Classification Of Banana Leaf

CLASSIFICATION OF BANANA LEAF DISEASES USING MACHINE LEARNING ALGORITHM A PROJECT REPORT Submitted by

AFRIN FATHIMA K

(Reg.No:2015104005)

ANCY A

(Reg.No:2015104015)

ANU PRIYA R

(Reg.No:2015104020)

In partial fulfillment for the award of the degree of

BACHELOR OF ENGINEERING IN ELECTRONICS AND COMMUNICATION ENGINEERING APRIL 2018

SETHU INSTITUTE OF TECHNOLOGY AN AUTONOMOUS INSTITUION AFFILIATED TO ANNA UNIVERSITY PULLOOR,KARIAPATTI-626 115.

i

SETHU INSTITUTIE OF TECHNOLOGY AN AUTONOMOUS INSTITUTION BONAFIDE CERTIFICATE

Certified that this technical project report “CLASSIFICATION OF BANANA

LEAF

DISEASES

USING

MACHINE

LEARNING

ALGORITHM” is the bonafide work of AFRIN FATHIMA.K (2015104005), ANCY. A (2015104015), ANU PRIYA.R (2015104020), who carried out the

technical project work under my supervision.

SIGNATURE

SIGNATURE

Mrs. Helina Rajini Suresh M.E.,(Ph.D.,)

Mrs. R.Sivaranjani M.E.,(Ph.D).,

HEAD OF THE DEPARTMENT

SUPERVISOR

Department of ECE

Professor/Department of ECE

Sethu Institute of Technology

Sethu Institute of Technology

Pulloor, Kariapatti-626 115

Pulloor,Kariapatti-626 115

Submitted for the 15UEC804 - Project Work End Semester Examination held at Sethu Institute of Technology on …………………….

INTERNAL EXAMINER

EXTERNAL EXAMINER

ii

ACKNOWLEDGEMENT

First we like to thank god the almighty for giving us the talent and opportunity to complete project. We wish to express our earnest great fullness to our honorable founder and chairman Mr. S. MOHAMED JALEEL B.Sc., B.L., for his encouragement extend to us to undertake this project. We wish to express our sense of gratitude to our principal Dr. A. SENTHIL KUMAR M.E., Ph.D., for being given guidance kind and cooperative encouragement, inspiration and keep interest show throughout the We would like to express our deep sense of gratitude to our Head of the Department Mrs. HELINA RAJINI SURESH M.E., (Ph.D.)., who extended their heartiest encouragement, advice and valuable guidance through this project. We would also like to acknowledge our deep sense of gratitude to our guide Mrs. R.SIVARANJANI M.E.,(Ph.D).,

for this enthusiastic

inspiration, constant encouragement, sustained guidance and scholarly advice impaired throughout the course of this project. We thank our parents, faculty members, supporting staff and friends for their extended during the time of our project.

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ABSTRACT Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. Leaf diseases are a noteworthy risk to sustenance security; however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non-attendance of the important foundation. Emergence of accurate techniques in the field of leaf based image detection and classification has shown impressive results. This work concentrates the detection and classification of affected and unaffected leaf. The proposed framework consists of four parts: Image Pre-processing, Segmentation of the leaf using K-means clustering to determine the diseased area, feature extraction and classification of diseases. Texture feature are extracted using second order statistical Gray Level Co-Occurance Matrix (GLCM) features and classification is done by using SVM classifier.The proposed method outperforms than the existing work listed in literature.

iv

TABLE OF CONTENTS CHAPTER No

1

2

TITLE

PAGE NO

ABSTRACT

iv

LIST OF FIGURES

vii

LIST OF ABBREVATIONS

viii

INTRODUCTION

1

1.1 IMAGE PROCESSING SYSTEM

1

1.1.1 DIGITIZER

1

1.1.2 IMAGE PROCESSER

2

1.1.3 DIGITAL COMPUTER

3

1.1.4 MASS STORAGE

3

1.1.5 HARD COPY DEVICE

3

1.1.6 OPERATOR CONSOLE

3

1.2 APPLICATIONS OF IMAGE PROCESSING

4

FUNDAMENTALS OF IMAGE PROCESSING

5

2.1 ILLUMINATION AND REFLECTION

5

2.2 GRAY SCALE

5

2.3 CLASSES IN IMAGE PROCESSING

6

2.3.1 IMAGE REPRESENTATION AND DESCRIPTION

6

2.3.2 IMAGE ENHANCEMENT

7

2.3.3 IMAGE RESTORATION

8

v

2.3.4 IMAGE RECOGNITION &INTERPRETATION

9

2.3.5 IMAGE SEGMENTATION

10

2.3.6 IMAGE RECONSTRUCTION

10

2.3.7 IMAGE DATA COMPRESSION

11

3

LITERATURE SURVEY

13

4

BANANA LEAF DISEASES

19

5

PROPOSED WORK

23

5.2 IMAGE DATASET

23

5.3 IMAGE PREPROCESSING

24

5.4 IMAGE SEGMENTATION

24

5.5 FEATURE EXTRACTION

26

5.5.1 GLCM

26

5.5.2 WORKING OF GLCM

27

5.5.3 HARALICK TEXTURE FEATURES

28

5.6 CLASSIFICATION

32

5.6.1 CONFUSION MATRIX 6

33

SOFTWARE DESCRIPTION

35

6.1 INTRODUCTION

35

6.1.1 THE MATLAB SYSTEM

36

7

RESULT AND DISCUSSION

37

8

CONCLUSION

43

REFERENCES

44

vi

LIST OF FIGURES TITLES

FIGURE NO.

PAGE NO.

