AGREE-YOLO: A Framework for Seafood Recognition and Cross-Cultural Gastronomic Recommendation.

Mingxin Hou, Shucheng Liu, Jianhua Wei, Kunfang Zhi, Mingxin Liu, Cong Lin

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Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Mingxin HouSchool of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China.ORCID 0000-0001-7751-4120

Shucheng LiuCollege of Food Science and Technology, Guangdong Ocean University, Zhanjiang 524088, China.ORCID 0000-0002-1775-8470

Jianhua WeiThe 75852 Troop of the Chinese People's Liberation Army, Guangzhou 510062, China.

Kunfang ZhiSchool of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Mingxin LiuSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Cong LinSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Funding

Guangdong Key R&D Program Project 2025B1111140001Innovation Team Project for Ordinary Universities in Guangdong Province 2024KCXTD041National Natural Science Foundation of China 62171143Natural Science Foundation of Guangdong Province 2025A1515012901 and 2025A1515011356

6 · The paper itself

Abstract

Real-time visual recognition systems integrated with culturally adaptive reasoning are urgently demanded in globalized culinary scenarios. An agent-oriented framework, Agent-based Gastronomy Recommender Enhanced Engine with YOLO (AGREE-YOLO), is proposed in this study, which integrates an optimized lightweight YOLOv13 detector and vision language model (VLM)-driven agents for cross-cultural seafood recipe recommendation. The improved YOLOv13 is equipped with group shuffle convolution (GSConv) modules and Wise-IoU (WIoU) loss, which is validated on a refined underwater seafood dataset targeting sea cucumbers, sea urchins and scallops. It achieves 91.2% precision and 87.3% recall, with 3.9% and 4.2% increments over the baseline model, and maintains 2.0 ms inference speed. Detection outputs are structured and stored in a MySQL database, and a novel ChatFlow pipeline is constructed in the Dify platform to support natural language database querying. VLM-powered agents retrieve structured data and generate culturally tailored recipes and dish images automatically. Operational validation verifies that the end-to-end pipeline realizes seamless conversion from seafood images to personalized cross-cultural recommendations. This work provides an integrated solution for intelligent, culturally adaptive gastronomy in food informatics.

Indexed as

agent-based systemsdeep learningfood styleseafood recognitionvision language models

Identifiers

PMID42195998

DOI10.3390/foods15101795

PMCPMC13206367

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