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 si...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en en en |
| Published: |
MDPI
2021
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/93207/7/93207_Ganoderma%20boninense%20Disease%20Detection.pdf http://irep.iium.edu.my/93207/8/93207_Ganoderma%20boninense%20Disease%20Detection_Scopus.pdf http://irep.iium.edu.my/93207/19/93207_Ganoderma%20boninense%20disease%20detection_WoS.pdf http://irep.iium.edu.my/93207/ https://www.mdpi.com/1424-8220/21/9/3052 |
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| Summary: | 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. |
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