Author
Contributions by role
Author 1
Olfa REBAI
Laboratory Ecochimie (LR21ES02), Department of Biological and Chemical Engineering, National Institute of Applied, Sciences and Technology, University of Carthage, Tunis, Tunisia.
Summary
Edited Journals
ICCK Contributions

Open Access | Research Article | 22 October 2025
AI-Powered Detection and Quantification of Local Date Varieties Using YOLO: Toward Intelligent Supply Chain Integration in Agri-Food Technology
Next-Generation Computing Systems and Technologies | Volume 1, Issue 1: 33-42, 2025 | DOI: 10.62762/NGCST.2025.936740
Abstract
This study presents an AI-powered approach to enhance quality control and traceability in the agri-food sector, focusing on the automated detection and classification of two Tunisian date varieties: Deglet Nour and "Bsir". The main objective is to develop a smart system that can quantitatively and qualitatively determine the proportion of any contamination of one variety by the other within a batch. To achieve this, state-of-the-art object detection YOLO models, v8 and v12, have been employed, trained on a custom annotated dataset which includes a wide range of real-world images, capturing the variability in the studied date fruit size, shape, and presentation. Both YOLO models were fine-tun... More >

Graphical Abstract
AI-Powered Detection and Quantification of Local Date Varieties Using YOLO: Toward Intelligent Supply Chain Integration in Agri-Food Technology