The potential loss of traditional Tunisian dishes threatens the sustainability of valuable cultural and nutritional traditions. To help preserve this rich heritage, a conversational AI system has been developed that employs advanced language processing and machine learning techniques to bring Tunisia’s culinary traditions to life in a digital space. Multilingual transformer models have been adapted to understand Tunisian dialects and combined with a detailed Food Heritage Knowledge Graph, allowing personalized, interactive access to authentic recipes and the stories behind them. A hybrid dialogue system operated by a chatbot has been implemented to ensure smooth, meaningful conversations t... More >

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 >