Staff View: Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar

Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar

One of the most common diseases in Malaysia in oil palm is Ganoderma basal stem rot (BSR) and causes significant yield loss during oil palm lifetime. Early disease detection of the Ganoderma BSR disease has been developed using lab-based technologies. However, those technologies require the sampling...

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Main Author:	Mohamad Izzuddin , Anuar
Format:	Thesis
Published:	2024
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://studentsrepo.um.edu.my/15370/2/Mohamad_Izzuddin_Anuar.pdf http://studentsrepo.um.edu.my/15370/1/Mohamad_Izzuddin_Anuar.pdf http://studentsrepo.um.edu.my/15370/
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spelling	my.um.stud.153702024-09-01T23:14:58Z Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar Mohamad Izzuddin , Anuar TK Electrical engineering. Electronics Nuclear engineering One of the most common diseases in Malaysia in oil palm is Ganoderma basal stem rot (BSR) and causes significant yield loss during oil palm lifetime. Early disease detection of the Ganoderma BSR disease has been developed using lab-based technologies. However, those technologies require the sampling of each oil palm in the field which consumes loads of labour and time. This study uses airborne optical remote sensing (AORS) images from multispectral and hyperspectral drones for detection of Ganoderma BSR disease in oil palm. Object-based Image Analysis (OBIA) was used to classify between Ganoderma BSR Disease Severity Index (GDSI). In OBIA, the segmentation parameters, the Edge is set to 30 while Merge is set to 70 to segment each of individual oil palm canopy fronds and avoid overlapping between canopies. Then, two classifiers which are 1) Support Vector Machines (SVM); and 2) K-Nearest Neighbour (KNN) were used to classify the segmented oil palm canopy into GDSI. Multispectral image classification using OBIA resulted a good accuracy (>90%) but unable to classify the mild/early disease severity. This study introduces a new procedure to process hyperspectral image for GDSI classification that starts with image colour-balancing, image transform using Continuum-Removed (CR) and First Derivative of Spectral Reflectance (FDSR), denoising using Savitzky-Golay filter and generating new images using significant wavelengths. Then, the new images were analysed using OBIA and results show high overall accuracy (92.5%) for GDSI classification with 85 % accuracy for early/mild Ganoderma BSR infection classification. The result showed that hyperspectral image performed better for detection of the early infection of Ganoderma BSR disease in oil palm compared to multispectral image 2024-07 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/15370/2/Mohamad_Izzuddin_Anuar.pdf application/pdf http://studentsrepo.um.edu.my/15370/1/Mohamad_Izzuddin_Anuar.pdf Mohamad Izzuddin , Anuar (2024) Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar. PhD thesis, Universiti Malaya. http://studentsrepo.um.edu.my/15370/
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Student Repository
url_provider	http://studentsrepo.um.edu.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohamad Izzuddin , Anuar Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar
description	One of the most common diseases in Malaysia in oil palm is Ganoderma basal stem rot (BSR) and causes significant yield loss during oil palm lifetime. Early disease detection of the Ganoderma BSR disease has been developed using lab-based technologies. However, those technologies require the sampling of each oil palm in the field which consumes loads of labour and time. This study uses airborne optical remote sensing (AORS) images from multispectral and hyperspectral drones for detection of Ganoderma BSR disease in oil palm. Object-based Image Analysis (OBIA) was used to classify between Ganoderma BSR Disease Severity Index (GDSI). In OBIA, the segmentation parameters, the Edge is set to 30 while Merge is set to 70 to segment each of individual oil palm canopy fronds and avoid overlapping between canopies. Then, two classifiers which are 1) Support Vector Machines (SVM); and 2) K-Nearest Neighbour (KNN) were used to classify the segmented oil palm canopy into GDSI. Multispectral image classification using OBIA resulted a good accuracy (>90%) but unable to classify the mild/early disease severity. This study introduces a new procedure to process hyperspectral image for GDSI classification that starts with image colour-balancing, image transform using Continuum-Removed (CR) and First Derivative of Spectral Reflectance (FDSR), denoising using Savitzky-Golay filter and generating new images using significant wavelengths. Then, the new images were analysed using OBIA and results show high overall accuracy (92.5%) for GDSI classification with 85 % accuracy for early/mild Ganoderma BSR infection classification. The result showed that hyperspectral image performed better for detection of the early infection of Ganoderma BSR disease in oil palm compared to multispectral image
format	Thesis
author	Mohamad Izzuddin , Anuar
author_facet	Mohamad Izzuddin , Anuar
author_sort	Mohamad Izzuddin , Anuar
title	Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar
title_short	Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar
title_full	Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar
title_fullStr	Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar
title_full_unstemmed	Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar
title_sort	airborne optical image classification model for detection of ganoderma disease in oil palm / mohamad izzuddin anuar
publishDate	2024
url	http://studentsrepo.um.edu.my/15370/2/Mohamad_Izzuddin_Anuar.pdf http://studentsrepo.um.edu.my/15370/1/Mohamad_Izzuddin_Anuar.pdf http://studentsrepo.um.edu.my/15370/
_version_	1811682651002634240
score	13.211869

Airborne optical image classification model for detection of Ganoderma disease in oil palm / Mohamad Izzuddin Anuar

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