classification of rice plant leaf diseases using feature matching

International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume I, Issue VII, November - 2014
CLASSIFICATION OF RICE PLANT LEAF
DISEASES USING FEATURE MATCHING
Dr.C.Kumar Charliepaul 1
Principal
A.S.L Pauls College of Engg & Tech, Coimbatore .
charliepaul1970@gmail.com
ABSTRACT—Technological advances have brought about drastic changes in farming like plant disease identification, pest outbreaks and crop
management. The proposed system identifies diseases of rice plant leaf by extracting features from the infected regions of the rice plant leaf
images. Fermi energy based segmentation method used to segment the infected region from its background region. Symptoms of the diseases are
characterized using features like color and shape of the infected portion and extracted feature used for identifying diseases. Color features are
determined by calculating mean and standard deviation of the infected and background pixels as well as change of color of the infected region
in comparison with the background in three different color planes, Red (R), Green (G) and Blue (B). Shape of the infected region is a major
symptom to predict the diseases. When the plant is infected by diseases having the symptoms of shapes like oval, circular and irregular spot.
DRSLE (Distance Regularized Level Set Evolution) algorithm used for identifying desired shape of the infected region. Rough Set theory
reduces the complexity of the system and minimizes loss of information by selecting core features. Finally using features matching predict
diseases of rice plant leaf images and provides superior result compare to traditional method.
Keywords: Fermi energy segmentation, Rough set theory, Feature Extraction, Rice plant leaf, Feature Matching.
I.
is similar with respect to some properties like color,
INTRODUCTION
intensity, or texture. Interested region provide more useful
Image processing is a process of analyzes and
information for the model. Feature Extraction reduces the
manipulation of digital image in order to improve the quality
amount of unnecessary information to describe a model. The
of image. Image is a collection of pixels. Pixel is a main
major problem in analyzing the complex data involves large
element in digital image. Digital image is processed in digital
number of variables. The large number of variable analyzes
computer. The digital image is composed of a finite number
require a large amount of memory and computational time.
of elements and each element has a particular value and
The color analyzes is the process of extracting the interested
location. Image Segmentation is the process of segmenting a
color information. The main element of image is color. The
digital image into several segments. The main aim of
analysis of color feature in image database retrieval is most
segmentation in image processing is to simplify or represent
important.
an image into more meaningful and also easier to analyze.
So the color feature is more domain independent
Image segmentation mainly used to isolate the objects and
compared to other feature. Color having three dimensional
boundaries in images. It assigns a label to every pixel in an
space Red, Green and Blue but RGB color space is not
image, the pixel with same label share certain characteristics.
uniform in images. It is used to eliminate the false hit. The
Thresholding is the simplest method of image segmentation.
Data contains both redundant and irrelevant features.
It can be used to isolate the interested region from its
Irrelevant feature does not provide more useful information
background region. Each of the pixels in an interested region
and redundant feature leads to computational process as
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International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume I, Issue VII, November - 2014
complex. Feature selection is the process of selecting
image of the disease infected plant or leaf, into the H, I3a
relevant and core feature for constructing the model. Feature
and
selection is also called as selection of variable, selection of
transformations are developed from a modification of the
variable subset and selection of attribute. It is subset of
original image in to color transformation to meet the
feature extraction. Feature extraction generate new feature
requirements of the plant disease data set. The transformed
from original feature while feature selection returns a subset
image is then segmented by analyzing the distribution of
of feature. Rough set theory is a feature selection method. It
intensities in a histogram. The threshold cut-off value is
is determined by lower and upper boundary of a set. It is a
determined according to their position in the histogram. This
mathematical concept dealing with uncertainty in data.
technique is particularly useful when the target in the image
Feature matching is a process of finding similar feature in
data set is one with a large distribution of intensities [2].
I3b
color
transformations.
The
I3a
and
I3b
different images in image processing. The feature of testing
S.L.S. Abdullah et al (2012) proposed improved
images is compared with the feature of training images. The
thresholding based technique for image segmentation.
highest feature matching images are taken as resultant
Different illuminations may produce different color intensity
images.
of the object surface and thus lead to inaccurate segmented
images and traditional methods were unable to produce good
quality segmented. Therefore, an improved thresholdingbased segmentation integrated with an inverse technique
(TsTN) that was able to partition natural images correctly.
