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UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom

Ganoderma disease in oil palm caused by Ganoderma spp. fungi have caused significant losses of Malaysia's economic income. Advances in remote sensed imagery and image processing using unmanned aerial vehicle (UAV) for Ganoderma disease detection could be developed to reduce operating cost and t...

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Main Author: Ezzati , Bahrom
Format: Thesis
Published: 2018
Subjects:
Q Science (General)
QH301 Biology
Online Access:http://studentsrepo.um.edu.my/12487/1/Ezzati.pdf
http://studentsrepo.um.edu.my/12487/
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spelling my.um.stud.124872021-09-23T23:48:05Z UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom Ezzati , Bahrom Q Science (General) QH301 Biology Ganoderma disease in oil palm caused by Ganoderma spp. fungi have caused significant losses of Malaysia's economic income. Advances in remote sensed imagery and image processing using unmanned aerial vehicle (UAV) for Ganoderma disease detection could be developed to reduce operating cost and time as well as cover wider oil palm areas. This study examines the performance of red-green-blue (RGB) and near- infrared (NIR) digital orthophoto image acquired using modified digital cameras mounted on the UAV for aerial detection of Ganoderma disease in oil palm. The orthophoto images were filtered using eight adaptive filters with window sizes of 7×7, 9×9 and 11×11. The filtered orthophoto images then were processed using three supervised image classifiers: Maximum Likelihood (ML), Mahalanobis Distance (MD) and Neural Net (NN). The classifiers were used to classify the Ganoderma disease severities into Experiment 1: T0 (healthy), T1 (mild), T2 (moderate) and T3 (severe); and Experiment 2: healthy and unhealthy. The classification outputs were assessed using a confusion matrix. Best result was obtained from Bit Error filter with 9×9 window size using the NN algorithm with an overall accuracy of 62.41% and a Kappa coefficient of 0.3890. This study demonstrated classification from UAV-based imagery can be improved using filters for Ganoderma disease detection mapping in oil palm plantation. 2018 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/12487/1/Ezzati.pdf Ezzati , Bahrom (2018) UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom. Masters thesis, University of Malaya. http://studentsrepo.um.edu.my/12487/
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 Q Science (General)
QH301 Biology
spellingShingle Q Science (General)
QH301 Biology
Ezzati , Bahrom
UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom
description Ganoderma disease in oil palm caused by Ganoderma spp. fungi have caused significant losses of Malaysia's economic income. Advances in remote sensed imagery and image processing using unmanned aerial vehicle (UAV) for Ganoderma disease detection could be developed to reduce operating cost and time as well as cover wider oil palm areas. This study examines the performance of red-green-blue (RGB) and near- infrared (NIR) digital orthophoto image acquired using modified digital cameras mounted on the UAV for aerial detection of Ganoderma disease in oil palm. The orthophoto images were filtered using eight adaptive filters with window sizes of 7×7, 9×9 and 11×11. The filtered orthophoto images then were processed using three supervised image classifiers: Maximum Likelihood (ML), Mahalanobis Distance (MD) and Neural Net (NN). The classifiers were used to classify the Ganoderma disease severities into Experiment 1: T0 (healthy), T1 (mild), T2 (moderate) and T3 (severe); and Experiment 2: healthy and unhealthy. The classification outputs were assessed using a confusion matrix. Best result was obtained from Bit Error filter with 9×9 window size using the NN algorithm with an overall accuracy of 62.41% and a Kappa coefficient of 0.3890. This study demonstrated classification from UAV-based imagery can be improved using filters for Ganoderma disease detection mapping in oil palm plantation.
format Thesis
author Ezzati , Bahrom
author_facet Ezzati , Bahrom
author_sort Ezzati , Bahrom
title UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom
title_short UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom
title_full UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom
title_fullStr UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom
title_full_unstemmed UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom
title_sort uav-based rgb/nir aerial imaging for the detection of ganoderma disease in oil palm plantation / ezzati bahrom
publishDate 2018
url http://studentsrepo.um.edu.my/12487/1/Ezzati.pdf
http://studentsrepo.um.edu.my/12487/
_version_ 1738506617068978176
score 13.211869

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