudin
Chalermwisutkul, Suramate
Akkaraekthalin, Prayoot
Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
description Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.
format Article
author Mas Ira Syafila, Mohd Hilmi Tan
Mohd Faizal, Jamlos
Ahmad Fairuz, Omar
Fatimah, Dzaharudin
Chalermwisutkul, Suramate
Akkaraekthalin, Prayoot
author_facet Mas Ira Syafila, Mohd Hilmi Tan
Mohd Faizal, Jamlos
Ahmad Fairuz, Omar
Fatimah, Dzaharudin
Chalermwisutkul, Suramate
Akkaraekthalin, Prayoot
author_sort Mas Ira Syafila, Mohd Hilmi Tan
title Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
title_short Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
title_full Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
title_fullStr Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
title_full_unstemmed Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
title_sort ganoderma boninense disease detection by near-infrared spectroscopy classification: a review
publisher MDPI
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/31641/1/Ganoderma%20boninense%20disease%20detection%20by%20near%E2%80%90infrared%20spectroscopy%20classification.pdf
http://umpir.ump.edu.my/id/eprint/31641/
https://doi.org/10.3390/ s21093052
https://doi.org/10.3390/ s21093052
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score 13.210693