The analysis results showed that TsTN has the ability to
produce good quality segmented images for dark images.
Furthermore, this segmentation technique was proven to be
more accurate than the traditional thresholding and clustering
II.
RELATED WORK
techniques [1].
A.K Das et al (2012) proposed SVM and Bayes’
Z.Xue et al (2003) proposed Bayesian shape model
classifier to classify the diseases of rice plant leaf. An
(BSM) to find contour points in the face. A full-face model
automated system has been developed to classify the leaf
consisting of the contour points is designed to describe the
brown spot and the leaf blast diseases of rice plant based on
face patch, using which the normalization of the extracted
the morphological changes of the plants caused by the
face patch can be performed efficiently. In BSM, the
diseases. Otsu method is used to isolate the infected region
prototype of the face contour can be adjusted adaptively
from the background. Radial distribution of the hue from the
according to its prior distribution. Moreover, an affine
center to the boundary of the spot images has been used as
invariant internal energy term is introduced to describe the
features to classify the diseases by Bayes’ and SVM
local shape deformations between the prototype contour in
Classifier [3].
the shape domain and the deformable contour in the image
R.Lu et al (2013) proposed Support Vector Machine
domain. The face patch is extracted and normalized using the
for classification and Otsu method for segmentation. This
piece-wise affine triangle warping algorithm.[5]
A.Camarago
et
al
(2009)
proposed
method automatically adjusts the classification hyper plane
color
calculated by using linear SVM and requires minimum
transformation method. This method converting the RGB
291
International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume I, Issue VII, November - 2014
training and time. It also avoids the problems caused by
with variation in color from gray to light brown at centers,
variations in the lighting condition and color of the fruit.
surrounded by distinct dark reddish brown margins.
Fig. 2. Leaf Brown Spot
Disadvantage of this method is segmentation is not accurate
[6].
3) Rice Blast
III.
PROPOSED WORK
Rice blast is caused by the fungus Magnaportheoryzae
Treatment the rice plant leaf based on diseases
and observed in both lowland and upland. Initially white to
saves the products from quantitative and qualitative loss and
grayish green circular lesions or spots with dark green
plays significant role in country’s economic growth. The
borders are found on the leaves.
proposed system aims at developing a predicting system to
Fig. 3. Rice Blast
predict the diseases of rice plant leaf by performing the
B. Fermi Energy Based Segmentation
steps: Identification of the Infected Region, Extraction of
Features, Selection of Features, Feature matching and
System quality depends on the segmented result of
Identification of Diseases.
infected leaf images. Thresholding is a widely used
segmentation technique that determines threshold value and
A. Description of Rice Plant Leaf Diseases
based on that value segment the infected leaf images. The
The Rice plant leaf disease classified by the
energy-based segmentation method consists in finding the
proposed system is described below.
optimal segmentation .This method is robust because the
1) Leaf Brown Spot
Brown spot symptoms are observed at tillering stage.
The shapes of the infected region vary from circular to oval
with light brown color to gray at the center and reddish
brown color at margin.
segmentation criteria are objectively defined in the energy
2) Sheath Rot
function and the optimization process is global and
automatic. Fermi energy based segmentation method is used
to predict the infected regions using RGB color components
of the images. The Fermi energy or Fermi level is expressed
in Eq. (1).
2
h2 2  3N  3
EF 


2ML2   
Fig. 1. Sheath Rot
(1)
Sheath rot caused by the pathogen Sarocladiumoryzae.
