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Machine Learning for Apple Fruit Diseases Classification System

  • Atrab A. Abd El-azizEmail author
  • Ashraf Darwish
  • Diego Oliva
  • Aboul Ella Hassanien
Conference paper
  • 181 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

There are a growing demand and an urgent need for fruits due to the increase in the world population. Fruit diseases cause a devastating problem in production losses worldwide. The healthy recognition of fruits and apples is an important issue for the economic and agricultural fields. In this paper, a recognition system for apple fruit diseases detection is proposed and experimentally validated. The K-Means based segmentation technique is applied. In regards to performance enhancement, different features extraction techniques are applied and classified using Support Vector Machine, K-NN, Multi-Class Support Vector Machine, and Multi-Label KNN (ML-KNN). The proposed model can significantly support accurate detection and automatic classification of apple fruit diseases. The average accuracy of diseases classification is achieved up to 97.5% and up to 99% for apple health classification.

Keywords

Multi-Class Support Vector Machine Multi-Label KNN Fruit K-means Agriculture 

References

  1. 1.
    Dubey, S.R., Jalal, A.S.: Application of image processing in fruit and vegetable analysis: a review. J. Intell. Syst. 24, 405–424 (2015)Google Scholar
  2. 2.
    Roberts, M.J., Schimmelpfennig, D., Ashley, E., Livingston, M.: The value of plant disease early-warning systems: a case study of USDA’s soybean rust coordinated framework. United States Department of Agriculture, Economic Research Service, Economic Research Report No. 18. Available via (2006)Google Scholar
  3. 3.
    Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S.: Automatic fruit and vegetable classification from images. Comput. Electron. Agric. 70, 96–104 (2010)CrossRefGoogle Scholar
  4. 4.
    Unay, D., et al.: Automatic grading of bi-colored apples by multispectral machine vision. Comput. Electron. Agric. 75(1), 204–212 (2011)CrossRefGoogle Scholar
  5. 5.
    Dubey, S.R., Jalal, A.S.: Detection and classification of apple fruit diseases using complete local binary patterns. In: Proceedings of the 3rd International Conference on Computer and Communication Technology, Allahabad, India, pp. 346–351 (2012)Google Scholar
  6. 6.
    Ashok, V., Vinod, D.S.: Automatic quality evaluation of fruits using probabilistic neural network approach. In: 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE (2014)Google Scholar
  7. 7.
    Dubey, S.R., Jalal, A.S.: Fruit disease recognition using improved sum and difference histogram from images. Int. J. Appl. Pattern Recognit. 1(2), 199–220 (2014)CrossRefGoogle Scholar
  8. 8.
    Dubey, S.R., Jalal, A.S.: Apple disease classification using color, texture and shape features from images. Signal Image Video Process. 10(5), 819–826 (2016)CrossRefGoogle Scholar
  9. 9.
    Singh, S., Singh, N.P.: Machine learning-based classification of good and rotten apple. In: Recent Trends in Communication, Computing, and Electronics, pp. 377–386. Springer, Singapore (2019)Google Scholar
  10. 10.
    Hrosik, R.C., et al.: Brain image segmentation based on firefly algorithm combined with K-means clustering. Stud. Inform. Control 28, 167–176 (2019)Google Scholar
  11. 11.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions, pp. 168–182. INRIA & Laboratoire Jean Kuntzmann, 655 avenue de l’Europe, Montbonnot 38330, France (2007)Google Scholar
  12. 12.
    Wei, Y., et al.: Multi-vehicle detection algorithm through combining Harr and HOG features. Math. Comput. Simul. 155, 130–145 (2019)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Thu, W.M., et al.: GLCM and LTP based classification of food types (2018)Google Scholar
  14. 14.
    Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Proceedings of the fourth ACM International Conference on Multimedia, New York, USA, pp. 65–73 (1997)Google Scholar
  15. 15.
    Sun, Z., et al.: Mutual information based multi-label feature selection via constrained convex optimization. Neurocomputing 329, 447–456 (2019)CrossRefGoogle Scholar
  16. 16.
    Gola, J., et al.: Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels. Comput. Mater. Sci. 160, 186–196 (2019)CrossRefGoogle Scholar
  17. 17.
    Mao, T., et al.: Defect recognition method based on HOG and SVM for drone inspection images of power transmission line. In: 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Atrab A. Abd El-aziz
    • 1
    • 5
    Email author
  • Ashraf Darwish
    • 2
    • 5
  • Diego Oliva
    • 3
    • 5
  • Aboul Ella Hassanien
    • 4
    • 5
  1. 1.Faculty of Computers and InformationKafrelsheikh UniversityKafr El-SheikhEgypt
  2. 2.Faculty of ScienceHelwan UniversityCairoEgypt
  3. 3.Departamento de Ciencias ComputacionalesUniversidad de Guadalajara, CUCEIGuadalajaraMexico
  4. 4.Faculty of Computers and Artificial IntelligenceCairo UniversityGizaEgypt
  5. 5.Scientific Research Group in Egypt (SRGE)CairoEgypt

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