Hardware/software co-design of a vision system for automatic classification of date fruits

Lazhar Khriji, Ahmed Chiheb Ammari, Medhat Awadalla

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper proposes a hardware/software (HW/SW) co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. Three artificial intelligence (AI) techniques are used for qualitative comparisons: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The accuracy performance of all AI classifiers is characterized for multiple color, shape, size, and texture feature combinations and for different critical parameter settings of the classifiers. In total, 600 date samples (100 dates/ variety) are selected and imaged each sample individually. The system starts with preprocessing and segmentation of the colored input images. A total of 19 features are extracted from each image for use in classification models. The ANN classifier is shown to outperform all other classifiers. 97.26% highest classification accuracy is achieved using a combination of 15 color and shape-size features.

Original languageEnglish
Pages (from-to)21-40
Number of pages20
JournalInternational Journal of Embedded and Real-Time Communication Systems
Issue number4
Publication statusPublished - Oct 1 2020

ASJC Scopus subject areas

  • Computer Science(all)


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