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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 5, September – October (2013), pp. 204-216
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IJCET
©IAEME

A REVIEW OF PLANT LEAF CLASSIFICATION FEATURES AND
TECHNIQUES
Garima Agarwal1, Rekha Nair2, Pravin Shrinath3
1

M.Tech (Computer Science), Mukesh Patel School of Technology Management & Engineering,
JVPD Vile Parle (West) Mumbai 400056, India.
2
Centre for Development of Advanced Computing, Gulmohar Cross Road No.9, Juhu,
Mumbai – 49, India.
3
Associate Professor, Department of Computer Engineering, MPSTME,
SVKM’s NMIMS University, Mumbai, India.

ABSTRACT
Plant Leaf classification has a broad application in agriculture and medicine, and it is mainly
significant to the biology diversity research. As plants play a very important role for environmental
protection, it is more important to identify and classify them accurately. Leaf classification is a
technique where leaf is classified based on its different morphological features like vein structure,
shape, color, texture etc. This paper provides an overview of different aspects of plant leaf
classification and various existing techniques used for classification.
Keywords: Leaf Classification, Leaf Recognition, Vein Extraction, GLCM, LVQ.
1.

INTRODUCTION

Plant is one of the most important forms of life on earth. Plants maintain the balance of
oxygen and carbon dioxide of earth’s atmosphere. The relations between plants and human beings
are also very close. In addition, plants are important means of livelihood and production of human
beings. Unfortunately, the overwhelming development of human civilization has disrupted this
balance to a greater extent than we realize. It is one of our biggest responsibilities to save the plants
from various threats, restore the diverseness of the plant community and put everything back to
balance.
The main step of protecting plants is to automatically recognize or classify them means
understand what they are and where they come from. There are a huge number of plant species
worldwide. To handle such volumes of information, development of a quick and efficient
classification method has become an area of active research. In addition to the conservation aspect,
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recognition of plants is also necessary to utilize their medicinal properties. There are several ways to
recognize a plant, like flower, root, leaf, fruit etc. In recent times computer vision methodologies and
pattern recognition techniques have been applied towards automated procedures of plant recognition.
Leaf classification and recognition is an important component of automated plant recognition
system. Plant identification using leaf images is a very challenging task. Leaf features contain
significant information that helps in plant species recognition. Leaf vein is an integral part of the leaf.
The type of the vein is an important morphological characteristic of the leaf. The shape, size, texture
and color of the leaves also play an important role in plant identification. In this paper, we have
carried out a survey of plant leaf classification and various existing techniques used for
classification.
The paper is organized as follows. The next section provides the detailed review of some of
the research carried out for plant leaf classification. Section 3 discusses the general approach used for
classification. Section 4 presents the various leaf feature extraction techniques and Section 5
discusses the different feature classification methods used.
2.

