Title: Image-Based Classification of Date Types Using Convolutional Neural Networks
Authors: Abedeleilah S. A. Elmahmoum, Dina Alborno, DaliaAl Harazine, Samy S. Abu-Naser
Volume: 9
Issue: 1
Pages: 10-16
Publication Date: 2025/01/28
Abstract:
This research focuses on the classification of nine varieties of dates using deep learning techniques. The study aims to develop an accurate and efficient model capable of identifying different types of dates based on images. A Convolutional Neural Network (CNN) was employed, trained on a dataset comprising thousands of date images, processed to enhance classification performance. The model was evaluated on multiple metrics, achieving high accuracy rates, demonstrating the feasibility of using deep learning in date classification. This approach can significantly aid in automating the identification process, which is crucial for the agricultural industry. The results indicate that deep learning techniques offer a robust solution for the classification of date varieties, with potential applications in quality control and market sorting.