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Machine Learning for Apple Fruit Diseases Classification System

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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References

  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. 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. Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S.: Automatic fruit and vegetable classification from images. Comput. Electron. Agric. 70, 96–104 (2010)

    Article  Google Scholar 

  4. Unay, D., et al.: Automatic grading of bi-colored apples by multispectral machine vision. Comput. Electron. Agric. 75(1), 204–212 (2011)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Wei, Y., et al.: Multi-vehicle detection algorithm through combining Harr and HOG features. Math. Comput. Simul. 155, 130–145 (2019)

    Article  MathSciNet  Google Scholar 

  13. Thu, W.M., et al.: GLCM and LTP based classification of food types (2018)

    Google Scholar 

  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. Sun, Z., et al.: Mutual information based multi-label feature selection via constrained convex optimization. Neurocomputing 329, 447–456 (2019)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Kafrelsheikh University, Kafr El-Sheikh, Egypt

    Atrab A. Abd El-aziz

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  3. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico

    Diego Oliva

  4. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Atrab A. Abd El-aziz, Ashraf Darwish, Diego Oliva & Aboul Ella Hassanien

Authors
  1. Atrab A. Abd El-aziz
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  2. Ashraf Darwish
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  3. Diego Oliva
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  4. Aboul Ella Hassanien
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Corresponding author

Correspondence to Atrab A. Abd El-aziz .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Abd El-aziz, A.A., Darwish, A., Oliva, D., Hassanien, A.E. (2020). Machine Learning for Apple Fruit Diseases Classification System. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_2

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