Where N is the number of particles, M is the mass
Rotting occurs on the leaf sheath that encloses the young
of the particles, L is the length of the cube and h is the
panicles. The lesions start as oblong or some irregular spots
Planck constant. When an image is acquired using a
physical source, the information content in the image is
292
International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume I, Issue VII, November - 2014
proportional to the energy radiated by the source. Fermi
with the background in three different color planes, Red (R),
EF of an image is computed using Eq. (1), where N
Green (G) and Blue (B).Mean and standard deviations are
energy
calculated by using the Histogram. Shape features are
is mapped as the number of pixels having distinct color
extracted using the DRLSE algorithm. The algorithm having
values in the image, number of grey levels is equivalent to
two step (1) edge detection (2) shape detection.
length L and mass of the image (M) is calculated by
Edge and Shape Detection
aggregating the mass of each pixel (i, j) using Eq. (2).
ij
rgb
m

H r , g ,b
pq
The RGB images are given as an input for edge
 r  g  b
(2)
detection. Output of the process is edge of the infected
region. Initially images are read from the specified folder
Where Hr,g,b is the number of pixels having a
and that image can be converted into grey scale image. The
particular intensity with r, g and b grey level values
computer generated curves that move within images to find
corresponding to Red, Green and Blue color planes
object boundary (Gradient Vector Flow).After the gradient
respectively and
p  q is
of the image computed, pixels with large gradient values
the size of the image. Energy
becomes possible edge pixel. In DRLSE Level Set method
E(i, j) at (i, j)th pixel position is calculated using Eq. (3) and
represents the outline of the irregular shape and also
compared with the threshold value
EF
for segmenting the
controls the changes in shape. Such as splitting and merging
in a natural and efficient way. The level set method
infected region of the image.
represents a closed curve using an auxiliary function
2
Ei, j  Er , g ,b 
If
,
2
h
r 2  g 2  b2 
ij
2 
2 mrgb  L
called the level set function.
(3)
level set of
Ei, j  EF then the pixel (i, j) is treated as part
is represented as the zero
by
  {( x , y ) |  ( x , y )  0}
(4)
of the infected region, otherwise in background region. To
reduce computational complexity constants h ,  and L are
is implicitly manipulated by level set method
eliminated from Eqs. (1) and (2) as the values are compared.
through the function
C. Feature Extraction
values inside the region
.The function
takes positive
and negative values outside. x,y
Change of color of the plant leaf due to infection,
are the coordinates of the infected region. The advantage of
shape of the spot (infected region) of the leaf is used as
this method is,it can perform number of computations
features to classify the diseases. There are several ways to
involving curves and surfaces on a fixed Cartesian grid
detect plant pathologies (diseases). Some diseases have
without
visible symptoms in leaf. Change of color of the infected
formulation with a distance regularization term and an
region is compared with the background is considered as
external energy term derives the motion of the zero level
one of the important features for disease identification.
contour towards the desired location. The level set evolution
Color features are determined by calculating mean and
is a gradient flow that reduces this energy function. The
standard deviation of the infected and background pixels as
level set evolution, uniformity of the LSF is maintained by a
well as change of color of the infected region in comparison
forward and backward diffusion that can be derived from
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parameterize
these
objects.
The
level
set
International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume I, Issue VII, November - 2014
the distance regularization term. The distance regularization
infected by corresponding diseases. In this method to
term with a potential function forces the gradient magnitude
perform number of computations involving curves and
of the level set function to one of its minimum point, so it
surfaces on a fixed Cartesian grid without parameterize
maintains the desired shape of the level set function.
these objects.
D. Rough Set Theory
IV.
PERFORMANCE COMPARISON
AND RESULT ANALYSIS
Rough set theory is a mathematical concept dealing
with uncertainty in data. The rough set is defined by the
A. Data Set The data set has been taken from
tuple ( PX , PX ).It is determined by the upper and lower
www.shutterstock.com which contains Blast, Sheath rot and
Brown spot images. Six images are taken for each disease,
boundary of a set. A discernibility matrix is constructed to
three images taken for training dataset and three images
represent the family of discernibility relations. Each cell in a
taken for testing dataset in each disease. The intensity, color
discernibility matrix (M) contains the features for which two
and shape are the major attributes of the image
objects are discernible. The element
mij
of M is defined by
B. Result Analysis
Eq. (5).
mij {ac : a(xi )  a(xj ) (d D, d(xi )  (dxj ))}, i,j
=1,2,3,……n
(5)
GT = ground truth
(7)
Fig. 4. Performance Evaluation of Segmentation Algorithm
Where
d(xi )
labels of objects xi and
and
xj
d(x j )
represent the class
respectively.
