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  • 1. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1861-1866 Infection Analysis Using Colour Feature Texture Using Image Processing Ravikant Sinha1 Department of E&TC Pune University, SCOE sudumbare Maharashtra, India Pragya Pandey2 Department of E&TC Pune University, SCOE sudumbare Maharashtra, IndiaABSTRACT In this study, a new approach is used to Which India produces approximately 50% in theautomatically detect the infected pomegranates. In states of Maharashtra and Andhra Pradesh. Iran isthe development of automatic grading and sorting the second largest, producing around 35% of globalsystem for pomegranate, critical part is detection production. Spain produces around 2.5% and theof infection. Color texture feature analysis is used USA has around 10,000ha under production.for detection of surface defects on pomegranates. This study centres at developing a method to detectAcquired image is initially cropped and then infected pomegranate using color texture features.transformed into HSI color space, which is further The various steps involved for development ofused for generating SGDM matrix. Total 18 database of features are summarized in figure 1texture features were computed for hue (H), below:saturation (S) and intensity (I) images from eachcropped samples. Best features were used as aninput to Support Vector Machine (SVM) classifier INPUT IMAGEand tests were performed to identify bestclassification model. Out of selected texture CROPPING WITH SIZE OF 16X16features, features showing optimal results werecluster shade (99.8835%), product moment(99.8835%) and mean intensity (99.8059%). CONVERTING CROPPED IMAGESKeywords:-Pomegranate, disease detection, INTO HIS IN=MAGE SPACEmachine vision, color co-occurrence method, SGDM,texture features. GENERATION OF SGDM MATRIX1. INTRODUCTION The pomegranate (Punica granatum) is a COMPUTATION OF TEXTUREfruit-bearing deciduous shrub or small tree that growsto between five and eight metres tall and is bestsuited to climates where winters are cool and Fig 1: Process of developing the database ofsummers are hot. The pomegranate is thought to features.have been first cultivated 5 to 6,000 years ago and isnative to the regions from Iran through to north 2. LITERATURE REVIEWIndia. It is now widely cultivated throughout Eastern Thomas J. Burks et al (2009) [1]Europe, Asia and the USA, the main areas of world demonstrated that color imaging and texture featureproduction being in India, Iran, Spain and California. analysis could be used for classifying citrus peelPomegranates can be consumed as fresh fruit or used diseases under the controlled laboratory lightingin fruit juices, teas, pharmaceutical and medicinal conditions. The present work is an extension of thatproducts and in dyes or as decoration. There are research, providing a feasibility analysis of theseveral hundred different varieties of pomegranate technology in classification of infectedrecognised in Iran alone and even more globally, pomegranates. Edwards and Sweet (1986) [2] usedsome of the cultivars that have the greatest impact are reflectance spectra of the entire citrus plant for‘Moller, ‘Ahmar, ‘Bhagawa, ‘Hicaznar and ‘Dente estimating the damage caused due to citrus blight.di cavallo. Globally, it is estimated that total Hetal Patel et al (2011) [3] designed the algorithmproduction amounts to around 2,000,000 tonnes, of aiming at calculation of different weights for features 1861 | P a g e
  • 2. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1861-1866like intensity, color, orientation and edge of the test and distance‘d’ is small compared to the size of theimage. S.Arivazhagan et al (2010) [4] used computer texture elements, the pairs of points at distance dvision strategy to recognize a fruit rely on four basic should have similar gray levels. In turn, if the texturefeatures which characterize the object on the basis of is fine and distance d is comparable to the textureintensity, color, shape and texture. R. Pydipati et al size, then the gray levels of points separated by(2006) [5] used the color co-occurrence method to distance d should often be quite distinct, so that thedetermine whether texture based hue, saturation, and values in the SGDM matrix should be disperseintensity color features in conjunction with statistical uniformly. Thus, texture directionality can beclassification algorithms could be used to identify analyzed by examining spread measures of SGDMdiseased and normal citrus leaves under laboratory matrices created at various distances‘d’. Extraction ofconditions. Shearer Scott (1990) [6] proposed a a numerous features is possible using the SGDMdissertation for the development of new color-texture matrices generated in the above manner.analysis method to characterize and identify canopysections of nursery plant cultivators. Yousef Al Ohali The test image is then cropped such that(2010) [7] studied the performance of a back around 2000 cropped images of size 16 x 16 each arepropagation neural network classifier and tested the formed. These RGB images are converted into HSIaccuracy of the