Skin defect detection of Pomegranates using Color Texture Features and DWT

Meenakshi M. Pawar

Dept. of Electronics & Telecommunication SVERIs College of Engineering Pandharpur,

Maharashtra, India minakshee2000@gmail.com

Meghana M. Deshpande

Dept. of Electronics & Telecommunication SVERIs College of Engineering Pandharpur,

Maharashtra, India meghana.deshpande24@gmail.com

Abstract Various Skin disorders lower the quality of fruits due to environmental stress such as high temperature and solar radiation some other skin disorders are induced by chemical treatments and pathogens. Skin defect detection is important in the development of automatic grading and sorting system for pomegranate, because manual sorting process is very expensive and time consuming to automate this process skin defect can be identified with the help of color texture feature and discrete wavelet transform. For color texture feature analysis, acquired image is transformed into HSI color space, which is further used for generating SGDM matrix. Total 12 texture features were computed for hue (H), saturation (S) and intensity (I) images from each image samples. Then wavelet transform is used to compute statistical features, Total 3 features were computed for R, G & B components of each image samples. Best features were used as an input to Support Vector Machine (SVM) classifier and tests were performed to identify best classification model. Features showing optimal results were mean (99%), variance (99.80), cluster shade (99.88%), cluster prominence (99.88%), Mean intensity (99.81%).

Keywords- pomegranate; color Co-occurrence method; texture features; discrete wavelet transform; support vector machine

I. INTRODUCTION Pomegranate (Punica granatum) is a high value crop. It is a fruit-bearing deciduous shrub or small tree that grows to between five and eight meters tall and is best suited to climates where winters are cool and summers are hot. The pomegranate is thought to have been first cultivated 5 to 6,000 years ago and is native to the regions from Iran through to north India. The main areas of world production being in India, Iran, Spain and California. Entire tree of pomegranate is of great economic importance. It can be consumed as fresh fruit or used in fruit juices, teas, pharmaceutical and medicinal products and in dyes or as decoration. Demand in the international market has inspired the automatic detection of quality of fruit in the agro-industry. Recently Katyal et al.[1] focused on fruit defect detection using 2D Gabor filter. Huang et al.[2] detected apple surface defect using Gabor wavelet transformation and SVM. Moradi et al.[3,4] extracted the fruit shape by ACM algorithm and segmentation has done using SHFCM approach then performed skin defect detection using background subtraction and segmentation is done using FCM. Arivazhagan et al.[5] used computer vision strategy

to recognize a fruit rely on four basic features which characterize the object on the basis of intensity, color, shape and texture. Blasco et al.[6] recognized skin damage in citrus fruit using multispectral data and morphological features and fruit sorting is done to identify the defect.

Figure 1. Process of developing the database of features

Figure 2.Typical Samples used in Dataset In order to detect skin defect present in pomegranates HSI model is used to obtain color texture features and discrete wavelet transform is used to obtain statistical feature Fig 1. Shows the process of developing the database features here input samples of skin infected and uninfected are taken. For each sample the image is cropped, HSI model is obtained for each cropped sample from which 12 texture features are calculated. Simultaneously each sample is transformed in to R,G,B components to obtain DWT from each layer and three statistical features are calculated. Fig 2. shows the typical samples of pomegranate used in dataset. Infected

Input Image

Cropping of image

Conversion into HSI Model

Separating RGB Component

Generation SGDM Matrix

Computation of DWT Coefficient

Calculation of Texture Matrix

Calculation of Statistical Matrix

2012 National Conference on Computing and Communication Systems (NCCCS)

978-1-4673-1953-9/12/$31.00 2012 IEEE

samples consist of images of Sunburn, Alternaria, Cercospora diseases. To acquire the original images, we used a digital camera in this work. The camera with focal length of 40.6 to 406 mm provided a resolution of 2M pixels (Spatial resolution of 640 480) The images were captured along all the angles of pomegranate and stored in JPEG format.

