{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:56:31Z","timestamp":1791928591176,"version":"4.3.4"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background\/Objectives: The objective evaluation of regional dietary intake remains a core challenge in personalized health management due to complex plate presentations and a lack of culturally specific dataset benchmarks. Methods: This study introduces a confidence-aware hybrid vision\u2013language framework engineered for traditional Turkish food recognition and structured nutritional assessment. Results: We curate a balanced dataset containing 14,711 verified images spanning 40 representative Turkish culinary classes to train and evaluate seven deep learning architectures. Among the visual models, EfficientNet V2-L achieved the highest standalone performance with an accuracy of 93.47%, 0.92 macro-precision, 0.92 macro-recall, and a 0.92 F1 score. To overcome visual ambiguity and automate content analysis, a confidence-aware routing strategy escalates uncertain predictions (\u03c4&lt;0.70) or user-rejected classifications to the Google Gemini 2.5 Flash multimodal large language model (MLLM). Conclusions: This hybrid paradigm yields a combined classification accuracy of 95.50% while validating portion weight estimations within a mean absolute error (MAE) of 18.42 g and total energy within 36.75 kcal. Fully realized as a cross-platform Flutter mobile application, the end-to-end pipeline demonstrates localized plate detection, adaptive portion analysis, and structured nutrient tracking, providing a scalable design for consumer-facing digital nutrition platforms.<\/jats:p>","DOI":"10.3390\/nu18152449","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:08:14Z","timestamp":1785161294000},"page":"2449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Confidence-Aware Hybrid Vision\u2013Language Framework for Food Recognition and Nutritional Monitoring"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6726-3679","authenticated-orcid":false,"given":"Furkan","family":"G\u00f6z","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0934-1069","authenticated-orcid":false,"given":"Muhammad","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"},{"name":"Wireless Information and Intelligent Systems (WINS) Research Center, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5694-8042","authenticated-orcid":false,"given":"Adnan","family":"Kavak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"},{"name":"Wireless Information and Intelligent Systems (WINS) Research Center, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-0213","authenticated-orcid":false,"given":"Sema","family":"Bayraktar","sequence":"additional","affiliation":[{"name":"Wireless Information and Intelligent Systems (WINS) Research Center, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2534-2413","authenticated-orcid":false,"given":"Ali Can","family":"Do\u011fru","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"},{"name":"Wireless Information and Intelligent Systems (WINS) Research Center, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9851-4103","authenticated-orcid":false,"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Sogang University, Seoul 04107, Republic of Korea"},{"name":"Research Center for Interneural Computing, China Medical University, Taichung 40402, Taiwan"},{"name":"Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5590-0784","authenticated-orcid":false,"given":"Hossein","family":"Fotouhi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, M\u00e4lardalen University (MDU), 72123 V\u00e4ster\u00e5s, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1080\/09637486.2017.1283683","article-title":"Automatic diet monitoring: A review of computer vision and wearable sensor-based methods","volume":"68","author":"Hassannejad","year":"2017","journal-title":"Int. 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