MACHINE LEARNING FOR DISEASE DETECTION USING ...eprints.utar.edu.my/3061/1/fyp_CN_2018_KWJ_-_1507159.pdfTable 6.2.1-T1 Result of testing on condition 1 (Healthy condition: Cabbage)

MACHINE LEARNING FOR DISEASE DETECTION USING RASPBERRY PI

WITH TENSORFLOW IN VEGETABLE FARMS

BY

KHOO WAH JIAN

A REPORT

SUBMITTED TO

Universiti Tunku Abdul Rahman

in partial fulfillment of the requirements

for the degree of

BACHELOR OF INFORMATION TECHNOLOGY (HONS)

COMMUNICATIONS AND NETWORKING

Faculty of Information and Communication Technology

(Kampar Campus)

MAY 2018

UNIVERSITI TUNKU ABDUL RAHMAN

MACHINE LEARNING FOR DISEASE DETECTION USING RASPBERRY PI


BY

KHOO WAH JIAN

A REPORT

SUBMITTED TO


in partial fulfillment of the requirements

for the degree of

BACHELOR OF INFORMATION TECHNOLOGY (HONS)

COMMUNICATIONS AND NETWORKING

Faculty of Information and Communication Technology

(Kampar Campus)

MAY 2018

ii BIT (HONS) COMMUNICATIONS & NETWORKING

Faculty of Information and Communication Technology (Kampar Campus), UTAR

DECLARATION OF ORIGINALITY

I declare that this report entitled “MACHINE LEARNING FOR DISEASE DETECTION

USING RASPBERRY PI WITH TENSORFLOW IN VEGETABLE FARMS” is my own

work except as cited in the references. The report has not been accepted for any degree and is

not being submitted concurrently in candidature for any degree or other award.

Signature : _________________________

Name : _________________________

Date : _________________________

iii BIT (HONS) COMMUNICATIONS & NETWORKING


ACKNOWLEDGEMENTS

I would like to thank my supervisor, Dr. Goh Hock Guan for his support and guidance

in this project. Without his guidance, I would never been able to come this far. His

willingness to offer assistance and guidance is generously appreciated.

Next, I would also like to thank my project teammate Choong Jian How for his

invaluable of knowledge and constructive feedbacks towards the project and I appreciate it a

lot.

Finally, I would like to thank my parents and family members for encouragement and

moral support. This project would not have been possible without them.

iv BIT (HONS) COMMUNICATIONS & NETWORKING


ABSTRACT

Plant disease has being one of the major factors that is preventing the farmers from

earning maximum profit from their harvest. This problem can be reduces if the farmers

monitor their crops closely start from the planting stage until harvesting stage. This method

could be working for small farm but if the farm is large, it could be a quite tedious task to be

completed.

The proposed system will provide a much better and convenient way for the farmers to

monitor their plants. This system provides a disease classification feature which will be

trained using a Machine learning technique called Transfer learning and it will deployed to a

Raspberry Pi connected with a camera. After the classification, it will return the classification

result and it will be forwarded into a cloud database. Then, a mobile application will retrieve

the data from the database. If there is any positive disease-presence result, it will send a

notification to the farmer that there is a plant in their farm got infected.

v BIT (HONS) COMMUNICATIONS & NETWORKING


TABLE OF CONTENTS

TITLE i

DECLARATION OF ORIGINALITY ii

ACKNOWLEDGEMENT iii

ABSTRACT iv

TABLE OF CONTENTS v

LIST OF FIGURES viii

LIST OF TABLES xi

LIST OF ABBREVIATION xiii

CHAPTER 1: INTRODUCTION 1

1.1 Problem Statement and Motivation 1

1.2 Project Objective 2

1.3 Project Scope 2

1.4 Impact, Significance and Contribution 3

1.5 Organization of the Report 4

CHAPTER 2: LITERATURE REVIEW 6

2.1 Review of the Technologies 6

2.1.1 Hardware Platforms 6

2.1.2 Summary of the Technologies Review 11

2.2 Review of Existing Systems/Applications 12

2.2.1 Plant Disease Detection using Raspberry Pi by K-means

Clustering Algorithm

12

2.2.2 Plant Diseases Detection using Image Processing Techniques 13

2.2.3 Deep Learning for Image-Based Cassava Disease Detection 14

2.2.4 Summary of the Existing Systems 15

2.3 Concluding Remark 16

CHAPTER 3: SYSTEM METHODOLOGY 17

3.1 System Development Models 17

3.1.1 Waterfall Model 17

3.1.2 V-Shaped Model 18

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3.1.3 Spiral Model 18

3.1.4 Prototype Model 19

3.1.5 Selected Model 20

3.2 System Requirement 20

3.2.1 Hardware 21

3.2.2 Software 24

3.3 Functional Requirement 25

3.3.1 Retrain 25

3.3.2 Image Capture 26

3.3.3 Label Image 26

3.3.4 Data View 26

3.3.5 Camera Stream 27

3.4 Expected Challenges 27

3.5 Project Milestone 28

3.6 Estimated Cost 30


CHAPTER 4: SYSTEM DESIGN 32

4.1 System Architecture 32

4.2 Functional Modules in the System 33

4.2.1 Retrain Module 33

4.2.2 Image Capture Module 34

4.2.3 Label Image Module 35

4.2.4 Data View Module 36

4.2.5 Camera Stream Module 37

4.3 System Flow 38

4.4 Database Design 39

4.5 GUI Design 40


CHAPTER 5: SYSTEM IMPLEMENTATION 43

5.1 Hardware Setup 43

5.1.1 Personal Laptop 43

vii BIT (HONS) COMMUNICATIONS & NETWORKING


5.1.2 Raspberry Pi 3 43

5.1.3 Status Indicator Setup 44

5.1.4 Camera Setup 45

5.2 Software Setup 46

5.2.1 Ubuntu 16.04 Installation 46

5.2.2 Android Studio Installation 47

5.2.3 Tensorflow Installation 47

5.2.4 UV4L Installation 49

5.3 Setting and Configuration 49

5.4 System Operation 50


CHAPTER 6: SYSTEM EVALUATION AND DISCUSSION 54

6.1 System Testing and Performance Metrics 54

6.2 System Testing and Result 55

6.2.1 Condition 1 (Without any hindrance on the camera) 55

6.2.2 Condition 2 (Hindrance on the camera) 65

6.3 Project Challenges 75

6.4 Objectives Evaluation 76


CHAPTER 7: CONCLUSION AND RECOMMENDATION 78

7.1 Conclusion 78

7.2 Recommendation 79

REFERENCES 80

APPENDIX 1 – BI WEEKLY REPORT

APPENDIX 2 – TURNITIN ORIGINALITY REPORT

POSTER

viii BIT (HONS) COMMUNICATIONS & NETWORKING


LIST OF FIGURES

Figure Number Title Page

Figure 2.1.1-F1 Jetson TK1 (Embedded Linux Wiki, Jetson TK1) 6

Figure 2.1.1-F2 Jetson TK1 full specifications (Embedded Linux Wiki, Jetson

TK1)

7

Figure 2.1.1-F3 Raspberry Pi board series (Network World from IDG) 8

Figure 2.1.1-F4 Raspberry Pi 3 Model B’s specifications (Raspberry Pi

Foundation, Raspberry Pi 3 Model B)

9

Figure 2.1.1-F5 Odroid-C2 (Odroid, Odroid-C2) 9

Figure 2.1.1-F6 Odroid-C2’s block diagram (Odroid, Odroid-C2) 10

Figure 2.1.1-F7 Odroid-C2’s specifications (Odroid, Odroid-C2) 10

Figure 2.2.1 Block diagram of the overall system (Plant Disease Detection

using Raspberry PI By K-means Clustering Algorithm, 2017, p

93)

12

Figure 2.2.2 General block diagram of Agrobot (Plant Diseases Detection

using Image Processing Techniques, 2016)

13

Figure 2.2.3 Overall accuracy for transfer learning using three machine

learning methods (Deep Learning for Image-Based Cassava

Disease Detection, 2017, p4)

15

Figure 3.1.1 Waterfall Model (Personal website – Software Engineering &

Architecture Practices, 2012)

17

Figure 3.1.2 V-Shaped Model (Personal website – Software Engineering &


18

Figure 3.1.3 Spiral Model (Personal website – Software Engineering &


19

Figure 3.1.4 Prototype Model (ISTQB Exam Certification, 2018) 20

Figure 3.2.1-F1 Raspberry Pi 3 Model B (Raspberry Pi Foundation) 21

ix BIT (HONS) COMMUNICATIONS & NETWORKING


Figure 3.2.1-F2 Raspberry Pi NoIR Camera V2 (element14) 22

Figure 3.2.1-F3 Kingston 16GB microSD card (Lazada) 22

Figure 3.2.1-F4 2.5A, 5.1V Micro USD B Power Supply (element14) 23

Figure 3.2.1-F5 5mm LEDs (guitarpedalparts) 23

Figure 3.2.2 Raspbian OS logo (Raspbian, 2012) 24

Figure 4.1 Full system architecture 32

Figure 4.2.1-F1 Tensorboard training summary about accuracy and cross entropy 33

Figure 4.2.1-F2 Tensorboard histograms about retraining layer weights, biases,

activations, etc

34

Figure 4.2.2 Flowchart for image capture module 34

Figure 4.2.3 Flowchart for label image module 35

Figure 4.2.4 Flowchart for data view module 36

Figure 4.2.5 Flowchart for camera stream module 37

Figure 4.3 Full flowchart of system flow 38

Figure 4.4-F1 Example of result of the classification that needed to be store in

database

39

Figure 4.4-F2 Example of data being stored in Firebase Database 39

Figure 4.5-F1 GUI design for the main page 40

Figure 4.5-F2 GUI design for data view 41

Figure 4.5-F3 GUI design for video stream 41

Figure 4.5-F4 GUI design for disease control method 42

Figure 5.1.2 Raspberry Pi 3 GPIO Header (element14, 2015) 44

Figure 5.1.3 Actual setup of the status indicator setup 45

Figure 5.1.4 Actual setup with the Raspberry Pi Camera 46

Figure 5.4-F1 Green LED lights up when the system starts to execute 50

x BIT (HONS) COMMUNICATIONS & NETWORKING


Figure 5.4-F2 Red LED lights up when the system stopped due to error 51

Figure 5.4-F3 Application shows the data retrieved from the database 52

Figure 5.4-F4 Application shows the video streaming service is running 52

Figure 5.4-F5 Application shows the notification feature 53

xi BIT (HONS) COMMUNICATIONS & NETWORKING


LIST OF TABLES

Table Number Title Page

Table 1.5 Organization of the report 4

Table 2.1.2 Summary of the Technologies review 11

Table 2.2.2 Summary of methods (Plant Diseases Detection using Image

Processing Techniques, 2016)