LITERATURE REVIEW

Rashad, et al.[1] , introduced an approach for classification of plants which was based on the
characterization of texture properties. They have used a combined classifier learning vector
quantization along with the radial basis function. The proposed system has an ability to classify and
recognize the plant from a small part of the leaf. The main advantage of this system is it neither
depends on the shape of the leaf nor on its color feature as the system depends only on the textural
features. This system is useful for the researchers of Botany who need to identify damaged plants.
This system is applicable as the combined classifier method produced high performance which is
superior to other tested methods as its correct recognition rate was 98.7% which has been revealed in
the result.
Kadir, et al.[2] , proposed a method that incorporates shape ,vein, color and texture features.
They have used Probabilistic Neural Network(PNN) as a classifier for Plant Leaf Classification.
There are several method available but none of them have captured color information because color
was not recognized as an important aspect to the identification. In this paper, Fourier descriptors,
slimness ratio, roundness ratio and dispersion were used to represent shape features. To represent
color, color moments that consist of mean, standard deviation and skewness were used. Twelve
texture features are extracted from lacunarity. The experimental result shows that the proposed
method gives average accuracy of 93.75% when it was tested on Flavia dataset which contains 32
kinds of plant leaves.
Hossain, et al.[3], proposed a method which works for the plants with broad flat leaves and
which were more or less two dimensional in nature. The method used Probabilistic Neural Network
for classification. In this method, the user selects the base point of the leaf and a few reference points
on the leaf blades. On the basis of these points, the leaf shape is extracted from the background and a
binary image is produced. Several morphological features were extracted such as eccentricity, area,
perimeter, major axis, minor axis, equivalent diameter, convex area and extent by aligning the leaf
horizontally with its base points on the left of the image. Several unique features were also extracted
by slicing across the major axis and parallel to the minor axis. The proposed has been tested using
ten-fold cross validation technique and it showed 91.41% average recognition accuracy.
Zheng, et al.[4] , proposed a new method of leaf vein extraction based on gray scale
morphology. The main idea of the method is to look upon the leaf vein as the noise on the leaf
surface and adopt the method of noise detection to extract the leaf vein. This paper includes five
steps : gray transformation, gray scale morphological processing, image enhancement, image
segmentation and processing on details. The main advantage of this method, it is applicable for
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uneven illumination images. The basic idea of the method is also applicable to other linear object
extraction.
Zheng, et al.[5] ,proposed a universal method for the extraction of the leaf vein using hue and
intensity information. Due to the color difference between the leaf vein and mesophyll, leaves are
classified as the contrasting color leaf and the concolorous leaf. The vein of a contrasting color leaf
can be extracted using hue information in HIS color space. Leaf vein extraction of concolorous leaf
needs both hue and intensity information. Compared with other methods the proposed method is
simple, fast and universal. The key problem is to identify a contrasting color leaf and a concolorous
leaf automatically.
Lee, et al.[6] , the proposed method implements a leaf recognition system using the leaf veins
and shape for plant classification. The method used the main vein and the frequency domain data by
using Fast Fourier Transform methods in conjunction with distance measurement between the
contours and centroids on detected leaf images. Total 21 The experimental results showed that the
performance of the proposed leaf recognition system is 97.19%.
Uluturk, et al[7] , proposed a simple method which was based on bisection of leaves for
recognition. After preprocessing techniques were applied on leaves, 7 low cost morphological
features and 3 additional features using half leaf images were extracted. Many of the leaf species
have morphological structure that resembles each other a lot. For these kind of leaves, the present
new morphological features for plant identification which was based on splitting the leaf images
vertically into two regions. Area, extent and eccentricity features were extracted for each part and
their proportions to each other were taken. These all 10 features were used as an input to
Probabilistic Neural Network. The results showed that the proposed method gives 92.5% recognition
accuracy.
The summary of literature review on plant leaf classification based on vein, shape, color and
texture is depicted in the Table 1.
Table 1 Summary of Literature Review
Research
Paper

Classific
ation
Based on

Classifiers

Features

Advantages

Disadvantages

[1] Plants
Images
Classificatio
n
Based
on
Textural
Features
using
Combined
Classifier
[Aug 2011]

Texture

Combined
Classifier
(LVQ +
RBF)

1. Ability of
classifying
and
recognizing the
plant from small
part of the leaf.
2. Useful in cases
when plant is
damaged etc.

1.High
Performance
2. No need
to consider
shape or
color of leaf.

1. Do not
consider
noise.

[2].Leaf
Classificatio
n
using shape,
color, and
texture
features
[Aug 2011].

Shape,
vein,
color, and
texture.

1. Make use of
several features
for classification.
2. Texture feature
is
based
on
lacunarity.
3. Color feature
consideration

1. Better
performance
of the system
due to
consideration
of several
features.
2. Works on
large dataset.

1. Lots of
Mathematical
calculation.

Probabilist
ic neural
network
(PNN).

206

Accuracy

Dataset

30

98.7%

32

93.75%

[3]Leaf
Shape
identificatio
n
Based
Plant
Biometrics
[Dec 2010]

Shape

[4]Leaf Vein
Extraction
Based
On
Gray Scale
Morphology

Vein

[5]Fast Leaf
Vein
Extraction
using Hue
and Intensity
Information

Vein

[6]
An
Implementat
ion of Leaf
Recognition
System

Vein and
shape

[7]Recogniti
on of Leaves
Based
on
Morphologic
al Features
Derived
From Two
HalfRegions

Shape

Probabilist
ic neural
network
(PNN).

