Local Tetra Pattern-Based Fruit Grading Using Different Classifiers

  • Ramanpreet Kaur
  • Mukesh Kumar
  • Mamta Juneja
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


Agriculture is an integral part of economic development, and thus, it becomes essential to lift the impact factor of agriculture development. In past years, researchers had introduced many nondestructive image processing technique to grade the food products. These techniques ensure the quality of food products, are consistent, and save the labor time as well. Many dedicated systems or techniques are available for grading particular type of fruit; therefore, there is need to devise common technique to grade various type of fruits. This paper introduces the common feature extraction method which uses local tetra pattern to grade fruits. In this research, we graded guava fruit into four categories (unripe, ripe, overripe, and defected). The performance of the proposed method is evaluated and compared using ensemble classifiers and compared using accuracy and error rate. The experimental results showed the highest accuracy of 93.8% by Subspace Discriminant Ensemble classifier. The proposed method can be easily adapted for any other spherical fruit or vegetable.


  1. 1.
    Kondo, N.: Fruit grading robot. In: International Conference on Advanced Intelligent Mechatronics, pp. 1366–1371 (2003)Google Scholar
  2. 2.
    Gay, P., Berruto, R.: Innovative techniques for fruit color grading (2002).
  3. 3.
    Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M.-F., Debeir, O.: Automatic grading of Bi-colored apples by multispectral machine vision. Comput. Electron. Agric. 75, 204–212 (2011)CrossRefGoogle Scholar
  4. 4.
    Suresha, M., Shilpa, N.A., Soumya, B.: Apples grading based on SVM classifier. Int. J. Comput. Appl. 0975–8878 (2012)Google Scholar
  5. 5.
    Blasco, J., Alexios, N., Molto, E.: Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. J. Food Eng. 81, 535–543 (2007)CrossRefGoogle Scholar
  6. 6.
    Arakeri, M.P., Lakshmana: Computer vision based fruit grading system for quality evaluation tomato in agriculture industry. In: 7th International Conference on Communication, Computing and Virtualization, vol. 79, pp. 426–433 (2016)Google Scholar
  7. 7.
    Mhaski, R.R., Chopade, P.B., Dale, M.P.: Determination of ripeness and grading of tomato using image analysis on Rasberry Pi. In: International Conference on Communication, Control and Intelligent Systems (2015). 978-1-4673-7541-2Google Scholar
  8. 8.
    Prabha, D.S., Kumar, J.S.: Assessment of banana fruit maturity by image processing technique. J. Food Sci. Technol. 52(3), 1316–1327 (2015)Google Scholar
  9. 9.
    Ji, W., Koutsidis, G., Luo, R., Hutchings, J., Akhtar, M., Megias, F., Butterworth, M.: A Digital Imaging Method for Measuring Banana Ripeness, vol. 38(3), pp. 364–374. Wiley Periodicals (2012)CrossRefGoogle Scholar
  10. 10.
    Hu, M.-H., Dong, Q.-L., Malakar, P.K., Liu, B.-L., Jaganathan, G.K.: Determining banana size based on computer vision. Int. J. Food Prop. 18(3), 508–520 (2015)CrossRefGoogle Scholar
  11. 11.
    Sanaeifar, A., Bakhshipour, A., Guardia, M.: Prediction of banana quality indices from color features using support vector regression 148, 54–61 (2016)Google Scholar
  12. 12.
    Ramya, M., Anitha Raghavendra: Ripeness evaluation of mangoes using Image processing. Int. J. Eng. Sci. Comput. 6(7) (2016)Google Scholar
  13. 13.
    Al Ohali, Y.: Computer vision based date fruit grading system: design and Implementation. J. King Saud Univ.–Comput. Inf. Sci. 23, 29–36 (2013)CrossRefGoogle Scholar
  14. 14.
    Dutta, M.K., Sengar, N., Minhas, N., Sarkar, B., Goon, A., Banerjee, K.: Image processing based classification of grapes after pesticide exposure. LWT-Food Sci. Technol. 72, 368–376 (2016)CrossRefGoogle Scholar
  15. 15.
    Sardar, H.: Fruit quality estimation by color for grading. Int. J. Model. Optim. 4(1) (2014)CrossRefGoogle Scholar
  16. 16.
    Murala, S., Maheshwari, R.P., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process. 21(5), 2874–2886 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.UIET, CSE DepartmentPanjab UniversityChandigarhIndia

Personalised recommendations