50120140502010

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 88-97 © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 2, February (2014), pp. 88-97
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IJCET
©IAEME

A NEW LEAF ANALYSIS AND CLUSTERING FOR TEA SPECIES
IDENTIFICATION
Arunpriya C.
P.S.G.R Krishnammal College for Women, Coimbatore, India
Antony Selvadoss Thanamani
Nallamuthu Gounder Mahalingam College, Pollachi, India.

ABSTRACT
Leaf is an important organ of the plant. It is widely used for many purposes such as in
medical field, chemical and other research purposes. Now it becomes active area for analysis of
plants as most of the plant species are at the risk of extinction. Most of the leaves cannot be analyzed
easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground
(e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and
cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea
leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This
proposed approach consists of three phases such as preprocessing, feature extraction, selection and
finally clustering of leaves. The tea leaf images are first preprocessed to remove the noise and
enhanced by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT and boundary
enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital
Morphological Features (DMFs) and Geometrical features are extracted and from that main features
are selected. They are given to the clustering process which is done by using Fuzzy C-Means
algorithm, it clearly cluster different type of tea leaves.
The Fuzzy C-Means is trained by 60 tea leaves to classify them into 6 types. Experimental
results proved that the proposed method clustered the tea leaves with more accuracy in less time.
Thus, the proposed method achieves more accuracy in clustering the leaf type.
Keywords: Leaf Recognition, Dual Tree Discrete Wavelet Transform (DT-DWT), Digital
Morphological Features (DMFs), K-Means Algorithm, Fuzzy C-Means (FCM).

88


I.

INTRODUCTION

Plant is an important thing in our environment. It has vital use in food stuﬀ, medicine and
industry. There is an difficulty arises while recognize plant species on earth. All over the world, there
are currently about 310 000–420 000 known plant species, and many are still unknown yet. [2]
At present, plant taxonomy usually adopts traditional classification method. And so far many
other classification methods, such as molecule biology, morphologic anatomy, cell biology and
phytochemistry, have also been used. These methods have good relation to do with biology and
chemistry. However, the acquisition of needed data from plant living body or specimen directly and
automatically by computer has not been implemented [1].
Plants are basically classified according to shapes, colors and structures of their leaves and
flowers [5, 6]. However, if we want to recognize the plant based on 2D images, it is difficult to
analyze shapes and structures of flowers since they have complex 3D structures. On the other hand,
the colors of leaves are always green; moreover, shades and the variety of changes in atmosphere and
season cause the color feature having low reliability. Therefore, we decided to recognize various
plants by the grey-level leaf image of plant. The leaf of plant carry useful information for
classification of various plants, for example, shape, aspect ratio and texture. [12]
In this work initially the leaf images are subjected to preprocessing, In that preprocessing the
leaf image is converted into gray scale and then from it noises are removed and it is enhanced to
desired level. From it several features are extracted and selection of features is made. Then they are
clustered based upon their features.
The paper can be organized as follows. Section II describes the related works involved
regarding leaf analysis. Section III describes about the proposed methodology. Experimental results
are illustrated in Section IV and Section V deals with the conclusion.
II.

RELATED WORKS

Ji-Xiang Du et al., takes leaf database from different plants is firstly constructed. Then, a new
classification method, referred to as move median centers (MMC) hypersphere classifiers, for the
leaf database based on digital morphological feature is proposed. Then says that it is more robust
than the one based on contour features since those significant curvature points are hard to find.
Finally, the efficiency and effectiveness of the method in recognizing different plants is
demonstrated by experiments.
Xiao Gu et al., [13] proposed a novel approach for leaf recognition by means of the result of
segmentation of leaf’s skeleton based on the integration of Wavelet Transform (WT) and Gaussian
interpolation. And then the classifiers, a nearest neighbor classifier (1-NN), a k -nearest neighbor
classifier (k-NN) and a radial basis probabilistic neural network (RBPNN) are employed, based on
Run-length Features (RF) obtained from the skeleton to identify the leaves. Ultimately, the efficiency
of this approach is illustrated by several experiments. The results reveal that the skeleton can be
effectively extracted from the entire leaf, and the recognition rates can be significantly improved.
The evolution of the curves may be driven by image gradient information (Kichenassamy et
al., 1995; Caselles et al., 1997), region information (Chan and Vese, 2001 [14]; Jehan-Besson et al.,
2003) [3], or their combination (Tsai et al., 2001) [4]. The image segments generated are enclosed by
closed contours and are unsupervised which make it an ideal candidate for image post processing.
These methods are more suitable for simple images and their performance degrades both in terms of
segmentation and complexity, with complicated images (Paragios and Deriche, 2000). In such cases,
the utilization of prior information is necessary for curve evolution methods in complicated image
segmentation applications.