Can
use
Clustering
Method

Can use
clustering
method

-

Probabilist
ic neural
network
(PNN).

1.Various
morphological
features
are
extracted.
2. Unique feature
Leaf width Factor
is used.

1.Easy to
implement
2.Identify the
type of plant
from a
partially
damaged or
broken leaf.

1.Require user
help in preprocessing
stage.
2.Inability to
work with
images with
complicated
background.

1. OTSU method
used to segment
leaf vein from
background.
2.Gray-scale
morphology used
to
remove
overlapping
between vein and
background.

1. Feasible
and more
practical.
2. Applicable
for uneven
illumination
images.

1.Processing
speed is
affected due to
the width of the
b structuring
element.

1. Due to color
difference
between leaf vein
and
mesophyll,
leaves
are
classified
into
two.
2.
Vein
of
contrasting color
leaf
extracted
using
hue
information and
vein
of
concolorous leaf
use both hue and
intensity
information.

1. Simple,
fast and
universal.

1. Identification
of contrasting
and
concolorous
leaf
automatically.

1. Main vein and
frequency domain
data is used for
extraction.
2.
Total
21
features
of
distance, FFT and
convex
hull
extracted.

1.
Recognition
rate of the
system
is
better than
the existing
system.

1. Bisection of
leaves done for
recognition.
2. New features
extracted
using
half leaf images.

1. Proposed
features are
adaptable
with the data
reduction
techniques.

207

30

91.41%

1

-

1

-

1. Leaf image
size and
position of the
dataset is not
constant.

32

97.19%

32

92.5%


3.

GENERAL CLASSIFICATION APPROACH

Classification process is carried out through number of steps. Initially a leaf image database
is constructed which consists of leaf sample pictures with their corresponding plant details. There is a
lack of standard leaf image database that can be used for plant classification.
First step for plant leaf classification is image acquisition which includes capturing the digital
image of leaf with digital camera, and it is termed as an input image.
In the second step, the input image is preprocessed to enhance the important features. In this
step grayscale conversion, image segmentation, binary conversion and image smoothing is done. The
main aim of image pre-processing is to improve image data so that it can remove undesired
distortions and enhances the image features that are relevant for further processing.
In the next step, the important features are extracted and are matched with the database
image. The input image is categorized to the plant whose leaf image has maximum match score
using some classifier giving the information of the inputted leaf.
The overall classification process is shown in the Fig. 1.

Figure 1. Block diagram for plant leaf classification
4.

LEAF FEATURE EXTRACTION METHODS

4.1
Texture Feature Extraction
4.1.1 GLCM
Grey Level Co-occurrence Matrices (GLCM) is a statistical method[15][16]. It is an old and
widely used feature extraction method for texture classification. It remains an important feature
extraction method in the domain of texture classification that computes the relationship between
pixel pairs in the image. Based on the GLCM four statistical parameters energy, entropy, contrast
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and correlation are computed. Lastly a feature vector is computed using the means and variances of
all the parameters.
4.1.2 Gabor Filters
Gabor filters also popular as the Gabor wavelets, is a widely used signal processing
method[15][17], The Gabor filters consists of parameters such as the radial center frequency,
orientation and standard deviation. It can be used by defining a set of radial center frequencies and
orientations. Even though orientation may vary, it usually covers 180° in direction in order to cover
all possible orientations. Since signal processing methods produces large feature size, the Gabor
filters needs to be downsized for the prevention of the dimensionality issues.
4.1.3 Fractal Measure (Lacunarity)
Other method to get texture features is using fractals[2]. Although, the fractal dimension is
not considered for a good texture description, there is a fractal measure known as “lacunarity” which
is a measure of nonhomogeneity of the data as well as measures lumpiness of the data. It defined in
term of the ratio of the variance over the mean value of the function. It may help in distinguishing
two fractals with the same fractal dimension. We can define lacunarity by some predefined formulas
which were originally applied to grayscale images. But we can also apply them to color images in
our implementation using RGB values in order to increase the number of features to represent texture
features.
Table 2 Summary of the Texture Features
Sr.
No.
[1]