4.1

PANAMA WILT

22

4.2

LEAF SPOT

22

4.3

ANTHRACNOSE

22

4.4

CIGAR END TIP ROT

22

4.5

CROWN ROT

22

4.6

STEM END ROT

22

4.7

PSEUDO STEM HEART ROT

22

4.8

MOKO DISEASE

22

4.9

BANANA BUNKY TOP VIRUS

22

4.10

MOSAIC VIRUS

22

5.1

BLOCK DIAGRAM OF THE PROPOSED WORK

23

5.2

NORMAL LEAF IMAGE

24

5.3

K-MEANS CLUSTERING

25

5.4

CREATION OF GLCM

28

5.5

SVM CLASSIFIER

33

5.6

CONFUSION MATRIX

33

5.7

SYSTEM DESIGN

34

7.1

HEALTHY LEAF

37

7.2

AFFECTED LEAF

38

7.3

CONFUSION MATRIX 1

40

7.4

CONFUSION MATRIX 2

41

vii

LIST OF ABBREVIATIONS

GLCM

-

Gray Level Co-Occurance Matrix

SVM

-

Support Vector Machine

ENT

-

Entropy

IDM

-

Inverse Difference Moment

E

-

Energy

RMS

-

Root Mean Square

viii

CHAPTER 1 INTRODUCTION The term digital image refers to processing of a two dimensional picture by a digital computer. In a broader context, it implies digital processing of any two dimensional data. A digital image is an array of real or complex numbers represented by a finite number of bits. An image given in the form of a transparency, slide, photograph or an X-ray is first digitized and stored as a matrix of binary digits in computer memory. This digitized image can then be processed and/or displayed on a high-resolution television monitor. For display, the image is stored in a rapid-access buffer memory, which refreshes the monitor at a rate of 25 frames per second to produce a visually continuous display.

1.1 The Image processing System A typical digital image processing system is given in Fig.1.1

Digitizer

Image Processor

Mass Storage

Digital Computer

Hard Copy Device

Display

Fig 1.1

Operator Console

Block Diagram of a Typical Image Processing System

1

1.1.1 Digitizer A digitizer converts an image into a numerical representation suitable for input into a digital computer. Some common digitizers are Microdensitometer. 1. Flying spot scanner 2. Image dissector 3. Videocon camera 4. Photosensitive solid- state arrays.

1.1.2 Image Processor An image processor does the functions of image acquisition, storage, preprocessing, segmentation, representation, recognition and interpretation and finally displays or records the resulting image. The following block diagram gives the fundamental sequence involved in an image processing system

Problem Domain

Image Acquisition

Preprocessing

Segmentation

Representation & Description

Knowledge

Recognition & interpretation

Result

Fig 1.2 Block Diagram of Fundamental Sequence involved in an image Processing system

As detailed in the diagram, the first step in the process is image acquisition by an imaging sensor in conjunction with a digitizer to digitize the image. The next step is the preprocessing step where the image is improved being fed as an input to the other processes. Preprocessing typically deals with

2

enhancing, removing noise, isolating regions, etc. Segmentation partitions an image into its constituent parts or objects. The output of segmentation is usually raw pixel data, which consists of either the boundary of the region or the pixels in the region themselves. Representation is the process of transforming the raw pixel data into a form useful for subsequent processing by the computer. Description deals with extracting features that are basic in differentiating one class of objects from another. Recognition assigns a label to an object based on the information provided by its descriptors. Interpretation involves assigning meaning to an ensemble of recognized objects. The knowledge about a problem domain is incorporated into the knowledge base. The knowledge base guides the operation of each processing module and also controls the interaction between the modules. Not all modules need be necessarily present for a specific function. The composition of the image processing system depends on its application. The frame rate of the image processor is normally around 25 frames/second.

1.1.3 Digital Computer Mathematical processing of the digitized image such as convolution, averaging, addition, subtraction, etc. are done by the computer.

1.1.4 Mass Storage The secondary storage devices normally used are floppy disks, CD ROMs etc.

1.1.5 Hard Copy Device The hard copy device is used to produce a permanent copy of the image and for the storage of the software involved.

1.1.6 Operator console The operator console consists of equipment and arrangements for verification of intermediate results and for alterations in the software as and

3

when require. The operator is also capable of checking for any resulting errors and for the entry of requisite data.

1.2 Applications of Image Processing Importance and necessity of digital image processing stems from two principal application areas: Improvement of pictorial information for human interpretation and Processing of scene data for autonomous machine perception. Digital image processing has a broad spectrum of applications such as remote sensing, image storage and transmission for business applications, medical imaging, acoustic imaging, and automated inspection of industrial parts. Images acquired by satellites are useful in tracking of earth resources, geographical mapping, prediction of agricultural crops, urban growth, weather, flood and fire control. Space imaging applications include recognition and analysis of objects contained in images obtained from deep space-probe missions. There are also medical applications such as processing of X-Rays, Ultrasonic scanning, Magnetic Resonance Imaging, Nuclear Magnetic Resonance Imaging, etc. In addition to the above mentioned applications, digital image processing is now being used in solving a wide variety of problems. Though unrelated, these problems commonly require methods capable of enhancing information for human interpretation and analysis. Image enhancement and restoration procedures are used to process degraded images of unrecoverable objects. Successful applications of image processing concepts are found in astronomy, defense, biology and industrial applications. The images may be used in the detection of tumors or for screening the patients. The current major area of application of digital image processing techniques is in solving the problem of machine vision.

4

CHAPTER 2 FUNDAMENTALS OF IMAGE PROCESSING The ultimate goal of any image processing technique is to help an observer interpret the content of an image.

2.1 Illumination and Reflectance The term image refers to a two dimensional light intensity function, denoted by f(x, y), where the value or amplitude of f at spatial coordinates (x, y) gives the intensity of the image at that point. Light is a form of energy and is hence nonzero and finite. The images people perceive in day to day life consist of light reflected from objects. The basic nature of the image may be characterized by two components a. The amount of source light being incident on the scene being viewed and b. The amount of light reflected by the objects in the scene.

The former is known as the Illumination and the latter is known as the Reflectance components of the image.

2.2 Gray Scale The intensity of a monochrome image f at coordinates (x, y) is known as the gray level (l) of the image at that point. It is evident that Lmin  l  Lmax

--------- (2.1)

Where, Lmin is the minimum gray level Lmax is the maximum gray level

And the only requirement is that Lmin be positive and Lmax be finite. 5

If i min

and i are the minimum and maximum values of the max

illumination and r

min

and r

max

are the minimum and maximum values of

reflectance respectively, we have i

r min min

-------- (2.2)

L i r max max max

-------- (2.3)

L min

The interval Lmin ,Lmax  is called the gray scale of the image. Normally the image is shifted to the interval [0,L] where l=0 is considered black and l=L is considered white.

