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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123893/ https://www.ncbi.nlm.nih.gov/pubmed/33925576 http://dx.doi.org/10.3390/s21093052 |
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author | Mohd Hilmi Tan, Mas Ira Syafila Jamlos, Mohd Faizal Omar, Ahmad Fairuz Dzaharudin, Fatimah Chalermwisutkul, Suramate Akkaraekthalin, Prayoot |
author_facet | Mohd Hilmi Tan, Mas Ira Syafila Jamlos, Mohd Faizal Omar, Ahmad Fairuz Dzaharudin, Fatimah Chalermwisutkul, Suramate Akkaraekthalin, Prayoot |
author_sort | Mohd Hilmi Tan, Mas Ira Syafila |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8123893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81238932021-05-16 Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review Mohd Hilmi Tan, Mas Ira Syafila Jamlos, Mohd Faizal Omar, Ahmad Fairuz Dzaharudin, Fatimah Chalermwisutkul, Suramate Akkaraekthalin, Prayoot Sensors (Basel) 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 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 /pmc/articles/PMC8123893/ /pubmed/33925576 http://dx.doi.org/10.3390/s21093052 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Mohd Hilmi Tan, Mas Ira Syafila Jamlos, Mohd Faizal Omar, Ahmad Fairuz Dzaharudin, Fatimah Chalermwisutkul, Suramate Akkaraekthalin, Prayoot Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review |
title | 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_short | Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review |
title_sort | ganoderma boninense disease detection by near-infrared spectroscopy classification: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123893/ https://www.ncbi.nlm.nih.gov/pubmed/33925576 http://dx.doi.org/10.3390/s21093052 |
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