Technique

Features

Advantages

Disadvantages

GLCM (Gray
Level CoOccurrence
Matrix)[15][1
6]

1. It is a tabulation of how
often
different
combinations
of pixel brightness values
(grey levels) occur in an
image.
2. It is usually defined for
aseries of “second order"
texture calculations.
1. It is a signal processing
method used for defining a
set of radial center
frequencies
and
orientations.

1. Smaller length of feature
vector.
2. Used to estimate image
properties
related
to
second-order statistics.
3. It can be improved to be
applied on different color
space
for
color
cooccurrence matrix.
1. It’s a multi-scale,
Multiresolution filter.
2. It has selectivity for
orientation,
spectral
bandwidth
and
spatial
extent.

1. They require a
lot
Of computation
(many matrices to
be computed).
2. Features are not
invariant to rotation
or Scale changes in
the texture.
1.Computational
cost
often high, due to
the
necessity of using a
large bank of filters
in
most applications

1. Lacunarity analysis is a
multi-scaled method of
determining the texture
associated with patterns of
spatial dispersion

1. It is easily implemented
on the computer and
provides
readily
interpretable
graphic
results.
2. Differences in pattern
can be detected even among
very sparsely

[2]

Gabor
Filters[15]

[3]

Fractal
Measures(La
cunarity)[2]

209

-


4.2
Color Feature Extraction
4.2.1 Color moments
Color moments represent color features to characterize a color image[2]. Features involved
are mean(µ), standard deviation(σ) and skewness(ϴ). In case of RGB color space, the three features
are extracted from each plane R, G and B.
4.2.2 Color Histogram
A histogram is the distribution of the number of pixels for an image[12][17].The number of
elements in a histogram depends on the number of bits in each pixel of an image. For eg. , if we
consider a pixel depth of n bit, the pixel values will be in between 0 and
-1, and the histogram
elements.
will have
4.2.3 Color Averaging Technique
Three color planes namely Red, Green and Blue are separated[11]. For each plane row mean
and column mean of colours are calculated. Pictorially the average row and column mean is
calculated as follows The average mean is calculated by adding up the mean of every row. The
average of all row means and all columns means is calculated for each plane. The features of all 3
planes are combined to form a feature vector. As the feature vectors are generated for all images in
the database, they are stored in a feature database.
Table 3 Summary of the Color Feature
Sr. No.

Technique
Color
Moments[2]

Feature
1. Characterise color
distribution in an
image
2. Mainly used for
color indexing
purposes

Color
Histogram
[12][17]

1. Represent color
distribution in an
image
2. It is statistic that
can be viewed as an
approximation of an
underlying
continuous distributio
n of colors values.

Color
Averaging
Technique
[11]

1. For each RGB
plane row mean and
column mean of
colours
are
calculated.

[1]

[2]

[3]

Advantages
1. There is no need to store
the
complete
color
distribution.
2. Color and Shape are good
feature to
use under
changing lighting condition.
3. Speeds up image retrieval
since there are less features
to compare.
1. Color information are
faster to compute compared
to other invariants.
2. color histogram are more
often used for threedimensional spaces like
RGB or HSV.

1. Size of the feature vector
is small.
2. Classification speed is
high.

Disadvantages
1. High order color
moments are not a part
of the color moments
as the required more
data in order to obtain
a good estimate of
their values.
2. It cannot handle
occlusion successfully.
1. It is based only on
color,
shape
and
texture of image are
ignored.
2. It have high
intensity towards noisy
interference such as
lightning
intensity
changes
and
quantization error.

