Content-Based Image Retrieval (CBIR) For Identifying … · Content based image retrieval (CBIR) [2, 9] offers efficient search and retrieval of images based on their content. With

Content-Based Image Retrieval (CBIR) For Identifying Image Based Plant Disease

Kamaljot Singh Kailey, Gurjinder Singh Sahdra

Department of Computer Science and Technology

[email protected] [email protected]

Lovely Professional University, Punjab, India

Abstract

This paper presents a method for identify plant

disease based on color, edge detection and histogram

matching. Farmers are suffering from the problem

rising from various types of plant traits/diseases.

Sometimes plant’s doctors are also unable to

recognize the disease that results in lack of

identification of right type of disease and this leads to

crop spoil if not taken care of at right time. The most

significant part of research on plant disease to

identify the disease based on CBIR (content based

image retrieval) that is mainly concerned with the

accurate detection of diseased plant. It has

significant perspective in field of agriculture. This

research describes effective; sample technique for

identify plant disease. The method used in this

research is divided into two major phases. First

phase concerns with training of healthy sample and

diseased sample. Second phase concerns with the

training of test sample and generates result based on

the edge detection and histogram matching.

Keywords

Edge detection, Image processing, CBIR, Color

histogram.

1. Introduction Plant diseases are turn into dilemma where it can

cause of significant reduction of the quality and

quantity of the agriculture products. Our research

focuses on the detection of plants diseases based on

color, edge detection and matching histogram

technique. We need two very significance

characteristic that is mainly concern with the

accuracy of detection and speed to recognize the

image diseases. Based on the color space, histogram,

and edge detection techniques, we can able to find

the disease of plant. Our research works on two

phases. First phase includes all the healthy and

disease leaves are given as input to the MATLAB. In

the training process, the RGB color components are

separated into three layers Red, Green and Blue i.e.

grayscale image and then apply the CANNY’s edge

detecting technique. After the edge detection

technique histogram is plot for each component of

healthy and disease leaf image and stored in the

systems. Second phase is mainly concern the test the

testing samples that are given as input to the MAT

LAB. In the training process of testing leaf , the RGB

color components of testing leaf image is separated

into red, green and blue components and apply

CANNY’s edge detection technique on each

component. To find the histogram plot for each

components and compare all the stored results and

identify disease infected or not in the plants leaf.

1.1 Problem of agricultural plant diseases

India is an [1] agricultural country; wherein about

70% of the population depends on agriculture.

Farmers have wide range of diversity to select

suitable Fruit and Vegetable crops. However, the

cultivation of these crops for optimum yield and

quality produce is highly technical. It can be

improved by the aid of technological support. The

management of perennial fruit crops requires close

monitoring especially for the management of diseases

that can affect production significantly and

subsequently the post-harvest life. It is [4] estimated

that 2007 plant disease losses in Georgia (USA) is

approximately $539.74 million. Of this amount,

around 185 million USD was spent on controlling the

diseases, and the rest is the value of damage caused

by the diseases. Those numbers are listed in Table 1. Automatic detection of plant diseases is an essential

research topic as it may prove benefits in monitoring

large fields of crops, and thus automatically detect

the symptoms of diseases as soon as they appear on

plant leaves. Therefore; looking for fast, automatic,

less expensive and accurate method to detect plant

disease cases is of great realistic significance [4].

Kamaljot Singh Kailey et al ,Int.J.Computer Technology & Applications,Vol 3 (3), 1099-1104

IJCTA | MAY-JUNE 2012 Available [email protected]

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mailto:[email protected]

mailto:[email protected]

Table 1: Summary [4] of total losses due to disease

damage and cost of control in Georgia, USA in 2007

.

1.2 Overview of Content Based Image

retrieval (CBIR)

Content based image retrieval (CBIR) [2, 9] offers

efficient search and retrieval of images based on their

content. With the abundance and increasing number

of images in digital libraries and the Internet in the

last decades, CBIR has become an active research

area. The retrieval may involve the relatively simpler

problem of finding images with low level

characteristics (e.g. finding images of sunset) or high

level concepts (e.g. finding pictures containing

bicycles).With the development of the Internet, and

the availability of image capturing devices such as

digital cameras, image scanners, the size of digital

image collection is increasing rapidly. Efficient

image searching, browsing and retrieval tools are

required by users from various domains, including

remote sensing, fashion, crime prevention,

publishing, medicine, architecture, etc. For this

purpose, many general purpose image retrieval

systems have been developed

1.3 Useful application of Image processing

for detecting disease Image processing is the enhancement of image that is

processing an image so that the results are more

suitable for a particular application. Processing an

image means sharpening or de-blurring an out of

focus image, highlighting edges, improving image

contrast, or brightening an image, removing noise.

