topic
*******name*********
IT*******
BSc (Hons) in Information Technology
Specializing in *********
Department of Information Technology
Sri Lanka Institute of Information Technology
Sri Lanka
month year
topic
******Name*****
IT*****
Dissertation submitted in partial fulfillment of the requirements for the Bachelor of Science
Special (Honors) in Information Technology
Specializing in ***** *****
Department of Information Technology
Sri Lanka Institute of Information Technology
Sri Lanka
month year
DECLARATION
I declare that this is my work. This proposal does not incorporate without acknowledgement
any material previously submitted for a degree or diploma in any other university or institute
of higher learning. To the best of our knowledge and belief, it does not contain any material
previously published or written by another person except where the acknowledgement is made
in the text.
Name
Student ID
Signature
Signature:
Date:
Signature of the Supervisor:
Date:
i
ABSTRACT
This research is to provide a smart mobile-based solution for identifying diseases and showing
possible remedies to control the diseases of selected spices plants, namely cinnamon, pepper,
and cloves. Proper identification of these plants is crucial for their effective disease
management. The proposed solution involves inputting an image of the plant, leaf, root,
branch, or trunk to the application for identification. To achieve this main objective, specific
objectives have been set out. Firstly, the visual symptoms of leaves, plant, root, branches, or
trunk of spice plants will be studied and understood. This will aid in identifying the diseases
affecting the plants. Secondly, the methods of capturing images on spice plants to train the
model will be studied and understood. This will ensure that accurate images are used to train
the model, which will enhance its accuracy. Thirdly, different techniques related to image
processing, deep learning, and machine learning will be studied and understood. These
techniques will be used to train the model to identify the diseases accurately. Finally, the study
aims to understand how to implement a mobile application to identify diseases and show
possible remedies to control the diseases. The application will be user-friendly, and the results
will be displayed in a format that is easy to comprehend and use. This study seeks to provide
a smart mobile-based solution for identifying diseases and showing possible remedies to
control the diseases of selected spices plants. It aims to achieve this by studying and
understanding the visual symptoms of the plants, how to capture images to train the model,
the techniques related to image processing, deep learning, and machine learning, and how to
implement a user-friendly mobile application. By providing an effective solution, the study
will contribute to the proper management of the diseases of cinnamon, pepper, and cloves.
Keywords : spices, identification, mobile application, disease management, deep learning
ii
ACKNOWLEDGEMENT
iii
TABLE OF CONTENT
DECLARATION ........................................................................................................................... i
ABSTRACT..................................................................................................................................ii
ACKNOWLEDGEMENT ..............................................................................................................iii
TABLE OF CONTENT ................................................................................................................. iv
LIST OF TABLES ......................................................................................................................... vi
LIST OF FIGURES ....................................................................................................................... vi
LIST OF ABBREVIATIONS ......................................................................................................... vii
LIST OF APPENDICES ............................................................................................................... vii
1
2
INTRODUCTION ................................................................................................................ 1
1.1
BACKGROUND & LITERATURE REVIEW .................................................................... 5
1.2
RESEARCH GAP ......................................................................................................... 6
1.3
RESEARCH PROBLEM................................................................................................ 7
1.4
RESEARCH OBJECTIVES ............................................................................................ 8
1.4.1
MAIN OBJECTIVE .............................................................................................. 8
1.4.2
SPECIFIC OBJECTIVES........................................................................................ 8
METHODOLOGY ............................................................................................................. 10
2.1
System Architecture ............................................................................................... 10
2.2
Development Process ............................................................................................ 11
2.2.1
2.3
Component ............................................................................................................ 11
2.4
COMMERCIALIZATION ASPECTS OF THE PRODUCT ............................................... 16
2.5
TESTING & IMPLEMENTATION ............................................................................... 17
2.5.1
3
Tools and technologies .................................................................................. 13
Testing ............................................................................................................ 17
RESULTS & DISCUSSION ................................................................................................. 20
3.1
RESULTS.................................................................................................................. 20
3.2
RESEARCH FINDINGS .............................................................................................. 21
3.3
DISCUSSION............................................................................................................ 22
4
SUMMARY OF EACH STUDENT’S CONTRIBUTION.......................................................... 23
5
CONCLUSION .................................................................................................................. 24
6
REFERENCES ................................................................................................................... 25
7
APPENDICES ................................................................................................................... 26
iv
v
LIST OF TABLES
Table 2.1 TEST CASES ........................................................................................................ 19
LIST OF FIGURES
Figure 1.1 Brain Identify what is the shape ................... Ошибка! Закладка не определена.
