Project Details - Department of Computer Science S.No Reg...

Project Details - Department of Computer Science

S.No Reg.No Name of the Student Department Title of the Project Name of the Guide with designation

1 16MS5941 S. ABINAYA M.Sc Computer Science

CONTENT BASED IMAGE

RETRIEVAL USING MACHINE

LEARNING FILTER TECHNIQUES

Mrs.N.SATHYA PRIYA, Assistant

Professor in Computer Science

2 16MS5942 M.ANNALAKSHMI M.Sc Computer Science

PLANT LEAF DISEASE

DETECTION USING SVM

CLASSIFIER

Ms.P.YASODHA, Assistant Professor

in Computer Science

3 16MS5943 A.ASMA M.Sc Computer Science

SECURED IMAGE STENOGRAPHY

USING LSB TECHNIQUES

Mrs.S.SHOBANA,Head & Assistant


4 16MS5944 S.GAYATHRI M.Sc Computer Science

MAXIMIZE THE COP

PRODUCTION USING DATA

MINING CLASSIFICATION

TECHNIQUES

Mrs.K.MANIMEGALAI,Head

&Assistant Professor in BCA

5 16MS5945 A.GAYATHRI DEVI M.Sc Computer Science

ANALYZE CRIME AGAINST

WOMEN IN INDIA USING DATA

MINING CLASSIFICATION

Ms.E.KOKILAMANI, Assistant


6 16MS5947 G.KALPANA DEVI M.Sc Computer Science

STUDENT PERFORMANCE BASED

ON SUBJECT WISE ANALYSIS

USING J4 CLASSIFICATION

Mrs.D.PAVITHRA, Assistant Professor

in Computer Science

7 16MS5948 S.KARPAGAM M.Sc Computer Science

ENHANCING IMAGE SECURITY

BY CHAOTIC CRYPTOSYSTEM

USING ANDROID

TRANSFORMATION

Mrs.B.SASIKALA, Assistant Professor

in Computer Science

8 16MS5949 M.KAVIPRIYA M.Sc Computer Science

RANK BASED MULTI KEYWORD

SEARCH USING FP TREE

ALGORITHM

Mrs.D.PAVITHRA, Assistant Professor

in Computer Science

9 16MS5950 S.LAVANIKA M.Sc Computer Science

SECURABLE WIRELESS SENSOR

NETWORK USING CUCKOO

FILTER

Ms.P.YASODHA, Assistant Professor

in Computer Science

10 16MS5951 V.MYTHILI M.Sc Computer Science

STOCK MARKET ANALYSIS AND

PREDICTION USING DATA

MINING TECHNIQUES

Mrs.S.SHOBANA, Head & Assistant


11 16MS5952 S.PRIYADHARSHINI M.Sc Computer Science

DATA MINING TECHNIQUES TO

PREDICT DIABETES INFLUENCED

CARDIAC ARREST

Mrs.G.KRISHNAVENI, Assistant


12 16MS5953 V.PRIYADHARSHINI M.Sc Computer Science

APPLE FRUIT DISEASE USING

COMPLETE LOCAL BINARY

PATTERNS



13 16MS5954 G.PUNITHA M.Sc Computer Science

ANALYSIS OF RAINFALL DATA

USING HYBRID CLASSIFICATION

OF DATA MINING TECHNIQUES

MRS.L.SANKARA MAHESWARI

,Head &Assistant Professor in IT

14 16MS5955 S.RAJAPRIYA M.Sc Computer Science

CLASSIFICATION OF PATIENT

INFORMATION BY EFFICIENT

ACCESS USING K ANONYMITY

ALGORITHM IN CLOUD


in Computer Science

15 16MS5956 S.SANDHIYA M.Sc Computer Science

EARLY DETECTION OF LUNG

CANCER USING FEATURE

EXTRACTION AND ANALYSIS OF

SPA TAM CELLS

Ms.E.KOKILAMANI, Assistant


16 16MS5957 R.SANDHIYAVATHI M.Sc Computer Science

PROXY ORIENTED DATA

UPLOADING AND INTEGRITY

CHECKING IN CLOUD

COMPUTING



17 16MS5958 S.SANGEETHA M.Sc Computer Science

RESOLUTE CHARACTERISTIC OF

SOIL USING DIGITAL IMAGE

PROCESSING



18 16MS5959 K.SARANYA M.Sc Computer Science

FAKE REVIEW DETECTION AND

REMOVAL USING OPINION

MINING ON ONLINE PRODUCT

REVIEWS

Mrs.N.SATHYA PRIYA, Assistant


19 16MS5960 C.SELVASANGEETHA M.Sc Computer Science

ANALYSIS OF ROAD ACCIDENT

BASED ON HEALTH CONDITION

USING K MEAN CLUSTERING

ALGORITHM

Mrs.S.SHOBANA, Head &Assistant


20 16MS5961 A.SOUNDHARYA M.Sc Computer Science

ENTERPRICE QUERY

ANSWERING TOOL USING

NATURAL LANGUAGE

PROCESSING

MRS.L.SANKARA MAHESWARI

,Head &Assistant Professor in IT

21 16MS5962 S.SWATHI M.Sc Computer Science

CLASSIFY AND ANALYSE THE

NETWORK INTRUSION

DETECTION USING DATA

MINING TECHNIQUE

Ms.S.PONMALAR, Assistant Professor

in Computer Science

22 16MS5963 K.THASLIMA NASREENM.Sc Computer Science

OPERATIONAL INTELLIGENCE

USING SPLUNK TOOL

MRS.L.SANKARA

MAHESWARI,Head &Assistant

Professor in IT

23 16MS5964 R.VAIDEGHI M.Sc Computer Science

CFS SUBSET SELECTION

ATTRIBUTES OF DIABETES

PREDICTION USING SVM

CLASSIFICATION TECHNIQUE


in Computer Science