The image processing has some useful applications

for detecting the various types of plant diseases such

as:

To detect edges of diseased leaf and stem

To find shape of affected area

To determine color of affected area

To separate the layers of image

For Image segmentation

2. Proposed approach step by step detail This approach starts with the digital images for both

the samples such as healthy leaf images and diseased

leaf images. The image is captured from the

environment using the Kodak digital camera of 9

megapixels. Then set the resolution of image at

390x425 dimensions. Once the database is acquired

of healthy and infected images of samples, the image

processing techniques are used to extract the useful

features that are useful for the analysis of next

phases. After that, histogram comparisons are used to

classify the image according to the specific problem

at hand.

PHASE-I

Step1. In the initial step of phase-I of disease image

detection, the RBG images of healthy and infected

plants are picked up. For this purpose, we need two

sample one for the healthy image and second for the

diseased image. In the healthy image sample we

choose a normal or uninfected image of leaf. And on

the other hand, we choose infected images of disease.

Figure1: Healthy and diseased image of plant

Step2. The second step of detection of plant diseases

start with the training process. In the training process,

first I separate the layers of RGB image into Red,

Green and Blue layers and then apply the CANNY’s

edge detection technique to detect the edges of

layered images. This technique is applied on both the

samples such that healthy sample as well as the

diseased sample of same plant. The edge detection

technique can not apply directly on the RGB image.

First, we need to convert it into grayscale image then

the CANNY’s edge detection technique is applied.



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The layers separation and the edge detection

technique of RGB image are shown as below:

Figure2: Red, green, and blue layers of RGB images

The above figure shows the layer separation of

healthy RGB leaf image. The separation of layer is

necessary for CANNY’s edge detection because of

the edge detection cannot directly applied on the

RGB image. The layers separation plays the

important role in detection of disease based on color

histogram. Once the layers are separated the

CANNY’s edge detection technique applied on each

layer. The edge detection technique is shown as:.

Figure3: CANNY’s Edge detection for Red Green

and Blue Layers

As the same way, we are applied these two

techniques on the diseased sample. First, we separate

the layers of diseased image then applied the

CANNY’s edge detection technique. So we obtained

the grayscale that is separated layers images and

corresponding edge detected images

PHASE-II

Step3. In the second phase, choose the test sample of

plant. When the testing sample is selected, the

training process is started again on the testing image.

Step4: In the training process, first I separate the

layers of tested image into Red, Green and Blue

layers and again apply CANNY’s edge detection

technique to detect the edges of layer’s images

Step5: Once the training process of first phase

samples is finished the histogram is generated for

both healthy leaf sample and diseased leaf sample

and saves in the memory, these histogram are

displayed, when we generate the histogram for the

testing image. In the second phase, after the training

process the histogram for testing sample is created or

generated suddenly. Once the his tograms are

generated for both samples and the testing image,

immediately we will applied the comparison

technique based on the histogram and edge detection

technique. The comparison is firstly with the testing

sample and the healthy sample if the testing sample is

diseased, it compare testing sample with the diseased

sample and these steps take few minute to display the

comparison result that is the testing sample is

diseased or not. The GUI (graphical user interface) is

used to show the overall process. When the

comparison is applied the waiting bar is display on

our display and results are also shown through the

GUI. This is beneficial for us because we are easily

understood the processing of implementation phase.

3. Experimental result and observation

3.1 Input data preparation and Observation

setting

Our experiment prepared two main phases, namely

(i) training of healthy and diseased sample, (ii)

training of testing sample. Once the database is

acquired of healthy and infected images of samples,

the image processing techniques are used to extract

the useful features that are useful for the analysis. For

this observation, we need two types of samples

images. The image is captured from the environment

using the Kodak digital camera of 9 megapixels.