2.3 Classes in Image Processing An image processing system may handle a number of problems and have a number of applications but it mostly involves the following processes known as the basic classes in image processing 1. Image Representation and Description 2. Image Enhancement 3. Image Restoration 4. Image Recognition and Interpretation 5. Image Segmentation 6. Image Reconstruction 7. Image Data Compression

2.3.1 Image Representation and Description Any processed image must be represented and described in a form suitable for further computer processing. Basically, representing a region involves two choices 1. In terms of its external characteristics (its boundary) and 2. in terms of its internal characteristics (the pixels comprising the region)

6

The next task is to describe the region based on the chosen representation. Generally an external representation is chosen when the primary focus is on shape characteristics. An internal representation is selected when the primary focus is on reflectivity characteristics such as color and texture. Some of the available representation approaches are, 1. Chain codes 2. Polygonal approximations 3. Signatures 4. Boundary segments

2.3.2 Image Enhancement The principle objective of enhancement technique is to process an image so that the result is more suitable than the original than for a specific application [1]. Most enhancement techniques are very much problem oriented and hence enhancement for one problem may turn out to be degradation for the other. Enhancement approaches may be classified into two broad categories.

1. Spatial domain enhancement techniques 2. Frequency domain enhancement techniques.

The former refers to processing the image in the image plane (pixels) itself while the latter techniques are based on modifying the Fourier (or any other) transform of an image. In general enhancement techniques for problems involve various combinations of methods from both the categories. Some examples of enhancement operations are edge enhancement, pseudo coloring, histogram equalization, noise filtering, unsharp masking, sharpening, magnifying, etc. The enhancement process does not increase the inherent information content present in the image but only tries to present it in a suitable manner. Enhancement operations may be either local or global. Global

7

operations operate on the entire image at a time while local operations define spatial masks (small sub images) over which the operation is to be performed.

2.3.3 Image Restoration The ultimate goal of restoration techniques (as in image enhancement) is to improve the image in some sense. However, unlike enhancement, restoration is a process that attempts to recover an image that has been degraded by using some apriori knowledge of the degradation phenomenon. Thus restoration techniques are oriented towards modeling the degradation and applying the inverse process in order to recover the original image. This approach usually involves formulating a criterion of goodness that will yield some optimal estimate of the desired result. Early techniques for digital image restoration were derived mostly from frequency domain concepts. However, modern methods take advantage of the algebraic approach. Although a direct solution by algebraic methods generally involves the manipulation of large systems of simultaneous equations, under certain conditions computational complexities can be reduced to the same level as required by traditional frequency domain restoration techniques. Restoration techniques may be either linear or non-linear. Image restoration may be classified into three major types. 1. Restoration models: Image formation, detector and recorder, noise model, sampled observation. 2. Linear filtering Inverse / pseudo-inverse filter, Wiener filter, FIR filter, Kalman filter, semi recursive filter. 3. Other methods Speckle noise reduction, maximum entropy restoration, Bayesian methods, blind deconvolution, etc.

8

2.3.4 Image Recognition and Interpretation Image recognition or analysis is a process of discovering, identifying and understanding patterns that are relevant to the performance of an image based task. One of the principle goals of image analysis is to endow a machine with the capability to approximate similar to human beings. An automated image analysis system is capable of exhibiting various degrees of intelligence. Some of the associated characteristics are, 1. The ability to extract pertinent information from a background of irrelevant details. 2. The capability to learn from examples and to generalize this knowledge. 3. The ability to make inferences from incomplete information.

Image analysis can be divided into three basic areas. 1. Low level processing which deals with functions requiring no intelligence 2. Intermediate level processing which deals with the task of extracting and characterizing components in an image resulting form a low level process and 3. High level processing which involves recognition and interpretation and is generally termed as intelligent cognition. The predominant concept underlying image interpretation methodologies is the effective organization and use of knowledge about a problem domain. Current techniques for image interpretation are mostly decision – theoretic methods, some of which are predicate logic, semantic networks, expert systems, etc.

9

2.3.5 Image Segmentation Image segmentation is a technique for extracting information from a image. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. Segmentation is stopped when the objects of interest in an application have been isolated. In general, autonomous segmentation is one of the most difficult tasks in image processing. This step determines the eventual success or failure of the analysis. Effective segmentation rarely fails to lead to a successful solution. Segmentation algorithms for monochrome images generally are based on one of two basic properties of gray level values 1. Discontinuity 2. Similarity In the first category, the approach is to partition an image based on abrupt changes in gray level. The principal areas of interest within this category are detection of isolated points and detection of lines and edges in an image. The principal approaches in the second category are based on thresholding, region growing, region splitting and region merging. The concept of segmenting an image based on discontinuity or similarity of the gray level value of its pixels is applicable to both static and dynamic images. In the latter cases, motion can be used as a powerful queue to improve the performance of segmentation algorithms. 2.3.6 Image Reconstruction An important problem in image processing is to reconstruct a cross section of an object from several images of its trans-axial projections. A projection is a shadow gram obtained by illuminating an object by penetrating radiation. Each horizontal line is a one dimensional projection of the horizontal slice of the project. Each pixel on the projected image represents the total absorption of the radiation along its path from the source to the detector. By 10

rotating the source detector assembly around the object, projection views for several different angles can be obtained. Image systems that generate such slice views

are

called

computerized

tomography

(CT)

scanners.

These

reconstructions are of several types. 1. Transmission tomography 2. Reflection tomography 3. Emission tomography 4. Magnetic resonance imaging 5. Nuclear magnetic resonance imaging If a three dimensional object is scanned by a parallel beam, then the entire three dimensional objects can be reconstructed from a set of two dimensional slices, each of which can be reconstructed using several available algorithms.

2.3.7 Image Data Compression An enormous amount of data is produced when a 2-D light intensity function is sampled and quantized to create a digital image. The amount of data generated may be so great that it results in impractical storage, processing and communication requirements. Image compression addresses the problem of reducing the amount of data required to represent a digital image. The underlying basis of the reduction process is the removal of redundant data. This amounts to transforming a 2-D pixel array into a statistically uncorrelated data set. The transformation is applied prior to storage or transmission of image. Later the compressed image is decompressed to reconstruct the original image or an approximation to it. Initial focus in this field was on the development of methods for reducing video transmission bandwidth, a process called bandwidth compression. Image compression is the natural technology for handling the increased spatial resolution of today’s imaging sensors and evolving broadcast television standards. Applications of data compression are in broadcast television, remote sensing via satellite, military communications via aircraft, radar and sonar, 11

teleconferencing, computer communications, facsimile transmission, document and medical imaging, hazardous waste control applications and the like.