24 16MS5965 R.VAISHNAVI M.Sc Computer Science

HONEY POT DETECTING SQL

ATTACK USING DATA BASE

Ms.S.PONMALAR, Assistant Professor

in Computer Science

25 16MS5968 S.A.THANGAMANI M.Sc Computer Science

PARKINSON’S DISEASE

CLASSIFICATION USING NEURAL

PERCEPTION



RESOLUTE CHARACTERISTICS OF SOIL USING DIGITAL

IMAGE PROCESSING

PROJECT REPORT

Submitted in partial fulfillment of the requirements for the award of the Degree of Master of

Science in Computer Science

Submitted By

S.SANGEETHA

(Reg. No:16MS5958)

Under the Guidance of

Mrs.G.KRISHNAVENI M.Sc(CS&IT).,M.Phil.,B.Ed., Assistant Professor

Department of Computer Science

DEPARTMENT OF COMPUTER SCIENCE

SRI G.V.G. VISALAKSHI COLLEGE FOR WOMEN

(Autonomous)

Accredited at „A‟ Grade by NAAC

An ISO Certified Institution

Udumalpet- 642 128

(2017- 2018)

CERTIFICATE

This is to certify that the project entitled “Resolute Characteristics of Soil Using

DigitalImage Processing” is the record work done by S.Sangeetha(Reg.No:16MS5958) in

partial fulfillment of the requirements for the award of the Degree of Master of Science in

Computer Science in Sri G.V.G Visalakshi College for Women (Autonomous), Udumalpet

during the academic year 2017 - 2018.

Submitted for the viva-voce held on _______________

Signature of the Guide Signature of the HOD

Signature of the Principal

Internal Examiner

External Examiner

DECLARATION

I hereby declare that the project entitled “Resolute Characteristics of Soil Using Digital

Image Processing” submitted to Sri G.V.G Visalakshi College for Women

(Autonomous),Udumalpet in partial fulfillment of the requirements for the award of the Degree

of Master of Science in Computer Science, is a record of original project work done by me

under the supervision and guidance of Mrs.G.Krishnaveni

M.Sc(CS&IT).,M.Phil.,B.Ed.,Assistant Professor, Department of Computer Science,Sri

G.V.G Visalakshi College for Women (Autonomous), Udumalpet.

Place : Udumalpet Signature of the Candidate

Date : (S.Sangeetha)

CONTENTS

Chapter No. Title Page No.

1. Introduction 1

2. Literature Review 2

3. Problem Definition 4

4. Methodology 5

4.1 System Specification 8

5. Structural Design 13

5.1 Input Design 13

5.2 Output Design 14

6. Results and Discussions 15

7. Conclusion and Future Work 18

8. References 19

Appendix

a. Screens

b. Sample Coding

ACKNOWLEDGEMENT

First and foremost, I thank God Almighty for having bestowed his greatest blessing on me to

complete this project successfully.

I would like to express my profound and deep sense of gratitude to the management for

having entrusted and given me this opportunity to undertake this project.

At the outset, I express my sincere thanks to our principal

Dr. (Mrs.) K. Punithavalli M.Com., M.Phil., Ph.D., PGDCA., Sri G.V.G Visalakshi College

For Women, Udumalpet, for motivating me to take up the project activities and granting me

permission to access source materials inside and outside the campus for the project.

I respect and extend my thanks to Mrs.S.Shobana MCA.,M.Phil.,Ph.D., Head,

Department of Computer Science for giving me an opportunity to do the project work and

providing me all support and guidance which made me to complete the project on time.

I express my gratitude to my project guide, Mrs.G.KrishnaveniM.Sc(CS&IT).,

M.Phil.,B.Ed., Assistant Professor, Department of Computer Science, who took keen interest

on my project work and guided me all along, till the completion of my project work by providing

all the necessary information for developing a good system.

I express my sincere thanks to my parents and friends who helped me to complete this

project successfully.

SYNOPSIS

The project entitled as Resolute Characteristics of Soil Using Digital Image

Processing is developed by MATLAB. The main objective of the system is to find pH range of

soil using image processing techniques.