Basically, the images that are captured with digital

camera of 9 megapixels are 3472x2614 dimensions.

We need to setting set up the resolution of image at

390x425 dimensions. Once the images are setting up

with the resolution, the training phases of healthy and

diseased leaf are started.

In the first phase of the implementation, we give the

right sample which is not diseased to the input of

MATLAB. After the training of healthy sample, we

will give the diseased sample as an input of

MATLAB. The training of the healthy leaf and

diseased leaf sample includes the training of separate

the RGB images into the three layers such as Red,

Green and Blue grayscale images. When the layers

are separated, the CANNY’S edge detection



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technique is applied on both sample’s layers. After

the CANNY’S edge detection technique, we generate

the color histogram for both the samples of plant and

save it in MATLAB memory display whenever it’s

needed. The CANNY’s edge detection technique is

used because of it’s the best technique of edge

detection as compared to other edge detection

techniques such as SOBEL’s edge detecting

technique. This is not perfect in detecting edges of

image.

When the first phase training process is completed,

the second phase of testing sample is started with the

selection of testing image. The testing image of plant

disease is also selected with the resolution of

390x425. This resolution is settled due to the speed

reason. In the second phase training process, first I

separate the layers of tested image into Red, Green

and Blue layers and used the CANNY’s edge

detection technique to detect the edges of layered

images. After the training process the histogram for

testing sample is created or generated suddenly. Once

the histograms are generated for both samples and the

testing image, immediately we will applied the

comparison technique based on the histogram and

edge detection technique. The comparison is firstly

with the testing sample and the healthy sample if the

testing sample is diseased, it compare testing sample

with the diseased sample and these steps take few

minute to display the comparison result that is the

testing sample is diseased or not.

3.2 Experimentation result

The experimentation start with the two samples, one

for the healthy leaf sample and second for the

diseased leaf sample. The experiment results for the

phase-1 which samples are the input to the

MATLAB. The training process is started on both the

samples. The experiment on the healthy sample is

shown below:

Figure4: experiment result of healthy sample

The above figure shows the observation result for the

healthy sample layers. These results show the layers

separation of RGB healthy sample image into Red,

Green, and Blue. The layers separation is necessary

for the edge detection. We cannot apply edge

detection directly on the RGB healthy leaf sample.

On the other hand, when we apply this technique on

the diseased sample, we obtain the following results

that are shown as:

Figure5: experiment result of diseased sample

The training process is necessary for further analysis.

If we are going to further analysis without including

the training process, it passes a message to us ―First

we required training process‖ in the MATLAB

command window. Once the first phase is finished

the implementation of second phase is started. The

second phase start with the implementation of testing

image. The implementation result for testing sample

is shown below:

Figure6: experiment result of testing sample

The above figure shows the experiment result on

testing sample which conveys the layers separation

result and the edge detection on each layer.

When training process for both samples and testing

sample are completed. The histogram is generated for

healthy leaf sample, diseased leaf sample and for

testing sample. Once the histograms for samples are

generated the comparison between histogram is

started, immediately we applied the comparison

technique based on the histogram and edge detection

technique. The generation of histogram and the

comparison is shown below. The above figure shows

the histogram for healthy leaf sample, diseased

sample and test sample. The comparison between



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these histograms is shown by the waiting bar in the

MATLAB. The waiting is shown as:

Figure7: histogram for samples

Figure8: Histogram matching waiting bar

First comparison is made between the test sample and

the healthy sample. If the test sample histogram

matched with the healthy leaf sample is generated the

result that is plant is not diseased. If it does not match

with the healthy leaf sample, the comparison is made

between the test sample and the diseased sample. If

the test sample is matched with the diseased sample,

it generates plant is diseased. The healthy and

diseased image is shown by the following figures:

Figure9 shows the result of not diseased leaf

Figure10 shown the result of diseased leaf

The diseased plant full screen result is shown below:

Figure11: full view of GUI of diseased leaf

detection

On the other hand, when we choose right image, it

generates the result of not diseased. They are shown

as below:

Figure12: Full screen result of not diseased image



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4. Conclusion and Future work To wind up all the information discuss above, I

should like to concludes that it is a efficient and

accurate technique for automatically detection of

plant diseased. In this research, plant diseased is

detected by using histogram matching. The histogram

matching is based on the color feature and the edge

detection technique. The color features extraction are

applied on samples that are contained the healthy leaf

of plant and the diseased leaf of the plant. The

training process includes the training of these

samples by using layers separation technique which

separate the layers of RGB image into red, green, and

blue layers and edge detection technique which

detecting edges of the layered images.