14

Table 2.2.4 Summary of the Existing Systems 15

Table 3.4-T1 Gantt chart showing the project milestones (FYP 1) 28

Table 3.4-T2 Gantt chart showing the project milestones (FYP 2) 29

Table 3.5 Estimated cost 30

Table 5.1.2 Table of pins and port being used and their function 43

Table 5.1.3 Table of LED colors, status and status description 44

Table 6.1 Table shows a clearer picture of the whole system testing 54

Table 6.2.1-T1 Result of testing on condition 1 (Healthy condition: Cabbage) 55

Table 6.2.1-T2 Result of testing on condition 1 (Healthy condition: Sweet

Pepper)

56

Table 6.2.1-T3 Result of testing on condition 1 (Healthy condition: Tomato) 57

Table 6.2.1-T4 Result of testing on condition 1 (Unhealthy condition: Bacterial

Soft Rot)

58

Table 6.2.1-T5 Result of testing on condition 1 (Unhealthy condition: Black Rot) 59

Table 6.2.1-T6 Result of testing on condition 1 (Unhealthy condition:

Anthracnose)

60

Table 6.2.1-T7 Result of testing on condition 1 (Unhealthy condition: Blossom

End Rot)

62

Table 6.2.1-T8 Result of testing on condition 1 (Unhealthy condition: Bacterial 63

xii BIT (HONS) COMMUNICATIONS & NETWORKING


Spot)

Table 6.2.1-T9 Result of testing on condition 1 (Unhealthy condition: Late

Blight)

64

Table 6.2.2-T1 Result of testing on condition 2 (Healthy condition: Cabbage) 65

Table 6.2.2-T2 Result of testing on condition 2 (Healthy condition: Sweet

pepper)

66

Table 6.2.2-T3 Result of testing on condition 2 (Healthy condition: Tomato) 67


Soft Rot)

68

Table 6.2.2-T5 Result of testing on condition 2 (Unhealthy condition: Black Rot) 70

Table 6.2.2-T6 Result of testing on condition 2 (Unhealthy condition:

Anthracnose)

71

Table 6.2.2-T7 Result of testing on condition 2 (Unhealthy condition: Blossom

End Rot)

72


Spot)

73

Table 6.2.2-T9 Result of testing on condition 2 (Unhealthy condition: Late

Blight)

74

xiii BIT (HONS) COMMUNICATIONS & NETWORKING


LIST OF ABBREVIATIONS

Abbreviation Meaning

GDP Gross Domestic Product

NoIR No Infrared

ARM Advanced RISC Machines

SBC Single Computer Board

LTS Long Term Support

OS Operating System

CPU Central Processing Unit

GPU Graphic Processing Unit

RAM Random Access Memory

SMS Short Message Service

K-NN K-Nearest Neighbors

DC Direct Current

SIFT Scale-Invariant Feature Transform

SVM Support Vector Machine

PCA Principal Component Analysis

BPNN Back Propagation Neural Network

SDLC Software Development Life Cycle

USB Universal Serial Bus

IDE Integrated Development Environment

GUI Graphical User Interface

LED Light Emitting Diode

API Application Programming Interface

URL Uniform Resource Locator

IP Internet Protocol

Machine Learning for Disease Detection using Raspberry Pi with Tensorflow in Vegetable Farms

Chapter 1: Introduction

1 BIT (HONS) COMMUNICATIONS & NETWORKING


CHAPTER 1: INTRODUCTION

1.1 Problem Statement and Motivation

Agriculture plays an important role in our day to day life. It provides food which is the

basic need of all human beings. Other than that, agriculture also generates our country‟s

economy. Based on an article from FFTC Agricultural Policy Platform, agriculture remains an

important sector of Malaysia‟s economy. In 2013, it contributed about 7.2% to the Malaysia‟s

GDP and also provided employment for 10.9% of the total employment in Malaysia (Rozhan

Abu Dardak 2015). Although agriculture is able contribute to our country‟s economy, there

are various problem preventing the farmers from gaining maximum profit from their crops

due to plant disease which can affect crops in their farm. Based on Asia One, the article states

that the income of some 200 farmers is in jeopardy after a disease ravaged their banana

plantations (R Sekaran 2015). In the past, many farmers relied on their direct eye observation,

waited until the plant disease symptoms appear due to uncertainly etc. These methods were

inaccurate and not reliable. If this problem cannot be resolve, not only farmers will lose their

income but our country also will suffer due to our country need to import more food from

foreign countries. Based on The Star Online, the article mentioned that Malaysia is currently

importing more food than it is producing and exporting, which puts us at the mercy of foreign

countries (Hariati Azizan 2016). Hence, a more reliable solution has to be implemented to

overcome this problem.

In this project, a disease detection system using machine learning will be developed.

This system will help the farmer to monitoring their crops where they can reduce the risk of

losing their harvest from the diseases. Using a camera to capture the image of crops everyday

and perform classification study on the images to determine whether the crops are healthy or

infected. With this system, farmers not only can monitoring their crops but they also can take

early precaution to prevent the plant disease from spreading as the system not only implement

with a camera but also with functionalities like notify the farmer when infect plants were

found and suggest a correct solution to take to prevent the disease. Farmer also can reduce

their workload with this system as they can monitor their farm automatically. This method is

much more less time consuming.





1.2 Project Objective

Objectives:

Design a disease detection system using machine learning technique for the

farmer to reduce the risk of losing their harvest from diseases.

High percentage for their harvest loss is due to plant diseases. This system works by

capturing the image of plants, if the images match one of the diseases on the pre-

trained and deployed artificial neural network on the system, it will alert the farmers to

take precaution measure.

To let farmers take early and correct precaution through the deployed disease

detection system by suggesting correct solution to take.

Sometimes even if the farmers able to discover there are infected plants, there is still

possibility that they are using incorrect method to kill off the disease. This can cause

them a big loss because chemical solution that required is a lot and expensive. By

using this system, farmer can not only correct precaution but also take early precaution

as this system included with a notification system to alert the farmers when there is

disease detected.

To develop and deploy an artificial neural network that is able to perform disease

classification on vegetable plants.

The artificial neural network developed for this system is used to classify that whether

the plant is in healthy state or unhealthy state. If the plant is unhealthy, then it will be

classified into which categories of disease that available in the artificial neural

network.

1.3 Project Scope

At the end of the project, the image classifier will be trained using a machine learning

technique which is called Transfer learning. Based on an article from Machine Learning

Mastery, Transfer learning is a machine learning technique where a model trained on one task

is re-purposed on a second related task (Brownlee 2017). Besides that, there are few





approaches for Transfer learning which the most common one is develop model approach,

pre-trained model approach, etc. Transfer learning with image data is being applied in this

project. It is a transfer learning method with pre-trained model approach. After trained the

classifier, the re-trained image classifier prototype will be deployed to the microcomputer

Raspberry Pi with a camera connected into it. The whole system will be powered up with a

5V, 1A power supply. The Pi NoIR Camera V2 will be used to capture image of the vegetable

plants and the Raspberry Pi will be responsible for classify the captured image using the re-

trained image classifier. After the classification, it will return the result and it also will be

forwarded to the Firebase database. Finally, a mobile application will be used to retrieve the

data from the database and if there is any positive disease-presence result found, it will send

notification to the farmer that there are plants got infected.

1.4 Impact, Significance and Contribution

In this project, the disease detection system provides user a much more convenient

way to monitoring their vegetable plants.

As the traditional agriculture method, farmers are required to monitoring their plants

manually. This method might be working fine with small sized-farm but if in a large sized-

farm, this method will be not convenient for the farmers because the farmers has to inspect the

plants one by one before can proceed to the next area. This will be consuming a lot of time

and a tedious task to be done and sometimes the farmers might miss some area of the farms. If

there is disease happening in the missed area of the farms, it might spread to the other area of

the farms too. By the time the farmers discovered, it might be too late and this can cause a lot

of income loss to the farmers. Even it is discovered by the farmers, there is disease happening.

There might be a chance where the farmer could use wrong cure method and this can cause

them a big loss as the chemical solution that required is a lot and it is expensive.

So in the proposed system, it can provide a much better and convenient way to reduce

the problems that the farmers having when monitoring their plants. By having this system, the

farm can be monitoring automatically without any missed area and able to notify the farmers

immediately if any diseases are found and suggesting the correct solution to take.





1.5 Organization of the Report

Chapters Description

Chapter 1: Introduction Introduction to the problem statement, motivation,

project objectives and project scope were defined and

explained.

The main contribution of this project also explained.

Chapter 2: Literature Review Different technologies that could use for this project

were reviewed and justification was made for the

chosen technology.

Three existing systems that similar to this project

where reviewed and were summarized into a table.

Chapter 3: System

Methodology

Four different system development models were

chosen and one of the development models was

selected. The selected model was justified why it was

selected.

The hardware, software and functional requirement

were identified and explained.

Expected project challenges were identified and

explained.

Project milestone for FYP 1 and estimated cost were

shown in Gantt chart form and table form

respectively.

Chapter 4: System Design Illustration of the system architecture was shown.

Functional modules were identified and system flow

was provided.

The design of database was shown.

The design of GUI was shown.





Chapter 5: System

Implementation

Hardware setup was explained.

Software setup was explained

Setting and configuration were provided and

explained

System operation was shown

Chapter 6: System Evaluation

and Discussion

System testing and performance metrics were

explained

System testing and result were shown and explained

Project challenges was identified and explained

Objective evaluation was explained

Chapter 7: Conclusion Discuss the outcome of the project

Project recommendation suggested

Table 1.5: Organization of the report


Chapter 2: Literature Review



CHAPTER 2: LITERATURE REVIEW

2.1 Review of the Technologies

There are variety of existing technologies can be used to implement this Plant Disease

Detection System. Each of the technologies has their pros and cons and hence each should be

evaluated carefully which is most suitable for this project. The technologies that will be

discussed are hardware platforms.