89


III.

METHODOLOGY

The tea leaf recognition method used in the proposed approach consists of three phases
namely image pre processing, feature extraction and selection then finally clustering.
3.1. Image Pre-Processing
The input tea leaf images undergo several processing steps as follows.
Converting RGB Image to Binary Image
The tea leaf image is obtained through scanners or digital cameras. An RGB image is firstly
converted into a grayscale image. Equation 1 is used to convert RGB value of a pixel into its
grayscale value.
gray=0.2989‫כ‬R+0.85870‫כ‬G+0.1140‫כ‬B

(1)

Where R, G and B corresponds to the color of the pixel, respectively.
Fuzzy Denoising Using Dual Tree Discrete Wavelet Transform
Here the denoising is done through Fuzzy shrinkage rule. In image denoising, where a tradeoff between noise suppression and the maintenance of actual image discontinuity should be made,
solutions are required to detect important image details and accordingly adapt the degree of noise
smoothing. With respect to this principle, use a fuzzy feature for single channel image denoising to
enhance image information in wavelet sub-bands and then using a fuzzy membership function to
shrink wavelet coefficients, consequently.
Dual Tree Discrete Wavelet Transform (DT-DWT) is used as a fuzzy denoising algorithm
which provides both shiftable sub-bands and good directional selectivity and low redundancy.
The 2-D dual-tree discrete wavelet transform (DT-DWT) of an image is employed using two
critically-sampled separable 2-D DWT’s in parallel .[16] The advantages of the dual-tree DWT (DTDWT) over separable 2D DWT is that, it can be used to employ 2D wavelet transforms which are
more selective with respect to orientation.
The real part or the imaginary part of DT-DWT [17] produces perfect reconstruction and
hence it can be employed as a stand-alone transform. Feature vector can be calculated using
magnitude of sub bands. The implementation of DT-DWT is easy. An input image is decomposed by
௔
௔
௕
௕
two sets of filter banks, (‫ܪ‬଴ , ‫ܪ‬ଵ ሻ ܽ݊݀ ‫ܪ‬଴ , ‫ܪ‬ଵ ሻ separately and filtering the image both horizontally
and vertically. Then eight sub bands are obtained: LLa, HLa , LHa , HHa , LLb , HLb , LHb and HHb.
Each high-pass subband from one filter bank is combined with the corresponding subband from the
other filter bank by simple linear operations: averaging or differencing. The size of every sub band is
the same as that of 2D DWT at the same level. [18] But there are six high pass sub bands instead of
three highpass sub bands at every level. The two low pass sub bands, LLb and LLa, are iteratively
decomposed up to a desired level within each branch.
The DT-DWT (K) can be designed in two ways to have required delays. The first is based on
Farras filters and the second employs Q-shift (quarter shift) filter design. The key issue in the design
of DT-DWT (K) is to obtain (approximate) shift invariance using any of the filter forms.[19]To use a
redundant transform for compression seems contradictory to the goal of compression which is to
reduce whatever redundancy as much as possible. However if coefficients of a redundant transform
are sparse enough, compression can even benefit from the introduced redundancy since most
coefficients are nearly zero.
Processing is usually result from a modification of the spatial correlation between wavelet
coefficients (often caused by zeroing of small neighboring coefficients) or by using DWT.
90

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
f
0976-6367(Print),

After the post processing process the enhanced leaf image is obtained as a result.
Boundary Enhancement
The margin of a leaf is highly focused in this pre processing step. Convolving the image with
a Laplacian filter of 3 × 3 spatial mask as in equation 2:
0
1
0

1
-4
1

Fig. 1 Enhanced image

0
1
0

(2)

Fig. 2 Boundary Enhancement

The Fig.1 shows the enhanced ima and Fig 2 shows boundary enhancement of the
image
proposed tea leaf recognition. To make boundary as a black curve on white background, the pixel
values “0” and “1” are swapped.
3.2.

Feature Extraction
The proposed approach uses Digital Morphological Features (DMFs) and geometrical
MFs)
features. For these features, computer can obtain feature values quickly and automatically. The
automatically
features used for extraction in the proposed method is described as follows.
A. Physiological Length
The only human interfered segment of the proposed algorithm is that, the two terminals of the
the
main vein of the leaf should be marked through mouse click. The distance between the two terminals
is the physiological length. It is represented as Lp. The red line in the Fig. 3 indicates the
physiological length of a leaf.

