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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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