2.1.1 Hardware Platform

The first embedded hardware platform that is suitable for this project is NVIDIA

Jetson TK1. This embedded hardware board is designed and developed by NVIDIA, one the

top leading manufacturer of graphics card today.

Figure 2.1.1-F1: Jetson TK1 (Embedded Linux Wiki, Jetson TK1)

This embedded board is really powerful where it comes with a quad-core 2.3Ghz

ARM Cortex-A15 CPU and the revolutionary Tegra K1 GPU. It is also equipped with a fan





for operation that is under heavy workloads. Next, this embedded board is running a Linux

distribution operating system which is Linux4Tegra OS. The Linux4Tegra OS is basically an

Ubuntu 14.04 OS with custom pre-configured drivers such as bootloader, kernel, OpenGL,

X.Org, Multimedia, etc. Other than that, this board not only includes some PC-oriented

features such as SATA, mini-PCIE but also have similar features as other embedded hardware

board such as Raspberry Pi. Lastly, this embedded hardware board is fairly pricey which cost

above RM1000 based on Lazada website (Lazada n.d.).

The following are the hardware features of Jetson TK1 (Embedded Linux Wiki n.d.):-

Figure 2.1.1-F2: Jetson TK1 full specifications (Embedded Linux Wiki, Jetson TK1)





The second embedded hardware board that is suitable for this project and which will

be discussed is Raspberry Pi. This embedded board was initially created by Eben Upton, the

creator of Raspberry Pi who has a goal to create a low-cost device that could improve

programming skills and hardware understanding at the pre-university level (Opensource.com

n.d.). It is only as big as a credit-card sized that can be plugs into a monitor and it uses a

standard keyboard and mouse just a regular computer desktop.

Figure 2.1.1-F3: Raspberry Pi board series (Network World from IDG)

Although it is slower compared with regular computer, but it is still equipped with a

complete Linux distribution operating system that can provide all the expected abilities that

implies, at low-power consumption. The price for this board is affordable where it cost less

than RM200 based on element14 website (element14 n.d.).





The following are the Raspberry Pi latest generation which is Raspberry Pi 3 Model

B‟s specifications (Raspberry Pi Foundation n.d.):-

Figure 2.1.1-F4: Raspberry Pi 3 Model B’s specifications (Raspberry Pi Foundation,

Raspberry Pi 3 Model B)

Finally, the last embedded hardware will be discuss is Odroid-C2. It is an embedded

hardware board equipped with a 64-bit quad-core single board computer (SBC) which is one

of the most cost-effective 64-bit development boards available. This board is small and

compact just like Raspberry Pi but it has much better specifications compared with the

Raspberry Pi board.

Figure 2.1.1-F5: Odroid-C2 (Odroid, Odroid-C2)





Figure 2.1.1-F6: Odroid-C2’s block diagram (Odroid, Odroid-C2)

It also same like all other embedded hardware boards which is running Linux

distribution operating system, Ubuntu 16.04 OS and other than that, it also capable running

Android 6.0 Marshmallow based on kernel 3.14LTS. The price for this board is also fairly

affordable where it cost around RM300 based on Odroid official website (Odroid n.d.).

The following are the Odroid-C2‟s specifications (Odroid n.d.):-

Figure 2.1.1-F7: Odroid-C2’s specifications (Odroid, Odroid-C2)





2.1.2 Summary of the Technologies Review

Jetson TK1 Raspberry Pi Odroid-C2

CPU NVIDIA "4-Plus-1"

2.32GHz ARM quad-core

Cortex-A15 CPU with

Cortex-A15 battery-saving

shadow-core

Quad Core 1.2GHz

Broadcom BCM2837

64bit CPU

Amlogic ARM®

Cortex®-

A53(ARMv8)

1.5Ghz quad core

CPUs

GPU NVIDIA Kepler "GK20a"

GPU with 192 SM3.2

CUDA cores (up to 326

GFLOPS)

Broadcom

VideoCore IV

Mali™-450 GPU (3

Pixel-processors + 2

Vertex shader

processors)

RAM 2GB 1GB 2GB

OS Supported Linux

(Linux4Tegra)

Linux

(Raspbian)

Linux

(Ubuntu

16.04)

Android

(Android 6.0

Marshmallow

based on

kernel

3.14LTS)

Price Above RM1000 Less RM200 Under RM300

Table 2.1.2: Summary of the Technologies review

Different hardware has their pros and cons. Based on the table above, we can see that

the best performance hardware is Jetson TK1 but the selections are limited to the price and

project scale. The prototype of the project only will be demonstrated rather than need to be

deployed on the real farm. Hence, it does not require a very powerful embedded board which

normally is expensive. Although, Raspberry Pi is much slower in performance compared the

other two embedded boards and due to the scale of the project, Raspberry Pi is more than

capable enough to handle the tasks and it is the cheapest among the three boards as we are

restricted by the cost budget.





2.2 Review of the Existing Systems/Applications

2.2.1 Plant Disease Detection using Raspberry PI by K-means Clustering Algorithm

There are similar projects that have been done in the past. Priyanka G. Shinde et.al

(2017) conducted a project to build a plant disease detection device using Raspberry Pi by K-

means Clustering Algorithm. The project model is using Raspberry Pi attached with a camera

that used to capture an image of crops and a monitor that used to display the detected disease

name and also the pesticide name and with another feature that can send message via sms or

email to notified farmer about the status of the plant (Figure 2.1).

Figure 2.2.1: Block diagram of the overall system (Plant Disease Detection using Raspberry

PI By K-means Clustering Algorithm, 2017, p 93)

In their project, the k clustering method applied together with k-NN which is also

known as k-nearest neighbors method to classify the capture images. K clustering method was

one of the classification steps that were used for image segmentation. Finally, the

segmentation will pass through the k-NN classifier for recognition and return the result to the

user. Even though, the k-NN classifier is one of the most common and easy to implement

machine learning method. It does come with few limitations such as high computation cost in

a large dataset and the requirement for storage of data.





2.2.2 Plant Diseases Detection using Image Processing Techniques

Shivani K. Tichkule & Dhanashri. H. Gawali (2016) conducted a project to build an

agricultural robot that used in plan disease detection using image processing technique. They

modeled a controller/processor as the heart of the system. The controller/processor is powered

up with a power supply or battery and it is attached with a webcam to capture the image of

crops. It also attached with a L293D motor driver and two DC motor (wheel) (Figure 2.2).

The motor driver and DC motor (wheel) is allowed the Agrobot to move around in the farm to

capture image of the crop. In this system, they also applied image processing technique and

machine learning methods to classify the plant diseases. They achieved a positive result by

using this method (Figure 2.3). The strength of their system is they installed DC motor

(wheel) into the system. This allowed the device capture images from various angle instead of

single angle. Next, their system also used different type of algorithms for different type of

plants to increase the accuracy of the result.

Figure 2.2.2: General block diagram of Agrobot (Plant Diseases Detection using Image

Processing Techniques, 2016)





Crops Algorithm/classifier/methods Accuracy

Soyabean leaves SIFT algorithm and SVM

classifier

Correctly recognize plant

species and accuracy is as

high as 93.79%

Cotton leaves PCA/KNN Overall accuracy 95%

Wheat leaves PCA & Morphological

features

96.7% for wheat powdery

mildew, 86.6% stripe rust

Grape leaf BPNN & K-means Efficient leaf disease color

extraction and for

Anthracnose 76.6%

Table 2.2.2: Summary of methods (Plant Diseases Detection using Image Processing

Techniques, 2016)

2.2.3 Deep Learning for Image-Based Cassava Disease Detection

Amanda Ramcharan et al (2017) proposed a project to develop a deep learning for

Cassava disease. The project model is using mobile application to do the classification of

plant images. In their project, they used a machine learning technique called transfer learning.

This is a machine learning method where a model that is trained on a large image dataset is

retrained to classify new classes. This method is much faster compared to traditional

convolutional neural network where extracting features is computationally intensive and

requires expert knowledge for robust performance. Transfer learning requires low

computational requirement and performance which is good for mobile applications.

The results they achieve in their project are accurate. They are using three different

machine learning methods to train the image classifier (Figure 2.2.3). Transfer learning

method is on the left and using pre-trained model, Inception. Based on the result, the overall

accuracy for transfer learning in classifying is better than k-NN training method and

comparable with SVM training method provided that transfer learning requires much lesser

computational requirement.





Figure 2.2.3: Overall accuracy for transfer learning using three machine learning methods

(Deep Learning for Image-Based Cassava Disease Detection, 2017, p4)

2.2.4 Summary of the Existing Systems

Existing System Advantages Disadvantages Critical Comments

Plant Disease

Detection Using

Raspberry PI by K-

means Clustering

Algorithm

Fast

computation

speed for small

dataset.

Easy to

implement.

Able to

suggest correct

pesticide to

use.

Dataset need to

be stored.

Computation

cost will be high

if dataset is

large.

K-NN approach

requires storage of

dataset which can be a

problem for embedded

devices as it has

limited storage space

and high computation

cost if the dataset is

large as it has limited

computation capacity.





Plant Diseases

Detection using Image

Processing Techniques

Able to take

image of plants

from various

angles.

Usage of

different

algorithms for

different

plants.

Lack of

flexibility

because of

particular

algorithm is only

for certain plant.

This project approach

requires a lot of studies

of different algorithms

which can be difficult

for a novice machine

learner.

Deep Learning for

Image-Based Cassava

Disease Detection

Shorter

training time.

Low

computational

requirement

which can be

deployed into

embedded

devices.

Requires large

and relevant

dataset to train

the classifier.

Transfer learning

approach is very

suitable for this project

as does not require

high computational

requirement and expert

in robust performance.

Table 2.2.4: Summary of the Existing Systems

2.3 Concluding Remarks

Few of the hardware platforms and existing plant disease detection systems were

discussed and studied. Every hardware and systems had their pros and cons. After a detail of

discussion and studies, this will be playing an important role for us to choose our hardware

platform and method approach that is most suitable to be applied in this project.


