Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

• Published in: International journal of embedded and real-time communication systems Vol. 11; no. 4; pp. 21 - 40
• Main Authors: Khriji, Lazhar, Ammari, Ahmed Chiheb, Awadalla, Medhat
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.10.2020
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Automatic classification; Automation; Cameras; Classification; Classifiers; Co-design; Color; Communication; Discriminant analysis; Embedded systems; Field programmable gate arrays; Fruits; Hardware; Image segmentation; Morphology; Neural networks; Personal computers; Real time; Software; Support vector machines; Vision systems
• ISSN: 1947-3176; 1947-3184
• DOI: 10.4018/IJERTCS.2020100102

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.