Fig. 3 Physiological length of a leaf
3:
91

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
f
0976-6367(Print),

B. Physiological Width
Drawing a line passing through the two terminals of the main vein, infinite lines can be
plotted orthogonal to that line. The count of intersection pairs between those lines and the leaf
margin is also infinite. At the physiological width, the longest distance between points of those
intersection pairs is defined. It is represented as Wp. As the coordinates of pixels are discrete, two
lines are considered as orthogonal if their degree is 90◦ ± 0.5◦.The red line in the Fig. 4 indicates the
90
.The
physiological width of a leaf.

Fig. 4 Physiological width of a leaf
C. Aspect Ratio
The ratio of physiological length Lp to physiological width Wp is called aspect ratio and it is
atio
given by equation(3),
(3)

D. Serration Angle
The teeth angle of a leaf can be defined as equation (4) using serration angle.
serrati

(4)
Where θ is the serration angle, a is the length of first recognizable teeth from the tip of the
angle and b is the breadth of first recognizable teeth from the tip of the angle.

rration
Fig. 5 Serration angle obtained from the tea leaf
The serration angle obtained from the tea leaf using equation 4 is shown in the Fig. 5.
92


E. Segment
The segment of a leaf can be defined as the ratio of first recognizable teeth in the left side
from the tip of the angle ‘a’ to the first recognizable teeth in the right side from the tip of the angle
‘b’ as defined in equation (5).
(5)
F. Segment maximum width to Physiological length ratio
The leaf is divided into 10 segments as shown in the Fig. 6. Each segment width to the
physiological length ratio can be determined for all the 10 segments.
G. Tip Angle
The angle which is formed from the tip of the leaf to the first recognizable teeth on either side
of the leaf is called tip angle.
The tip angle can be calculated using the formula as defined in equation (6).

(6)

where θ is the tip angle, a and b are the first recognizable teeth from the tip of the angle on
left and right side respectively. The tip angle formed from the tea leaf is shown in the Fig. 7.
θ

a

Fig. 6 Segment of a leaf

b

Fig. 7 Tip angle obtained from the tea leaf

3.3.

Feature Selection
Feature selection is the process of selecting the features which are essential for its
classification or clustering. While extracting the features user may extract many features. And when
all the extracted features are given for clustering, it may fails in clustering. The computational
complexity arises while large numbers of features are given for clustering and time consumption also
becomes high. For these reasons feature selection is made and here appropriate features are selected
based on its further process.
93


The clustering process here is done according to the selected features. The leaf images here
are grouped based on it.
In this paper several morphological and geometrical features are extracted. The geometrical
features such as Physiological Length, Physiological Width, Serration Angle, Leaf Layer Segment –
Layer 1, Layer 2, Layer 3, Layer 4, Layer 5, Layer 6, Layer 7, Layer 8, Layer 9 and Layer 10
maximum distance, Segment maximum width to Physiological length ratio and Tip Angle are
extracted.
Morphological feature of Aspect Ratio is extracted. These are the unique features by this
clustering is made. Each type of leaf differs by these features.
3.4.

K-means clustering
To cluster the images based on the selected features k-Means Clustering is used. K-Means
algorithm is one of the clustering algorithms that classify the input data points into multiple classes
based on their inherent distance from each other. Suppose that the data features form a vector space
and this algorithm tries to find natural clustering in them. The points are clustered around centroid
µ୧ ‫׊‬୧ ൌ 1 … k which are obtained by minimizing the objective as given in equation (7)
ଶ

ܸ ൌ ∑୩ ∑୶ౠ ‫ א‬౩ ൫x୨ െ µ୧ ൯
୧ୀଵ

(7)

౟

where there are k clusters R ୧,୨ ൌ 1, 2, … … , k and µ୧ is the centroid or mean point of all the
points x୨ ‫ א‬R ୧
The algorithm takes a 2 dimensional image as an input. Various steps in the algorithm are as follows:
•
•
•
•

Compute the intensity distribution of the intensities.
Initialize the centroids with k random intensities.
Repeat the following steps until the cluster labels of the image do not change anymore.
Cluster the points based on distance of their intensities from the centroid intensities which
can be defined as equation (8).
ଶ

c ሺ୧ሻ ‫׷‬ൌ arg min ቛx ሺ୧ሻ െ µ୨ ቛ
୨

•

ሺ8ሻ

Compute the new centroid for each of the clusters as in equation (9).
ߤ௜ ؔ

∑௠ 1 ൛ܿሺ௝ሻ ൌ ݆ൟ‫ ݔ‬ሺ௜ሻ
௜ୀଵ
∑௠ 1 ൛ܿሺ௝ሻ ൌ ݆ൟ
௜ୀଵ

ሺ9ሻ

where k is a parameter of the algorithm (the number of clusters to be obtained), i iterates over
the all the intensities, j iterates over all the centroids and µ୧ are the centroid intensities. [15].
3.5.