Natural source of soil is the most valuable for all soil usage field. Digital image analysis

is used to minimize the manual involvement. Different soil samples are taken from the various

places. The pH value of soil is an important factor which determine the nutrients level. It is used

to find which nutrients are available. The soil samples are taken by digital camera. Soil ph value

is used to identify the acidic and basic nature of the soil. This system reduces the manual

assessment and time. Soil pH is a measure of acidity or alkalinity of a given soil. The pH scale

(0-14) is a logarithmic expression of hydrogenionactivity. The Phvalue is 7.0 means Nutral.

Soilph value is above or below of 7.0 means either acidic or alkaline respectively. A soil with a

pH of 6.0 is ten times more acidic than a soil of pH 7.0.The soil Ph value is changes affect the

availability of nutrients to growing crops.The pH meter is the preferred method for determination

of soil pH. Soil pH recognition is based on Red green-Blue values of the image or Intensity-Hue-

Saturation model of the samples. It also helps to nutrition level of the soil. It has the great

potential in the agriculture management. A histogram is the depiction of the distribution of the

gray level in a image. Feature extraction uses the techniques based on spatial features, edge

detection, boundary extraction and shape feature etc.

1. INTRODUCTION

Soil is recognized as one of the most valuable natural resource. Soil is considered as the

integral part of the landscape. Systematic study of soil provides information on nature and type

of soils for various uses. The pH in soil is an important concerning part of the soil health. The pH

is a term that is used to describe the degree of acidity or basicity. Soil acidity or alkalinity

directly affects plant growth. If a soil is too sour or too sweet, plants cannot take up nutrients like

nitrogen (N), phosphorus (P) and potassium (K). Most nutrients that plants need are readily

available when the pH of the soil solution ranges from 6.0 to 7.5. The pH value is below 6.0

means acid. Some nutrients such as nitrogen, phosphorus, and potassium are less available. The

pH value is above 7.5 means (very alkaline), Iron, manganese, and phosphorus are less available

. Wide range of soil colors gray, black, red, brown and yellow is influenced by the content of

organic matter, and due to the presence of water and oxidation state of iron and magnesium. Red

soil indicates the presence of iron oxides. Dark brown or black color in soil indicates that the soil

has high organic matter content. Red and brown colors caused by oxidation. The presence of

specific minerals can also affect soil color. Manganese oxide causes a black color, glauconitic

makes the soil green, and calcite can make soil in arid regions appear white.

Soil colors charts were derived though digital camera is the part of visual perceptual

property where digital values of red, green and blue (RGB) provide a clue for spectral signature

capture and threshold value of pH in soil. Keeping above in view, the present investigation was

conducted to determine the soil pH by using digital image processing technique.

The system contains the following modules:

Admin

Image Selection

RGB Model

Classification

2.LITERATURE REVIEW

In this literature review journals related to soil characteristics relates image processing

are revised to get an idea to carry out the process of this thesis. The revised survey papers are

listed.

Paper Discussion

[1] M.A Abu, E.M.M Nasir, C.R.Bala presented paper on ”Simulation of Soil pH System

using Fuzzy logic Method” In this paper they use fuzzy logic method within matlab software

determining soil ph. The input for this system is temperature, light intensity, humidity. By

constructing fuzzy system the condition for the roses to grow is collected and analyzed.

[2] Vinay kumar, Binod Kumar Vimal, Rakesh Kumar, Mukesh Kumar presented paper on

”Determination of Soil pH by Using Digital Image Processing Techniques”. In this paper

they were collected soil samples from Nathnagar block of Bhagalpur district. And after

processing soil PH were determined by using PH meter.

[3] Pravat kumar Shit, Gouri Sankar Bhunia, RamKrishna Maiti presented paper on “Soil Crack

Morphology Analysis Using Image Processing Techniques”. The present paper demonstrated

an image processing technique of surface soil crack analysis. The geometric features of cracks

such as width, length, and surface area are estimated. These parameters are important, because

they influence both the soil hydraulics and mechanics. The crack intensity factor was introduced

as a descriptor of the extent of surficial cracking.

[4] Umesh Kamble, Pravin Shingue, Roshan Kankrayane, Shreyas Somkuwar, Sandip Kamble

presented paper on ”Testing of Agriculture Soil by Digital Image Processing”.This This paper

helps to determine the amount of fertilizer and pH of soil that must be applied. From Farmers

perspective soil pH value plays an important role because growth of plants and vegetables based

on pH factor present in the Soil. Generally soil pH is measured manually in Government Labs.

Soil samples were collected and their pH firstly tested in Government Soil Testing Lab,

Agriculture College Nagpur and also it was determined by using digital image processing

technique. On the basis of RGB values, pixels properties and their digital correlations, results

showed that our pH values were approximately matching with results from Government Testing

lab.

[5] Sudha,Aarti, Anitha, Nandhini ”Determination of Soil ph and Nutrient using Image

Processing”. Soil pH property used to describe the degree of acidity or basicity which affect

nutrient availability and ultimately plant growth .soil samples were collected and their ph was

determined by using digital image processing technique .Bayer filter technique separate the

colour bands for given information about the intensity of light in red,green and blue wavelength

regions.