Figure 1.2 Google Quick Draw ...................................... Ошибка! Закладка не определена.
Figure 1.3 Let'sdraw.it ................................................... Ошибка! Закладка не определена.
Figure 1.4 Designing Drawing Apps for Youngsters: Artistic and Technological Factors - UI
....................................................................................... Ошибка! Закладка не определена.
Figure 1.5 HOW TO DRAW APP UI............................ Ошибка! Закладка не определена.
Figure 2.1 System Overview Diagram ............................ Ошибка! Закладка не определена.
vi
LIST OF ABBREVIATIONS
Abbreviations
Description
LIST OF APPENDICES
vii
1
INTRODUCTION
Spice plants such as cinnamon, pepper, and cloves can be susceptible to various
diseases that can have a negative impact on their growth and yield. Understanding the
types of diseases that these plants can develop and how they affect them is crucial for
proper disease management. One of the common diseases that affect pepper plants is
Mosaic Virus, which is caused by a virus that can affect more than 150 types of plants
including fruits, vegetables, and flowers. This disease is mainly spread by insects,
specifically aphids and leafhoppers, and can cause yellowing, distortion, and mottling
of leaves, as well as stunted growth and reduced yields [1].
Another disease that can affect spice plants is bacterial canker, which is characterized
by a thick, liquid exudate that is primarily composed of bacteria. This disease can cause
cankers on the plant tissue, which can lead to the death of the affected plant. The
symptoms of plant disease, such as changes in color, shape, or function of the plant,
can help identify the type of disease affecting the plant [2]. Infectious plant diseases
can also be caused by fungi or viruses and can range in severity from mild leaf or fruit
damage to death. For example, cinnamon can be affected by powdery mildew, which
is a fungal disease that can cause a white, powdery growth on leaves, stems, and
flowers, and can eventually cause the plant to die. Meanwhile, pepper plants can be
affected by bacterial wilt, which is a bacterial disease that can cause wilting, yellowing,
and death of the plant. Cloves can be affected by a fungal disease called anthracnose,
which can cause leaf spots, stem cankers, and fruit rot [3]. Proper identification and
management of these diseases are crucial to ensure healthy plant growth and maximum
yield. This can involve measures such as regular scouting for signs and symptoms of
disease, maintaining proper sanitation practices, and implementing disease-resistant
varieties of plants.
Spices plants are an essential part of the culinary world, providing flavors and aromas
that can make or break a dish. However, they are not immune to diseases that can harm
their growth, yield, and quality. Identifying these diseases is crucial to managing them
effectively and preventing their spread. Farmers and gardeners can use various
methods to identify plant diseases, including books, the internet, and local garden
1
centers [4]. They can also take a small cutting of an infected plant or a photograph of
it to their local garden center or extension service for diagnosis. It is important to note
that accurate diagnosis requires careful observation of the plant's appearance,
including the leaves, stems, flowers, and fruit [5].
One of the most common diseases in spices plants, as well as in other plants, is the
mosaic virus [6]. It is a viral disease that affects more than 150 types of plants,
including fruits, vegetables, and flowers. The virus causes mottled patterns of light and
dark green on the leaves, which can distort the plant's growth and yield. Other diseases
that can affect spices plants include bacterial and fungal infections. Symptoms of
bacterial infections include wilting, yellowing, and brown spots on the leaves and
stems [5]. Fungal infections can cause various symptoms, including powdery mildew,
black spots, and rust-like growths on the leaves, stems, and flowers [6].
To identify diseases in spices plants, it is essential to look for signs and symptoms of
the pathogen involved. Signs of plant diseases are physical evidence of the pathogen,
such as fungal fruiting bodies, bacterial ooze, or viral particles [2]. Symptoms, on the
other hand, are the visible effects of the disease on the plant, including a detectable
change in color, shape, or function of the plant as it responds to the pathogen [5].
Identifying diseases in spices plants is crucial to managing them effectively and
preventing their spread. Farmers and gardeners can use various methods, such as
books, the internet, and local garden centers, to learn about plant diseases and how to
identify them. Accurate diagnosis requires careful observation of the plant's
appearance, including the leaves, stems, flowers, and fruit. Signs of plant diseases are
physical evidence of the pathogen, while symptoms are the visible effects of the
disease on the plant. By understanding these concepts and being observant, it is
possible to prevent and control diseases in spices plants.
