International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS)

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1 International Association of Scientific Innovation and Research (IASIR) (An Association Unifing the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) ISSN (Print): ISSN (Online): A Comparative Analsis of Feature Etraction Methods for Fruit rading Classifications P.Deepa 1 Dr.S.N.eethalakshmi 1 Ph.D Research Scholar Professor Department of computer science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore-64104, Tamil Nadu, India Abstract: Fruit plas an important role in our life. India is eporting large number of fruits to abroad in that mosambi is one among. Before eporting the fruits, the fruits have to be graded according to their qualit. This paper presents an evaluation and comparison of the performance of three different etraction methods for classification of defect and non defect fruits. Three different feature etraction methods are LCM (re Level Co-occurrence Matri), shape features and intensit based features. The performance of each feature etraction method is evaluated and compared based on PNN (Probabilistic Neural Network) classifier. The eperimental results suggest that shape feature outperformed than other two methods. Kewords: PNN, Fruit grading, LCM, Shape, Intensit and PNN. I. INTRODUCTION India being an agricultural countr, eports a huge quantit of fruits abroad. But still in our produce is wasted for want of facilities for preservation and processing. The fruits have to be graded before being packed and sent to the international market for pricing. rading is done on the basis of various criteria like weight, shape, color, size etc. prior to eport; mosambi should be free from damages and defects. Presence of defects and without defect can be seen as in fig 1. Currentl the qualit inspection of mosambi is done manuall. Human inspection involves labor intensive work and judgment made can be ver subjective depending on the mood, condition, and knowledge about the fruit grading person involved. Further more, the manual process can be ver time consuming and inefficient especiall when dealing with high production volume. a) With defect (b) without defect Fig 1. Eample of mosambi with defect and without defect Various supervised classifiers such as PNN are used for classification problems in the area of fruit grading. Image feature etraction is important step in fruit classification. These features are etracted using image processing techniques. Several features are etracted from mosambi, such as teture feature, shape feature and intensit based features. Tetures are one of the important features used for man applications. Teture features have been widel used in fruit qualit classification. The teture features are abilit to distinguish between defect and non defect mosambi. Teture is an alteration and variation of surface of the image. In general, teture can be characterized as the space distribution of gra levels in a neighborhood. Teture feature have been proven to be useful in differentiating defect and non defect pattern in the fruit. Etracted teture features provide information about tetural characteristics of the image. Different classifier used in agricultural applications including neural network, support vector machine and fuzz classifier. Neural network have been widel used for fruit grading. There are two tpes of teture measure: first order and second order [4]. In the first order, teture measures are statistics calculated from an individual piel and do not consider piel neighbor relationships. In the second order, measures consider the relationship between neighbor piels. The LCM and shape features is a second order teture calculation and intensit based features are first order teture calculation. Teture features has been and used as parameter to enhance the classification result. This paper presents a comparison among three tpes of features used in fruit classification. A teture is a method of capturing pattern in the image. These features are calculated using statistical measures such as IJETCAS ; 013, IJETCAS All Rights Reserved Page 1

