Document Type : Research Article


1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

2 Computers and Control Engineering Deptartment, Faculty of Engineering, Tanta University, Tanta, Egypt.


Date fruits are considered essential food and the most important agricultural crop in Saudi Arabia. Where Saudi Arabia produces many of the types of dates per year. Collecting large data for date fruits is a difficult task and consumed
time, besides some of the date types are seasonal. Wherein convolutional neural networks (CNN) model needs large datasets to achieve high classification accuracy and avoid the overfitting problem. In this paper, an augmented date fruits dataset was developed using deep convolutional generative adversarial networks techniques (DCGAN). The dataset contains 600 images for three varieties of dates (Sukkari, Suggai and Ajwa). The performance of DCGAN was evaluated using Keras and MobileNet models. An extensive simulation shows the classify using DCGAN with the MobileNet model achieved 88% of accuracy. Whilst 44% for the Keras. Besides, MobileNet achieved better classification in the original dataset.


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