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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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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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 |
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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 |
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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 |
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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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