Fuzzy C-Means Clustering
The Fuzzy C-Means (FCM) clustering algorithm was first introduced by Dunn [20] and later
was extended by Bezdek [21]. The algorithm is an iterative clustering method that produces an
optimal c partition by minimizing the weighted within group sum of squared error objective function
JFCM defined in equation (10) [21].

94

௡

௖

‫ܬ‬ி௉஼ெ ൌ ෍ ෍ሺ‫ݑ‬௜௞ ሻ௤ ݀ଶ ሺ‫ݔ‬௞ , ‫ݒ‬௜ ሻ
௞ୀଵ ௜ୀଵ

ሺ10ሻ

Where X = ‫ݔ‬ଵ , ‫ݔ‬ଶ , … , ‫ݔ‬௡ ሽ ‫ ܴ ه‬௣ is the data set in the p-dimensional vector space, ݊ is the
number of clusters with 2 ൑ ܿ ൏ ݊, ‫ݑ‬௜௞ is the degree of membership of ‫ݔ‬௞ in the ith cluster, q is a
weighting exponent on each fuzzy membership, vi is the prototype of the centre of cluster i,
݀ଶ ሺ‫ݔ‬௞ , ‫ݒ‬௜ ሻ is a distance measure between object ‫ݔ‬௞ and cluster centre ‫ݒ‬௜ . A solution of the object
function JFCM can be obtained via an iterative process.
IV.

EXPERIMENTAL RESULTS

The tea leaf clustering is taken in the proposed approach. For each type of tealeaf, 10 pieces
of leaves from testing sets are used to test the accuracy. The dataset used in this approach is UPASI
dataset.
For clustering using FCM algorithm, input as features are given by those features they are
clustered. Here for this UPASI dataset there are 6 types of tea leaves. Each type has nearly same
feature values and they are far for the other type.
TABLE I: DIFFERENT TYPES OF TEA LEAVES TESTED IN THE PROPOSED METHOD
Tea Leaf Name
Serration Angle Feature Values
Tip Angle Feature Values
TRF 1

110 to 140

35 to 50

UPASI - 3

35 to 120

35 to 75

UPASI - 9

80 to 140

28 to 46

UPASI - 10

110 to 150

45 to 55

UPASI -17

100 to 120

42 to 52

UPASI - 22

60 to 150

45 to 100

The feature values obtained for serration and tip angle are shown in table I. Likewise all other
remaining 14 different features are extracted. From the above table, we can see that the range of
feature values differs for each type of leaves.
The performance of the proposed approach is evaluated based on the following parameters:
Accuracy and Execution time. The different tea leaves taken in the proposed approach is shown in
the Table II.
TABLE II: DIFFERENT TYPES OF TEA LEAVES TESTED IN THE PROPOSED METHOD
Tea Leaf Name
Tested Samples
Number of Correct Clustered
TRF 1

57

54

UPASI - 3

62

52

UPASI - 9

56

51

UPASI - 10

56

46

UPASI -17

60

56

UPASI - 22

63

56

95


TABLE III: COMPARISON OF THE EXECUTION TIME
Classification
Time (seconds)
Techniques
Fuzzy C-Means
0.83
K-Means
0.74
EM
0.92
Table III reveals the execution time of the classification algorithms. The execution time of
the proposed K-Means clustering approach is less when compared with EM and Mean Shift
Clustering algorithm. Thus this k-means clustering is well suits for clustering of images in reduced
executed time.
V.

CONCLUSION

A new approach of tea leaf feature selection and clustering is proposed in this paper. This
approach consisted of three phases namely the preprocessing, feature extraction and selection finally
clustering. The image can be preprocessed using fuzzy denoising by Dual Tree Discrete Wavelet
Transform (DT-DWT) and Boundary enhancement. The main features of leaf can be extracted by
using morphological and geometrical feature techniques. And the related features are selected and
given as an input to the clustering. FCM clustering algorithm is used as a clustering algorithm. The
computer can automatically cluster 60 kinds of plants via the leaf images loaded from digital
cameras or scanners. The performance of the proposed approach is evaluated based on the accuracy
and execution time. The proposed algorithm produces better accuracy and takes very less time for
execution.
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