Image data compression methods fall mainly into 3 categories, 1. Pixel coding a. Run length coding b. Bit plane coding c. PCM/Quantization 2. Predictive coding a. Delta modulation b. Line by line DPCM c. 2-D DPCM 3. Transform coding a. Zonal coding b. Threshold coding c. Multi-dimensional techniques

12

CHAPTER 3 LITERATURE SURVEY

[1]

Pooja.V, Ragul Das and Kanchana, “Identification Of Plant

Leaf Diseases Using Image Processing Techniques”, IEEE, 2017.

This paper proposes a disease detection and classification technique with the help of machine learning mechanisms and image processing tools. Initially, identifying and capturing the infected region is done and latter image preprocessing is performed. Further, the segments are obtained and the area of interest is recognized and the feature extraction is done on the same. Finally the obtained results are send through SVM Classifiers to get the results. The Support Vector Machines outperforms the task of classification of diseases; results show that the methodology put forward in the paper provides considerably better results than the previously used disease detection techniques.

[2]

R.Meena Prakash; G.P.Saraswathy; G.Ramalakshmi,” Detection

of Leaf Diseases and Classification using Digital Image Processing”, IEEE, 2017.

This paper, image processing techniques are used to detect the plant leaf diseases. The objective of this work is to implement image analysis & classification techniques for detection of leaf diseases and classification. The proposed framework consists of four parts. They are (1) Image preprocessing (2) segmentation of the leaf using K-means clustering to determine the diseased areas (3) feature extraction using statistical Gray-Level Co-Occurrence Matrix 13

(GLCM) features and classification is done using Support Vector Machine (SVM).

[3]

Prajwala TM , Alla

Pranathi , Kandiraju Sai Ashritha ,

Nagaratna B. Chittaragi and Shashidhar G. Koolagudi, “Tomato Leaf Disease Detection using Convolutional Neural Networks” , IEEE , 2018.

This paper adopts a slight variation of the convolutional neural network model called LeNet to detect and identify diseases in tomato leaves. The main aim of the proposed work is to find a solution to the problem of tomato disease detection using the simplest approach while making use of minimal computing resources to achieve results comparable to state of the art techniques. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94-95% indicating the feasibility of the neural network approach even under unfavourable conditions.

[4]

Vijai Singh, Varsha and Misra, “Detection of unhealthy region

of plant leaves using Image Processing and Genetic

Algorithm”,

IEEE, 2015.

This paper presents an algorithm for image segmentation techniques used for automatic detection as well as classification of plant leaf disease and survey on different disease classification techniques that can be used for plant leaf disease. Image segmentation, which is an important aspect for disease, is done by using genetic algorithm.

14

[5]

Yogesh Dandawate and Radha Kokare, “An Automated

Approach for Classification of Plant Diseases Towards Development of Futuristic Decision Support System in Indian Perspective”, IEEE, 2015.

This paper focus on the approach based on image processing for detection of diseases of soybean plants. The soybean images are captured using mobile camera having resolution greater than 2 mega pixel. The purpose of the proposed project is to provide inputs for the Decision Support System (DSS), which is developed for providing advice to the farmers as and when require over mobile internet. Our proposed work classifies the images of soybean leaves as healthy and diseased using Support Vector Machine (SVM). The algorithm comprises of four major steps: image acquisition analysis and classification. The SVM classifier proves its ability in automatic and accurate classification of image. Finally, it can be concluded from the experimental results that this approach can classify the leaves with an average accuracy of 93.79%. The proposed system will enable the farmers to get advice from the agriculture experts with minimal efforts.

[6]

Davoud Ashourloo, Hossein Aghighi; Ali Akbar Matkan;

Mohammad

Reza Mobasheri; and Amir Moeini Rad,

“An

Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement”, IEEE, 2016.

15

This paper investigated on 1) using partial least square regression (PLSR), v support vector regression (v-SVR), and Gaussian process regression (GPR) method for wheat leaf rust disease detection, 2) evaluating the impact of training sample size on the results, 3) the influence of disease symptoms effects on the predictions performances of the above-mentioned methods, and 4) comparisons between the performance of SVIs and machine learning techniques. In this study, the spectra of the infected and non- infected leaves in different

diseases

symptoms

were

measured

using

a

non-image

spectroradiometer in the electro- magnetic region of 350 to 2500nm. In order to produce a ground truth dataset, we employed photos of a digital camera to compute the disease severity and disease symptoms fraction. The result represent that the machine learning techniques is contrast to SVIs are not sensitive to different diseases symptoms and their results are reliable.

[7]

Halil Durmu, Ece Olcay Gune and Murvet Kirci, “Disease

Detection on the Leaves of the Tomato Plants by Using Deep Learning”, IEEE, 2017.

This paper physical changes in the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architecture were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf image from the Plant Village dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet. 16

[8]

Jobin Francis , Anto Sahaya Dhas D , Anoop B K ,

“IDENTIFICATION OF LEAF DISEASES IN PEPPER PLANTS USING SOFT COMPUTING TECHNIQUES” , IEEE , 2016.

This paper provides an evaluation study on the existing disease detection is plants. The case of a plant, the term disease is defined as any impairment happening to the normal physiological function, producing characteristics symptoms. The studies of plant disease refer to studying the visually observable pattern of a particular plant. The identification of plants, leaves, stems and finding out pests or diseases, or its percentage is founded very effective in the successful cultivation of crops. The naked eye observation is the approach adopted by many of the farmers for the detection and identification of plant diseases. It requires continuous monitoring and found less useful on large farms. Also, the farmers are unaware of non-native diseases. With the aid of imaging technology the plant disease detection systems automatically detects the symptoms that appear on the leaves and stem of a plant and helps in cultivating healthy plants in a farm. These systems monitor the plant such as leaves and stem and any variation observed from its characteristic features, variation will be automatically identified and also will be informed to the user.