II. COLOR CO-OCCURRENCE MATRIX Kim et al. [7] used the color co-occurrence method for citrus peel fruit classification. Pydipati et al. [8] utilized the color co-occurrence method to extract various textural features from the color RGB images of citrus leaves. There are two main analysis methods for calculation of texture viz. 1) Structural Approach 2) Statistical Approach Statistical approach, which is used here, is a quantitative measure of arrangement of intensities in a region. Statistical methods use second order statistics to describe the relationships between pixels within the region by constructing Spatial Gray-level Dependency Matrices (SGDM). A SGDM matrix is the joint probability occurrence of gray levels i and j for two pixels with a defined spatial relationship in an image. Distanced and angle are used to define the spatial relationship. If the texture is coarse and distanced is small compared to the size of the texture elements, the pairs of points at distance d should have similar gray levels. In turn, if the texture is fine and distance d is comparable to the texture size, then the gray levels of points separated by distance d should often be quite distinct, so that the values in the SGDM matrix should be disperse uniformly. Thus, texture directionality can be analyzed by examining spread measures of SGDM matrices created at various distanced. Extraction of a numerous texture features are possible using the SGDM matrices generated in the above manner. The following steps were performed to generate SGDM matrices: The test image is then cropped such that around 2000 cropped image. These RGB images are converted into HSI color space representation. Then each pixel map is used to generate a color co-occurrence matrix, resulting in three CCM matrices, one for each of the H, S and I pixel maps. 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. For a position operator p, we can define a matrix Pij that counts the number of times a pixel

with grey-level i occurs at position p from a pixel with grey-level j. For example, if we have four distinct grey-levels 0, 1, 2 and 3, then one possible SGDM matrix P (i, j, 1, 0) is given below as shown: The SGDMs are represented by the function P (i, j, d, ) where i represents the gray level of the location (x, y) in the image I(x, y), and j represents the gray level of the pixel at a distance d from location (x, y) at an orientation angle of . The nearest neighbour mask is exemplified in Fig.3, where the reference pixel is shown as an asterisk.

Figure 3. Nearest neighbour mask for calculating spatial Gray-level

dependence matrix (SGDM) Statistical methods use order statistics to model the relationships between pixels within the region by constructing Spatial Gray-level Dependency Matrices (SGDMs). A SGDM matrix is the joint probability occurrence of gray levels i and j for two pixels with a defined spatial relationship in an image.

III. TEXTURE FEATURE CALCULATION Texture has no unique definition, it is one of the characteristics that segments images into regions of interest (ROI) and classifies those regions. It provides information about the spatial organization of the intensities in an image. In general, texture can be defined as a characteristic of image that can provide a higher-order description of the image and includes information about the spatial distribution of tonal variations or gray tones. Texture involves the spatial distribution of gray levels. Hence SGDM matrices were used to calculate various texture features. These features are useful in carrying out differentiation algorithms for skin defect detection purpose ahead. For differentiating infected pomegranate from the uninfected ones the following features were calculated from the components H, S and I: Variance:

)()(1

0

iPMiiVNg

ix

=

= (1)

Cluster Shade:

( ) ),(311,

jiPPjPiCsNg

jiyx

=

+= (2)

0 1 3 31 0 0 13 0 2 33 1 3 0

P =

1 0 3 02 2 0 3

( , )3 2 0 21 3 2 3

I x y =

Cluster Prominence:

),()(41

1,jiPPjPiC

Ng

jiyxp

=

+= (3)

Mean Intensity: )(1

0

iiPMiNg

ix

=

= (4)

Where, Co (i, j) is the (i, j) th entry in SGDM matrix and Ng is total number of gray levels considered for generation of SGDM matrix. Px and Py are defined by

=

=

=

1

0

1

0

),(Ng

j

Ng

ix jiPiP

=

=

=

1

0

1

0

),(Ng

i

Ng

jy jijPP

IV. WAVELET FEATURE CALCULATION Wavelet analysis is an advanced feature extraction algorithm which is based on windowing technique with variable sized regions. The window size can be kept wide for low frequencies and narrow for high frequencies which lead to an optimum time frequency resolution for complete frequency range Mukane et al.[9]. A discrete wavelet transform (DWT) is the wavelet transform process in which the wavelets in numerical analysis and functional analysis are discreetly sampled. Temporal resolution is a key advantage of wavelet transform over Fourier transform in which it captures both frequency and location information. In this study single level 2-D wavelet transform decomposition is used for features extraction. The 2-D wavelet transform will give four matrices the approximation coefficients matrix and detailed coefficients matrices horizontal, vertical, and diagonal, respectively. The approximation matrix CA is obtained for the three different layers of red, green and blue from which mean feature is obtained

