Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin

The red palm weevil is an indigenous species from South East Asia, has recently become one of the most dangerous pests of palms around the globe. The early detection of Red Palm Weevil (RPW) infestations is critical to prevent palm death and thus serves as an essential element of a successful Integr...

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Main Author:	Shamsudin, Mohamad Ikhwan
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Geographic information systems
Online Access:	https://ir.uitm.edu.my/id/eprint/33210/1/33210.pdf
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id	my-uitm-ir.33210
record_format	uketd_dc
spelling	my-uitm-ir.332102020-08-10T05:53:04Z Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin 2020-08-05 Shamsudin, Mohamad Ikhwan Geographic information systems The red palm weevil is an indigenous species from South East Asia, has recently become one of the most dangerous pests of palms around the globe. The early detection of Red Palm Weevil (RPW) infestations is critical to prevent palm death and thus serves as an essential element of a successful Integrated Pest Management (IPM) program used to control RPW. However, early detection of RPW is not easy because palm trees do not show any visual evidence of infection. The aim of this study is to determine the influential factor that affecting infestation of Red Palm Weevil at UITM Perlis by using RS & GIS, statistical and machine learning approach. To achieve the aim, the objective of this study are to analyze the RPW dataset for year 2017 and 2018 with suitable parameter using simple linear regression, to predict the pest infestation using different machine learning classifier tool and to map the parameter influenced by pest infestation using GIS interpolation techniques. In this study, there are two variable such as climatic data, and RPW. To accomplish the objective, the software that used including Knowledge Analysis (WAIKATO) and Microsoft Excel, ERDAS and Arch Map Software. The study area focusing at coconut Plantation in UITM Perlis. The methodology of this study is including data acquisition and data processing. The data are collected from Meteorological Department, Malaysia Space Agency (MSA), Department of Agriculture and at the ground. The expected outcome is get the influential parameters to detect RPW infestation. The contribution of this study is to help identify the factor that influence Red Palm Weevil infestation on coconut trees for further investigation and tree monitoring. 2020-08 Thesis https://ir.uitm.edu.my/id/eprint/33210/ https://ir.uitm.edu.my/id/eprint/33210/1/33210.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surverying (R)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Geographic information systems
spellingShingle	Geographic information systems Shamsudin, Mohamad Ikhwan Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin
description	The red palm weevil is an indigenous species from South East Asia, has recently become one of the most dangerous pests of palms around the globe. The early detection of Red Palm Weevil (RPW) infestations is critical to prevent palm death and thus serves as an essential element of a successful Integrated Pest Management (IPM) program used to control RPW. However, early detection of RPW is not easy because palm trees do not show any visual evidence of infection. The aim of this study is to determine the influential factor that affecting infestation of Red Palm Weevil at UITM Perlis by using RS & GIS, statistical and machine learning approach. To achieve the aim, the objective of this study are to analyze the RPW dataset for year 2017 and 2018 with suitable parameter using simple linear regression, to predict the pest infestation using different machine learning classifier tool and to map the parameter influenced by pest infestation using GIS interpolation techniques. In this study, there are two variable such as climatic data, and RPW. To accomplish the objective, the software that used including Knowledge Analysis (WAIKATO) and Microsoft Excel, ERDAS and Arch Map Software. The study area focusing at coconut Plantation in UITM Perlis. The methodology of this study is including data acquisition and data processing. The data are collected from Meteorological Department, Malaysia Space Agency (MSA), Department of Agriculture and at the ground. The expected outcome is get the influential parameters to detect RPW infestation. The contribution of this study is to help identify the factor that influence Red Palm Weevil infestation on coconut trees for further investigation and tree monitoring.
format	Thesis
qualification_level	Bachelor degree
author	Shamsudin, Mohamad Ikhwan
author_facet	Shamsudin, Mohamad Ikhwan
author_sort	Shamsudin, Mohamad Ikhwan
title	Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin
title_short	Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin
title_full	Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin
title_fullStr	Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin
title_full_unstemmed	Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin
title_sort	prediction method of rpw infestation using gis, rs, machine learning and statistical approach / mohamad ikhwan shamsudin
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surverying (R)
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/33210/1/33210.pdf
_version_	1783734226406866944

Prediction method of RPW infestation using GIS, RS, machine learning and statistical approach / Mohamad Ikhwan Shamsudin

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