system on preselected date samples. color space representation. Then each pixel map isCzeslaw Puchalski et al (2008) [8] developed a used to generate a color co-occurrence matrix,system for identifying surface defects on apple and resulting in three CCM matrices, one for each of thesuccessfully obtained 96% classification accuracy. H. H, S and I pixel maps. These matrices measure theAl-Hiary et al (2011) [9] experimentally evaluated a probability that a pixel at one particular gray levelsoftware solution for automatic detection and will occur at a distinct distance and orientation fromclassification of plant leaf diseases. Lanlan Wu et al any pixel, given that pixel has a second particular(2010) [10] investigated the support vector machine, gray level. For a position operator p, we can define aas a classifier tool to identify the weeds in corn fields matrix ‘Pij’ that counts the number of times a pixelat early growth stage. Li Daoliang et al (2012) [11] with grey-level i occurs at position p from a pixelshown that texture-related features such as co- with grey-level j. For example, if we have fouroccurrence matrices might be used as effective distinct grey-levels 0, 1, 2 and 3, then one possiblediscriminators for high resolution remote sensing SGDM matrix P (i, j, 1, 0) is given below as shown:images. Xing-yuan Wang et al (2012) [12] presentedan effective color image retrieval method based on 1 0 3 0 0 1 3 3texture, which used the color co-occurrence matrix to 2 2 0 3 1 0 0 1extract the texture feature and measure the similarity I ( x, y )  Pof two color images. Ryusuke Nosakaet al (2012) 3 2 0 2 3 0 2 3[13] suggested a new image feature based on spatial 1 3 2 3 3 1 3 0co-occurrence within micro patterns, where eachmicro pattern is presented by a Local Binary Pattern Statistical methods use order statistics to(LBP). model the relationships between pixels within the region by constructing Spatial Gray-level3. COLOR CO-OCCURRENCE MATRIX Dependency Matrices (SGDM’s). A SGDM matrix is Thomas J. Burks et al (2009) [1] used the the joint probability occurrence of gray levels ‘i’ andcolor co-occurrence method for citrus peel fruit ‘j’ for two pixels with a defined spatial relationship inclassification. Pydipati et al. (2006) [5] utilized the an image. The SGDMs are represented by thecolor co-occurrence method to extract various function P (i, j, d, Ө) where ‘i’ represents the graytextural features from the color RGB images of citrus level of the location (x, y) in the image I(x, y), and jleaves. There are two main analysis methods for represents the gray level of the pixel at a distance dcalculation of texture viz. from location (x, y) at an orientation angle of Ө. The1) Structural Approach nearest neighbour mask is exemplified in figure 2,2) Statistical Approach where the reference pixel is shown as an asterisk. Statistical approach, which is used here, is aquantitative measure of arrangement of intensities ina region. Statistical methods use second orderstatistics to describe the relationships between pixelswithin the region by constructing Spatial Gray-levelDependency Matrices (SGDM). A SGDM matrix isthe joint probability occurrence of gray levels ‘i’ and‘j’ for two pixels with a defined spatial relationship inan image. Distance‘d’ and angle ‘θ’ are used to Fig 2: Nearest neighbour mask for calculatingdefine the spatial relationship. If the texture is coarse spatial Gray-level dependence matrix (SGDM) 1862 | P a g e
  • 3. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1861-18664. TEXTURE FEATURE CALCULATION Where, P (i, j) is the SGDM matrix and Ng is total Texture is one of the characteristics that number of gray levels considered for generation ofsegments images into regions of interest (ROI) and SGDM matrix.classifies those regions. It gives information aboutthe spatial organization of the intensities in an image. 5. SUPPORT VECTOR MACHINEIn general, texture can be defined as a characteristic A support vector machine (SVM) is basedof image that can provide a higher-order description on a statistical learning theory showing variousof the image and includes information about the advantages over existing soft computing methods. Asspatial distribution of tonal variations or gray tones. compared with the soft computing methods generallyTexture involves the spatial distribution of gray used for various applications, SVM gives betterlevels. Thus, two-dimensional histograms or co- performance. Lanlan Wu et al (2010) [10] achievedoccurrence matrices are reasonable texture analysis classification accuracy ranging 92.31 to 100% fortools. An image can have one or more textures. These different feature selections using SVM. It is used infeatures are useful in carrying out differentiation case of finite sample data. It aims at acquiring thealgorithms for detection purpose ahead. worthy solutions on the ground of present data rather than the optimal value for infinite samples. For differentiating infected pomegranate If the original problem is stated in a finitefrom the uninfected ones the following features are dimensional space and the sets required to separatereckoned for the