Spices plants such as cinnamon, pepper, and cloves can be affected by various diseases
that can cause visible symptoms on their leaves, branches, trunk, or roots.
Understanding these visual symptoms is important for identifying and treating plant
diseases. Here are some ways to understand the visual symptoms of spice plant
diseases. To begin with, it is important to carefully observe the plant to identify any
2
physical evidence of the pathogen involved. Farmers and gardeners often use books
and the internet to identify plant diseases. They can also take a small cutting of an
infected plant (or a photograph of it) to a local garden center or extension service for
help with diagnosis [7].
One of the most common visual symptoms of plant diseases is the formation of galls,
which are growths or swellings found on leaves, stems, and/or roots. Galls can be
caused by various factors such as bacteria, fungi, or insects. Scale insects can infest a
wide variety of trees and shrubs, forming "bumps" on leaves and/or stems that can be
flicked off with a knife point. Leaves or other surfaces can become shiny and sticky
from the sugary excrement from the insects.
Another visual symptom of plant diseases is a change in color, shape, or function of
the plant as it responds to the pathogen. For example, a common viral disease that
affects pepper plants is mosaic virus, which causes a mottled pattern of light and dark
green on the leaves. This disease is mainly spread by insects, specifically aphids and
leafhoppers [9]. In contrast, bacterial canker is a disease that affects fruit trees such as
cherry, peach, and plum. The thick, liquid exudate is primarily composed of bacteria
and is a sign of the disease, although the canker itself is composed of plant tissue and
is a symptom [8].
Moreover, there are several types of visual symptoms that can indicate problems with
the vascular tissue of the plant, which is responsible for transporting water, nutrients,
and photosynthates throughout the plant. Wilting, yellowing, or browning of leaves
can be a sign of vascular wilt diseases caused by fungi or bacteria. Root rot is another
disease that affects the roots of plants, causing them to turn brown and mushy. These
diseases can also cause stunting of the plant, reduced vigor, and premature death [8].
The presence of fungal fruiting bodies on leaves, stems, or branches is a visual
symptom of fungal diseases such as powdery mildew or black spot. Powdery mildew
is a common fungal disease that affects many different plants, including cinnamon,
pepper, and cloves. It appears as a white or gray powder on the leaves and stems,
causing them to become distorted and wilted [9].
3
Technology has played an increasingly important role in the study and management of
plant diseases. From the detection and diagnosis of plant diseases to the development
of new management strategies, technology has greatly improved our understanding of
plant diseases and how to combat them. One way in which technology is involved in
plant disease is through the development of diagnostic tools [10]. Diagnostic
technologies such as DNA sequencing, polymerase chain reaction (PCR), and enzymelinked immunosorbent assay (ELISA) have allowed for more accurate and rapid
detection of plant pathogens. These tools can help identify the pathogen responsible
for a plant disease and allow for the early detection of new or emerging diseases.
Another way technology is involved in plant disease is using remote sensing and
imaging [11]. Remote sensing involves the use of drones, satellites, and other
technologies to collect data on plant health and disease. This data can be used to
identify areas of stress in crops and monitor the spread of disease over large areas.
Imaging technologies such as hyperspectral imaging can also be used to detect subtle
changes in plant physiology that may indicate the presence of disease.
Technology has also facilitated the development of new management strategies for
plant diseases [12]. For example, precision agriculture techniques such as variable rate
application of fertilizers and pesticides can reduce the risk of disease outbreaks by
providing targeted application of inputs. Biological control agents, such as beneficial
insects and microbes, can also be used to manage plant diseases. Furthermore, artificial
intelligence (AI) and machine learning algorithms can help predict disease outbreaks
and inform management decisions.
4
1.1
BACKGROUND & LITERATURE REVIEW
5
1.2
RESEARCH GAP
Features
Systems
Proposed
System



6

1.3
RESEARCH PROBLEM
7
1.4
RESEARCH OBJECTIVES
1.4.1

MAIN OBJECTIVE
The main objective of this mobile-based solution is to provide a tool for
identifying diseases of selected spice plants, including cinnamon, pepper, and
cloves. By inputting an image of the plant, leaf, root, branch, or trunk to the
application, the tool will identify the disease and show possible remedies to
control it.
1.4.2

SPECIFIC OBJECTIVES
To study and understand visual symptoms of leaves, plant, root, branches, or
trunk of spice plants:
This objective involves studying the visual symptoms of the selected spice plants,
which can include changes in color, shape, texture, or structure that indicate the
presence of a disease. By studying these visual symptoms, the researchers can develop
a comprehensive understanding of the diseases that commonly affect the selected spice
plants and identify the most effective methods for diagnosing and treating these
diseases.