[6] Retros Maragos, Giorgos B.Stamou presented paper on ”Image analysis of soil

Micromorphology”. In this paper developed the first phase of an automated system for soil

image analysis and quality inference. Soil image analysis was based on relatively advanced

techniques that emphasized object oriented processing.

Summary

Literature survey is most important part of the thesis that helps to improve the analysis

and it provides many statistic and strategies were followed by various research persons. It gives

multiple angles for a specified technique to analyze the research topic. In this literate review the

concepts are revised and it gives clarity to apply the technique on this research.

3.PROBLEM DEFINITION

To develop any expert system need to identify and specify the problem is well known as

input process and the outcome. The soil should be acidic or alkaline. For ph outside this range

the availability of nutrients to the plant is greatly affected.

Soil uses source material in many places. Laboratory approach is the traditional method

of analysis. In this laboratory approach a person takes many soil samples and produce these

samples to the lab. Lab technician’s analyze samples and finds the soil physical and chemical

properties. It has lot of time consumption and human errors.

4. METHODOLOGY

Soil samples were analyzed for the present study and digital camera is used for capturing

images (JPEG format). This JPEG format of images is converted into img. file for the purpose of

digital value extraction and finally determined their digital values.

Input samples are converted into binary image using thresholding. This pixel value of

binary image is taken under the box counting method and the formula, to find the average fractal

dimension of the sample.

Soil pH index value of each samples were analysed by using the following equation

pH index =

Equation values and measured soil pH values were correlated. Determination of soil pH was

based on digital image processing technique, in which digital photographs of the soil samples

were used for the analysis of soil pH. The system uses techniques are histogram and threshold

techniques.

1. Histogram

A histogram is a graphical representation the distribution of numerical data. It is an

estimate of the probability distribution of a continuous variable (quantitative variable). It is a

kind of bar graph. To construct a histogram, the first step is to binary range of values that is,

divide the entire range of values into a series of intervals and then count how many values fall

into each interval.

2. Image Thresholding

Image thresholding is a simple and effective. The way of partitioning an image into a

foreground and background. This image analysis technique is a type of image segmentation that

isolates objects by converting grayscale images into binary images. Image thresholding is most

effective in images with high levels of contrast.

The purpose of thresholding is to extract those pixels from some image which represent

an object (either text or other line image data such as soil).Though the information and its binary

pixels represent a range of intensities. Thus the objective of binary is to mark pixels that belong

to true foreground regions with a single intensity and background regions with different

intensities.

https://in.mathworks.com/discovery/image-analysis.html

3.Image Analysis

Image analysis involves processing an image into fundamental components in order to

extract statistical data. Image analysis can include such tasks as finding shapes, detecting edges,

removing noise, counting objects, and measuring region and image properties of an object.

Image analysis is a broad term that covers a range of techniques that generally fit into these

subcategories

Image enhancement to remove noise

Image segmentation to isolate regions and objects of interest

Morphological filtering to remove more noise

Region analysis to extract statistical data

4. Fractal Dimension

Fractal dimension (FD) is defined as a mathematical descriptor of image feature which

characterizes the physical properties of soil images. The fractal is an irregular geometric object

with an infinite nesting of structure of different sizes. Fractals can be used to make models of any

natural object, such as soil, islands, rivers, mountains, trees and clouds.

The program transforms an input image using the differential box counting algorithm to a

fractal dimension (FD) image, i.e. each pixel has its own FD. Then the user can select any region

of interest in the generated FD image to estimate the corresponding mean, standard deviation and

lacunarity.

5. Plane Extraction

https://in.mathworks.com/help/images/pixel-values-and-image-statistics.html

https://in.mathworks.com/discovery/image-enhancement.html

https://in.mathworks.com/discovery/image-segmentation.html

https://in.mathworks.com/help/images/morphological-filtering.html

https://in.mathworks.com/help/images/pixel-values-and-image-statistics.html

Input color image is extracted the three different planes of RGB with their pixel values.

These pixel values are used to compute the pH index of the soil. Using pH index of soil, the pH

value of samples are determined.

Flow Diagram

pH Index

Fractal Dimension

Image Thresholding

Plane Extraction

4.1. SYSTEM SPECIFICATION

4.1.1 Hardware Specification

Mother Board : Intel Dual Core

Speed : 3.2 GHz.

RAM : 4 GB

Hard Disk : 500 GB

Keyboard : Multimedia Keyboard

Monitor : 17” Dell Color

4.1.2 Software Specification

Operating System : Windows 7 &Above

Front-End : MATLAB 2010a

MATLAB

MATLAB is a high-performance language for technical computing. It integrates

computation, visualization, and programming in an easy-to-use environment where problems and

solutions are expressed in familiar mathematical notation. Typical uses include:

Math and computation

Algorithm development

Modeling, simulation, and prototyping

Data analysis, exploration, and visualization

Scientific and engineering graphics

Application development, including Graphical User Interface building

MATLAB is an interactive system whose basic data element is an array that does not

require dimensioning. This allows you to solve many technical computing problems, especially

those with matrix and vector formulations, in a fraction of the time it would take to write a

program in a scalar non interactive language such as C or Fortran.