[9]

Shima Ramesh; Mr. Ramachandra Hebbar, “Plant Disease

Detection Using Machine Learning”, IEEE, 2018.

This paper makes use of Random Forest in identifying between healthy and diseased leaf from the data sets created. Our proposed paper includes various phases of implementation namely dataset creation, feature extraction, training the classifier and classification. The created datasets of diseased and 17

healthy leaves are collectively trained under Random Forest to classify the diseased and healthy image. For extraction features of an image we use Histogram of an Oriented Gradient (HOG). Overall, using machine learning to train the large data sets available publicly gives us a clear way to detect the disease present in plants in a colossal scale.

[10] Pranjali B Padol; Prof. Anjali A. Yadav, “SVM Classifier Based Grape Leaf Disease Detection”, IEEE, 2016.

This paper is intended to aid in the detection and classification leaf diseases of grape using SVM classification techniques. First the diseased region is found using segmentation by K-means clustering, then both color and texture feature are extracted. Finally classification technique is used to detect the type of leaf disease. The proposed system can successfully detect and classify the examined disease with accuracy of 88.89%.

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CHAPTER 4 BANANA LEAF DISEASES Panama Wilt: This is a soil-borne fungal diseases and gets entry in the plant body through roots. It is most serious in poorly drained soil. Initial symptoms or yellowing of lower leaves, including leaf blades and petioles. The leaves hang around the Pseudostem and wither. In the Pseudostem of the diseased plant, yellowish to reddish streaks are noted with intensification of colour towards the rhizome. Wilt is severe in poor soil with continuous cropping of banana. Warm soil temperature, poor drainage, light soil and high soil moisture are congenial for the spread of the diseases. Leaf spot, Leaf streak or Sigatoka diseases: Yellow Sigatoka is one of the serious diseases affecting the banana crop. Initial symptoms appear in the form of light yellowish spots on the leaves. A small number of these enlarge, become oval; the color also changes to dark brown. Still later, the center of the spot dies, turning light grey surrounded by a brown ring. In severe cases, numerous spots coalesce, killing large part of the leaf. Rainfall, dew and temperature determine the spread of the disease. Condition favouring mass infection is most common during the rainy season with temperature above 21 0c. Anthracnose: The disease attacks banana plants at all stages of growth. Disease attacks the flowers, skin and distal ends of banana heads. The symptoms appear as large brown patches covered with a crimson growth of the fungus. The disease fruit turns black and the fruit is shrivelled. Cigar End Tip Rot: A black necrosis spread from the perianth into the tip of immature fingers. The rotted portion of the banana finger is dry and tends to adhere to fruits.

19

Crown Rot: The characteristics symptoms are blackening of the crown tissues, which spreads to the pulp through the pedicel resulting rotting of the injected portion and separation of fingers from the hand. Stem-end Rot: The fungus enters through the cut stem or hand. The invaded flesh becomes soft and water-soaked. Pseudostem Heart Rot : The first indication of heart rot is the presence of heart leaves with part of the lamina missing or decayed. In severe cases, the inner leaves of the crown first turn yellow, then brown and finally die. In more severe cases all the leaves and the plant die. Head Rot : Newly planted suckers get affected, leading to rotting and emitting foul odour. In older plants rotting at the collar region and leaf bases are seen. In advance cases trunk base becomes swollen and split. Bacterial Wilt or Moko Disease : The young plants are affected severely. In the initial stages the bacterial wilt is characterized by the yellowish discolouration of the inner leaf lamina to the petiole. The leaf collapses near the junction of the lamina with the petiole. Within a week most of the leaf exhibit wilting symptoms. The presence of yellow fingers in otherwise green stem often indicates the presence of moko disease. The most characteristics symptoms appear on the young suckers that have been cut ones and begin regrowth. These are blackened and stunte. The tender leaves from the suckers yellow necrotic. Banana Bunchy Top Virus: The diseases transmitted to the plant by the aphit vector Pntaloni nigronervosa and dwarf bananas are very susceptible to this disease. Primary symptoms of the disease are seen when infected suckers are planted. Such infected suckers putforth narrow leaves, which are chlorotic and exhibit mosaic symptoms. The affected leaves re brittle with their margins rolled upwards. Characteristics symptoms of bunchy top virus is the presence of 20

interrupted dark green strikes long the secondary veins of the lamina or the midrib of the petiole. The diseased plants remain stunted and do not produce bunch of any commercial value. Banana Streak Virus: A prominent symptom exhibited by BSV is yellow streaking of the leaves, which become progressively necrotic producing black streaked appearance in older leaves. This virus is transmitted mostly through infected planting materials, though mealy bugs and more probably Saccharicoccus sacchari are also believed to transmit it. Shoot tip culture does not eliminate it from vegetatively propagated materials. Mosaic Virus: The disease characterized by typical mosaic symptoms on the leaves. Mosaic plants easily are easily recognized by their dwarf growth and mottled, distorted leaves. The earliest symptoms appear on younger leaves ass light green or yellowish streaks and bands giving a mottled appearance. The aphit vector aphis gossypii transmits the disease. Banana Bract Mosaic Virus: This symptoms appear as yellow green bands or mottling over an entire are of young leaves. The affected leaves shows abnormal thickening of veins. Bunch development is affected.

21

Fig 4.1 : Panama Wilt

Fig 4.4 : Cigar End Tip Rot

Fig 4.2 : Leaf Spot

Fig 4.5: Crown Rot

Fig 4.3 : Anthracnose

Fig 4.6: Stem End Rot

Fig 4.7: Pseudostem Heart Rot Fig 4.8: Moko Disease Fig 4.9: Banana Bunchy Top Virus

Fig 4.10: Mosaic Virus

22

CHAPTER 5 PROPOSED WORK In the proposed work consist of four parts. They are: Image Preprocessing Image Segmentation Feature Extraction Classification

5.1 BLOCK DIAGRAM

Fig 5.1 : Block Diagram of the proposed work

23

5.2 IMAGE DATASET In this step the sample images are collected from the dataset.For which a training set of 360 images and a testing set of 260 images is constructed. The standard jpg format is used to store these images. Then the input image is resized to 256×256 pixels.