-

4.3
Shape Feature Extraction
4.3.1 Digital morphology features
he features are extracted from the contours of leaf[3,6,7,8]. The digital morphology features
(DMF) generally include geometrical features (GF) and invariable moment features (MF). The
210


geometrical features consist of aspect ratio, rectangularity, area ratio of convexity, perimeter ratio of
convexity, circularity, sphericity, eccentricity, form factor, etc.
4.3.2 Polar Fourier Transform
Polar Fourier Transform (PFT) is very useful to capture shape of a leaf. The descriptors
extracted from PFT are invariant under the action of translation, rotation, and scaling[8][2].
4.3.3 Moment Invariant
The moments are also widely used as the features for image processing and classification,
which provide a more geometric and intuitive meaning than the morphological features[8][14]. It
was Hu who first set out the mathematical foundation for two-dimensional moment invariants. Hu
defined seven invariant moments computed from central moments through order three that are also
invariant under object translation, rotation and scaling.
Table 4 Summary of the Shape Feature
Sr .
No.

Feature

Advantages

Digital
Morphology
Feature
[3,6,7,8]

1. Feature is extracted from
the contour of leaf.
2.It consist of geometrical
features such as aspect ratio,
area, perimeter etc.

Polar Fourier
Transform

[1]

Technique

1. Image is converted from
Cartesian space to polar
space.
2.The descriptors extracted
from PFT are invariant under
the action of translation,
scaling, and rotation.
1.It provide a more geometric
and intuitive meaning than
the morphological features.

1.
It
provide
critical
information of the visual
representation of the leaf.
2.It gives efficient
classification of the
leaves and for the detection of
deformations and holes, in
order to classify deformed
samples,
1. Classification using PFT
either in contours or regions
are simple to compute, robust
to noise and compact.

[2]

Moment
Invariant

1. Computationally simple.
2. They are invariant to
rotation, scaling and
Translation

[3]

Disadvantages

-

-

1. Since the basis is
not orthogonal, these
moments suffer from a
High degree of
information
redundancy
2. Higher-order
moments are very
sensitive to noise

4.4
Vein Features
4.4.1 Gray Scale Morphology
Extracting leaf vein from the leaf in the image is usually regarded as a problem of image
segmentation[4][13]. Image segmentation cannot be conducted directly on the gray image because
the color difference is local between the leaf vein and its background. The gray overlap in the whole
image leaf vein and the whole background should be eliminated before the image segmentation
process. The purpose of gray-scale morphology processing is just to get rid of the gray overlap in the
whole leaf vein and the whole background.

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4.4.2 Using Hue And Intensity Information
Since there is a color difference between the leaf vein and mesophyll, leaves can be classified
as the contrasting leaf and concolorous leaf[5]. A contrasting color leaf is a leaf with an obvious
difference between leaf vein and mesophyll. If the colors of leaf vein and mesophyll are similar or
belong to a same color category, such a leaf is a concolorous leaf. The vein of a contrasting color leaf
can be extracted using hue information in HIS color space. Leaf vein extraction of a concolorous leaf
needs both hue and intensity information.
4.4.3 Independent Component Analysis
Independent Component Analysis (ICA) is a signal processing method to extract independent
sources given only observed data that are mixtures of the unknown sources[10]. This method is
applied to the patches of leaf images to learn basis function and then the basis functions are used as
the pattern map for vein detection. A gray scale image is transformed into a pattern map in which the
leaf, edge, background and other pixels are classified into different classes by pattern matching. High
accuracy of vein detection is achieved and it is free of the influences of illumination and there is no
need to preprocessing.
Table 5 Summary of the Vein Feature
Sr.
No.

[1]

[2]

[3]

5.

Technique

Features

Advantages

Gray Scale 1. The purpose of gray-scale
Morphology[ morphology processing is just
4][13]
to get rid of the gray overlap
in the whole leaf vein and the
whole background
Using
Hue 1. Due to color difference
and Intensity between leaf vein and
Information
mesophyll,
leaves
are
[5]
classified into two.
2. Vein of contrasting color
leaf extracted using hue
information and vein of
concolorous leaf use both hue
and intensity information
Independent
1. Method is applied to the
Component
patches of leaf images to
Analysis
learn basis function and then
[10]
the basis functions are used as
the pattern map for vein
detection.
2.
Method
to
extract
independent sources.