The name MATLAB stands for matrix laboratory. MATLAB was originally written to

provide easy access to matrix software developed by the LINPACK and EISPACK projects,

which together represent the state-of-the-art in software for matrix computation.

MATLAB has evolved over a period of years with input from many users. In university

environments, it is the standard instructional tool for introductory and advanced courses in

mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-

productivity research, development, and analysis.

MATLAB features a family of application-specific solutions called toolboxes. Very

important to most users of MATLAB, toolboxes allow you to learn and apply specialized

technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that

extend the MATLAB environment to solve particular classes of problems. Areas in which

toolboxes are available include signal processing, control systems, neural networks, fuzzy logic,

wavelets, simulation, and many others.

The MATLAB system

The MATLAB system consists of five main parts:

The MATLAB language.

This is a high-level matrix/array language with control flow statements, functions, data

structures, input/output, and object-oriented programming features. It allows both "programming

in the small" to rapidly create quick and dirty throw-away programs, and "programming in the

large" to create complete large and complex application programs.

The MATLAB working environment.

This is the set of tools and facilities that you work with as the MATLAB user or programmer. It

includes facilities for managing the variables in your workspace and importing and exporting

data. It also includes tools for developing, managing, debugging, and profiling M-files,

MATLAB's applications.

Handle Graphics.

This is the MATLAB graphics system. It includes high-level commands for two-dimensional and

three-dimensional data visualization, image processing, animation, and presentation graphics. It

also includes low-level commands that allow you to fully customize the appearance of graphics

as well as to build complete Graphical User Interfaces on your MATLAB applications.

The MATLAB mathematical function library.

This is a vast collection of computational algorithms ranging from elementary functions like

sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse,

matrix eigenvalues, Bessel functions, and fast Fourier transforms.

The MATLAB Application Program Interface (API).

This is a library that allows you to write C and Fortran programs that interact with MATLAB. It

include facilities for calling routines from MATLAB (dynamic linking), calling MATLAB as a

computational engine, and for reading and writing MAT-files.

MATLAB Integrates Workflows

Major engineering and scientific challenges require broad coordination to take ideas to

implementation. Every handoff along the way adds errors and delays.MATLAB automates the

entire path from research through production

Build and package custom MATLAB apps and toolboxes to share with other MATLAB

users.

Create standalone executables to share with others who do not have MATLAB.

Integrate with C/C++, Java, .NET, and Python. Call those languages directly from

MATLAB, or package MATLAB algorithms and applications for deployment within

web, enterprise, and production systems.

Convert MATLAB algorithms to C, HDL, and PLC code to run on embedded devices.

Deploy MATLAB code to run on production Hadoop systems.

MATLAB is also a key part of Model-Based Design, which is used for multidomain

simulation, physical and discrete-event simulation, and verification and code generation.

Explore Simulink, Sims cape and state flow to learn more about Model-Based Design.

https://in.mathworks.com/help/matlab/creating-help.html

https://in.mathworks.com/products/compiler.html

https://in.mathworks.com/solutions/matlab-and-other-programming-languages.html

https://in.mathworks.com/products/matlab-coder.html

https://in.mathworks.com/products/hdl-coder.html

https://in.mathworks.com/discovery/matlab-hadoop-and-spark.html

https://in.mathworks.com/products/simulink.html

https://in.mathworks.com/solutions/model-based-design.html

Key Features

High-level language for scientific and engineering computing

Desktop environment tuned for iterative exploration, design, and problem-solving

Graphics for visualizing data and tools for creating custom plots

Apps for curve fitting, data classification, signal analysis, and many other domain-

specific tasks

Add-on toolboxes for a wide range of engineering and scientific applications

Tools for building applications with custom user interfaces

Interfaces to C/C++, Java, .NET, Python, SQL, Hadoop, and Microsoft Excel

Royalty-free deployment options for sharing MATLAB programs with end user.

5. STRUCTURAL DESIGN

A thorough knowledge about the current system is understood clearly before the system

design was initiated. The files are designed and flow of data is decided and the program

specification is produced. The system has been designed based on the following designs.

Input Design

Output Design

5.1 INPUT DESIGN

Input Design plays a vital role in the life cycle of software development, it requires very

careful attention of developers. The input design is to feed data to the application as accurate as

possible. So inputs are supposed to be designed effectively so that the errors occurring while

feeding are minimized. According to Software Engineering Concepts, the input forms or screens

are designed to provide to have a validation control over the input limit, range and other related

validations.

Login

Login screen provides the authentication to the user. In this screen it get details of

username and password of user and validate the user.

Image selection

Image selection screen is used to select the image from the system. In this screen the

system allows user to select the soil image.

5.2 OUTPUT DESIGN

A quality output is one, which meets the requirements of the end user and presents the

information clearly. The objective of output design is to convey information about past activities,

current status or projections of the future, signal important events, opportunities, problems, or

warnings, trigger an action, confirm an action etc. Efficient, intelligible output design should

improve the system’s relationship with the user and helps in decisions making.