To study and understand how to capture images on spice plants to train model:
This objective involves studying the different techniques and methods for capturing
clear and accurate images of the spice plants. High-quality images are essential for
training the model effectively, and researchers may need to use specialized equipment,
such as cameras with high resolution and specialized lenses or lighting, to capture these
images. The researchers may also need to develop specific protocols for capturing
images of different parts of the plant, such as leaves, branches, or roots.

To study and understand types of techniques which are related to image
processing, deep learning, and machine learning to train a model:
This objective involves studying various techniques related to image processing, deep
learning, and machine learning to train the model to accurately identify the disease.
Image processing techniques can be used to enhance the quality of the input images,
8
while deep learning and machine learning techniques can be used to analyze the
images and identify patterns or features that indicate the presence of a disease. The
researchers may need to experiment with different techniques and approaches to
determine the most effective methods for training the model.

To study and understand how to implement a mobile application to identify
diseases and show possible remedies to control the diseases:
This objective involves studying how to develop and implement a mobile
application that can identify the disease and provide possible remedies to control it.
The application may need to be user-friendly and accessible, with clear instructions
and intuitive interfaces. The researchers may also need to identify reliable sources of
information on possible remedies for the identified diseases and integrate this
information into the application. Additionally, the application may need to incorporate
feedback mechanisms to allow users to report their experiences and provide additional
information on the effectiveness of different remedies.
9
2
2.1
METHODOLOGY
System Architecture
Figure 2.1 System overview diagram
The main objective of the project is to provide farmers with a smart mobile solution
that can help them diagnose and manage the diseases of certain spice plants. This is
achieved by using the suggested method, which allows farmers to input a photograph
of the surface of the spice plant into the mobile application. The application then uses
image processing and deep learning techniques to identify the disease present in the
spice plant and recommend effective methods of disease control. The proposed system
is designed to minimize the severe impact of plant diseases by visualizing the spread
of illnesses according to their severity and notifying farmers whenever an infected tree
is discovered. This way, farmers can take prompt action to prevent the spread of the
disease and minimize the damage to their crops. Overall, the project aims to provide a
simple and user-friendly solution for farmers to identify and manage the diseases of
their spice plants, ultimately helping them to improve their crop yields and
profitability.
10
2.2
2.2.1
Development Process
Component
Figure 2.2 Component Diagram
This component of the proposed system is responsible for processing the captured image of
the spice plant and identifying any diseases or symptoms present in the leaves. It uses a
Convolutional Neural Network (CNN) to accurately identify the edges and shapes on the leaf
images. The CNN is trained to identify various disease patterns and classify them accordingly.
If the leaf turns brown, the CNN uses color segmentation to identify brown color patterns in
the disease, and extract features to detect the symptoms. This allows for more accurate
11
identification of the disease compared to other basic image processing and machine learning
techniques.
To further improve the image processing, the OpenCV library is used to denoise the images
before processing. OpenCV is a widely-used computer vision library that provides various
functions
for
image
processing,
including
fastNlMeansDenoising
and
fastNlMeansDenoisingColored, which remove noise from images and improve their clarity.
The proposed system specifically uses the MobileNetV2 architecture from scratch to solve
the problem of identifying diseases in spice plants. This architecture is optimized for mobile
devices and has been trained on large datasets, making it a suitable choice for the proposed
mobile application. Overall, this component plays a crucial role in accurately identifying
diseases in spice plants and providing effective solutions to farmers.
12
2.2.2
Tools and technologies
React Native
React Native is a cross-platform mobile application development framework that can
be used to develop the proposed system for identifying diseases and showing possible
remedies to control the diseases of selected spice plants. React Native allows
developers to build mobile applications that can run on both iOS and Android
platforms with a single codebase, which makes it a cost-effective solution for mobile
application development. To achieve the main objective, React Native can be used to
create a mobile application that allows users to input an image of the plant, leaf, root,
branch, or trunk of selected spice plants, such as cinnamon, pepper, and cloves, to
identify the disease. The application can be designed to process the input image using
image processing, deep learning, and machine learning techniques to accurately
identify the disease and suggest possible remedies for disease control.