Disadvantages

1. Method is 1. Processing speed
feasible
and is affected due to the
more practical. width of the b
structuring element.
1. Method is 1. Identification of
faster and more contrasting
and
applicable
concolorous
leaf
automatically

1.
High
accuracy
of
vein detection
is achieved.
2.No need of
preprocessing

1.The performance
of ICA algorithm is
not better than those
of Prewitt.
2.Results of ICA
algorithm on whole
image are not good.

FEATURE CLASSIFICATION METHODS

5.1

k-Nearest Neighbor
k-Nearest Neighbor classifier is used to calculate the minimum distance between the given
point and other points to determine which class the given point belongs. It selects the training
samples with the closest distance to the query sample[9][15]. Conceptually, this simple classifier
computes the distance from the query sample to every training sample and selects the neighbor or
neighbors that are having minimum distance. In terms of plant leaf classification; the distance to be
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calculated is termed as Euclidian distance. k-NN is a popular implementation where k number of best
neighbors is selected (i.e. k is a small positive integer, k = 1). And the appropriate class is decided
based on the highest number of votes from the k neighbors. The nearest neighbor is popular as
simpler classifier since it does not include any training process. It is mainly applicable in case of a
small dataset which is not trained. However, it suffers the limitation that the speed of computing
distance increases according to the number available training samples
5.2

Learning Vector Quantization
Learning Vector Quantization (LVQ) can be understood as a special case of an artificial
neural network, and is a predecessor to Self-organizing maps (SOM)[9].It is a supervised version of
vector quantization that can be used when we have labeled input data. An LVQ system can be
represented as a set of prototypes given by W= (w(i),..., w(n)) which are defined in the observed
data’s feature space. According to a given distance for each data point, the prototype that is much
closer to the input is measured and the winner prototype is then adapted. If it gets incorrectly
classified then moves away. An advantage of LVQ is that it creates easy to interpret prototypes used
by an experts in the respective application domains and also applies to multi-class classification
problems yielding variety of practical applications. A key issue in LVQ is the choice of an
appropriate measure of distance or similarity for training and classification.
5.3

Probabilistic Neural Network (PNN)
PNN is derived from Radial Basis Function (RBF) Network and it has parallel distributed
processor that has a natural tendency for storing experiential knowledge[9]. It is predominantly a
classifier that maps any input pattern to a number of classifications and can be forced into a more
general function approximator. A PNN is an implementation of a statistical algorithm called kernel
discriminate analysis in which the operations are organized into a multilayered feed forward network
having four layers such as Input layer, Pattern layer, summation layer, and output layer.
5.4 Radial Basis Function
A radial basis function (RBF) is a real-valued function whose value depends only on the
distance from the origin. Any function that satisfies this property is a radial function[9]. The
frequently used measuring norm is Euclidean distance. Basically, RBF’s are the networks where the
activation of hidden units is based on the distance between the input vector and a prototype vector.
There are several properties associated with variety of scientific disciplines. This includes function
approximation, density estimation, regularization theory, and interpolation in the presence of noise. It
allows for a straightforward interpretation of the internal representation produced by the hidden layer
and training algorithms for RBFs are significantly faster than those for Probabilistic Neural
Networks.
5.5

Support Vector Machine
Support vector machine (SVM) is a non-linear classifier, which is a newer trend in machine
learning algorithm and is popularly used in many pattern recognition problems, including texture
classification. In SVM, the input data is non-linearly mapped to linearly separated data in some high
dimensional space providing good classification performance[9][15]. SVM maximizes the marginal
distance between different classes. SVM is designed to work with only two classes by determining
the hyper plane to divide two classes. This ca be done by maximizing the margin from the hyper
plane to the two classes. The samples closest to the margin that were selected to determine the hyper
plane is known as support vectors. The main advantage of SVM is its simple geometric interpretation
and a sparse solution. Unlike neural networks, the computational complexity of SVMs does not
depend on the dimensionality of the input space. One of the drawbacks of the SVM is the large
213


number of support vectors used from the training set to perform classification task. However, SVM
is still considered to be powerful classifier, soon to be replacing the ANNs.
5.6