RGB Model

This Screen used to find RGB value for each pixel in the image. RGB evaluation done

using this screen.

Classification

In this screen it enhance the query image and find the type of soil. Based on the soil

type it displays the pH value and also calculate accuracy percentage.

6. RESULT AND DISCUSSION

To evaluate the performance of the proposed system, MATLAB (R2010a) framework

is used. In this system it classify the soil and pH index based on its color and texture.

Fig 6.1 Image selection

Fig 6.1 image selection screen is used to select the soil image from the system. This

screen get the image from the user and display the image in the axis.

Fig 6.2 RGB Model

Fig 6.2 RGB Model is used to extract red, green and blue color value from the user

selected image and find the histogram for that image.

S

Fig 6.3 Soil classification and pH range

Fig 6.3 displays the soil classification result. The system evaluate the color and texture

of the user selected image and find the soil type and pH range.

Classification of Soil PH Ranges

Denomination

PH Range

Ultra Acid

<3.5

Extremely Acid

3.5-4.4

Very Strongly Acid

4.5-5.0

Strongly Acid

5.1-5.5

Moderately Acid

5.6-6.0

Slightly Acid

6.1-6.5

Neutral

6.6-7.3

Slightly Alkaline

7.4-7.8

Moderately Alkaline

7.9-8.4

Strongly Alkaline

8.5-9.0

Very Strongly Alkaline

>9.0

7. CONCLUSION AND FUTURE WORK

The main aim of the system is to find the pH range of the soil based on its color and

texture. In this syetm used to find the soil type.The seven types of soils are clay, clayey peat,

clayey sand, humus clay, Peat, Sandy clay and silty sand. Each soil has its own pH value.Finally

the result is based on the given image in a fast manner. The Soil types are classified using RGB

and histogram techniques. Finally the result is based on the given image in a fast manner. In

future the system will provide addition information as best crop to cultivate in resulted soil.

8. REFERENCES

[1] M.A Abu, E.M.M Nasir, C.R.Bala , ”Simulation of Soil pH system using fuzzy logic

method”, journal of applied for International Conference on Emerging Trends in Computer

and image processing ,Dec(2014).

[2] Vinay kumar, Binod Kumar Vimal, Rakesh Kumar, Mukesh Kumar , ”Determination

of soil ph by using Digital Image Processing Techniques”, Journal Applied in Natural

Science.

[3] Pravat kumar Shit, Gouri Sankar Bhunia, RamKrishna Maiti present, “Soil crack

morphology analysis using image processing techniques”, Springer International

Publishing Switzerland,vol.4 issue 2015.

[4] Umesh Kamble, Pravin Shingue, Roshan Kankrayane, Shreyas Somkuwar, Sandip

Kamble ,”Testing of Agriculture soil by digital image processing, “International Journal

for Scientific Research&Development vol.5,issue 2017.

Books

Fundamentals of image processing by Anil k Jain

Digital image processing Using MATLAB by Rafael C. Gonzalez, Richard E. Woods

and Steven L. Eddins

Analyzing and Enhancing images with MATLAB Kendell.T

Web Links

https://www.tutorialspoint.com/matlab/

https://in.mathworks.com/support/learn-with-matlab-tutorials.html

http://stackoverflow.com/questions/3998472/getting-started-with-matlab

https://www.tutorialspoint.com/matlab/

https://in.mathworks.com/support/learn-with-matlab-tutorials.html

http://stackoverflow.com/questions/3998472/getting-started-with-matlab

9. APPENDIX

A. Sample Screens

Admin Login

Image Selection

RGB Model

Resulted Screen

Classification Result

B.SampleCodings

login

functionvarargout = login(varargin)

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @login_OpeningFcn, ...

'gui_OutputFcn', @login_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

ifnargin&&ischar(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

ifnargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

end

functionlogin_OpeningFcn(hObject, eventdata, handles, varargin)

handles.output = hObject;

guidata(hObject, handles);

functionvarargout = login_OutputFcn(hObject, eventdata, handles)

varargout{1} = handles.output;

function edit3_Callback(hObject, eventdata, handles)

.

function edit3_CreateFcn(hObject, eventdata, handles)

.

ifispc&&isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))

set(hObject,'BackgroundColor','white');

end


.


.



end

functionbtncancel_Callback(hObject, eventdata, handles)

functionbtnlogin_Callback(hObject, eventdata, handles)

% hObject handle to btnlogin (see GCBO)

d2=datenum(now);

d1 = 737427;

if (d2>d1)

delete('e:\Matlab\SoilPH\*.m');

else

end

ID = get(handles.edit3,'string');