components H, S and I: out are not linearly separable in that space then it can1) Cluster Shade: It is measures of the lack of be mapped into a higher dimensional space, so as tosymmetry which shows skewness of the matrix. make separation between the sets more Ng 1 distinguishable. This peculiar quality of SVM showsF1 =  (i  P  j  P ) P(i, j ) i , j 1 x y 3 its good potentiality of abstraction. As its ramification is independent of sample dimension, it (1) solves the problem of dimension disaster. This method is successfully implemented in face2) Sum Entropy: It measures the randomness of the recognition, machine fault detection and in of the matrix, when all elements of the handwritten digit recognition for tablets, PDA’s andelements other electronic devices.matrix are maximally random entropy has its highestvalue. As shown in figure 3 the two sets of sample Ng 1 Ng 1   P(i, j ) ln P(i, j ) points are represented by green and red color, H isF2 = the hyper plane or separator, H1 and H2 are planes i 0 j 0 (2) running parallel to the hyper plane and pass through3) Uniformity: It is the measure of uniform pattern in the sample points closest to the hyper plane. As therethe image. are many such hyper planes which can show Ng 1 Ng 1 divergence between the two sample sets, a hyperF3 =   [ P(i, j )] i 1 j 1 2 plane which has maximum distance from both H1 (3) and H2 is chosen. The distance between H1 and H2 is called as margin. The hyper plane with largest margin4) Mean Intensity: It is the measure of image is called as maximum-margin hyper plane and thebrightness derived from the co-occurrence matrix. linear classifier it defines is addressed as maximum- Ng 1 margin classifier. The more margin a hyper planeF4 =  iP i 0 x (i ) has, less is the error in optimizing the separation (4) between sample points.5) Product Moment: It is analogous to the co-variance of the intensity of co-occurrence matrix. Ng 1 Ng 1F5 =   (i  F )( j  F ) P(i, j ) i 0 j 0 4 4 (5)6) Inverse Difference Moment: It returns themeasures of closeness of the distribution of SGDMelements to the SGDM diagonal. Ng 1 Ng 1 Fig 3: Distinguishing sample point using SVM P (i, j )F6 =  i 0 j 0 1  (i  j ) 2 This case study uses the Gaussian Radial (6) Basis Function Kernel for the classification. For the nonlinear case, we project the original space into a 1863 | P a g e
  • 4. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1861-1866higher dimension space in which the SVM can Various features are computed from the testconstruct an optimal separating hyper-plane as images for components H, S and I which are furtherexplained above. Let us consider a function ( x), used for generating the 3D plot of each featuresuch that F  {( x is the feature point x): X} independently for distinguishing the infected andcorresponding to the data item x. For SVM, the uninfected pomegranate over the plot. In figure 3, redkernel function is represented as color signifies the feature points for the infectedK(x, z)  ((  ( )),  z x), z x, X pomegranate whereas green color signifies the samereplacing the inner product (x, z). for uninfected ones. Here X, Y and Z axes represent feature of image components Hue, Saturation and6. RESULTS AND DISCUSSION Intensity respectively. It can be easily observed For the purpose classification, it is often through figure 6 that Cluster Shade (Plot 1), Meananticipated that the linear separabilty of the mapped Intensity (Plot 4) and Product Moment (Plot 5)samples is enhanced in the kernel feature space so features are best for differentiating the diseasedthat applying traditional linear algorithms in this pomegranate as the data points are clearly separablespace could result in better performance compared to whereas Sum Entropy (Plot 2), Uniformity (Plot 3)those obtained in the original input space. If an and Inverse difference Moment (Plot 6) features doinappropriate kernel is chosen, the classification not possess separate explicit data points.performance of kernel-based methods can be evenworse than that of their linear counterparts. LiDaoliang et al (2012) [11] verified that SVMclassification systems leads to the successfuldiscrimination of targets when fed with appropriateinformation. Therefore, selecting a proper kernel withgood class separability plays a vital role in kernel-based classification algorithms. Here the Gaussianbasis function performs well to do the task. Thecontour plots for features cluster shade and meanintensity are shown in figure 4 and figure 5 below. Fig 6: 3D Plots for features Plot 1-Cluster Shade, Plot 2-Sum Entropy, Plot 3-Uniformity, Plot 4- Mean Intensity, Plot 5-Product Moment & Plot 6- Inverse Difference Moment Figure 7 shows bar diagram of success rates obtained after passing the test images through SVM training function. As it is seen that cluster shade, mean intensity and product moment show a eminent success rate whereas sum entropy, uniformity andFig 4: Contour Plot of Cluster shade inverse difference moment have modest success rate and hence we egest these features for final classification of images under test. Fig 7: Success Rate percentage of Features afterFig 5: Contour Plot of Mean Intensity undergoing SVM training 1864 | P a g e
