Gonzalez, BryanGarcia, GonzaloVelastin, Sergio AGholamHosseini, HamidTejeda, LinoRamirez, HeilymFarias, Gonzalo2026-01-222026-01-222025-12-22Sensors (Basel), ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 26(1), 76-76. doi: 10.3390/s260100761424-82201424-8220http://hdl.handle.net/10292/20526The 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-recall 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's 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.© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.artificial intelligencecomputer visiondeep learningfood weight estimation46 Information and Computing Sciences4603 Computer Vision and Multimedia ComputationNetworking and Information Technology R&D (NITRD)Assistive TechnologyMachine Learning and Artificial IntelligenceBioengineering0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareAlgorithmsAnimalsArtificial IntelligenceDeep LearningFoodHumansImage Processing, Computer-AssistedAlgorithmsArtificial IntelligenceHumansFoodImage Processing, Computer-AssistedDeep LearningAnimalsAlgorithmsArtificial IntelligenceHumansFoodImage Processing, Computer-AssistedDeep LearningAnimalsJournal ArticleOpenAccess10.3390/s26010076