Artificial intelligent techniques for palm date varieties classification

Lazhar Khriji*, Ahmed Chiheb Ammari, Medhat Awadalla

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The demand on high quality palm dates is increasing due to its energy value and nutrient content, which are of great importance in human diet. To meet consumer and market standards with large-scale production, in Oman as among the top date producer, an inline classification system is of great importance. This paper addresses the potentiality of using Machine-Learning (ML) techniques in classifying automatically, without any physical measurement, the six most popular date fruit varieties in Oman. The effect of color, shape, size, and texture features and the critical parameters of the classifiers on the classification efficiency has been endeavored. Three different ML techniques have been used for automatic classification and qualitative comparison: (i) Artificial Neural Networks (ANN), (ii) Support Vector Machine (SVM), and (iii) K-Nearest Neighbor (KNN). Based on the merge of color, shape and size features contributes to achieve the highest accuracy. Experimental results show that the ANN classifier outperforms both SVM and KNN with the highest classification accuracy of 99.2%. This developed vision system in this paper can be successfully integrated in the packaging date factories.

Original languageEnglish
Pages (from-to)489-495
Number of pages7
JournalInternational Journal of Advanced Computer Science and Applications
Issue number9
Publication statusPublished - 2020


  • Computer vision
  • Feature extraction
  • Machine learning
  • Palm date

ASJC Scopus subject areas

  • Computer Science(all)


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