@article {10.3844/jcssp.2025.851.868,
article_type = {journal},
title = {Computer Vision for Reducing Food Waste in an Institutional Canteen: A Literature Review and Performance Analysis},
author = {Correia, Ana and Aidos, Clara and Caldeira, João M. L. P. and Soares, Vasco N. G. J.},
volume = {21},
number = {4},
year = {2025},
month = {Mar},
pages = {851-868},
doi = {10.3844/jcssp.2025.851.868},
url = {https://thescipub.com/abstract/jcssp.2025.851.868},
abstract = {Food waste in today's society has been the subject of growing interest and discussion, given its economic, environmental, social, and nutritional implications. Although food waste is present throughout the food supply chain, in developed countries it tends to be higher in the final stages of consumption (e.g., households and food services). This study focuses on institutional canteens, where food waste includes prepared meals that have not been sold (i.e., leftovers), as well as food served that is left on plates after the meal has been consumed (i.e., scraps). It presents a first step towards developing a prototype/solution based on computer vision techniques to identify and quantify food waste in an institutional canteen. It begins by introducing the related concepts. It then surveys the state-of-the-art and categorizes existing solutions, presenting their main characteristics, strengths, and limitations. Inception-V3 and ResNet-50 are identified as the most promising computer vision techniques, and their performance has been evaluated. Information is also provided on open questions and research directions in this area},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}