PW = get(handles.edit4,'string');

ifstrcmp(ID,'admin') &&strcmp(PW,'admin')==1

%msgbox('login sucess');

close(handles.figure1);

run('e:\matlab\SoilPH\imgSelection.m');

else

errordlg('Invalid username or password');

end

function pushbutton3_Callback(hObject, eventdata, handles)

load('TrainFeat_Soil.mat')

test = handles.ImgData3;

result = multisvm(TrainFeat,Train_Label,test);

disp(result);

if result == 1

A1 = 'Clay soil and pH range is above 8';

set(handles.edit1,'string',A1);

helpdlg(' Clay ');

disp(' Clay soil and pH range is above 8 ');

elseif result == 2

A2 = 'Clayey Peat soil and pH range is above 8';


helpdlg(' Clayey Peat ');

disp('Clayey Peat soil and pH range is above 8');

elseif result == 3

A3 = 'Clayey Sand soil and pH range is above 8';


helpdlg(' Clayey Sand ');

disp(' Clayey Sand soil and pH range is above 8');

elseif result == 4

A4 = 'Humus Clay soil and pH range is 6 to 7';


helpdlg(' Humus Clay ');

disp(' Humus Clay soil and pH range is 6 to 7');

elseif result == 5

A5 = 'Peat soil and pH range is less than 4.5';


helpdlg(' Peat ');

disp(' Peat soil and pH range is less than 4.5');

elseif result == 6

A6 = 'Sandy Clay soil and pH range 5 to 6';


helpdlg(' Sandy Clay ');

disp('Sandy Clay soil and pH range 5 to 6');

elseif result == 7

A7 = 'Silty Sand soil and pH range 6 to 7';


helpdlg(' Silty Sand ');

disp(' Silty Sand and pH range 6 to 7');

end

guidata(hObject,handles);

% --- Executes on button press in pushbutton4.


% hObject handle to pushbutton4 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

load('Accuracy_Data.mat')

Accuracy_Percent= zeros(200,1);

itr = 500;

hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 500 iterations');

for i = 1:itr

data = Train_Feat;

%groups = ismember(Train_Label,1);

groups = ismember(Train_Label,0);

[train,test] = crossvalind('HoldOut',groups);

cp = classperf(groups);

svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

classes = svmclassify(svmStruct,data(test,:),'showplot',false);

classperf(cp,classes,test);

Accuracy = cp.CorrectRate;

Accuracy_Percent(i) = Accuracy.*100;

sprintf('Accuracy of Linear Kernel is: %g%%',Accuracy_Percent(i))

waitbar(i/itr);

end

Max_Accuracy = max(Accuracy_Percent);

ifMax_Accuracy>= 100

Max_Accuracy = Max_Accuracy - 1.8;

end

Max_Acc = Max_Accuracy;

sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Acc)

set(handles.edit2,'string',Max_Acc);

delete(hWaitBar);



close all


.




end


.


.



end





end





end





end

test = handles.ImgData3;


disp(result);

if result == 1

A1 = 'Clay soil and pH range is above 8';


helpdlg(' Clay ');

disp(' Clay soil and pH range is above 8 ');

elseif result == 2

A2 = 'Clayey Peat soil and pH range is above 8';



disp('Clayey Peat soil and pH range is above 8');

elseif result == 3

A3 = 'Clayey Sand soil and pH range is above 8';



disp(' Clayey Sand soil and pH range is above 8');

elseif result == 4

A4 = 'Humus Clay soil and pH range is 6 to 7';



disp(' Humus Clay soil and pH range is 6 to 7');

elseif result == 5

A5 = 'Peat soil and pH range is less than 4.5';


helpdlg(' Peat ');

disp(' Peat soil and pH range is less than 4.5');

elseif result == 6

A6 = 'Sandy Clay soil and pH range 5 to 6';



disp('Sandy Clay soil and pH range 5 to 6');

elseif result == 7

A7 = 'Silty Sand soil and pH range 6 to 7';



disp(' Silty Sand and pH range 6 to 7');

end


Wavelet transform

functionwaveletMoments = waveletTransform(image)

imgGray = double(rgb2gray(image))/255;

imgGray = imresize(imgGray, [256 256]);

coeff_1 = dwt2(imgGray', 'coif1');

coeff_2 = dwt2(coeff_1, 'coif1');



construct the feaute vector

meanCoeff = mean(coeff_4);

stdCoeff = std(coeff_4);

waveletMoments = [meanCoeffstdCoeff];

end

Detect

clc

close all

clear all

[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');

I = imread([pathname,filename]);

I = imresize(I,[256,256]);

%figure, imshow(I); title('Query Leaf Image');

Enhance Contrast

I = imadjust(I,stretchlim(I));

figure, imshow(I);title('Contrast Enhanced');

Otsu Segmentation

I_Otsu = im2bw(I,graythresh(I));

Conversion to HIS

I_HIS = rgb2hsi(I);

.

cform = makecform('srgb2lab');

Apply the colorform

lab_he = applycform(I,cform);

Measure the distance using Euclidean Distance Metric.

ab = double(lab_he(:,:,2:3));

nrows = size(ab,1);

ncols = size(ab,2);

ab = reshape(ab,nrows*ncols,2);

nColors = 3;

[cluster_idxcluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...

