Advancements in Food Recognition: A Comprehensive Review of Deep Learning-Based Automated Food Item Identification

Food recognition is a vital component of various applications, including dietary monitoring, nutritional analysis, and restaurant menu optimization. In recent years, deep learning techniques have shown remarkable promise in automating the process of food item identification from images, significantly improving accuracy and efficiency. This review paper provides a comprehensive overview of the advancements in food recognition using deep learning, offering insights into the evolution of techniques and datasets employed in this domain. The paper begins by discussing the motivations behind automated food recognition, highlighting its significance in addressing health-related concerns and improving food-related services. It then delves into the fundamental concepts of deep learning and its relevance to image-based food recognition, providing readers with a solid foundation for understanding the subsequent sections. A major focus of this review is the analysis of state-of-the-art deep learning architectures and methodologies employed in food recognition. Furthermore, the review presents an extensive survey of publicly available food image datasets, evaluating their suitability for training and benchmarking deep learning models. It concludes by highlighting key research gaps and suggesting potential directions for future advancements in this rapidly evolving field.