An entry containing minimum number of features
implies that the features are sufficient to distinguish
associated diseases and considered as the most important
The Figure 4 shows that Fermi energy method
features or core. The core feature set, say CR is defined by
contains less noise in segmentation when compared with the
Eq. (6)and remaining are treated as noncore features, say
Otsu and K-means. The gray scale image is input to the
(NC).
Otsu and K-Mean method but color component of the image
CR  {mij || mij || 1, i, j 1,2,3...., n}
is input to the Fermi energy method. Because of the color
(6)
component is used as an input, infected images are correctly
E. Feature Matching
segmented. From the result analysis, the Fermi energy based
segmentation performs better than traditional segmentation.
Minimal feature subset or reduct is considered for
Computational time is reduced due to core feature selection.
Feature matching where feature match is represented as IF-
If the disease prediction system uses Otsu and K-means
THEN form consisting of antecedent and consequent. IF the
Segmentation method, produces 50% and 45% accurate
color and shape of trained dataset images is equal to the
result respectively, but results 75% of accuracy when it uses
color and shape of test dataset image then the plant leaf is
Fermi Energy segmentation method. In traditional method
294
International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume I, Issue VII, November - 2014
greyscale images taken for segmentation. In uninfected
Communication Program and her ideas and suggestions,
region some of the pixel treated as infected and in infected
which have been very helpful in the project. I am so deeply
region some of the pixel treated as uninfected. So the
grateful for her help professionalism and valuable guidance
infected region not correctly segmented.
throughout this project.
1) Fermi Energy Based Segmentation
Fig.5. Segmentation Algorithm
REFERENCES
[1] Abdullah.SL.S, Hambalia.H.A, and Jamil.N (2012), ‘Segmentation of
Color component of the images are input to the Fermi
Natural Images Using an Improved Threshold Based technique,’
energy method. So the infected region correctly segmented.
International Symposium of Robotics and Intelligent Sensors, Vol. 50,No.
3, pp. 938-944.
CONCLUSION AND FUTURE WORK
[2] Camargoa.A, and Smith.J.S (2009), ‘An Image Processing Based
Algorithm to Automatically Identify Plant Disease Visual Symptoms,’
International Journal of Biosystem Engineering, Vol. 102, No. 1, pp. 9-21.
Predictive system has been developed for the
prediction of rice plant leaf diseases using the symptoms
[3] Das.A.K, Phadikar.S, and Sil.J (2012), ‘Classification Rice Leaf Diseases
Based on Morphological Changes,’ International Journal of Information
created by the diseases. Fermi energy based region
and Electronics Engineering, Vol. 20, No. 2, pp. 80-95.
extraction method correctly segments the infected region.
[4] Das.A.K, Phadikar.S, and Sil.J (2013), ‘Rice Diseases Classification using
Color features are determined by calculating mean and
Feature Selection and Rule Generation Techniques,’ International Journal
of Computers and Electronics Engineering, Vol. 90, No. 3, pp. 76-85.
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well as change of color of the infected region .The desired
extraction and recognition,’ International Journal of Pattern recognition,
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Apple Sorting and Grading Using Support Vector Machine and Otsu’s
are selected which minimizes loss of information and
Method,’ International Journal of. Computer and Electronics in
reduces the computational time. Finally, the testing dataset
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[7] Salamo.M, and Sanchez.M.L (2011), ‘Rough
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features, by using highest match features, diseases are
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classified. The result shows that it performs well on nonuniform illumination images and also reduces the noisy
Author Biography:
features. The predictive system 75% correctly predicts the
Kumar Charlie Paul, Principal of A.S.L Pauls College of
Engineering & Technology. Had did many National and
International Conferences and published many papers in
journals. He also guided many students for their Ph.D
project works. Having more than 23 years of experience in
teaching field.
rice plant leaf diseases. In future, it can be extended using
more dataset from various kind of plant leaf and also can
find risk factor of diseases.
ACKNOWLEDGMENT
First of all I would like to extend my sincere
gratitude to my supervisor Dr.C.Nalini for providing me the
opportunities of taking the part in Master of Computer and
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