Fig 5.2 : Normal Leaf Image

5.3 IMAGE PREPROCESSING Image pre-processing is used to enhance the quality of the image necessary for further processing and analysis. It includes color space conversion and image enhancement. The RGB images of leaves are converted into L*a*b* color space. The color transformation is done to determine the luminosity and chromaticity layers. The color space conversion is used for the enhancement of visual analysis.

5.4 IMAGE SEGMENTATION Image segmentation is the process used to simplify the representation of an image into meaningful form, such as to highlight object of interest from background. The K-means clustering algorithm performs segmentation by minimizing the sum of squares of distances between the image intensities and the cluster centroids. The main idea is to define k centers, one for each cluster. These

centers

should

be

placed

in

a

cunning

way

because

of

different location causes different result. So, the better choice is to place

24

them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest centre.

Fig 5.3 : K-Means Clustering When no point is pending, the first step is completed and an early group age is done. At this point we need to re-calculate k new centroids as barycentre of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the

same

data

set

points and the nearest new centre. A loop has been generated. As a result of this loop we may notice that the k centers change their location step by step until no more changes are done or in other words centers do not move any more. Finally, this algorithm aims at minimizing an objective function know as squared error function given by: 𝑣(𝑣) = ∑𝑣

2 (‖𝑣𝑣 − 𝑣 𝑣‖)

𝑣,𝑣=1

------ (5.1)

Algorithmic steps for K-means clustering: Let X = {x1,x2,x3,……..,xn} be the set of data points and V = {v1,v2,…….,vc} be the set of centers. 25

1) Randomly select ‘c’ cluster centers. 2) Calculate the distance between each data point and cluster centers. 3) Assign the data point to the cluster center whose distance from the cluster center is minimum of all the cluster centers.. 4) Recalculate the new cluster center using: 1 𝑣𝑣 = (1⁄𝑣1 ) ∑ 𝑣 𝑣=1 𝑣

------ (5.2)

1

where, ‘ci’ represents the number of data points in ith cluster. 5) Recalculate the distance between each data point and new obtained cluster centers. 6) If no data point was reassigned then stop, otherwise repeat from step 3).

5.5 FEATURE EXTRACTION A statistical method of examining texture that considers the spatial relationship of pixels is the gray-level co-occurrence matrix (GLCM), also known as the gray-level spatial dependence matrix. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix. (The texture filter functions, described in Texture Analysis cannot provide information about shape, that is, the spatial relationships of pixels in an image).

5.5.1 GRAY LEVEL CO-OCCURANCE MATRIX(GLCM) In 1973, Haralick introduced the co-occurrence matrix and texture features which are the most popular second order statistical features today. Haralick proposed two steps for texture feature extraction. First step is 26

computing the co-occurrence matrix and the second step is calculating texture feature based on the co-occurrence matrix. This technique is useful in wide range of image analysis applications from biomedical to remote sensing techniques.

5.5.2 WORKING OF GLCM Basic of GLCM texture considers the relation between two neighbouring pixels in one offset, as the second order texture. The gray values relationships in a target are transformed into the co-occurrence matrix space by a given kernel mask such as 3×3, 5×5, 7×7 and so forth. In the transformation from the image space into the co-occurrence matrix space, the neighbouring pixels in one or some of the eight defined directions can be used; normally, four direction such as 0°, 45°, 90°, and 135° is initially regarded, and its reverse direction (negative direction) can be also counted into account. It contains information about the positions of the pixels having similar gray level values. Each element (i, j) in GLCM specifies the number of times that the pixel with value I occurred horizontally adjacent to a pixel with value j. In Figure computation has been made in the manner where, element (1, 1) in the GLCM contains the value 1 because there is only one instance in the image where two, horizontally adjacent pixels have the values 1 and 1. Element (1, 2) in the GLCM contains the value 2 because there are two instances in the image where two, horizontally adjacent pixels have the values 1 and 2. Element (1, 2) in the GLCM contains the value 2 because there are two instances in the image where two, horizontally adjacent pixels have the values 1 and 2. The GLCM matrix has been extracted for input dataset imagery. Once after the GLCM is computed, texture features of the image are being extracted successively.

27

Fig 5.4 :Creation of GLCM

5.5.3 HARALICK TEXTURE FEATURES Haralick extracted thirteen texture features from GLCM for an image. The important texture features for classifying the image into water body and non-water body are Energy (E), Entropy (Ent), Contrast (Con), Inverse Difference Moment (IDM) and Directional Moment (DM). The thirteen texture features are,  Contrast  Correlation  Energy  Homogeneity  Mean  Standard  Entropy  RMS  Variance  Smoothness  Kurtosis  Skewness 28

 IDM. Andrea Baraldi and Flavio Parmiggiani (1995) discussed the five statistical parameter energy, entropy, contrast, IDM and DM, which are considered the most relevant among the 14 originally texture features proposed by Haralick et al. (1973). The complexity of the algorithm also reduced by using these texture features. Let i and j are the coefficients of co-occurrence matrix, M i, j is the element in the co-occurrence matrix at the coordinates i and j and N is the dimension of the co-occurrence matrix.

a) CONTRAST Contrast measures intensity contrast of a pixel and its neighbour pixel over the entire image. If the image is constant, contrast is equal to 0. The equation of the contrast is as follows, Contrast  

N 1 i, j 0

( p )(i 

j)

2

------ (5.3)

ij

b) ENERGY Energy is a measure of uniformity with squared elements summation in the GLCM. Range is in between 0 and 1. Energy is 1 for a constant image. The equation of the energy is given by equation, N 1

Energy  

i, j 0

2

------ (5.4)

(p ) ij

c) HOMOGENEITY Homogeneity measures the similarity among the pixels. Its range is between 0 and 1. Homogeneity is 1 for a diagonal GLCM. The equation of the Homogeneity is as follows,

Homogeneity  

N 1

i, j 0

( p2 )

------ (5.5)

ij 2

[1  (i 

d) CORRELATION 29

j) ]

Correlation measures how correlated a pixel is to its neighbourhood. Its range is in between -1 and 1. Correlation  