Chapter 3: System Methodology



CHAPTER 3: SYSTEM METHODOLOGY

3.1 System Development Models

System Development Model which can be refers as SDLC. It is a framework defining

tasks performed at each step in the software development process. It also consists of a detailed

plan of how to develop, maintain and replace specific software. There are few common SDLC

models such as waterfall model, v-shaped model, spiral model and prototype model will be

evaluated.

3.1.1 Waterfall Model

Waterfall Model is a development model that will be proceeds in sequence manner.

The project moves methodically from one phase to next phase without overlapping. Thus,

each phase is finished before the next phase begins. This model also does not define the

process to go back to the previous phase to handle changes in requirement. This model is most

suitable for projects that have their requirements clear and well-defined.

Figure 3.1.1: Waterfall Model (Personal website – Software Engineering & Architecture

Practices, 2012)





3.1.2 V-Shaped Model

V-shaped model is an extension of the waterfall model but instead of going down

sequentially, the process steps are going upwards after implementation and coding phase.

When this unique approach is completed, it will form the V shape.

Figure 3.1.2: V-Shaped Model (Personal website – Software Engineering & Architecture

Practices, 2012)

The difference between V-shaped model and Waterfall mode is that there is the early

testing phase in V-shaped model. The early testing phase is a stage where verification and

validation will be done to reduce errors and increase the chance of success over the waterfall

model. This model is most suitable for small projects that have their requirements clearly

defined and known.

3.1.3 Spiral Model

Spiral model combines both elements of design and prototyping-in-stages. In an effort

to combine advantages of top-down and bottom-up concepts, spiral model combines the

feature of the prototyping model and the waterfall model to achieve that. Spiral model re-uses

many of the same phases in the waterfall model and it is separated by planning stages. It has





risk assessment, prototyping and simulation. Due to the combination of two models in it, this

model is most suitable for those large, expensive and complicated projects.

Figure 3.1.3: Spiral Model (Personal website – Software Engineering & Architecture

Practices, 2012)

3.1.4 Prototype Model

Prototype model is a model that performs the analysis phase, design phase and

implementation phase concurrently. With user feedbacks, all these phases are performed

repeatedly until the final system is completed. By using this model, the client can get an

“actual feel” of the system much earlier instead of they have to wait for the final system to be

completed. This allow any misunderstanding of requirements, additional features and possible

errors to be detected much earlier, before the actually system is finalized. This model is most

suitable for projects whose requirements cannot be known in detail ahead of time.





Figure 3.1.4: Prototype Model (ISTQB Exam Certification, 2018)

3.1.5 Selected Model

After some evaluations and comparisons between waterfall model, v-shaped model,

spiral model and prototype model. The prototype model was selected for this project. This is

due several reasons such as project scale, system requirements and able to get prototype

system ready early. As this project is not a very large scale project, hence the prototype model

is suitable. Besides, this model also allows any changes or modification to be made which can

reduces the chance of failure. Due to system is for those farmers, they might not know their

requirements clearly. By using prototype model, any changes or addition of requirement can

be done easily. Finally, as this system is for those farmers, it is important to have system

prototype to be ready early to get user feedback to make changes or improvement.

3.2 System Requirement

This project will be employing a Raspberry Pi 3 Model B connected with a Raspberry

Pi NoIR Camera V2 which allows the Raspberry Pi to capture the image of plants and 2 LEDs

to indicate the current status of the system. The software components include Ubuntu OS,

Raspian OS, Firebase Database, Tensorflow, Android Studio and UV4L video streaming

driver software.





3.2.1 Hardware

Raspberry Pi 3 Model B

The Raspberry Pi 3 is the heart of the entire system. It has moderate specifications for

an embedded device to run machine learning process but it is reasonable in pricing and easy

to get. It also has a complete Linux distribution which is specifically for Raspberry Pi which

is an embedded device powered by an ARM processor.

Figure 3.2.1-F1: Raspberry Pi 3 Model B (Raspberry Pi Foundation)

Raspberry Pi NoIR Camera V2

This camera module has a Sony IMX219 8-megapixel sensor. It also does not employ

an infrared filter which gives us the ability to see in the dark with infrared lighting but by

daylight the pictures will look decidedly curious. It also comes with a little square of blue gel

film which can be used to monitor the health of green plants but we do not use this feature in

this project.





Figure 3.2.1-F2: Raspberry Pi NoIR Camera V2 (element14)

Kingston 16GB MicroSD Card

This microSD card is used as the storage for both operating system and program files

for Raspberry Pi 3 as it has a slot for microSD card. 16GB storage is more than enough to

store the operating system, the captured image and the re-trained image classifier.

Figure 3.2.1-F3: Kingston 16GB microSD card (Lazada)





2.5A, 5.1V Micro USB B Power Supply

The prototype of this project is just for demonstration purpose only and do not require

to be deployed in a real farm. Hence, a power supply is being used rather than portable

rechargeable power supply such as power bank.

Figure 3.2.1-F4: 2.5A, 5.1V Micro USD B Power Supply (element14)

5mm LEDs

This electronic component is used in the system to indicate the current status of the

system. As the actual setup of the system does not have the display for user to view. The

LEDs serve as an indicator to user that whether the system is running, crashing or idling.

Figure 3.2.1-F5: 5mm LEDs (guitarpedalparts)





Personal Computer (Laptop)

This equipment is used in developing and training dataset for the image classifier

which will be deployed later into the Raspberry Pi. As the embedded device is limited in

resources, all the heavy resource work will be carried out using laptop.

3.2.2 Software

Ubuntu 16.04.4 LTS

Ubuntu operating system is a free Linux Distribution for desktop, server and cloud.

This operating system will installed in the laptop and the developing and training for the

image classifier will be done on Linux environment.

Raspbian Operating System

Figure 3.2.2: Raspbian OS logo (Raspbian, 2012)

Raspbian OS is a free Linux Distribution based on Debian optimized for the Raspberry

Pi hardware. Raspbian started back in 2012 when the first generation Raspberry Pi was

released, a version of Debian for devices with the ARM processor. Later, the software was

optimized specifically for Raspberry Pi and a new distribution was released which was known

as Raspbian.

Python Language

Python language is an interpreted, object-oriented, high-level programming with

dynamic semantics. Python is widely in embedded systems due for its writability, error

reduction and readability.





Firebase (Database)

A Firebase database will be created for the system where this database will be hosted

on cloud. Firebase database is a real-time database that is cloud-hosted which was developed

by Google.

Tensorflow

Tensorflow is an open source software library for numerical computation using data

flow graphs. This library provides a lot APIs that are mainly designed for artificial neural

network models. Transfer learning using pre-trained models will be used in this project.

Android Studio

The implementation of the GUI is an android mobile application. Hence, Android

Studio is being used in this project. It is an IDE use to develop android application by Google.

UV4L Video Streaming Driver Software

A video streaming feature will be implemented for the mobile application as an

optional feature for the user to video stream from the mobile application via Raspberry Pi

camera. Hence, video streaming server for Raspberry Pi is needed to accomplish that. UV4L

is a modular collection of Video4Linux2-compliant, cross-platform, user space drivers for real

or virtual video input and output devices and over the years it also includes a generic purpose

streaming server plug-in which is especially made for IoT devices.

3.3 Functional Requirement

3.3.1 Retrain

Containing all the python scripts, pre-trained model and dataset will be required to

train the image classifier.





In this project, the image classifier needs to be trained. Due to the limited resources on

the Raspberry Pi, the training process will be taking place using a laptop as it has much more

powerful CPU, GPU and more RAM. The image classifier will be trained using Tensorflow

which is an open source software library developed by Google. Transfer learning technique

will be applied in the training process because this technique reduces the complexity and time

requires for training.

3.3.2 Image Capture

Define the functionalities of the camera that will be connects to the Raspberry Pi, the

LEDs and auto reboot. Python script are studied and implemented. The pi camera is used to

capture image of plants to let the system able to do evaluation to classify the plant current

condition. The pi camera is also being programmed to be able capture image every 6 hours

automatically. The LEDs are used to indicate the current status of the system and if the

current status indicates that the system is crashed. It will be auto rebooted after some time.

3.3.3 Label Image

Define the functionalities of the pre-trained image classifier such as where are the

image path, load the image and many more. After that the image classifier will evaluate the

captured image. After the evaluation, the classifier will display the result and it will be pushed

to the cloud database for data storage. If the system is crashed during this process, it will be

auto rebooted after some time.

3.3.4 Data View

Define the functionalities of the data viewing in the mobile application such as

database URL, comparing classification date with current date and compare the classification

of state condition. After the classification result is pushed to the cloud database, the date of

classification result will be compared with the current date. This is to make sure that only the

latest data will be displayed to user and not the old results. Other than that, the state condition





will also be compared. If the state condition result is unhealthy, a notification alert will be

send to the user. If the user clicks on the notification, control methods will be displayed.

3.3.5 Camera Stream

Define the functionalities of video streaming in the mobile application such as obtain

IP address, connect to the camera and disconnect from the camera. This is an optional feature

in the mobile application where allows user to video stream from the mobile application via

the Pi camera which is attached on the Raspberry Pi. IP address must be obtained before the

video stream could be running and this will be done in automatically by this function itself.

All the user need to do is to press connect if they wish to start the video streaming and press

disconnect to end the streaming.

3.4 Expected Challenge

One of the challenges to be expected in this project will be the required image dataset.

To train a high accuracy neural network, large quantity and suitable image dataset is required.

In this project, we are required to gather images about vegetable plants. In a normal situation,

an unhealthy plant is rarely happen in farm. Thus, it makes the gathering of unhealthy

vegetable plant images difficult.

Another challenge to be expected in this project will be limited resources. This due to

our system will be deployed into Raspberry Pi which has a very limited resources compared

to a laptop or desktop. Hence, careful planning for deployment is needed to prevent not

enough resources in the Raspberry Pi.





