Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi

Large scale planting of coconut trees requires on-time detection of diseases such as Red Palm Weevil (RPW) is more than 60% of coconut tree plantations in the Peninsula of Malaysia. In order to overcome this problem, remote sensing offers a better solution to detect pest diseases and map the coconut...

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Main Author:	Nik Effendi, Nik Ahmad Faris
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Aerial geography Remote Sensing Geomatics
Online Access:	https://ir.uitm.edu.my/id/eprint/33509/1/33509.pdf
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id	my-uitm-ir.33509
record_format	uketd_dc
spelling	my-uitm-ir.335092020-08-25T06:37:20Z Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi 2020-08-12 Nik Effendi, Nik Ahmad Faris Aerial geography Remote Sensing Geomatics Large scale planting of coconut trees requires on-time detection of diseases such as Red Palm Weevil (RPW) is more than 60% of coconut tree plantations in the Peninsula of Malaysia. In order to overcome this problem, remote sensing offers a better solution to detect pest diseases and map the coconut trees healthiness by the disease on time. It is also useful tool to monitor the development of coconut plantations. Remote Sensing satellite image can provide user requirement data and has the ability to acquire data in narrow and contiguous spectral bands, enabling to detect the healthiness and pest infestation by using remote sensing technique. In this study, two different classification technique used to detect and classify the healthiness of coconut trees such as unsupervised classification method based on Normalized Different Vegetation Index (NDVI) value and supervised classification method based on Maximum Likelihood (ordinary method) and Support Vector Machine (machine learning). Results: All these methods usually showed better results, as it could provide overall accuracy about 88% and the kappa value is 0.7533 when compare the NDVI value with the ground truth data. Besides, supervised classification method with different technique that is ML and SVM also provide better results in classify the healthiness of coconut tree. Based on the result, it can conclude that Remote Sensing technique can be a used to detect the healthiness of coconut tree and RPW infestation. 2020-08 Thesis https://ir.uitm.edu.my/id/eprint/33509/ https://ir.uitm.edu.my/id/eprint/33509/1/33509.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surverying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Aerial geography Remote Sensing Geomatics
spellingShingle	Aerial geography Remote Sensing Geomatics Nik Effendi, Nik Ahmad Faris Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi
description	Large scale planting of coconut trees requires on-time detection of diseases such as Red Palm Weevil (RPW) is more than 60% of coconut tree plantations in the Peninsula of Malaysia. In order to overcome this problem, remote sensing offers a better solution to detect pest diseases and map the coconut trees healthiness by the disease on time. It is also useful tool to monitor the development of coconut plantations. Remote Sensing satellite image can provide user requirement data and has the ability to acquire data in narrow and contiguous spectral bands, enabling to detect the healthiness and pest infestation by using remote sensing technique. In this study, two different classification technique used to detect and classify the healthiness of coconut trees such as unsupervised classification method based on Normalized Different Vegetation Index (NDVI) value and supervised classification method based on Maximum Likelihood (ordinary method) and Support Vector Machine (machine learning). Results: All these methods usually showed better results, as it could provide overall accuracy about 88% and the kappa value is 0.7533 when compare the NDVI value with the ground truth data. Besides, supervised classification method with different technique that is ML and SVM also provide better results in classify the healthiness of coconut tree. Based on the result, it can conclude that Remote Sensing technique can be a used to detect the healthiness of coconut tree and RPW infestation.
format	Thesis
qualification_level	Bachelor degree
author	Nik Effendi, Nik Ahmad Faris
author_facet	Nik Effendi, Nik Ahmad Faris
author_sort	Nik Effendi, Nik Ahmad Faris
title	Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi
title_short	Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi
title_full	Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi
title_fullStr	Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi
title_full_unstemmed	Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi
title_sort	classification technique for determining the healthiness of coconut tree / nik ahmad faris nik effendi
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surverying
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/33509/1/33509.pdf
_version_	1783734244073275392

Classification technique for determining the healthiness of coconut tree / Nik Ahmad Faris Nik Effendi

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