Automatic Classification of Mango Using Statistical Feature and SVM

  • Santi Kumari BeheraEmail author
  • Shrabani Sangita
  • Amiya Kumar Rath
  • Prabira Kumar SethyEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 41)


Natural fruit like mango contributes a major part of national growth. Due to flavor, nutrition, and taste, mango is one of the popular fruits. There are around 283 types of mangoes present in India only, from that 30 types of mangoes are well known. Human vision sometimes leads to mismatch between the varieties of mango fruit. Using machine vision technique, it helps to reduce human effort and achieve a better result. The aim of this paper is to extract the features of mango fruit with GLCM (Gray-Level Co-Occurrence Matrix) and classify the variety of mango fruits by multiclass Support Vector Machine (SVM) with K-means clustering.


Image processing SVM GLCM K-means 


  1. 1.
    Nandi, C.S., Tudu, B., Koley, C.: A machine vision technique for grading of harvested mangoes based on maturity and quality. IEEE Sensors J. 16(16), 6387–6396 (2016)CrossRefGoogle Scholar
  2. 2.
    Pham, V.H., Lee, B.R.: An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Vietnam J. Comput. Sci. 2(1), 25–33 (2015)CrossRefGoogle Scholar
  3. 3.
    Ashok, V., Vinod, D.S.: A comparative study of feature extraction methods in defect classification of mangoes using neural network. In: 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), IEEE (2016)Google Scholar
  4. 4.
    Kumar, C., Chauhan, S., Narmadha Alla, R.: Classifications of citrus fruit using image processing-GLCM parameters. In: 2015 International Conference on Communications and Signal Processing (ICCSP), IEEE (2015)Google Scholar
  5. 5.
    Mustafa, N.B.A. et al.: Classification of fruits using probabilistic neural networks-improvement using color features. In: TENCON 2011–2011 IEEE Region 10 Conference. IEEE (2011)Google Scholar
  6. 6.
    Mendoza, F., Dejmek, P., Aguilera, J.M.: Calibrated color measurements of agricultural foods using image analysis. Postharvest Biol. Technol. 41(3), 285–295 (2006)CrossRefGoogle Scholar
  7. 7.
    Yam, K.L., Papadakis, S.E.: A simple digital imaging method for measuring and analyzing color of food surfaces. J. Food Eng. 61(1), 137–142 (2004)CrossRefGoogle Scholar
  8. 8.
    Gongal, A., et al.: Sensors and systems for fruit detection and localization: a review. Comput. Electron. Agric. 116, 8–19 (2015)CrossRefGoogle Scholar
  9. 9.
    Payne, A.B., et al.: Estimation of mango crop yield using image analysis–segmentation method. Comput. Electron. Agric. 91, 57–64 (2013)CrossRefGoogle Scholar
  10. 10.
    Qureshi, W.S., et al.: Machine vision for counting fruit on mango tree canopies. Precis. Agric. 18(2), 224–244 (2017)CrossRefGoogle Scholar
  11. 11.
    Khairunniza-Bejo, S., Kamarudin, S.: Chokanan mango sweetness determination using hsb color space. In: 2011 Third International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM), IEEE (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVSSUTBurlaIndia
  2. 2.Department of ElectronicsSambalpur UniversitySambalpurIndia

Personalised recommendations