George Papakostas

By G.A. Papakostas, D.E. Koulouriotis, E.G. Karakasis and V.D. Tourassis

Part of the book: Biomedical Engineering, Trends in Electronics

By G.A. Papakostas, D.E. Koulouriotis and V.D. Tourassis

Part of the book: Human-Centric Machine Vision

By Michail Marinis, Evangelos Georgakoudis, Eleni Vrochidou and George A. Papakostas

The use of machine learning for visual food ingredient recognition has been at the forefront in recent years due to its involvement in numerous applications and areas such as recipe discovery, diet planning, and allergen detection. In this work, all relevant publications from 2010 to 2023 were analyzed, including databases such as Scopus, IEEE Xplore, and Google Scholar, aiming to provide an overview of the methodologies, challenges, and potential of this emerging field. Challenges, such as visual differences and complicated ingredient composition, are highlighted, along with the importance of data preprocessing, image preparation methods, and the use of deep learning techniques for state-of-the-art performances. The potential applications of this technology in the fields of automation and robotics are explored, and existing datasets are provided. Research concluded that among the several machine learning techniques being used, the reported performances of convolutional neural networks (CNNs) rate them on top of all approaches that are currently being used.