3.5 Project Milestone

Task Project Week

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Data Collection

Define project

objective and scope

Analysis for literature

review

Define technologies

involved

Determine system

development model

Determine system

and functional

requirements

Outline system

architecture

Outline system flow

Train image classifier

and implement to

Raspberry Pi

Presentation

Documentation

Table 3.4-T1: Gantt chart showing the project milestones (FYP 1)





Task Project Week

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Begin creating

database

Begin development of

GUI

Add different plant

image dataset

Finalizing the

development and

implementation of

database and GUI

Finalizing the

functional

requirement, system

architecture and

system flow

Finalizing system for

presentation

System testing and

performance

Presentation

Documentation

Table 3.4-T2: Gantt chart showing the project milestones (FYP 2)





3.6 Estimated Cost

Items For Final Year Project

Development

For Commercialisation

Raspberry Pi 3 Model B RM162.75 RM162.75

Raspberry Pi NoIR Camera

V2

RM116.25 RM116.25

Kingston 16GB MicroSD

Card

RM24.99 RM24.99

2.5A, 5,1V Micro USB B

Power Supply

RM63.99 RM63.99

LED 5mm RM1.80 RM1.80

Laptop RM0 --

Ubuntu OS RM0 --

Raspbian OS RM0 --

Firebase (Database) RM0 Vary

Tensorflow RM0 --

Android Studio RM0 --

UV4L RM0 RM0

RM369.78 RM369.78

Table 3.5: Estimated cost

Based on the table above, there will be no spending needed as the required items are

mostly personal belonging of mine. As for commercialization, the Raspberry Pi 3 Model B,

Raspberry Pi NoIR Camera V2, Kingston 16GB MicroSD card and power supply adapter will

cost RM162.75, RM116.25, RM24.99 and RM63.99 respectively. As for the database, it

comes with different subscription plan for user to choose. Hence the final price the database

plan is depend on the user how much data that they want to store. The provided plans are free,

RM96.56 a month and pay as you go. The price was refer based on their official website.






Different system development model were evaluated and prototype model was

selected for the development of this project. The system requirement and functional

requirement was identified to ensure the project is on correct path. Expected challenge of this

project was identified. Project milestone was also illustrated in Gantt chart format to show the

estimation time taken to complete the project. Finally, the cost for development and

commercialization was shown and explained.


Chapter 4: System Design



CHAPTER 4: SYSTEM DESIGN

4.1 System Architecture

Figure 4.1: Full system architecture

System architecture is a conceptual model that defines a system. It allows the reader to

understand and clear of how the system actually works.

Based on figure 4.1, it shows that how different component works together to make

the whole system working. The Raspberry Pi will be the heart of the system which will be

controlling the Pi camera and the trained image classifier. The Pi camera will capture the

image and it will store in a specific file inside the Raspberry Pi storage. Then, the re-trained

image classifier will begin classify the image. It will return the result and it will be pushed to

Firebase cloud. Finally, the user can retrieve the data from firebase via an android mobile

application. The android mobile application only will display the latest classification data and

send notification alert if the result is classify as unhealthy.





4.2 Functional Modules in the system

4.2.1 Retrain Module

As this module is not a part of the system, it will not be discuss completed. This

module is responsible for training the image classifier that will be deployed into the

Raspberry Pi. In order to train the classifier, there should be a module which has all the

required files such as the training scripts, pre-trained model which will be used to reduce the

training time and image dataset which will be use for training. This module also provides a

function called Tensorboard which display training summary to allow easier to understand,

debug and optimize. Basic functions such as parameter tuning, changing different pre-trained

models will be available in this module.

Figure 4.2.1-F1: Tensorboard training summary about accuracy and cross entropy





Figure 4.2.1-F2: Tensorboard histograms about retraining layer weights, biases, activations,

etc

4.2.2 Image Capture Module

Figure 4.2.2: Flowchart for image capture module





In this module, all the setting for the camera, timer and LEDs function will be written

in python code and implemented. This module is responsible for tuning the pi camera setting,

controlling the timer for image capture, saving the image in specific file, indicating the status

of the system and auto rebooting the system where there is error occurs.

4.2.3 Label Image Module

Figure 4.2.3: Flowchart for label image module

This module is responsible for classifying the capture images. Firstly, this module will

be locating the path of captured image being stored and load the image. Next, the image

classifier model will be called in this module to classify the image. After classification, this

module also responsible for displaying the classification result and push the classification

result into cloud database. If any of the process has error, the system will be auto rebooted.





4.2.4 Data View Module

Figure 4.2.4: Flowchart for data view module

This module is responsible for retrieving classification data from the database and

display to user. First this module will be retrieving all the classification data from database

and each of the classification result date will be compared with the current date. Only the

latest data will be displayed. Next, the classification state result also will be compared to

determine that whether the classification result is healthy or unhealthy. If the result is

unhealthy, a notification alert will be send to the user.





4.2.5 Camera Stream Module

Figure 4.2.5: Flowchart for camera stream module

This module is an optional feature and it is responsible for the video streaming feature

in the mobile application. First this module will be obtaining the IP address from the

classification result in the database. Only the latest IP address will be obtained. Next, the IP

address will be inserted into the streaming URL and this must be completed before the user

start the video streaming feature. Once this is done, user can press connect and the Pi camera

will be start video streaming. Lastly, user also can disconnect from the video streaming by

disconnecting it.





4.3 System Flow

The full system flow of this project will be represented in a flow chart form. As the

project is completed, the process from Raspberry Pi power on till cloud database could be

done automatically. From the flow chart, it emulates a situation where the plant disease

detection system will be capturing every 6 hours and then the trained image classifier will

begin classify the captured image, return the classification result and push the result to cloud

database. After the classification result is stored in the cloud database, user can retrieve the

data via an android mobile application developed for this project. The application will

compare the classification result date with the current date and display the latest result.

Finally, the result also will be compared to determine whether it is healthy or unhealthy. If the

result is unhealthy, a notification alert will be send to user.

Figure 4.3: Full flowchart of system flow





4.4 Database Design

The database design for this project is a cloud-based database (Firebase). The reason

for using this kind of database design is because of the limited resources on the Raspberry Pi

and the provided API for android mobile application. The purpose of the database is to store

the result of the classification which will be used on the mobile application. The developed

mobile application will retrieve the data of classification from the database and it will notify

the user if there is any positive disease-presence result.

Figure 4.4-F1: Example of result of the classification that needed to be store in database

Figure 4.4-F2: Example of data being stored in Firebase Database





4.5 GUI Design

The GUI implementation for this project is an android mobile application which will

be retrieving the classification result from the Firebase database. By comparing the date in the

classification result with the current date, the application is able to display the latest

classification data for the user. Other than that, the classification state result also will be

compared to determine that whether the result is healthy or unhealthy. If it is unhealthy, a

notification alert will be send to the user. Besides that, the user also can click the on

notification alert on the application. This will display control methods for the respective plant

diseases. This feature is to allow the user take correct precaution method to get rid the plant

disease from spreading in their farm. Finally, this application also provided with an optional

feature where the user can video stream from the application via Raspberry Pi camera. This

could be useful if the user is receiving a notification from this application and the user wish to

view the condition of the plant immediately.

Figure 4.5-F1: GUI design for the main page





Figure 4.5-F2: GUI design for data view

Figure 4.5-F3: GUI design for video stream





Figure 4.5-F4: GUI design for disease control method


The full system design was shown and explained in this chapter. First, full system

architecture was illustrated using a diagram and explained to let the user know how the

system works. Besides that, all functional modules were also identified and explained what

each of the modules do in the system. Next, the full system flow of the system was illustrated

and explained. Finally, the database design and GUI design were discussed and what it is

going to do in this project.


Chapter 5: System Implementation



CHAPTER 5: SYSTEM IMPLEMENTATION

5.1 Hardware Setup

5.1.1 Personal Laptop

In this project, a personal laptop is used for training the image classifier and

developing the android mobile application. Due to the limitation of resources in the Raspberry

Pi, the training of the image classifier is conducted using laptop and later deployed into the

Raspberry Pi. As this project is not using any third-party software for data retrieving and

viewing, a laptop is used to develop an android mobile application for this purpose. Other

than that, the mobile application is also capable of sending notification to user if there is any

disease-positive result obtained and camera streaming in the application via Pi Camera on the

Raspberry Pi.

5.1.2 Raspberry Pi 3

There are two hardware implementations for the Raspberry Pi. First implementation is

the status indicator setup which uses 3 pins out of the 40 GPIO pins provided in the Raspberry

Pi. Next, the implementation for the camera setup which uses the CSI camera port that

provided by the Raspberry Pi also. The table below shows the hardware design for the

Raspberry Pi used in this project. Besides that, a figure of the 40 GPIO pins is also shown.

Pin/Port Function

Ground Connect to ground

GPIO 17 Control the output of green LED

GPIO 27 Control the output of red LED

CSI Camera Port Control the camera

Table 5.1.2: Table of pins and port being used and their function





Figure 5.1.2: Raspberry Pi 3 GPIO Header (element14, 2015)

5.1.3 Status Indicator Setup

In this project, total of two different colors LEDs (2 pins) are needed. These LEDs are

indicating the status of the system. Each color LED is representing different status of the

system as in the full system there is no display for the user to view the status of the system.

This allows the user to be aware that what the system is currently doing.

LED color Status Status Description

Green The LED is on. The system software is running.

Red The LED is on. The software stopped due to error and waiting for

reboot.

Table 5.1.3: Table of LED colors, status and status description

Table 5.1.3 shows the different colors LEDs and their status description. If green LED

is on, it indicates that the system software is currently running. Next if the red LED is on, it





shows that the software stopped due to error and waiting for rebooting. Finally if there is no

LEDs are lighting up, it indicates the system is idle.

Figure 5.1.3: Actual setup of the status indicator setup

The actual implementation of the LEDs is shown in Figure 5.1.3 above. Each LED is

connected with different GPIO pins at one end and same ground pin in another end.

5.1.4 Camera Setup

There is a camera setup in this project and any brand of webcams would work if

provided that there are appropriate drivers could be found online and installed on the

Raspberry Pi. In this case, Raspberry Pi NoIR Camera V2 is used in this project as this

camera is a personal belonging of mine and the compatibility with Raspberry Pi. In the

embedded device itself, there is a CSI camera port for connecting this Raspberry Pi Camera.

The setup for this camera is rather easy. Once the camera is connected to the camera

port and the camera interface is activated, the camera is ready to be use. Figure 5.1.4 below

shows the actual setup with the Raspberry Pi Camera connected.