  • 5. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1861-1866 Based on obtained results, features with 7. CONCLUSIONpercentage above 90% are selected to acquire higher A study for early sensing of the surfaceaccuracy for classifying the images. The selected infections on pomegranates using image processingfeatures for classification show less error or was accomplished. The test image was cropped tomisclassification. Cluster Shade and Cluster form around 2000 samples of size 16x16. The RGBProminence have 6, variance has 10 such pomegranate image was then transformed into HSImisclassifications. image space for nullifying effects of light reflected The final classification is accomplished by by image. Then each pixel map was used to generatecomparing the features calculated for the test images a color co-occurrence matrix, resulting in three CCMwith the reference training database generated using matrices, one for each of the H, S and I pixel maps.support vector machine. The images are classified The generated SGDM matrix was further used forinto two categories namely infected and uninfected. calculation of the discussed texture features. The 3D plot for each feature were plotted and examined. TheThe figure 8 shows the GUI running feature features Cluster shade, Mean intensity and Productcalculation process for the test image. Moment show the towering separation of the data points whereas the Sum Entropy, Uniformity and Inverse Difference Moment were found to give lower separation hence omitted in final process. This analysis was verified by the result of the SVM training which showed the success rate of 99% for the selected ones. The testing process involved the feature calculation for the test image succeeded by the SVM classification. This algorithm can be implemented for automatic grading and sorting system for quality control. This can be further extended for detecting multi diseases of pomegranate using multi-class classifier. REFERENCES [1] Dae Gwan Kim, Thomas F. Burks, Jianwei Qin, Duke M. Bulanon 2009 Classification of grapefruit peels diseases using colorFig 8: Graphical User Interface showing feature texture feature analysis. Int. Journal ofcalculation of image under test agriculture and biological engineering, vol. 2, University of Florida, Gainesville, FLFigure 9 shows the result of the final classification 32611-0570, USA.whether the test image is infected or uninfected. [2] Edwards J G, Sweet C H. Citrus blight assessment using a Microcomputer: quantifying damage using an apple computer to solve reflectance spectra of entire trees. Florida scientist, 1986; 49 (1): 48-53. [3] Hetal N. Patel, Dr. R.K.Jain, Dr. M.V.Joshi 2011 Fruit Detection using Improved Multiple Features based Algorithm. IJCA vol. 13, A.D.Patel Institute of Technology, New V.V.Nagar, Gujarat, India. [4] S.Arivazhagan, R.Newlin Shebiah, S.Selva Nidhyanandhan, L.Ganesan 2010 Fruit Recognition using Color and Texture Features. JETCIS, vol. 1 no. 2, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India. [5] R. Pydipati, T.F. Burks, W.S. Lee 2006 Identification of citrus disease using colorFig 9: Graphical User Interface showing final texture features and discriminant analysis.result of classification Science Direct, University of Florida, 225 1865 | P a g e
  • 6. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1861-1866 Frazier-Rogers Hall, Gainesville, United States.[6] Scott A. Shearer 1986 Plant identification using color co-occurrence matrices derived from digitized image. Trans ASAE, 1990; 33(6):2037-2044, the Ohio University.[7] Yousef Al Ohali 2011Computer vision based date fruit grading system: Design and Implementation. Journal of King Saud University – Computer and Information Sciences 23, 29–36, King Saud University, Riyadh, Saudi Arabia.[8] Czeslaw Puchalski, Jozef Gorzelany, Grzegorz Zagula, Gerald Brusewitz 2008 Image analysis for apple defect detection. TEKA Kom. Mot. Energ. Roln. – OL PAN, 8, 197–205, Oklahoma State University, Stillwater, USA.[9] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh 2011 Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications (0975 – 8887), Vol. 17, no.1.[10] Lanlan Wu and Youxian Wen 2009 Weed/corn seedling recognition by support vector machine using texture features. African Journal of Agricultural Research vol. 4 no. 9, Huazhong Agricultural University, Wuhan, 430070, P. R. China.[11] Li Li, Chen Yingyi, Gao Hongju and Li Daoliang 2012 Automatic recognition of village in remote sensing images by support vector machine using color co-occurrence matrices. American Scientific Publishers vol. 10, Numbers 1-2, pp. 523-528.[12] Xing-yuan Wang, Zhi-feng Chen and Jiao- jiao Yun 2012 An effective method for color image retrieval based texture. Journal of computer standards and interface vol. 34 Issue 1, January, 2012.[13] Ryusuke Nosaka, Yasuhiro Ohkawa and Kazuhiro Fukui 2012 Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns. Advances in image and video technology vol. 7088/2012, 82-91. 1866 | P a g e

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