Machine Learning-Based Classification of Good and Rotten Apple

  • Shiksha SinghEmail author
  • Nagendra Pratap Singh
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)


An apple is one of the most cultivated and consumed fruits in the world and continuously being praised as a delicious and miracle food. It is a rich source of Vitamin A, Vitamin B1, Vitamin B2, Vitamin B6, Vitamin C, and folic acid etc, whereas the rotten fruits affect the health of human being as well as cause big economical loss in agriculture sectors and industries. Therefore, identification of rotten fruits has become a prominent research area. This paper focuses on the classification of rotten and good apple. For classification, first extract the texture features of apples such as discrete wavelet feature, histogram of oriented gradients (HOG), Law’s Texture Energy (LTE), Gray level co-occurrence matrix (GLCM) and Tamura features. After that, classify the rotten and good apples by applying various classifiers such as SVM, k-NN, logistic regression, and Linear Discriminant. The performance of proposed approach by using SVM classifier is 98.9%, which is found better with respect to the other classifiers.


Apple images Texture features Machine learning Classification 


  1. 1.
    Roberts, M. J., Schimmelpfennig, D. E., Ashley, E., Livingston, M. J., Ash, M. S., Vasavada, U., et al. (2006). The value of plant disease early warning systems: a case study of usda’s soybean rust coordinated framework. Technical report, United States Department of Agriculture, Economic Research Service.Google Scholar
  2. 2.
    Dubey, S. R., & Jalal, A. S. (2016). Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing, 10(5), 819–826.CrossRefGoogle Scholar
  3. 3.
    Dubey, S. R., & Jalal, A. S. (2014). Adapted approach for fruit disease identification using images. arXiv:1405.4930.
  4. 4.
    Sindhi, K., Pandya, J., & Vegad, S. (2016). Quality evaluation of apple fruit: A survey. International Journal of Computer Applications (0975–8887), 136(1).CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Singh, N. P., Srivastava, R. (2016). Segmentation of retinal blood vessels by using a matched filter based on second derivative of gaussian. International Journal of Biomedical Engineering and Technology, 21(3), 229–246.CrossRefGoogle Scholar
  7. 7.
    Seo, J. W., & Kim, S. D. (2013). Novel pca-based color-to-gray image conversion. In 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 2279–2283. IEEE.Google Scholar
  8. 8.
    Singh Rajeev, N. P. (2018). Extraction of retinal blood vessels by using an extended matched filter based on second derivative of gaussian. In Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 2016.Google Scholar
  9. 9.
    Jain, A. K. (1989). Fundamentals of digital image processing. In Prentice-Hall information and system sciences series. Prentice-Hall.Google Scholar
  10. 10.
    Ivars, D. J. B., & Garca, D. S. C. (2018). Image database: Apple golden’. Retrieved January 15, 2018, from
  11. 11.
    Leemans, V., Destain, M.-F. (2004). A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering, 61(1), 83–89.CrossRefGoogle Scholar
  12. 12.
    Unay, D., & Gosselin, B. (2005). Artificial neural network-based segmentation and apple grading by machine vision. In 2005. IEEE International Conference on Image Processing, ICIP, (Vol. 2, p. II–630). IEEE.Google Scholar
  13. 13.
    Zhu, B., Jiang, L., Luo, Y., & Tao, Y. (2007). Gabor feature-based apple quality inspection using kernel principal component analysis. Journal of Food Engineering, 81(4), 741–749.CrossRefGoogle Scholar
  14. 14.
    Wang, J.-J., Zhao, D., Ji, W., Tu, J., & Zhang, Y. (2009). Application of support vector machine to apple recognition using in apple harvesting robot. In 2009 ICIA’09 International Conference on Information and Automation (pp. 1110–1115). IEEE.Google Scholar
  15. 15.
    Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M.-F., & Debeir, O. (2011). Automatic grading of bi-colored apples by multi-spectral machine vision. Computers and Electronics in Agriculture, 75(1), 204–212.CrossRefGoogle Scholar
  16. 16.
    Arlimatti, S. R. (2012). Window based method for automatic classi_cation of apple fruit. International Journal of Engineering Research and Applications, 2(4), 1010–1013.Google Scholar
  17. 17.
    Dubey, S. R., & Jalal, A. S. (2012). Detection and classification of apple fruit diseases using complete local binary patterns. In 2012 Third International Conference on Computer and Communication Technology (ICCCT), (pp. 346–351). IEEE.Google Scholar
  18. 18.
    Jhuria, M., Kumar, A., & Borse, R. (2013). Image processing for smart farming: Detection of disease and fruit grading. In 2013 IEEE Second International Conference onImage Information Processing(ICIIP), (pp. 521–526). IEEE.Google Scholar
  19. 19.
    Ashok, V., & Vinod, D. S. (2014). Automatic quality evaluation of fruits using probabilistic neural network approach. In 2014 International Conference on Contemporary Computing and Informatics (IC3I), (pp. 308–311). IEEE.Google Scholar
  20. 20.
    Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing (2nd ed.). Prentice Hall.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMMM University of TechnologyGorakhpurIndia

Personalised recommendations