Agricultural Plant Leaf Disease Detection Using Image Processing.

Walchand College Of Engineering, Sangli.

(An Autonomous Institute)

Department of Electronics Engineering

M. Tech. Part II

Dissertation Phase I

SYNOPSIS

1. Name of Student : Mr.Nitin Pandit Kumbhar.

2. Name of Course : M. Tech. in Electronics Engineering

3. Date of Registration : July, 2012

4. Name of Guide : Prof. S. B. Dhaygude.

5. Proposed Title of Dissertation : Agricultural Plant Leaf Disease Detection using image processing.

6. Synopsis of the work :

A Problem definition and Relevance

Plant diseases cause periodic outbreak of diseases which leads to large scale

death and famine. The naked eye observation of experts is the main approach adopted

in practice for detection and identification of plant diseases. But, this requires

continuous monitoring of experts which might prohibitively expensive in large farms.

Further, in some developing countries, farmers may have to go long distances to contact

experts, this makes consulting experts too expensive and time consuming and

moreover farmers are unaware of non native diseases. Detection of plant diseases is an

important research topic as it may prove benefits in monitoring large fields of crops.

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Agricultural Plant Leaf Disease Detection Using Image Processing.

B. Introduction

Plant disease diagnosis is an art as well as science.The diagnostic proces

(i.e.recognition of symptomsand signs), is inherently visual and requires intuitive judgment

as well as the use of scientific methods.Photographic images of symptoms and signs of

plant’s diseases used extensively to enhance description of plant diseases are invaluable in

research, teaching and diagnostics etc. Farmers are very much concerned about the huge costs

involved in these activities. Automatic identification and classification of diseases based on

their particular symptoms are very useful to farmers and also agriculture scientists. Early

detection of diseases is a major challenge in agriculture science.

C. Survey of the possible development approaches

1. Color Transformation Structure: First, the RGB images of leaves are converted

into Hue Saturation Intensity (HSI) color space representation. Hue is a color attribute that

refers to the domi ant color as perceived by an observer. Saturation refers to the relative purity

or the amount of white light added to hue and intensity refers to the amplitude of the light

2. Masking green pixels: In this step, identify the mostly green colored pixels.

After that, based on specified threshold value that is computed for these pixels, the mostly

green pixels are masked as follows: if the green component of the pixel intensity is less than

the pre-computed threshold value, that pixels are assigned to a value of zero. This is done

in sense that the green colored pixels mostly represent the healthy areas of the leaf and they

do not add any valuable weight to disease identification and furthermore this significantly

reduces the processing time.

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Agricultural Plant Leaf Disease Detection Using Image Processing.

3 .Segmentation: The infected region is then segmented into a number of patches

of equal size. The size of the patch is chosen in such a way that the significant information is

not lost. The next step is to extract the useful segments. Not all segments contain significant

amount of information. So the patches which are having more than fifty percent of the

information are taken into account for the further analysis.

4 Color co-occurrence Method: The color co-occurrence texture analysis

method is developed through the Spatial Gray-level Dependence Matrices (SGDM). The

gray level co-occurrence methodology is a statistical way to describe shape by statistically

sampling the way certain gray-levels occur in relation to other gray levels . These matrices

measure the probability that a pixel at one particular gray level will occur at a distinct

distance and orientation from any pixel given that pixel has a second particular gray level

5 Texture Features: Texture features like Contrast, Energy, Local homogeneity,

Cluster shade and Cluster prominence are computed for the Hue content of the image

7. The Proposed Work

To study of Detection of plant leaf disease with color co-occurrence matrix .

Implement MATLAB code with GUI for Detection of Agricultural plant

leaf disease.

Software Architecture

MATLAB 7 R2012a

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Agricultural Plant Leaf Disease Detection Using Image Processing.

8. Facility Available

A. Post-graduate Laboratory.

B. Central Library.

C. Computers.

D. Internet.

9. Estimated Cost : RS. 2000/- only (approximately).

10. Expected Date of Completion : June 2013.

Mr. Nitin Pandit Kumbhar prof. S. B. Dhaygude

(Student) (Guide)

Dr. Mrs. S. S. Deshapande

(H. O. D.)

Electronics Department

Walchand College of Engineering, Sangli

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Agricultural Plant Leaf Disease Detection Using Image Processing.

12 References :

1. Ananthi, S. Vishnu Varthini, Detection And Classification Of Plant Leaf Diseases

International Journal of Research in Engineering & Applied Sciences, Volume 2,

Issue 2 (February 2012) ISSN: 2249-3905

2. 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 (0975-8887), Volume 17-No.1.March 2011.

3. Dheeb Al Bashish, Malik Braik, and Sulieman Bani-Ahmad , (2010)A Framework

for Detection and Classification of Plant Leaf and Stem Diseases, International

Conference on Signal and Image Processing pp 113-118

4. Dae Gwan Kim, Thomas F. Burks, Jianwei Qin, Duke M. Bulanon, Classification of

grapefruit peel diseases using color texture feature analysis, International Journal on

Agriculture and Biological Engineering, Vol:2, No:3,September 2009. Pp 41-50.

5. Sabine D. Bauer , Filip Korc, Wolfgang Forstner, The Potential of Automatic

Methods of Classification to identify Leaf diseases from Multispectral images,

Published online: 26 January 2011,Springer Science+Business Media, LLC 2011.,

Precision Agric (2011) 12:361–377, DOI 10.1007/s11119-011-921

6. Muhammad Hameed Siddiqi1, Suziah Sulaiman, Ibrahima Faye and Irshad Ahmad,

A Real Time Specific Weed Discrimination System Using Multi-Level Wavelet

Decomposition, International Journal of Agriculture & Biology, ISSN Print: 1560–

8530; ISSN Online: 1814-9596 ,09–118/YHP/2009/11–5–559–565

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