{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T23:28:01Z","timestamp":1777764481131,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food catering services. Specifically, it focuses on content identification and portion size estimation in a dining hall setting, typical of corporate and educational settings. An RGB camera is employed to capture the tray delivery process in a self-service restaurant, providing test images for content identification algorithm comparison, using standard evaluation metrics. The approach utilizes the YOLO architecture, a widely recognized deep learning model for object detection and computer vision. The model is trained on labeled image data, and its performance is assessed using a precision\u2013recall curve at a confidence threshold of 0.5, achieving a mean Average Precision (mAP) of 0.873, indicating robust overall performance. The weight estimation procedure combines computer vision techniques to measure food volume using both RGB and depth cameras. Subsequently, density models specific to each food type are applied to estimate the detected food weight. The estimation model\u2019s parameters are calibrated through experiments that generate volume-to-weight conversion tables for different food items. Validation of the system was conducted using rice and chicken, yielding error margins of 5.07% and 3.75%, respectively, demonstrating the feasibility and accuracy of the proposed method.<\/jats:p>","DOI":"10.3390\/s26010076","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T14:27:51Z","timestamp":1766586471000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms: Phase 2"],"prefix":"10.3390","volume":"26","author":[{"given":"Bryan","family":"Gonzalez","sequence":"first","affiliation":[{"name":"Escuela de Ingenier\u00eda El\u00e9ctrica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2362804, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9968-960X","authenticated-orcid":false,"given":"Gonzalo","family":"Garcia","sequence":"additional","affiliation":[{"name":"College of Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6775-7137","authenticated-orcid":false,"given":"Sergio","family":"Velastin","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK"},{"name":"Department of Computer Engineering, Universidad Carlos III de Madrid, 28903 Getafe, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0135-2687","authenticated-orcid":false,"given":"Hamid","family":"GholamHosseini","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lino","family":"Tejeda","sequence":"additional","affiliation":[{"name":"Sourcing, Santiago 7550000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7593-2123","authenticated-orcid":false,"given":"Heilym","family":"Ramirez","sequence":"additional","affiliation":[{"name":"Departamento de Electrotecnia e Inform\u00e1tica, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Vi\u00f1a del Mar 2520000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2186-4126","authenticated-orcid":false,"given":"Gonzalo","family":"Farias","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda El\u00e9ctrica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2362804, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, Y., Xu, C., Khanna, N., Boushey, C.J., and Delp, E.J. (2013, January 15\u201319). Food Image Analysis: Segmentation, Identification and Weight Estimation. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), San Jose, CA, USA.","DOI":"10.1109\/ICME.2013.6607548"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ando, Y., Ege, T., Cho, J., and Yanai, K. (2019, January 21). DepthCalorieCam: A Mobile Application for Volume-Based Food Calorie Estimation Using Depth Cameras. Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management (MADiMa\u201919), Nice, France.","DOI":"10.1145\/3347448.3357172"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"012014","DOI":"10.1088\/1742-6596\/1963\/1\/012014","article-title":"Study for Food Recognition System Using Deep Learning","volume":"1962","author":"Salim","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_4","unstructured":"Zunair, H., and Hamza, A.B. (2025, December 17). 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