Mean : = =

=

N

i

N

jjix

NMk

1 12 ),(

1 (5)

V. SUPPORT VECTOR MACHINE SVM maps input vectors to a higher dimensional space where a maximal separating hyperplane is constructed Pawar et al [10]. SVM maps the input patterns into a higher dimensional feature space through some nonlinear mapping chosen a priori. A linear decision surface is then constructed in this high dimensional feature space. Thus, SVM is a linear classifier in the parameter space, but it becomes a nonlinear classifier as a result of the nonlinear mapping of the space of the input patterns into the high dimensional feature space. The mathematical background of SVM formulation is explained briefly in this section. Detailed

discussion on SVM is available in references Cortes and Vapnik [11, 12]. For linearly separable data set of {(xi , ci)}, the hyperplane obtained by SVM for classifying the data into two classes can be written as:

0. =bxw Which implies

1).( bxwc ii ni 1 Where, xi is input sample and ci output having either 1 or -1, a constant denoting the class. The vector w is perpendicular to the separating hyperplane and the offset parameter b allows in increasing the margin. For the linearly separable training data, these hyperplanes can be considered to maximize the distance between the extreme points of each class. The distance between the hyperplanes can be

given as w/2

. Therefore, maximization of the distance between the hyperplanes becomes a problem of

minimization of w

. The primal form of optimization problem becomes a quadratic programming optimization which can be written as,

Minimize 2)2/1( w

;

subject to 1).( bxwc ii ni 1 The factor of 1/2 is used for mathematical convenience. The dual form of this optimization problem leads to a classification problem which is only a function of the support vectors, i.e., the training data that lie on the margin. Originally, the SVM algorithm was developed for as a linear classifier. However, it was further extended Boser et al [13] to create a nonlinear classifier by applying the kernel trick. Some common kernels include, Polynomial (homogeneous):

dxxxxk ),(),( = ;

Polynomial (inhomogeneous): dxxxxk )1,(),( += ;

Radial Basis Function: 2)exp(),( xxxxk =

, for 0> ; Gaussian Radial Basis Function:

= 2

2

2exp),(

xxxxk

VI. NUMERICAL RESULTS Various features are computed from the test images for components H, S and I which are further used for generating the 3D plot of each feature independently for analyzing the infected and uninfected pomegranate over the plot. In Fig. 4, red color signifies the feature points for the uninfected pomegranate whereas green color signifies the same for infected ones. Here X, Y and Z axes represent feature of image components Hue, Saturation and Intensity

respectively. Variance (Plot a), Cluster Shade (Plot b), Cluster Prominence (Plot c), Mean Intensity (Plot d) and Mean (Plot e) features were analyzed. It can be easily observed that for differentiating the diseased pomegranate from healthy can be easily done as the data points are clearly separable.

a) Variance

b) Cluster Shade

c) Cluster Prominance

d) Mean Intensity

e) Mean

Figure 4. 3D plots for feature analysis a) Variance b) Cluster Shade c) Cluster Prominance d) Mean Intensity e) Mean

For classification purposes, it is often anticipated that the linear separabilty of the mapped samples is enhanced in the kernel feature space so that applying traditional linear algorithms in this space could result in better performance compared those obtained in the original input space. If an inappropriate kernel is selected, the classification performance of kernel-based methods can be even worse than that of their linear counterparts. Therefore, selecting a proper kernel with good class separabilty plays a significant role in kernel-based classification algorithms. Here the Gaussian basis function performs well to do the job. The contour plots for features Variance (Plot a), Cluster Prominence (Plot b), Mean Intensity (Plot c) is shown in Fig. 5 below. two feature classifications for black colored dashed line shows the separating hyperplane which separates the two classes of infected and healthy pomegranate red color shows the support vectors which forms classification boundary black color shows (x) Infected class data, and yellow color shows ( ) Healthy class data, from figures it can be easily conclude that two classes can be clearly separable using kernel Gaussian function.

a) Varience

b) Cluster Prominence

c) Mean Intensity

Figure 5. Contour plot of SVM classifier a) Variance b) Cluster Prominence c) Mean Intensity

The performance of the SVM classification is measured in terms of success rate which is the ratio of number correctly classified sample against the total number of samples used for classification.