2 Deepa et al., International Journal of Emerging Technologies in Computational and Applied Sciences,4(), March-Ma 013, pp. 1-5 entrop, contrast and uniformit etc. Automatic classification into defect and non defect pattern is based on the teture features etracted from the fruit images. The paper is structured as follows. In section related works are discussed. Section 3 deals with the proposed methodolog. In section 4 performance measures are eplained in detail. Section 5 is the eperimental results, followed b conclusions at section 6. II. RELATED WORK In the literature, various numbers of techniques are described to detect and classif the presence of defects in the fruit images. DevrimUna et.al,[1] used statistical features to identif the defected areas in the apple images. And the are classified using various classifiers such as Linear Discriminant, Nearest Neighbour, Fuzz Nearest Neighbor and SVM. Using these classifiers the obtained 90% recognition of defected fruit and non defect fruit. Slamet Riadi et.al,[] used centroidal profile to etract features of mean, diameter, maimum of diameter was etracted from papaa object. These features sets are then fed separatel to the neural network for the size grade classification. The proposed technique has shown satisfactor result with classification accurac of more than 95%. S.Arivazhagan et.al, [3] used intensit, color, shape and teture features for fruit recognition. The recognition is done b the minimum distance classifier based upon the statistical and co-occurrence features derived from the Wavelet transformed sub- bands. In order to classif mangoes we need to be aware of the mango grading standard. Color and the size are the most significant criteria that are used to sort fruits. However, for sorting of mangoes there is another major factor which is the skin teture of mangoes that can improve the accurac of the classification sstem [4]. The attributes chosen for the oranges, peaches and apples [4] are size, color, stem location and detection of eternal blemishes. Size has been identified using machine vision b measuring area or diameter of fruits. The averaged surface color is a good indicator for these tpes of fruits. To determine the stem location, the take the images of the random fruit from oranges, apples and peaches. Then, the images analsis algorithms were applied and shown centroid of the stem in the computer. The eternal blemishes were detected using the combination of infrared and visible information. [5]. Devrim Una, [6] used statistical, teture and geometric features are etracted from apple images and various classifiers are used to grade the fruit according to the European standards and the obtained 93.5% accurac. III. METHODOLOY The flowchart for proposed fruit classification is shown in figure. A. Image Acquisition To evaluate the proposed method, 00 mosambi fruits were collected from the market. The images of mosambi were then captured at random orientation from perpendicular views. The camera was setup in a fied position to get an appropriate silhouette of the object as shown in figure 3. Camera Object Fig 3. Image acquisition set up 1. Image preprocessing The pre-processing task involves some procedures to prepare the images to be read for image processing. The images were initiall normalized to produce uniformit in terms of image size and to reduce the processing time. The original image with dimensions of piels was resized to one third of its normal size. An appropriate silhouette should be obtained to get an accurate processing result. For this task, the RB image was converted to a grascale. B. Feature etraction Feature etraction is a method of capturing visual content of an image. The objective of feature etraction process is to represent raw image in its reduced form to facilitate decision making process such as pattern classification. A variet of technique used for teture feature etraction such as LCM, shape and intensit based features. Feature etraction step is important step to get high classification rate. A set of features are etracted in order to allow a classifier to distinguish between defect and non defect pattern. The non defect fruit can be identified on the basis of tetural appearance. Etracted features are used in neural classifier to IJETCAS ; 013, IJETCAS All Rights Reserved Page

3 Deepa et al., International Journal of Emerging Technologies in Computational and Applied Sciences,4(), March-Ma 013, pp. 1-5 train it for the recognition of particular class either defect or non defect. The abilit of the classifier to assign the unknown object to the correct class is dependent on the etracted features. 1. LCM A LCM element P, d (i, j) is the joint probabilit of the gra level pairs i and j in a given direction separated b distance of d units. In this work seven features are determined for teture discrimination: Autocorrelation, contrast, cluster shade, entrop, sum of squares, and sum of entrop. Their definitions are given b the equations (1-5). Each subdivided block is an independent ROI. Multi-distance and multi-direction can be used to etract a large number of features. In our method we etract LCM features using one distance d = {1}, and four direction = {0 0, 90 0, 180 0, 70 0 }, which result in 0 i.e. (1 4 5) features etracted for each block. Autocorrelation = 1 1 ( p Contrast = p i j 0 Energ = 1 1 Entrop = 1 Cluster shade = Sum of entrop = )( p ) (1) j)( i j) () ( 3 i, j )( i j (3) 3 ) j) (4) 1 j 0 j)log( p( i, j)) (5) p ( i)log p ( i)) (6) Sum of squares = 1` 1 p( i, j)( i ) (7) i 0 j 0. Shape Features The shape features are also called geometric or morphological features. The shape of the defect is valuable feature. These tpes of features are based on the shape of ROIs. This feature does not consider the intensit of piels in the region; take the shape of the segmented region. Area =, Centroid = I(, ) N 1 I( i, i ) N 1 i, Diameter = ma d( 1 Perimeter = k, 1 R N 1 i 1 i i 1 k ) Minor ais = Minimum ais: it is the smallest distance connecting one point along the region boundar to another point on the region boundar going through the center of the region. Maimum ais = It is the largest distance connecting one point along the region. Maimum ais = It is the largest distance connecting one point along the region boundar to another point on the region boundar going through the center of the region. 3. Intensit based features Piel intensities are simplest available feature useful for pattern recognition. Intensit features are first order statistics depends onl on individual piel values. The intensit and its variation inside the fruit images can be measured b features like: median, mode, standard deviation and variance. Average intensit of ever column in the image is m n, then the total number of mean is n. Sample features for the three feature etraction method is shown in Table 4. C. CLASSIFICATION The proposed method used probabilistic neural network. The schematic representation of neural network with n inputs m hidden units and one output unit [7].The etracted features are considered as input to the neural IJETCAS ; 013, IJETCAS All Rights Reserved Page 3