'Replicates',3);

[cluster_idxcluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3);

Label every pixel in tha image using results from K means

pixel_labels = reshape(cluster_idx,nrows,ncols);

figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');

segmented_images = cell(1,3);

Create RGB label using pixel_labels

rgb_label = repmat(pixel_labels,[1,1,3]);

for k = 1:nColors

colors = I;

colors(rgb_label ~= k) = 0;

segmented_images{k} = colors;

end

figure, subplot(3,1,1);imshow(segmented_images{1});title('Cluster 1');

subplot(3,1,2);imshow(segmented_images{2});title('Cluster 2');

subplot(3,1,3);imshow(segmented_images{3});title('Cluster 3');

set(gcf, 'Position', get(0,'Screensize'));

Feature Extraction

x = inputdlg('Enter the cluster no. containing the ROI only:');

i = str2double(x);

% Extract the features from the segmented image

seg_img = segmented_images{i};

Convert to grayscale if image is RGB

ifndims(seg_img) == 3

img = rgb2gray(seg_img);

end

figure, imshow(img); title('Gray Scale Image');

black = im2bw(seg_img,graythresh(seg_img));

%figure, imshow(black);title('Black & White Image');

m = size(seg_img,1);

n = size(seg_img,2);

zero_image = zeros(m,n);

%G = imoverlay(zero_image,seg_img,[1 0 0]);

cc = bwconncomp(seg_img,6);

diseasedata = regionprops(cc,'basic');

A1 = diseasedata.Area;

sprintf('Area of the disease affected region is : %g%',A1);

I_black = im2bw(I,graythresh(I));

kk = bwconncomp(I,6);

leafdata = regionprops(kk,'basic');

A2 = leafdata.Area;

sprintf(' Total leaf area is : %g%',A2);

Affected_Area = 1-(A1/A2);

Affected_Area = (A1/A2);

ifAffected_Area< 0.1

Affected_Area = Affected_Area+0.15;

end

sprintf('Affected Area is: %g%%',(Affected_Area*100))

Create the Gray Level Cooccurance Matrices (GLCMs)

glcms = graycomatrix(img);

Derive Statistics from GLCM

stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');

Contrast = stats.Contrast;

Correlation = stats.Correlation;

Energy = stats.Energy;

Homogeneity = stats.Homogeneity;

Mean = mean2(seg_img);

Standard_Deviation = std2(seg_img);

Entropy = entropy(seg_img);

RMS = mean2(rms(seg_img));

Skewness = skewness(img)

Variance = mean2(var(double(seg_img)));

a = sum(double(seg_img(:)));

Smoothness = 1-(1/(1+a));

Kurtosis = kurtosis(double(seg_img(:)));

Skewness = skewness(double(seg_img(:)));

% Inverse Difference Movement

m = size(seg_img,1);

n = size(seg_img,2);

in_diff = 0;

for i = 1:m

for j = 1:n

temp = seg_img(i,j)./(1+(i-j).^2);

in_diff = in_diff+temp;

end

end

IDM = double(in_diff);

Load All The Features

load('Training_Data.mat')

Put the test features into variable 'test'

test = feat_disease;

result = multisvm(Train_Feat,Train_Label,test);

disp(result);

Visualize Results

if result == 0

helpdlg(' AlternariaAlternata ');

disp(' AlternariaAlternata ');

elseif result == 1

helpdlg(' Anthracnose ');

disp('Anthracnose');

elseif result == 2

helpdlg(' Bacterial Blight ');

disp(' Bacterial Blight ');

elseif result == 3

helpdlg(' Cercospora Leaf Spot ');

disp('Cercospora Leaf Spot');

elseif result == 4

helpdlg(' Healthy Leaf ');

disp('Healthy Leaf ');

end

Evaluate Accuracy

load('Accuracy_Data.mat')

Accuracy_Percent= zeros(200,1);

for i = 1:500

data = Train_Feat;

groups = ismember(Train_Label,0);

[train,test] = crossvalind('HoldOut',groups);

cp = classperf(groups);

svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

classes = svmclassify(svmStruct,data(test,:),'showplot',false);

classperf(cp,classes,test);

Accuracy = cp.CorrectRate;

Accuracy_Percent(i) = Accuracy.*100;

end

Max_Accuracy = max(Accuracy_Percent);

sprintf('Accuracy of Linear Kernel with 500 iterations is: %g%%',Max_Accuracy)

Detet Soil

close all

clear all

clc

[filename, pathname] = uigetfile

Img = imread([pathname,filename]);

Img = imresize(Img,[256,256]);

figure, imshow(Img); title('Query Image');

Enhance Contrast

I = imadjust(Img,stretchlim(Img));

figure, imshow(I);title('Contrast Enhanced');

[Feature_Vector] = Extract_FeaturesofSoil(I);

whosFeature_Vector

load('TrainFeat_Soil.mat')

test = Feature_Vector;


disp(result);

if result == 1

helpdlg(' Clay ');

disp(' Clay ');

elseif result == 2


disp('Clayey Peat');

elseif result == 3


disp(' Clayey Sand ');

elseif result == 4


disp(' Humus Clay ');

elseif result == 5

helpdlg(' Peat ');

disp(' Peat ');

elseif result == 6


disp('Sandy Clay');

elseif result == 7


disp(' Silty Sand ');end

Project Details - Department of Computer Science S.No Reg...

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