Genetic Algorithm
Genetic Algorithms are mainly used for feature classification and feature selection. The basic
purpose of genetic algorithms (GAs) is optimization[9] .GAs give a heuristic way of searching the
input space for optimal x that approximates brute force without enumerating all the elements and
therefore bypasses performance issues specific to exhaustive search. Genetic algorithm is used
effectively in the evolution to find a near-optimal set of connection weights globally without
computing gradient information and without weight connections initialization. Though solution
found by genetic algorithms is not always best solution. It finds “good” solution always. Main
advantage of GA is that is adaptable and it possess inherent parallelism. Genetic Algorithms handle
large, complex, non differentiable and multi model spaces for image classification and many other
real world applications.
Table 6 Summary of the Feature Matching Techniques
Sr.No.

Techniques
k-Nearest
Neighbor(kNN)[9][15]

Advantages
1. Simpler classifier since exclusion
of any training process.
2. It is mainly applicable in case of
a small dataset which is not trained.

Disadvantages
1. The speed of computing distance
increases according to the numbers
available in training samples.
2. Expensive testing of each instance.
3. Sensitiveness to noisy or
Irrelevant inputs.

Learning
Vector
Quantization
(LVQ)[9]

1. The choice of an appropriate
measure of distance or similarity for
training and classification.

Radial Basis
Function(RB
F)[9]

1. It creates easy to interpret
prototypes.
2. This can be applied to multiclass classification problems and
useful in classifying textural
features too.
1. Tolerant of noisy inputs and
virtually no time consumed to
train..
2. Instances can be classified by
more than one output.
3. Adaptive to changing data.
1. Training phase is faster.
2. The hidden layer is easier to
interpret.

Support
Vector
Machine(SV
M)[9][15]

1. Simple geometric interpretation
and a sparse solution.
2. SVMs can be robust, even when
the training sample has some bias.

Genetic
Algorithm[9]

1. Handle large, complex, non
differentiable and multi model
spaces.
2. Refining irrelevant and noise
genes.

[1]

[2]

[3]

[4]

[5]

[6]

Probabilistic
Neural
Networks(P
NN)[9]

214

1. Long training time.
2. Large complexity of network
structure.
3. Need lot of memory for training
data.
1. When training is finished and it is
being used it is slower. So when speed
is a factor then it is slower in
execution.
1. Slow training.
2. Difficult to understand structure of
algorithm.
3. Large no. support vectors are
needed from the training set to
perform classification task.
1. Complications involved in the
representation of training/output data.
2. Not the most efficient method to
find some optima, rather than global.


As per the literature survey, the LVQ method is giving the best accuracy in classifying the
plant leaf [1] [9]. We can also combine these techniques with other methods in order to achieve a
higher accuracy. For example, Rashad, et al., [1] has invented the combined classifier i.e. (LVQ +
RBF) giving the maximum accuracy (98.7%) in classifying plant leaf.
6.

CONCLUSION

In this survey, we have discussed a brief overview on Plant classification and its importance
in recent years. We have also discussed the different ways in which the problem of accurate plant
leaf classification has been formulated in literature. An overview of the literature on various
techniques that can be used for extraction and classification of various leaf features are also
discussed.
In our survey we have found that there are various techniques present to extract the features
but only few gives the best result like GLCM which is a new and popular texture extraction method,
color moments for color feature extraction, moment invariant for shape extraction and gray scale
morphology for vein extraction.
REFERENCES
[1]