Figure 5.1.4: Actual setup with the Raspberry Pi Camera

5.2 Software Setup

5.2.1 Ubuntu 16.04 Installation

Ubuntu OS need to be installed in the laptop for the training of image classifier. The

Linux platform is chosen over the Windows platform due to compatibility with the

Tensorflow library during the time when the project was conducted. To make things easier,

dual boot (Windows 10 and Ubuntu 16.04) method is used. This method helps us to save a lot

of time from backup the important files from our previous OS and it also allows us to boot

either into Windows or Linux. The following link provides a complete guide on how we can

setup our computer into dual-booting: (https://www.tecmint.com/install-ubuntu-16-04-

alongside-with-windows-10-or-8-in-dual-boot/)

https://www.tecmint.com/install-ubuntu-16-04-alongside-with-windows-10-or-8-in-dual-boot/

https://www.tecmint.com/install-ubuntu-16-04-alongside-with-windows-10-or-8-in-dual-boot/





5.2.3 Android Studio Installation

Due to this project is not using third-party application for data retrieving and viewing,

notification services and video streaming, an android mobile application is developed. To

develop android application, we need to install Android Studio which is the official IDE for

android development. To install Android Studio, go to Android Studio official website and

download the latest version. Make sure the system requirement is fulfill and proceed to install.

5.2.3 Tensorflow Installation

Before we can start train and deploy the artificial neural network, we need to

download and install the Tensorflow library. The installation of Tensorflow for laptop is

rather long and the guide is easy to find online. The Tensorflow version that we used in this

project is an older version which Tensorflow 1.4. The available installation guide for Ubuntu

OS can be found in: (https://www.tensorflow.org/versions/r1.4/install/install_linux). There are

total of 4 mechanisms we can install the Tensorflow:

virtualenv

“native” pip

Docker

Anaconda

In this project, we are installing the Tensorflow with “native” pip. After the

installation is completed, we can begin train the artificial neural network.

Next will be the installation of Tensorflow for the Raspberry Pi. When this project is

conducted, there is no any official installation guide or support for the Raspberry Pi. We are

using an installation guide provided by other developers. These are the following instruction

for installation of Tensorflow in Raspberry Pi:

1. First, make sure Raspberry Pi is running at least Raspbian 8.0 (Jessie).

2. Open terminal and type in sudo apt-get update.

3. After that, install python 2.7 by typing sudo apt-get install python-pip python-dev.

4. Next, download the wheel file from this repository:

https://www.tensorflow.org/versions/r1.4/install/install_linux





wget https://github.com/samjabrahams/tensorflow-on-raspberry-

pi/releases/download/v1.0.1/tensorflow-1.0.1-cp27-none-linux_armv7l.whl.

After the file is downloaded, install the file by typing sudo pip install tensorflow-

1.0.1-cp27-none-linux_armv7l.whl. For your information, the installation for

Tensorflow might take some time and please wait patiently.

5. We also need to reinstall the mock library to keep it from throwing and error when we

import Tensorflow. First, we will type in sudo pip uninstall mock to uninstall the mock

library. After it is done, type in sudo pip install mock to install back the mock library.

6. Finally, we can run a simple program to verify that the Tensorflow is installed

correctly.

python

import tensorflow as tf

hello = tf.constant (“Hello, Testing here!”)

sess = tf.Session()

print (sess.run (hello))

It should display “Hello, Testing here!” on the terminal if Tensorflow is working

correctly.

Note: The installation of Tensorflow for Raspberry Pi provided from the instruction above

only for python 2.7. If you wish to install for python 3.3+, please view this link for

information: (https://www.instructables.com/id/Google-Tensorflow-on-Rapsberry-Pi/). By the

time of this project is completed, Tensorflow is officially supporting Raspberry Pi and you

wish to use their official installation guide. This following provides further information for

that: (https://www.tensorflow.org/install/install_raspbian).

https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v1.0.1/tensorflow-1.0.1-cp27-none-linux_armv7l.whl

https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v1.0.1/tensorflow-1.0.1-cp27-none-linux_armv7l.whl

https://www.instructables.com/id/Google-Tensorflow-on-Rapsberry-Pi/

https://www.tensorflow.org/install/install_raspbian





5.2.4 UV4L Installation

UV4L is a software driver for video streaming service in Raspberry Pi. This driver

software is installed to allow the mobile application able to stream video via the Raspberry Pi

Camera. The following link provided a full detail guide for the installation of this driver for

Raspberry Pi: (https://www.linux-projects.org/uv4l/installation/). After the installation is

done, video streaming service will be able to run.

5.3 Setting and Configuration

The CD contains two folders which is called Classifier and Mobile app. These folders

contain all the necessary source codes to program the system as well as a mobile application

for data retrieving and viewing, notification service and video streaming. To set up the whole

system for demonstration, these are the following instructions:

1. First, copy the folder named „Classifier‟ from the CD into the Raspberry Pi.

2. Check the image path for the capture_image.py and label_image.py. After that, script

path for the run.sh and start.sh by opening each of them with the provided text editor

software in Raspberry Pi. Make sure all the paths are correct before proceed to next

step.

3. Open a file called setup.txt in the classifier folder. In this file contains 2 command

lines that allow the system run automatically. Open the terminal and type in sudo

crontab –e and copy the following command file and paste it at the bottom of the file.

Press ctrl+x and Y to save the file. The system should be able to run automatically

after the Raspberry Pi is rebooted.

4. Before rebooting and run the system, we need to install Firebase library. Please type in

this following command line into the terminal:

sudo apt-get update

sudo pip install requests == 1.1.0

sudo pip install python-firebase

5. After Firebase library is installed. Proceed to the Mobile app folder which contains of

the mobile application source codes and an apk file. Copy the apk file into your

https://www.linux-projects.org/uv4l/installation/





mobile phone and make sure the option for unknown sources is turned on from Setting

-> Privacy -> Unknown sources. Install the apk file into your mobile phone.

6. Finally, reboot the Raspberry Pi and wait for the system to execute.

5.4 System Operation

Once the configuration for the Raspberry Pi and the mobile device from section 5.3 is

been setup. The system should be able to execute automatically and when the system is

executing, the green LED should be lighted up as shown in Figure 5.4-F1. The green LED is

indicating that the system software is currently running. If the system encounters any error,

the green LED will be turned off and then the red LED will be lighted up as shown in Figure

5.4-F2. The red LED is indicating that the system software is stopped due to error and after

some time, the system will be auto reboot.

Figure 5.4-F1: Green LED lights up when the system starts to execute





Figure 5.4-F2: Red LED lights up when the system stopped due to error

After the system done executing once, open the mobile application and click on data.

User should be able to view the classification result. User also can click on camera to use

video streaming service. The notification will appear if the classification result is matched the

plant diseases in the image classifier model. Finally if the user clicks on the notification, the

application will display the control method for the respective plant diseases which is shown in

section 4.5.





Figure 5.4-F3: Application shows the data retrieved from the database

Figure 5.4-F4: Application shows the video streaming service is running





Figure 5.4-F5: Application shows the notification feature

5.5 Concluding Remark

All the necessary hardware setup and software setup were explained in detail. By

following the instructions in the setting and configuration, the system and the mobile

application should be able to run without any problem. All the required software is included

in the CD. Finally, system operation was shown and explained to show how the system is

working.


Chapter 6: System Evaluation and Discussion



CHAPTER 6: SYSTEM EVALUATION AND DISCUSSION

6.1 System Testing and Performance Metrics

A series of testing carried out in order to ensure the accuracy of the image

classification. In the system, there are total 3 types of vegetable plants which are in healthy

condition and 2 types of plant diseases for each vegetable plant which consider as unhealthy

condition. To ensure this, each vegetable plant condition will be tested with 20 trails under 2

conditions. The first condition is that the system testing will be carried out without any

hindrance for the camera which means each captured image that obtained from the camera is

in good condition. For second condition, the system testing will be carried out with hindrance

to the camera such as bad lighting. This is to show that whether the system is able to ensure

the accuracy of image classification to remain high despite in such environment condition. In

total there will be 18 sets of system testing in this project and same test images will be used

for both environmental conditions. The acceptance requirement for this system test is that the

number of correct classification results must be more than the number of wrong classification

results.

Condition 1: Without any hindrance on the

camera

Healthy condition: Cabbage, Sweet pepper,

Tomato

Unhealthy condition: Bacterial soft rot

(Cabbage), Black rot (Cabbage), Anthracnose

(Sweet pepper), Blossom end rot (Sweet pepper),

Bacterial spot (Tomato), Late blight (Tomato)

Condition 2: Hindrance on the camera (Bad

lighting)

Healthy condition: Cabbage, Sweet pepper,

Tomato

Unhealthy condition: Bacterial soft rot

(Cabbage), Black rot (Cabbage), Anthracnose

(Sweet pepper), Blossom end rot (Sweet pepper),

Bacterial spot (Tomato), Late blight (Tomato)

Table 6.1: Table shows a clearer picture of the whole system testing





6.2 Testing Setup and Result

The table below indicates the number of correct results of image classification for each

vegetable plants condition under 2 different environmental.

6.2.1 Condition 1 (Without any hindrance on the camera)

Healthy condition: Cabbage

Number of trails Classification Result Correct/Wrong

1 Healthy Cabbage Correct



4 Black Rot Wrong


6 Black Rot Wrong





11 Black Rot Wrong










Table 6.2.1-T1: Result of testing on condition 1 (Healthy condition: Cabbage)





Number of trails: 20

Number of correct classification result: 17

Number of wrong classification result: 3

Correct rate: 17/20*100% = 85%

Wrong rate: 3/20*100% = 15%

Based on the statistic above, the outcome of healthy cabbage in condition 1 is

considered as high accurate as it only failed 3 times out of 20 trials. In other words, the wrong

rate is 15% out of 100% and the correct rate is 85% out of 100%. This happens because of the

captured image is in good quality and the gathering of sample images for healthy cabbage are

available easily.

Healthy condition: Sweet Pepper


1 Healthy Sweet Pepper Correct















16 Healthy Tomato Wrong









Table 6.2.1-T2: Result of testing on condition 1 (Healthy condition: Sweet Pepper)




Correct rate: 19/20*100% = 95%

Wrong rate: 1/20*100% = 5%

Based on the statistic above, the outcome of healthy sweet pepper in condition 1 is



captured image is in good quality and the gathering of sample images for healthy sweet

pepper are available easily.