4 Deepa et al., International Journal of Emerging Technologies in Computational and Applied Sciences,4(), March-Ma 013, pp. 1-5 classifier. PNN is a feed forward neural network. It is a supervised neural network that is widel used in the area of pattern recognition, nonlinear mapping, and estimation of the probabilit of class membership and likelihood ratios [8]. The neural network trained b adjusting the weights so as to be able to predict the correct class. The desired output was specified as 1 for defect and for non defect. The input features are normalized between 1 and.the classification process is divided into the training phase and the testing phase. In the training phase the known fruit images are given. In the testing phase, unknown images are given and the classification is performed using the classifier after training. The accurac of the classification depends on the efficienc of the training. Fig. Flow chart for proposed method Autocorrelation Contrast Cluster shade Energ Entrop Table 1: LCM feature values for defect fruit Area Centroid diameter perimeter Major length Table 3; shape features for defect fruit Mean Std Median Maimum Minimum Autocorrelation Contrast Cluster shade Energ Entrop Table : LCM feature values for non defect fruit Area Centroid diameter perimeter Major length Table4: shape features for non defect fruit Mean Std Median Maimum Minimum Table 5: Intensit based feature for defect fruit Table 6: Intensit based feature for non defect fruit IV. EXPERIMENTAL RESULTS The efficienc of three different feature etraction methods is trained and tested using PNN classifier. The dataset used for this eperiment is composed of 00 mosambi images. Which includes 100 with defect and 100 IJETCAS ; 013, IJETCAS All Rights Reserved Page 4

5 Deepa et al., International Journal of Emerging Technologies in Computational and Applied Sciences,4(), March-Ma 013, pp. 1-5 without defect, 80% (150 out of 00) set of images are used for training and 0% (100 out of 00) used for testing. The effectiveness of the three different feature etraction methods were evaluated and compared. Three eperiments are conducted. In each eperiment, the architecture of the PNN, training and testing samples are same. In the first eperiment LCM features were etracted and its classification was done using PNN classifier. In the second eperiment shape features were etracted and its classification was done and in the third eperiment the intensit features were etracted and was classified. The results shows that LCM features based PNN is giving 96% classification rate, shape feature is giving 100% classification rate and intensit features is giving 9% classification rate LCM INTENSITY SHAPE classification accurac V. CONCLUSION This paper eamined the three different tpes of feature etraction method. The result proves that shape feature based PNN is giving higher classification rate of 100%. The shape gives a better performance when compared with LCM and intensit features. In future, the fruits can be graded with Indian standards with different classifiers to find out the optimum classification procedure. VI. REFERENCES 1. Devrim Una et al, 010, Automatic grading of Bi-colored apples b multispectral machine vision, Journal of Computers and Electronics in Agriculture, Elsevier.. S.Arivazhagan et al, Oct 010, Fruit Recognition using Color and Teture Features, Journal of Emerging Trends in Computing and Information Sciences, Vol. 1, No.. 3. Slamet Riadi, et al, 007, Papaa Size rading using Centroidal Profile Analsis of Digital Image, 6th wseas international conference on circuits, sstems, electronics,control & signal processing. 4. Blasco, J., Aleios, N. and Molto, E(003). Machine Vision Sstem Qualit rading of Fruit, Biosstems Engineering, Leemansa, V., Mageinb, H. and Destain, M.-F.(00). On-line Fruitrading According to their Eternal Qualit using Machine Vision. Journal of Biosstems Engineering, 83, D. F. Specht; H. Romsdahl, 1994,.Eperience with adaptive probabilistic neural network and adaptive general regression neural network, In Proceedings of the IEEE International Conference on Neural Networks, volume, pp N.K. Bose and P. Liang. Neural network fundamentals with graphs, algorithms and application. New Delhi: Mcraw-Hill R.O. Duda, P.E. Hart and D.. Stork. Pattern Classification. CA: John Wille & Sons. 001 IJETCAS ; 013, IJETCAS All Rights Reserved Page 5