Advertisement

Multi-class SVM Based Classification Approach for Tomato Ripeness

  • Esraa Elhariri
  • Nashwa El-Bendary
  • Mohamed Mostafa M. Fouad
  • Jan Platoš
  • Aboul Ella Hassanien
  • Ahmed M. M. Hussein
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

Abstract

This article presents a content-based image classification system to monitor the ripeness process of tomato via investigating and classifying the different maturity/ripeness stages. The proposed approach consists of three phases; namely pre-processing, feature extraction, and classification phases. Since tomato surface color is the most important characteristic to observe ripeness, this system uses colored histogram for classifying ripeness stage. It implements Principal Components Analysis (PCA) along with Support Vector Machine (SVM) algorithms for feature extraction and classification of ripeness stages, respectively. The datasets used for experiments were constructed based on real sample images for tomato at different stages, which were collected from a farm at Minia city. Datasets of 175 images and 55 images were used as training and testing datasets, respectively. Training dataset is divided into 5 classes representing the different stages of tomato ripeness. Experimental results showed that the proposed classification approach has obtained ripeness classification accuracy of 92.72%, using SVM linear kernel function with 35 images per class for training.

Keywords

image classification features extraction ripeness principal component analysis (PCA) support vector machine (SVM) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brezmes, J., Llobet, E., Vilanova, X., Saiz, G., Correig, X.: Fruit ripeness monitoring using an electronic nose. Sensors and Actuators B-Chem. Journal 69(3), 223–229 (2000)CrossRefGoogle Scholar
  2. 2.
    May, Z., Amaran, M.H.: Automated ripeness assessment of oil palm fruit using RGB and fuzzy logic technique. In: Demiralp, M., Bojkovic, Z., Repanovici, A. (eds.) Proc. the 13th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering (MACMESE 2011), Wisconsin, USA, pp. 52–59 (2011)Google Scholar
  3. 3.
    Polder, G., van der Heijden, G.W.A.M., Young, I.T.: Spectral Image Analysis for Measuring Ripeness of Tomatoes. Transactions-American Society of Agricultural Engineers International Journal 45(4), 1155–1162 (2002)Google Scholar
  4. 4.
    Jaffar, A., Jaafar, R., Jamil, N., Low, C.Y., Abdullah, B.: Photogrammetric Grading of Oil Palm Fresh Fruit Bunches. International Journal of Mechanical & Mechatronics Engineering (IJMME) 9(10), 18–24 (2009)Google Scholar
  5. 5.
    Paulraj, M.P., Hema, C.R., Krishnan, R.P., Radzi, S.S.M.: Color Recognition Algorithm using a Neural Network Model in Determining the Ripeness of a Banana. In: Proc. the International Conference on Man-Machine Systems (ICoMMS), Penang, Malaysia, pp. 2B7-1–2B7-4 (2009)Google Scholar
  6. 6.
    Rizam, S., YAsmin, A.R.F., Ihsan, M.Y.A., Shazana, K.: Non-destructive Watermelon Ripeness Determination Using Image Processing and Artificial Neural Network (ANN). International Journal of Intelligent Technology 4(2), 130–134 (2009)Google Scholar
  7. 7.
    Suganthy, M., Ramamoorthy, P.: Principal Component Analysis Based Feature Extraction, Morphological Edge Detection and Localization for Fast Iris Recognition. Journal of Computer Science 8(9), 1428–1433 (2012)CrossRefGoogle Scholar
  8. 8.
    Ada, RajneetKaur: Feature Extraction and Principal Component Analysis for Lung Cancer Detection in CT scan Images. International Journal of Advanced Research in Computer Science and Software Engineering 3(3) (2013)Google Scholar
  9. 9.