React Native can also be used to implement specific objectives, such as studying and
understanding the visual symptoms of leaves, plant, root, branches, or trunk of spice
plants, and how to capture images on spice plants to train the model. The application
can provide features that allow users to learn about the visual symptoms of diseases in
selected spice plants and how to capture clear and accurate images of the plants for
training the model. Additionally, React Native can be used to implement the study and
understanding of types of techniques related to image processing, deep learning, and
machine learning to train a model. The mobile application can use these techniques to
train the model to identify diseases accurately, and the application can be designed to
update the model regularly with new data to improve its accuracy.
13
Image Processing and Convolutional Neural Networks (CNN) play a crucial role in
the identification and management of diseases in selected spice plants like cinnamon,
pepper, and cloves. The process involves capturing an image of the plant or its parts
(leaf, root, branch, or trunk) and uploading it to the mobile application for analysis.
The image is then processed using Image Processing techniques, which are used to
extract relevant features from the image that can help identify the disease.
CNN is a deep learning technique that is commonly used in image analysis
applications. It is used to train the model to identify patterns in the images that
correspond to specific diseases. CNN is an accurate method for detecting edges,
shapes, and patterns on leaf images. Once the model has been trained, it can be used
to identify diseases in new images with a high degree of accuracy. The mobile
application will use the trained model to identify the disease based on the features
extracted from the image. The identified disease will then be matched with a database
of possible remedies to suggest the most effective treatment.
Overall, Image Processing and CNN are essential components of the mobile
application for identifying and managing diseases in selected spice plants. They allow
for quick and accurate identification of diseases, which can help prevent the spread of
the disease and minimize the impact on crop yields.
14
Image Processing – CNN
Image Processing and Convolutional Neural Networks (CNN) play a crucial role in
the identification and management of diseases in selected spice plants like cinnamon,
pepper, and cloves. The process involves capturing an image of the plant or its parts
(leaf, root, branch, or trunk) and uploading it to the mobile application for analysis.
The image is then processed using Image Processing techniques, which are used to
extract relevant features from the image that can help identify the disease.
CNN is a deep learning technique that is commonly used in image analysis
applications. It is used to train the model to identify patterns in the images that
correspond to specific diseases. CNN is an accurate method for detecting edges,
shapes, and patterns on leaf images. Once the model has been trained, it can be used
to identify diseases in new images with a high degree of accuracy. The mobile
application will use the trained model to identify the disease based on the features
extracted from the image. The identified disease will then be matched with a database
of possible remedies to suggest the most effective treatment.
Overall, Image Processing and CNN are essential components of the mobile
application for identifying and managing diseases in selected spice plants. They allow
for quick and accurate identification of diseases, which can help prevent the spread of
the disease and minimize the impact on crop yields.
15
2.3
COMMERCIALIZATION ASPECTS OF THE PRODUCT
Market Potential: The market potential for a disease identification application for spice
plants is significant, as farmers and growers are always looking for new and innovative
ways to manage and prevent diseases. This application could provide a valuable tool
for growers to quickly identify and treat diseases, potentially leading to increased crop
yields and profits.
Licensing and Distribution: The application could be licensed and distributed to
agricultural companies, nurseries, and farming organizations. These companies could
then distribute the application to their clients or members, providing a value-added
service and potentially increasing their revenue.
Partnership Opportunities: The development of this application could lead to
partnership opportunities with companies that specialize in agriculture, such as
fertilizer or pesticide companies. These companies could use the application to identify
specific diseases and provide targeted solutions to their customers.
Subscription Model: The application could be offered as a subscription-based service,
where growers pay a monthly or yearly fee to access the disease identification and
treatment information. This model could provide a recurring revenue stream and
potentially increase customer loyalty.
Expansion into Other Crops: Once the application is developed and tested for spice
plants, it could potentially be expanded to include other crops, such as fruits and
vegetables. This would broaden the market potential and potentially increase the
application's revenue.
16
2.4
2.4.1
Test
case
ID
T_1
TESTING & IMPLEMENTATION
Testing
Test case Test input Values
Name
Test
Procedure
T_2
17
Expected
Output
Actual
Result
Test
Resul
t
T_3
T_4
18
T_5
T_6
Table 2.1 TEST CASES
19
3
3.1
RESULTS & DISCUSSION
RESULTS
20
3.2
RESEARCH FINDINGS
21
3.3
DISCUSSION
22
4
SUMMARY OF EACH STUDENT’S CONTRIBUTION
Member
Components
Tasks
23
5
CONCLUSION
24
6
REFERENCES
25
7
APPENDICES
26