Machine Learning for Apple Fruit Diseases Classification System

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


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.


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


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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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