M. Z. Rashad, B. S. el-Desouky,and Manal S. Khawasik, Plants Images Classification Based
on Textural Features using Combined Classifier, International Journal of Computer Science
& Information Technology (IJCSIT), Vol 3, No. 4, August 2011,pp.93-100.
[2] Abdul Kadir, Lukito Edi Nugroho, and Paulus Insap Santosa, Leaf classification using shape,
color, and texture, International Journal of Computer Trends & Technology (IJCTT), JulyAugust 2011,pp.225-230.
[3] Javed Hossain, M. Ashraful Amin, Leaf Shape Identification Based Plant Biometrics, 978-14244-8494-2/10/$26.00 ©2010 IEEE
[4] Xiaodong Zheng, Xiaojie Wang, Leaf Vein Extraction Based on Gray-scale Morphology, I.J.
Image, Graphics and Signal Processing, 2010, 2, 25-31 Published Online December 2010 in
MECS (http://www.mecs-press.org/)
[5] Xiaodong Zheng, Xiaojie Wang, Fast Leaf Vein Extraction using Hue and Intensity
Information, 978-1-4244-4994-1/09/$25.00 ©2009 IEEE
[6] Kue-Bum Lee, Kwang-Woo Chung and Kwang-Seok Hong, An Implementation of Leaf
Recognition System, ISA 2013, ASTL Vol. 21, pp. 152 - 155, 2013 © SERSC 2013
[7] Caner Uluturk, Aybars Ugur, Recognition of Leaves Based on Morphological Features
Derived From Two Half-Regions 978-1-4673-1448-0/12/$31.00 ©2012 IEEE.
[8] A. Kadir,L.E. Nugroho, A. Susanto and P.I. Santosa, A Comparative Experiment of Several
Shape Methods In Recognizing Plants , International Journal of Computer Science &
Information Technology (IJCSIT), Vol 3, No 3, June 2011
[9] Prof. Meeta Kumar, Mrunali Kamble, Shubhada Pawar, Prajakta Patil, Neha Bonde, Survey
on Techniques for Plant Leaf Classification , International Journal of Modern Engineering
Research (IJMER) www.ijmer.com Vol.1, Issue.2, pp-538-544 ISSN: 2249-6645
[10] Yan Li, Zheru Chi, David D. Feng, Leaf Vein Extraction Using Independent Component
Analysis, 1-4244-0100-3/06/$20.00 C2006 IEEE
[11] Dr. H.B.Kekre ,Dhirendra Mishra ,Stuti Narula ,Vidhi Shah, Color Feature Extraction For
CBIR, ISSN : 0975-5462 Vol. 3 No.12 December 2011
[12] Jayamala K. Patil, Raj Kumar, Color Feature Extraction of Tomato Leaf Diseases, ISSN:
2231-5381 http://www.internationaljournalssrg.org

215


[13] Xiaodong Zheng,Xiaojie Wang, Leaf vein extraction using a combined operation of
mathematical morphology, 978-1-4244-7941-2/10/$26.00 ©2010 IEEE
[14] A.H. Kulkarni, Dr. H.M.Rai, Dr. K.A.Jahagirdar, P.S.Upparamani, A Leaf Recognition
Technique for Plant Classification Using RBPNN and Zernike Moments, International
Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 1,
January 2013
[15] Jing Yi Tou, Yong Haur Tay, Phooi Yee Lau, Recent Trends In Texture Classification: A
Review, Symposium On Progress In Information & Communication Technology 2009
[16] Gurpreet Kaur, Gurpinder Kaur, Classification of Biological Species Based on Leaf
Architecture, International Journal of Engineering Research and Development ISSN: 2278067X, Volume 1, Issue 6 (June 2012), PP.35-42 www.ijerd.com
[17] Hanife Kebapci, Berrin Yanikoglu and Gozde Unal, Plant Image Retrieval Using Color,
Shape and Texture Features, The Computer Journal Advance Access published April 9, 2010.
[18] Nisargkumar Patel, Prof. V.V. Shete and Ashwini Charantimath, “LVQ Based Person
Identification System”, International Journal of Computer Engineering & Technology
(IJCET), Volume 4, Issue 4, 2013, pp. 185 - 193, ISSN Print: 0976 – 6367, ISSN Online:
0976 – 6375.
[19] Ajeesh S. S. and Indu M.S., “Feature Extraction Techniques on CBIR-A Review”,
International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4,
2013, pp. 467 - 474, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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