Healthy condition: Tomato


1 Healthy Tomato Correct
























Table 6.2.1-T3: Result of testing on condition 1 (Healthy condition: Tomato)




Correct rate: 20/20*100% = 100%

Wrong rate: 0/20*100% = 0%

Based on the statistic above, the outcome of healthy tomato in condition 1 is



captured image is in good quality and the gathering of sample images for healthy tomato are

available easily.

Unhealthy condition: Bacterial Soft Rot (Cabbage)


1 Not Vegetable Wrong

2 Bacterial Soft Rot Correct






















20 Black Rot Wrong

Table 6.2.1-T4: Result of testing on condition 1 (Unhealthy condition: Bacterial Soft Rot)




Correct rate: 18/20*100% = 90%

Wrong rate: 2/20*100% = 10%

Based on the statistic above, the outcome of bacterial soft rot for cabbage in condition

1 is considered as high accurate as it only failed 2 times out of 20 trials. In other words, the

wrong rate is 10% out of 100% and the correct rate is 90% out of 100%. This happens

because of the captured image is in good quality and the distinct disease symptoms compared

with other diseases.

Unhealthy condition: Black Rot (Cabbage)


1 Black Rot Correct

2 Black Rot Correct

3 Black Rot Correct

4 Black Rot Correct

5 Black Rot Correct

6 Bacterial Soft Rot Wrong






8 Black Rot Correct

9 Black Rot Correct

10 Black Rot Correct




14 Healthy Cabbage Wrong







Table 6.2.1-T5: Result of testing on condition 1 (Unhealthy condition: Black Rot)




Correct rate: 15/20*100% = 75%

Wrong rate: 5/20*100% = 25%

Based on the statistic above, the outcome of black rot for cabbage in condition 1 is

considered as accurate as it only failed 5 times out of 20 trials. In other words, the wrong rate

is 25% out of 100% and the correct rate is 75% out of 100%. This happens because of the

captured image is in good quality and the similarity of disease symptoms compared with other

diseases.

Unhealthy condition: Anthracnose (Sweet Pepper)


1 Anthracnose Correct

2 Healthy Sweet Pepper Wrong










8 Blossom End Rot Wrong











19 Blossom End Rot Wrong


Table 6.2.1-T6: Result of testing on condition 1 (Unhealthy condition: Anthracnose)




Correct rate: 17/20*100% = 85%

Wrong rate: 3/20*100 = 15%

Based on the statistic above, the outcome of anthracnose for sweet pepper in condition

1 is considered as high accurate as it only failed 3 times out of 20 trials. In other words, the


because of the captured image is in good quality and the distinct disease symptoms compared

with other diseases.





Unhealthy condition: Blossom End Rot (Sweet Pepper)


1 Blossom End Rot Correct


3 Anthracnose Wrong













16 Anthracnose Wrong

17 Bacterial Spot Wrong

18 Late Blight Wrong



Table 6.2.1-T7: Result of testing on condition 1 (Unhealthy condition: Blossom End Rot)




Correct rate: 15/20*100% = 75%

Wrong rate: 5/20*100% = 25%

Based on the statistic above, the outcome of blossom end rot for sweet pepper in

condition 1 is considered as accurate as it only failed 5 times out of 20 trials. In other words,





the wrong rate is 25% out of 100% and the correct rate is 75% out of 100%. This happens

because of the captured image is in good quality and the similarity of disease symptoms

compared with other diseases.

Unhealthy condition: Bacterial Spot (Tomato)


1 Bacterial Spot Correct




















Table 6.2.1-T8: Result of testing on condition 1 (Unhealthy condition: Bacterial Spot)








Correct rate: 17/20*100% = 85%

Wrong rate: 3/20*100% = 15

Based on the statistic above, the outcome of bacterial spot for tomato in condition 1 is



captured image is in good quality and the distinct disease symptoms compared with other

diseases.

Unhealthy condition: Late Blight (Tomato)



2 Late Blight Correct























Table 6.2.1-T9: Result of testing on condition 1 (Unhealthy condition: Late Blight)




Correct rate: 11/20*100% = 55%

Wrong rate: 9/20*100% = 45%

Based on the statistic above, the outcome of late blight for tomato in condition 1 is

considered as low accurate as it only failed 9 times out of 20 trials. In other words, the wrong

rate is 45% out of 100% and the correct rate is 55% out of 100%. This happens because lack

of sufficient sample image dataset and the similarity of disease symptoms compared with

other diseases despite with the good quality of captured image.

6.2.2 Condition 2 (Hindrance on the camera)

Healthy condition: Cabbage



2 Black Rot Wrong

3 Black Rot Wrong

4 Black Rot Wrong




8 Black Rot Wrong



11 Black Rot Wrong

12 Black Rot Wrong

13 Black Rot Wrong






15 Black Rot Wrong




19 Black Rot Wrong


Table 6.2.2-T1: Result of testing on condition 2 (Healthy condition: Cabbage)




Correct rate: 6/10*100% = 30%

Wrong rate: 14/10*100% = 70%

Based on the statistic above, the outcome of healthy cabbage in condition 2 is

considered as not accurate as it only failed 14 times out of 20 trials. In other words, the wrong

rate is 70% out of 100% and the correct rate is 30% out of 100%. This happens because of

bad lighting effect which affects the image quality.

Healthy condition: Sweet pepper


























Table 6.2.2-T2: Result of testing on condition 2 (Healthy condition: Sweet pepper)




Correct rate: 12/20*100% = 60%

Wrong rate: 8/20*100% = 40%

Based on the statistic above, the outcome of healthy sweet pepper in condition 2 is


is 40% out of 100% and the correct rate is 70% out of 100%. This happens because of distinct

appearance of the sweet pepper where it slightly overcomes the bad lighting effect that affects

the captured image.

Healthy condition: Tomato


























Table 6.2.2-T3: Result of testing on condition 2 (Healthy condition: Tomato)




Correct rate: 13/20*100% = 65%

Wrong rate: 7/20*100% = 35%

Based on the statistic above, the outcome of healthy tomato in condition 2 is



appearance of the tomato where it slightly overcomes the bad lighting effect that affects the

captured image.

Unhealthy condition: Bacterial Soft Rot (Cabbage)










5 Black Rot Wrong
















Table 6.2.2-T4: Result of testing on condition 2 (Unhealthy condition: Bacterial Soft Rot)




Correct rate: 9/20*100% = 45%

Wrong rate: 11/20*100% = 55%

Based on the statistic above, the outcome of bacterial soft rot for cabbage in condition

2 is considered as not accurate as it only failed 11 times out of 20 trials. In other words, the


because of bad lighting effect which affects the image quality.





Unhealthy Condition: Black Rot (Cabbage)



2 Black Rot Correct

3 Black Rot Correct


5 Black Rot Correct

6 Not Cabbage Wrong

7 Anthracnose Wrong














Table 6.2.2-T5: Result of testing on condition 2 (Unhealthy condition: Black Rot)




Correct rate: 6/20*100% = 30%

Wrong rate: 14/20*100% = 70%

Based on the statistic above, the outcome of black rot for cabbage in condition 2 is

considered as not accurate as it only failed 14 times out of 20 trials. In other words, the wrong





rate is 70% out of 100% and the correct rate is 30% out of 100%. This happens because of

bad lighting effect which affects the image quality.

Unhealthy condition: Anthracnose (Sweet Pepper)






















Table 6.2.2-T6: Result of testing on condition 2 (Unhealthy condition: Anthracnose)




Correct rate: 10/20*100% = 45%

Wrong rate: 11/20*100% = 55%





Based on the statistic above, the outcome of anthracnose for sweet pepper in condition

2 is considered as average accurate as it only failed 11 times out of 20 trials. In other words,

the wrong rate is 55% out of 100% and the correct rate is 45% out of 100%. This happens

because of bad lighting effect which affects the image quality.

Unhealthy condition: Blossom End Rot (Sweet Pepper)


1 Healthy Sweet pepper Wrong












13 Not vegetable Wrong








Table 6.2.2-T7: Result of testing on condition 2 (Unhealthy condition: Blossom End Rot)








Correct rate: 7/20*100% = 35%

Wrong rate: 13/20*100% = 65%


condition 2 is considered as not accurate as it only failed 13 times out of 20 trials. In other

words, the wrong rate is 65% out of 100% and the correct rate is 35% out of 100%. This

happens because of bad lighting effect which affects the image quality.

Unhealthy condition: Bacterial Spot (Tomato)


























Table 6.2.2-T8: Result of testing on condition 2 (Unhealthy condition: Bacterial Spot)




Correct rate: 11/20*100% = 55%

Wrong rate: 9/20*100% = 45%

Based on the statistic above, the outcome of bacterial spot for tomato in condition 2 is



disease symptoms of bacterial spot for tomato where it slightly overcomes the bad lighting

effect that affects the captured image.

Unhealthy condition: Late Blight (Tomato)













12 Black Rot Wrong













Table 6.2.2-T9: Result of testing on condition 2 (Unhealthy condition: Late Blight)




Correct rate: 8/20*100% = 40%

Wrong rate: 12/20*100% = 60%


condition 2 is considered as not accurate as it only failed 12 times out of 20 trials. In other

words, the wrong rate is 60% out of 100% and the correct rate is 40% out of 100%. This

happens because of bad lighting effect which affects the image quality.

6.3 Project Challenges

Undoubtedly, this project is more difficult than initial expected. The completion of

this system would be not possible if most of the challenges that faced in this project were not

solved. The following are the challenges in this project:

Suitable dataset – To train a high accuracy artificial neural network, large quantity and

suitable image dataset is required. In a normal situation, unhealthy plant‟s picture is

hard to obtain as this rarely happen in farm. Thus, it makes the gathering of unhealthy

plant images difficult.

Video streaming network – The purpose of this feature is to allow the user able to

view their plants from other locations where the mobile devices are not connecting in

the same network as the system. Unfortunately, both mobile device and the system





have to being in the same network in order to use this video streaming service. This is

because the limited feature of the video streaming software for Raspberry Pi.

Raspberry Pi Camera – The Raspberry Pi Camera is mainly used to capture images

and for video streaming. However, this camera has 2 flaws which are unable to auto

focus and poor image quality when in poor lighting environment. This causes the

accuracy of the artificial network drops when the captured image is in poor quality.

Machine learning – Due to limited and minimal knowledge about machine learning,

the image classifier function requires longer development time to explore and make it

works. A simple and small artificial neural network was trained to classify few

vegetable plants condition.