N 1

(p )

i, j0

(i   )( j   )



ij

------ (5.6)

2

e) ENTROPY This concept comes from thermodynamics. Entropy (Ent) is the measure of randomness that is used to characterize the texture of the input image. Its value will be maximum when all the elements of the co-occurrence matrix are the same. It is also defined as in Equation Entropy  i, j0 M (i, j)( ln( M (i, j)))  N 1

------ (5.7)

f) INVERSE DIFFERENCE MOMENT Inverse Difference Moment (IDM) is a measure of image texture as defined in Equation . IDM is usually called homogeneity that measures the local homogeneity of an image. IDM feature obtains the measures of the closeness of the distribution of the GLCM elements to the GLCM diagonal. IDM has a range of values so as to determine whether the image is textured or non-textured. IDM 

N 1

1

  1 (1 j)

M (i, j)

------ (5.8)

2

i, j0

g) SKEWNESS Skewness is a measure of the asymmetry of the data around the sample mean. If skewness is negative, the data spreads out more to the left of the mean than to the right. If skewness is positive, the data spreads out more to the right. The skewness of the normal distribution is zero. The skewness of a distribution is defined as, Skewness 

E( x



30

)

2

2

------ (5.9)

h) KURTOSIS Kurtosis is a measure of how outlier-prone a distribution is. The kurtosis of the normal distribution is 3. Distributions that are more outlier-prone than the normal distribution have kurtosis greater than 3; distributions that are less outlier-prone have kurtosis less than 3. Some definitions of kurtosis subtract 3 from the computed value, so that the normal distribution has kurtosis of 0. The kurtosis function does not use this convention. The kurtosis of a distribution is defined as,

Kurtosis 

E( x



)

4

------ (5.10)

4

i) ROOT MEAN SQUARE The RMS block computes the true root mean square (RMS) value of the input signal. The true RMS value of the input signal is calculated over a running average window of one cycle of the specified fundamental frequency,

RMS  1 t f  T t T

t  2

------ (5.11)

where f(t) is the input signal and T is 1/(fundamental frequency). As this block uses a running average window, one cycle of simulation must complete before the output gives the correct value. For the first cycle of simulation, the output is held to this specified initial RMS value. j) STANDARD DEVIATION For a random variable vector A made up of N scalar observations, the standard deviation is defined as

31



1 N S   N  1 i 1

------ (5.12)

A   i

where μ is the mean of A:



1 N

 A N

i 1

------ (5.13)

i

The standard deviation is the square root of the variance. Some definitions of standard deviation use a normalization factor of N instead of N-1, which you can specify by setting w to 1.

5.6 CLASSIFICATION Classification is done by using SVM Classifier to produce the better accuracy. Support Vector Machine (SVM) is kernel-based supervised learning algorithm used as a classification tool. The training algorithm of SVM maximizes the margin between the training data and class boundary. The resulting decision function depends only on the training data called support vectors, which are closest to the decision boundary. It is effective in high dimensional space where number of dimensions is greater than the number of training data. SVM transforms data from input space into a high-dimensional feature space using kernel function. Nonlinear data can also be separated using hyper plane in high dimensional space. The computational complexity is reduced by kernel Hilbert space The idea of support vector machine is to create a hyper plane in between data sets to indicate which class it belongs to. The feature vector is given as input to the classifier. The feature vectors of the database images are divided into training and testing vectors. The classifier trains on the training set and applies it to classify the testing set. The

performance of the classifier is

measured by comparing the predicted labels and actual values.

32

Fig 5.5 SVM Classifier

5.6.1 CONFUSION MATRIX A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.

Fig 5.6 Confusion matrix 

True positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease. True positive=TP/actual yes



------ (5.14)

True negatives (TN): We predicted no, and they don't have the disease. True negatives =TN/actual no

33

------ (5.15)



False positives (FP): We predicted yes, but they don't actually have the disease. (Also known as a "Type I error.") False positive=FP/actual yes



------ (5.16)

False negatives (FN): We predicted no, but they actually do have the disease. (Also known as a "Type II error.") False negatives =FN/actual no

5.7 SYSTEM DESIGN The following flow chart is the proposed model;

Fig 5.7 : System Design

34

------ (5.17)

CHAPTER 6 SOFTWARE DESCRIPTION 6.1 INTRODUCTION 6.1.1 THE MATLAB SYSTEM: MATLAB is an abbreviation of matrix laboratory. It is multi-paradigm numerical computing software which uses the 4th generation of programming Language (MATLAB programming language) developed by Mathworks. MATLAB is able to do plotting of functions and data, matrix manipulations, creation of user interfaces, implementation of algorithm, and interfacing with programs written in other languages. It is widely used by the people from different background of field and knowledge such and science, engineering, economist, and the researchers. 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 formulation, in a

35

fraction of the time it would take to write a program in a scalar non interactive language such as C or FORTRAN. The name MAATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software development by the LINPACK and ARPACK projects. Today, MATLAB uses software developed by the LAPACK and ARPACK projects, which together represent the state-ofthe-art in software for matrix computation. MATLAB features a family of application-specific solution 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-file) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control system, neural networks, fuzzy logic, wavelets, simulation and many others.

36

CHAPTER 7 RESULT AND DISCUSSION The proposed work is evaluated over the database of 120 images of banana leaves. The database consists of images of 60 healthy leaves and 40 diseased leaves. A detailed study is completed to investigate the use of advance in image processing for the detection of plant diseases. A system for diagnosis the detection of plant disease has been developed using the Matlab application. The image data of the leaves is collected by using a digital camera. Algorithms for segmentation, feature extraction and classification based on image processing techniques were designed .The normal images and the cluster images of banana leaves are shown in Fig7.1. The Feature extraction process used gray level co-occurrence methodology (GLCM method), shape and texture based featured extraction. HEALTHY LEAF

(a)

(b)

(e)

(f)

(c)

(g)

37

(d)

(h)

(i)

(j)

(k)

(l)

Fig 7.1 : (a),(e),(i) and (m) Healthy leaf , (b),(f),(j) and (n) Cluster 1 , (c),(g),(k) and (o) cluster 2 ,(d),(h),(l) and (p) Cluster 3 AFFECTED LEAF

(a)

(b)

(c)