Once the histograms are generated for both samples

and the testing image, immediately we applied the

comparison technique based on the histogram. The

comparison is firstly with the testing sample and the

healthy sample if the testing sample is diseased, it

compare testing sample with the diseased sample and

these steps take few minute to display the comparison

result that is the testing sample is diseased or not. The

GUI (graphical user interface) is used to show the

overall process. When the comparison is applied the

waiting bar is display on our display and results are

also shown through the GUI. This is beneficial for us

because we are easily understood the processing of

implementation phase.

The future work mainly concerns with the large

database and advance feature of color extraction that

contains a better result of detection. Another work

concerns with research work in a particular field with

advance features and technology.

5. References

[1] Jayamala K. Patil, Raj Kumar, [2011] ―Advances in

image processing for detection of plant diseases‖, Journal of Advanced Bioinformatics Applications and Research

ISSN 0976-2604 Vol 2, Issue 2, pp 135-141

[2] Y Liua, D Zhanga, G Lua,Wei-Ying Mab, ―Asurvey of

content-based image retrieval with high-level semantics‖,

Pattern Recognition 40 (2007),pp. 262 – 282. [3] H Müller, N Michoux, D Bandon, A Geissbuhler, ―A

review of content-based image retrieval systems in medical

applications–—clinical benefits and future directions‖,

International Journal of Medical Informatics (2004) 73, pp.

1—23. [4] H.-W. Dehne, G. Adam, M. Diekmann, J. Frahm, A.

Mauler-Machnik, P. van Halteren, ―Diagnosis and

Identification of Plant Pathogens (Developments in Plant

Pathology)‖, October 31, 1997, Edition-1, pp 1-4.

[5] H Al-Hiary, S Bani-Ahmad, M Reyalat, M Braik and Z ALRahamneh, ―Fast and Accurate Detection and

Classification of Plant Diseases‖, International Journal of

Computer Applications 17(1): pp. 31-38, March 2011.

Published by Foundation of Computer Science.

[6] B. S. Anami1, Suvarna N. and Govardhan A, ―A text

based approach to content based information retrieval for

Indian medicinal plants‖, International Journal of Physical Sciences Vol. 3 (11), pp. 264-274, November, 2008.

[7] Basavaraj S Anami, Suvarna S Nandyal and A

Govardhan. Article: A Combined Color, Texture and Edge

Features Based Approach for Identification and

Classification of Indian Medicinal Plants. International Journal of Computer Applications 6(12):45–51, September

2010. Published By Foundation of Computer Science.

[8] B.Sathya Bama, S.Mohana valli, S.Raju, V.Abhai

Kumar, content based leaf image retrieval (CBLIR) using

shape, color and texture features, Indian Journal of Computer Science and Engineering (IJCSE) Vol. 2 No. 2

Apr-May 2011.

[9] Hanife Kebapci, Berrin Yanikoglu, Gozde Unal [2009],

Plant Image Retrieval Using Color and Texture Features,

Computer and Information Sciences, IEEE Transactions on, pp. 82 – 87

Kamaljot Singh was born in Nakodar on 4th

September. He received his Msc(CS) Degree from

Guru Nanak Dav University, Amritsar and M.TECH

Degree from Lovely Professional University in 2010

and 2012 respectively. His Research Interests include

Image Processing, Software Engineering and wireless

network, neural network etc.

Gurjinder Singh Sahdra was born in Nawanshahar

on 11th

February 1987. He received his Msc(CS) Degree from Guru Nanak Dav University, Amritsar and M.TECH Degree from Lovely Professional

University in 2010 and 2012 respectively. His Research Interests include Image Processing, cloud computing and wireless network, neural network etc

.



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Content-Based Image Retrieval (CBIR) For Identifying … · Content based image retrieval (CBIR) [2, 9] offers efficient search and retrieval of images based on their content. With