6.4 Objectives Evaluation

The first objective was to design a disease detection system using machine technique

for the farmer to reduce the risk of losing their harvest from diseases. This was successfully

achieved as the system is running an image classifier which was train using a machine learn

technique called Transfer Learning. It also shows that the image classifier is able to classify

the image into healthy and unhealthy state based on the testing result in section 6.2.

The second objective was to allow the farmers to take early and correct precaution

through the deployed disease system by suggesting correct solution to take. This was also

successfully achieved in the proposed system mobile application. In the application it has a

notification feature that will alert the farmers if there is any disease state result from the

retrieved data in the mobile application. It also provided a solution for the farmers to take

when they received the notification alert via the mobile application.

Finally, the last objective of this project was to develop and deploy an artificial neural

network that is able to perform disease classification on vegetable plants. This has been

accomplished as the system model deployed in the Raspberry Pi was an artificial neural

network developed using a machine learning technique called Transfer Learning. Based on

the testing result in section 6.2, the trained model was able to perform disease classification

on 3 different vegetable plants which is cabbage, sweet pepper and cucumber.





6.5 Concluding Remark

The system testing was carried out and performance metrics were obtained to prove

the accuracy of the image classifier. From the testing results, it shown that the accuracy of the

image classifier can be worse if the quality of the captured image is not in good condition.

The project challenges were also identified. Finally, it can be said that all the objectives were

achieved. Hence, the final outcome of this project is quite successful.


Chapter 7: Conclusion and Recommendation



CHAPTER 7: CONCLUSION AND RECOMMENDATION

7.1 Conclusion

The intention of this project is to provide a more reliable method for the farmers to

monitor their farm. The purpose that they need to monitor their farm is because plant disease

is one of the major factors that destroying their harvest. Traditionally, they relied on a non-

practical way to monitor their farm which is direct eye observation. If their farm is big-sized

farm, they might miss some spots in their farm by using this method.

So, at the end of this project, a working full prototype disease detection system was

developed. In this system, it will be capturing image every 6 hours interval and the image

classifier is able to classify the image based on healthy state and category unhealthy one into

different type of disease. The full prototype system is implemented on a Raspberry Pi 3

connected with a Raspberry Pi camera. The system is able to run automatically once the

Raspberry Pi is booted up and all the classification result will be stored in a cloud database.

Finally, an android mobile application is also developed for user to view the data which is

stored in the cloud database. The android mobile application also comes with a notification

service to alert the user when the classification result is disease-positive and a video streaming

feature from the mobile application via Raspberry Pi Camera.

Although this project is proven to be difficult but the project objectives are able to

convert into deliverables such as a disease detection system for farmers, allows farmers to

take early and correct precaution through this system and develop and deploy an artificial

neural network to perform disease classification on vegetable plants. Through the system

testing, this project also can be considered relatively reliable. Provided that the image

captured was in good condition.

Nevertheless, the evolution of IoT technologies is growing fast nowadays. The

potential of this project is so helpful that it can improve the agriculture sector into the next

level.


Chapter 7: Conclusion and Recommendation



7.2 Recommendation

There are still many improvements and enhancements can be done in this project.

Firstly, the image classification model can be further increase its accuracy by adding bad

condition images such as bad lighting, noise and etc into the model training. This is because

when the camera is capturing the image which is in bad condition it can greatly affect the

result of image classifier. By doing this, the image classifier still able classify the image

accurately despite the captured images are in bad condition.

Next, the system could also be leverage by using servo or a drone with the camera

which allows the system to capture the image of the plants in multiple angles instead of one.

In the current system it only allows to capture image in one fixed angle which makes the

system have blind spot. If the disease started to grow in the blind spot of the disease detection

system, it would be problematic to the user. Hence, this recommendation is to prevent this

blind spot issue.

Furthermore, addition sensors such as temperature sensor and humidity sensor could

be added into the system. This allows the system to make future prediction result instead of

only getting current classification result. Temperature and humidity are one of the few

attributes that need to be analyzed whether a plant disease will happen. As different level

temperature and humidity will be suitable for certain disease to grow. This recommendation is

to prevent this from happening.



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APPENDIX 1 – BI WEEKLY REPORT

FINAL YEAR PROJECT WEEKLY REPORT Project II

Trimester, Year: Year 3 Semester 3 Study week no.: 2

Student Name & ID: Khoo Wah Jian - 1507159

Supervisor: Dr. Goh Hock Guan

Project Title: Machine Learning for Disease Detection using Raspberry Pi with

Tensorflow in Vegetable Farms

1. WORK DONE

- Discussed more in detail about GUI design for the project.

- Discussed about expanding the image classifier model by training more vegetable plants.

2. WORK TO BE DONE

- Begin the development of GUI.

- Begin research about what vegetable plants to be added into the image classifier model.

3. PROBLEMS ENCOUNTERED

- None.

4. SELF EVALUATION OF THE PROGRESS

- Knowledge about android development and vegetables improved.

_________________________ _________________________

Supervisor‟s signature Student‟s signature







1. WORK DONE

- Discussed and determined the full system architecture, full system flow and functional

requirements.

- System hardware setup is completed

2. WORK TO BE DONE

- Finalized the GUI development.

- Begin to train the new image classifier model.


- Android application not able to retrieve data from the firebase.

- Camera streaming is not able to start.


- Knowledge about agriculture improving.

- Work is behind schedule. Need a proper planning so that I do not need to rush for GUI and

database completion.

_________________________ _________________________








1. WORK DONE

- GUI and database is development is completed.

- Successfully trained the new image classifier with more type of vegetable plants.

2. WORK TO BE DONE

- Begin testing the new image classifier.

-Begin to combine the system hardware with my teammate.

-Begin writing the report


- Having difficulty in developing the video streaming feature.

- Having trouble to obtain image dataset for image classifier training


- Work was progressing as scheduled.

_________________________ _________________________




Student Name & ID: Khoo Wah Jian – 1507159




1. WORK DONE

- System architecture, system flow and functional requirement were finalized.

- The new image classifier was deployed and tested.

- System hardware combine was completed.

2. WORK TO BE DONE

- Improve the image classifier by tuning different parameter in training process.

- Begin mobile application software combine with my teammate.

- Finalize the draft report.


- Having difficult writing system testing in report.

- Having difficult combining the source codes for hardware part.


- System hardware combination is done. Have better understand on overview of the project.

- Work is progressing as scheduled.

_________________________ _________________________








1. WORK DONE

- System hardware and software combine was completed.

- Draft report was completed.

2. WORK TO BE DONE

- Start full system testing.

- Modify the draft report and finalize the final report.


- None.


- Have better understand on the full system.

_________________________ _________________________


FINAL YEAR PROJECT WEEKLY REPORT

Project II






1. WORK DONE

- Full system testing was completed.

- Final report was completed.

2. WORK TO BE DONE

- Prepare presentation slides

- Make minor configuration on the system for demonstration.


- Having Trouble is finalizing the final report.


- Confident for demonstration of the full functionalities of the system.

_________________________ _________________________


POSTER

APPENDIX 2 – TURNITIN ORIGINALITY REPORT

PLAGIARISM CHECK RESULT

FACULTY OF INFORMATION AND COMMUNICATION TECHNOLOGY

Full Name(s)of Candidate(s)

Khoo Wah Jian

ID Number(s)

1507159

Programme /Course CN

Title of Final Year Project Machine Learning for Disease Detection using Raspberry Pi

with Tensorflow in Vegetable Farms


Similarity Supervisor’s Comments (Compulsory if parameters of originality exceeds the limits approved by UTAR)

Overall similarity index:___ %

Similarity by source Internet Sources: _______________% Publications: _________ % Student Papers:_________ %

Number of individual sources listed of more than 3%similarity:

Parameters of originality required and limits approved by UTAR are as Follows: (i) Overall similarity index is 20% and below, and

(ii) Matching of individual sources listed mustbelessthan3%each, and (iii)Matching texts in continuous block must not exceed 8 words

Note: Parameters (i ) - (ii) shall exclude quotes, bibliography and text matches which are less than 8 words.

Note Supervisor/Candidate(s) is/are required to provide softcopy off full set of the originality report to

Faculty/Institute Based on the above results, I hereby declare that I am satisfied with the originality of the Final Year

Project Report submitted by my student(s) as named above. ______________________________ ______________________________ Signature of Supervisor

Signature of Co-Supervisor

Name: __________________________

Name: __________________________

Date: ___________________________ Date: ___________________________


Form Title :Supervisor’s Comments on Originality Report Generated by Turnitin for Submission of Final Year Project Report (for Undergraduate Programmes) Form Number: FM-IAD-005 Rev No.:0 Effective Date: 01/10/2013 Page No.: 1 of 1

UNIVERSITI TUNKU ABDUL RAHMAN

FACULTY OF INFORMATION & COMMUNICATION

TECHNOLOGY (KAMPAR CAMPUS)

CHECKLIST FOR FYP2 THESIS SUBMISSION

Student Id 1507159

Student Name Khoo Wah Jian

Supervisor Name Dr. Goh Hock Guan

TICK (√) DOCUMENT ITEMS

Your report must include all the items below. Put a tick on the left column after you have

checked your report with respect to the corresponding item.

Front Cover

Signed Report Status Declaration Form

Title Page

Signed form of the Declaration of Originality

Acknowledgement

Abstract

Table of Contents

List of Figures (if applicable)

List of Tables (if applicable)

List of Symbols (if applicable)

List of Abbreviations (if applicable)

Chapters / Content

Bibliography (or References)

All references in bibliography are cited in the thesis, especially in the chapter of literature review

Appendices (if applicable)

Poster

Signed Turnitin Report (Plagiarism Check Result - FormNumber:FM-IAD-005)

*Include this form (checklist) in the thesis (Bind together as the last page) I, the author, have checked and confirmed all the items listed in the table are included in my report. ______________________ (Signature of Student) Date:

Supervisor verification. Report with incorrect

format can get 5 mark (1 grade) reduction. ______________________ (Signature of Supervisor) Date:

Documents

MACHINE LEARNING FOR DISEASE DETECTION USING ...eprints.utar.edu.my/3061/1/fyp_CN_2018_KWJ_-_1507159.pdfTable 6.2.1-T1 Result of testing on condition 1 (Healthy condition: Cabbage)