    El-Bendary, N., Zawbaa, H.M., Hassanien, A.E., Snasel, V.: PCA-based Home Videos Annotation System. The International Journal of Reasoning-based Intelligent Systems (IJRIS) 3(2), 71–79 (2011)Google Scholar
  10. 10.
    Xiao, B.: Principal component analysis for feature extraction of image sequence. In: Proc. International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE), Chengdu, China, vol. 1, pp. 250–253 (2010)Google Scholar
  11. 11.
    Shahbahrami, A., Borodin, D., Juurlink, B.: Comparison between color and texture features for image retrieval. In: Proc. 19th Annual Workshop on Circuits, Systems and Signal Processing (ProRisc 2008), Veldhoven, The Netherlands (2008)Google Scholar
  12. 12.
    Soman, S., Ghorpade, M., Sonone, V., Chavan, S.: Content Based Image Retrieval using Advanced Color and Texture Features. In: Proc. International Conference in Computational Intelligence (ICCIA 2012), New York, USA (2012)Google Scholar
  13. 13.
    Wu, Q., Zhou, D.-X.: Analysis of support vector machine classification. J. Comput. Anal. Appl. 8, 99–119 (2006)MathSciNetMATHGoogle Scholar
  14. 14.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Abraham, A.: SVM-based Soccer Video Summarization System. In: Proc. the Third IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC 2011), Salamanca, Spain, pp. 7–11 (2011)Google Scholar
  15. 15.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Kim, T.-H.: Machine learning-based soccer video summarization system. In: Kim, T.-H., Gelogo, Y. (eds.) MulGraB 2011, Part II. CCIS, vol. 263, pp. 19–28. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Tzotsos, A., Argialas, D.: A support vector machine approach for object based image analysis. In: Proc. International Conference on Object-based Image Analysis (OBIA 2006), Salzburg, Austria (2006)Google Scholar
  17. 17.
    Zhang, Y., Xie, X., Cheng, T.: Application of PSO and SVM in image classification. In: Proc. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, China, vol. 6, pp. 629–631 (2010)Google Scholar
  18. 18.
    Suralkar, S.R., Karode, A.H., Pawade, P.W.: Texture Image Classification Using Support Vector Machine. International Journal of Computer Applications in Technology 3(1), 71–75 (2012)Google Scholar
  19. 19.
    Yu, H., Li, M., Zhang, H.-J., Feng, J.: Color texture moments for content-based image retrieval. In: Proc. International Conference on Image Processing, New York, USA, vol. 3, pp. 929–932 (2002)Google Scholar
  20. 20.
    Liu, Y., Zheng, Y.F.: One-against-all multi-class SVM classification using reliability measures. In: Proc. IEEE International Joint Conference on Neural Networks (IJCNN 2005), Montreal, Quebec, Canada, vol. 2, pp. 849–854 (2005)Google Scholar
  21. 21.
    U.S.D.A. United States Standards for Grades of Fresh Tomatoes, U.S. Dept. Agric./AMS, Washington, DC (1991), http://www.ams.usda.gov/standards/vegfm.htm (accessed: March, 2013); Nose, Sensors and Actuators B 2000 69, 223–229; Signals and fruit quality indicators on shelf-life measurements with pinklady apples. Sensors and Actuators B 2001 80, 41–50

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Esraa Elhariri
    • 1
    • 2
  • Nashwa El-Bendary
    • 3
    • 2
  • Mohamed Mostafa M. Fouad
    • 3
    • 2
  • Jan Platoš
    • 4
    • 2
  • Aboul Ella Hassanien
    • 5
  • Ahmed M. M. Hussein
    • 6
  1. 1.Faculty of Computers and InformationFayoum UniversityFayoumEgypt
  2. 2.Scientific Research Group in Egypt (SRGE)GizaEgypt
  3. 3.Arab Academy for Science,Technology, and Maritime TransportCairoEgypt
  4. 4.Department of Computer Science, FEECS and IT4 InnovationsVSB-Technical University of OstravaOstravaCzech Republic
  5. 5.Faculty of Computers and InformationCairo UniversityCairoEgypt
  6. 6.Dept. of Genetics, Faculty of AgricultureMinia UniversityMinyaEgypt

Personalised recommendations