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UNIVERSITI TEKNOLOGI MARA
Prediction Method of RPW Infestation Using GIS, RS, Machine Learning and Statistical Approach
MOHAMAD IKHWAN BIN SHAMSUDIN
Thesis submitted in fulfilment Of requirement for degree of
Bachelor of Surveying Science and Geomatic (Hons)
Faculty of Architecture Planning and Surveying
January 2020
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AUTHOR’S DECLARATION
I declare that the work in this thesis/dissertation was carried out in accordance with the regulations of Universiti Teknologi MARA. It is original and is the results of my own work, unless otherwise indicated or acknowledged as referenced work. This thesis has not been submitted to any other academic institution or non-academic institution for any degree or qualification.
I, hereby, acknowledge that I have been supplied with the Academic Rules and Regulations for Post Graduate, Universiti Teknologi MARA, regulating the conduct of my study and research.
Name of Student : Mohamad Ikhwan Bin Shamsudin Student I.D. No. : 2015162643
Programme : Bachelor of Surveying Science and Geomatics (Honours) – AP220 Faculty : Architecture, Planning & Surveying Thesis/Dissertation
Title
: Prediction Method of RPW Infestation Using GIS, RS, Machine Learning and Statistical Approach
Signature of Student
:
………..
Date : January 2020
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ABSTRACT
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.
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Table of Contents
CONFIRMATION BY PANEL OF EXAMINERS ... ii
AUTHOR’S DECLARATION ... iii
SUPERVISOR’S DECLARATION ... iv
ABSTRACT ... v
ABSTRAK ... vi
ACKNOWLEDGEMENTS ... vii
LIST OF ABBREVIATIONS ... xv
CHAPTER 1 ... 1
INTRODUCTION ... 1
1.1 Background of study... 1
1.2 Problem statement ... 3
1.3 Aim and Objectives of Research ... 4
1.4 Research Question ... 4
1.5 Scope of Research ... 5
1.6 Significant of Research... 5
1.7 Structure of Thesis ... 6
CHAPTER 2 ... 7
LITERATURE REVIEW ... 7
2.1 Introduction ... 7
2.2 Description of Pest Diseases and Pest Infestation at Palm Trees ... 7
2.3 Types of insect pest attacking different palm tree species ... 8
2.3.1 Red Palm Weevil... 8
2.3.2 Metisa Plana ... 12
2.3.3 Rhinoceros Beetle ... 13
2.4 Abiotics and Biotics factor influence the pest infestation at palm trees ... 15
2.5 Remote Sensing and GIS Technology in determining the Pest Diseases and Pest Infestation ... 15
2.6 Machine Learning Techniques in determining the Pest Diseases and Pest infestation ... 16
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2.7 Previous research on pest/disease of outbreak/infestation using RS, GIS and
Machine learning approach ... 16
2.7.1 In Malaysia ... 16
2.8 Summary of Literature review... 17
CHAPTER 3 ... 18
METHODOLOGY ... 18
3.1 Introduction ... 18
3.2 Overall Methodology... 18
3.3 Study Area ... 20
3.4 Data Acquisition ... 21
3.4.1 Data of Remote Sensing ... 21
3.4.2 Data of Meteorology ... 21
3.4.3 Data of RPW ... 21
3.5 Data Processing ... 22
3.6 Prediction of pest outbreak or infestation using simple linear regression ... 22
3.6.1 Stepwise Regression... 22
3.6.2 RPW versus Rainfall ... 23
3.6.3 RPW versus Temperature ... 25
3.6.4 RPW versus Relative Humidity ... 27
3.6.5 RPW versus Wind Speed ... 29
3.7 Prediction of pest outbreak or infestation using machine learning ... 31
3.7.1 Regression Algorithm ... 31
3.7.2 Classification Algorithm ... 31
3.8 Preparation of Spatial Interpolation Maps ... 32
3.8.1 Kriging Interpolation ... 32
3.8.2 Image Pre-Processing ... 32
3.9 Summary of Research Methodology ... 36
CHAPTER 4 ... 37
RESULT AND ANALYSIS ... 37
4.1 Introduction ... 37
4.2 Comparison of Prediction performances using regression method ... 37