38

(d)

(e)

(f)

(i)

(m)

(g)

(j)

(h)

(k)

(n)

(o)

(l)

(p)

Fig 7.2 : (a),(e),(i) and (m) Affected Leaf , (b),(f),(j) and (n) Cluster 1 , (c),(g),(k) and (o) Cluster 2 ,(d),(h),(l) and (p) Cluster 3 In GLCM method, both the color and texture of an image are taken into account, to arrive at unique features, which represent that image.The affected and the clustered images of banana leaves are shown in Fig 7.2 The manual feeding of the datasets, in the form of digitized RGB color photographs was implemented for feature extraction and training the data. After training, the test data sets were used to analyze the performance of accurate classification. The main characteristics of disease detection are speed and accuracy. Hence, there is working on development of automatic, efficient, fast and accurate which is use for detection disease on unhealthy leaf. To segment the leaf area, the K-means clustering technique is used for segmentation of image then feature extraction is done using both texture as well as color features. Then finally SVM 39

classification technique is used to detect the type of leaf diseases. This algorithm helps in identifying the presence of diseases by observing the visual symptoms seen on the leaves of the plant. Our approach is in the form of table of confusion (sometimes also called a confusion matrix), is a table with two rows (predicted class) and two columns(actual class) that reports the number of false positives, false negatives, true positives, and true negatives. This allows more detailed analysis than mere proportion of correct classifications (accuracy).

OUTPUT

Fig 7.3 : Confusion Matrix 1

40

This confusion matrix shows the accuracy rate of 77%, when we take sample images of training(40) and testing(20) for classification of healthy and affected banana leaf.

Fig 7.4 : Confusion Matrix 2 This confusion matrix shows the accuracy rate of 85%,when we take sample images of training(100) and testing(10) for classification of healthy and affected banana leaf. Comparing to the above confusion matrix it produce the higher accuracy because of using higher amount of sample images in the training set.

41

CHAPTER 8 CONCLUSION Agricultural sector is still one of the most important sector over which the majority of the Indian population relies on. Detection of diseases in banana leaf is hence critical to the growth of the economy. The Detection of banana leaf Diseases has been developed using the MATLAB Application. The segmentation of the diseased part is done using K-Means segmentation. Then, GLCM texture features are extracted and classification is done using SVM. SVM classifier was used for the accurate classification of the diseases which will help the farmers to reduce the pesticide usage as well as to increase the crop yield. Future work can be done by considering the different Support Vector Machine kernels to obtain higher accuracy with lower execution time and more features can be considered to increase the accuracy.

42

REFERENCES [1] Vijay Singh , Varsha and A.K.Misra , “Detection of Unhealthy Region of Plant leaves using Image Processing and Genetic Algorithm” , International Conference on advances in Computer Engineering and Applications , Page No: 1028-1032 , June 2015. [2] Yogesh Dhandawate and Radha Kokare, “An automated approach for classification of Plant diseases towards Development of Futuristic Decision Support System in Indian Perspective”, IEEE, Page No: 794-799, 2015. [3] Gulam Mustafa Choudthary and Vikrandh Gulati, “Advance in Image Processing for Detection of Plant Diseases”, Internal Journal of Advanced Research in Computer Science and Software Engineering, Vol No: 05, Issue No: 07, Page No: 1090-1093, July 2015. [4] Anand.R , S.Veni and J.Aravindh , “An Application of Image Processing Techniques for Detection of Diseases on Brinjal leaves using K means Clustering Method” , International Conference on recent trends in Information Technology , 2016. [5] Davoud Ashourloo et al. , “An Investigation into Machine Learning Regression Techniques for the Leaf rust Diseases Detection using Hyper Spectral Measurement” , IEEE , Page No: 1-8 , 2016. [6] Jobin Francis , D.Anto Sahaya Dhas and B.K.Anoop , “Identification of Leaf Diseases in Pepper Plants using Soft Computing Techniques” , IEEE , 2016.

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[7] Pranjali B.Padol and Anjali A.Yadav, “SVM Classifier Based Grape Leaf Diseases Detection”, Conference on Advances in Signal Processing (CASP), Page No: 175-179, June 2016. [8] Vijay Singh, A.K.Misra, “Detection of Plant Leaf Disease using Image Segmentation and Soft Computing Techniques”, Information Processing in Agriculture, Page No: 41-49, November 2016. [9] Halil Durmus , Ece Olcay Gunes and Murvet Kirci , “Disease Detection on the Leaves of the tomato Plants by using Deep Learning” , I.T.U.TARBIL Environmental Agriculture Informatics Applied Research Centre , 2017. [10] V.Pooja , Ragul Dhas and V.Kanjana , “Identification of Plant Leaf Disease using Image Processing Techniques” , International Conference on Technological Innovation in ICT for Agriculture and Rural Development , Page No: 130-133 , 2017. [11] R.Meena Prakash , G.P.Sarawathi and G. Rama Lakshmi , “Detection of Leaf Diseases and Classification using Digital Image Processing” , International Conference on Innovations in Information , Embedded and Communication Systems(ICIIECS) , 2017. [12] Anjna and Rajandeep Kaur, “Review of Image Segmentation Techniques”, International Journal of Advanced Research in Computer Science”, Vol No: 08, Issue No: 04, Page No: 36-39, May 2017. [13] Melike Sardogan, Adem Tuncer and Yunes Ozen, “Plant Leaf Disease

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Detection and Classification based on CNN with LVQ

Algorithm”,

International Conference on Computer Science and Engineering, Page No: 382385, 2018. [14] Harjitha Poojary and Sabari Shedthi , “A Survey on Plant Diseases Detection using Support Vector Machine” , International Conference on Control , Power , Communication and Computing Technologies(ICCPCCT) , Page No: 292-295 , 2018. [15] Seema Ramesh et all. , “Plant Disease Detection using Machine Learning”, International Conference on Design Innovation for 3Cs Compute Communicate Control, DOI 10.1109/ICDI3C.2018.00017, Page No: 41-45, 2018. [16] Prajwala TM , Alla Pranadhi et all. , “Tomato Leaf Disease Detection using Convolutional Neural Networks”, Eleventh International Conference on Contemporary Computing(IC3) , August 2018.

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