SR= 100*NNc

(6)

Performance of SVM is estimated for different groups of features as shown in Table 1 SVM is tested for five set of features viz. Mean, Variance ,cluster shade ,cluster prominence and Mean Intensity. Almost all features are showing success rate above 99%.The Overall success rate is 99.67% it shows that the SVM is best suited for this application.

TABLE I. SUCCESS RATE

Sr. No. Feature Success Rate 1. Mean 99% 2. Variance 99.80% 3. Cluster Shade 99.88% 4. Cluster Prominence 99.8835% 5. Mean Intensity 99.81%

VII. CONCLUSION In this paper, the application of nonlinear feature extraction for Pomegranate is presented. The features are obtained using HSI color model and wavelet transform, SVM is used for classification of the data. The result of skin defect detection of Pomegranate using SVM shows that all features are showing success rate above 99%, this gives the confidence that this algorithm can be used for automatic detection of quality of fruit.

REFERENCES [1] V. Katyal and D. Srivastava Efficient Fruit Defect Detection

and Glare removal Algorithm by anisotropic diffusion and 2D Gabor filter, International Journal of Engineering Science & Advanced Technology, Vol. 2 ,pp. 352 357, 2012.

[2] W.Huang , C. Zhang and B. Zhang Identifying Apple Surface Defects Based on Gabor Features and SVM Using Machine Vision, journal of computer and computing technologiesin agriculture(IFIP Advances in Information and Communication Technology), Vol. 370,pp. 343-350, 2012.

[3] G. Moradi, M. Shamsi ,M. Sedaghi and R. Alsharif, Fruit defect detection from color images using ACM and MFCM algorithms, International Conference on Electronic Devices, Systems and Applications (ICEDSA), 2011, pp. 182 - 186 , 25-27 April 2011.

[4] G. Moradi,Apple Defect Detection Using Statistical Histogram Based Fuzzy C-Means Algorithm, Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1-5, 16-17 Nov. 2011.

[5] S.Arivazhagan, R.Newlin Shebiah, S.Selva Nidhyanandhan and L.Ganesan, Fruit Recognition using Color and Texture Features, Journal of Emerging Trends in Computing and Information Sciences, vol. 1 no. 2 , 2010.

[6] J. Blasco, N. Aleixos, J. Gomez-Sanchand E. Molto Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features, Journal of biosystems engineering, vol.103,pp.137-145,2009

[7] D. Kim, Thomas, F. Burks, Q. Jianwei and M. Duke, Classification of grapefruit peel diseases using colour texture

feature analysis in Int. Journal of agriculture and biological engineering, vol. 2 no. 3,pp. 41-50, 2009.

[8] R. Pydipati, T.F. Burks and W.S. Lee, Identification of citrus disease using color texture features and discriminant analysis, in Science Direct, University of Florida, 225 Frazier-Rogers Hall, Gainesville, United States, 2006.

[9] M. Mukane, S. Gengaje, and S. Bormane, On Scale Invariance Texture Image Retrival using Fuzzy Logic and Wavelet Co-roccurrence based Features, International journal of computer applications, Vol.18(3), pp.10-17,2011.

[10] Pawar p. and Jung s,Support Vector Machine based Online Composite Helicopter Rotor Blade Damage Detection System , Journal Of Intelligent Material Systems And Structures, vol. 19, pp. 1217-1228, 2007.

[11] Cortes C. and Vapnik V. , Support Vector Networks, Machine Learning, pp.273-297, 1995

[12] Vapnik V.. The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.

[13] Boser , I. Guyon, and V. Vapnik , A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual workshop on Computational learning Theory Pittsburgh Pennsyl vania USA, pp.144-152, 1992.

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