从车间到云端：AI Agent 在食品制造企业的落地之路
一、为什么食品制造业值得做 Agent
食品制造业流程重、合规严、毛利薄、数据散。一家中型食品厂往往同时跑着 MES、WMS、LIMS、ERP，外加一堆 Excel 和纸质记录。传统”上一套大系
统”式改造周期长、见效慢，而 AI Agent 的价值在于：不推翻现有系统，而是在系统之间长出一层”会思考的胶水”。
二、四个典型应用场景
1. 质量异常自主排查。 某批次指标超标时，传统流程要质量工程师人工翻查 LIMS 报告、批
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污水处理设备、虾类自动投料机等三十余项国家专利，同时还具备独立设计制造养殖设备、智能化养殖控制系统及实际应用的能力。公司核心技术包括
产的情况下，逐步引入石斑鱼、优质螺类等更多高价值水产，打造绿色品牌水产品矩阵。”侯宗元说。产学研联动 输出“智慧标准”对于侯宗元这代“
的一个基地。其中，红树林农业与北部湾大学达成合作，联合开发渔业养殖AI大模型，通过校企联合，进一步完善模型理论数据库。广西水产科学研究院
本文作者：广西农业农村厅
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方案是可行的，并可以通过图像识别技术实现，从而为淡水鱼加工行业的自动化提供了可靠的技术支持。
图2 鳙鱼鱼体图像分割算法可视化输出图像
3.基于机器学习的草鱼分割产品冷链物流新鲜度预测模型与系统
及时准确预测水产品新鲜度，对于优化冷链运输策略、减少经济损失至关重要。本研究团队基于支持向量回归（SVR）技术构建了草鱼分割产品新鲜度预测模型，用于评估草鱼在0～12℃贮藏条件下的新鲜度指标变化（图3）。均方根误差(RMSE)和
6月10日，南海区委书记顾耀辉到九江镇调研农业现代化工作。他要求，要制定南海水产业发展规划；要继续支持企业用AI技术赋能水产业；要学会借助文体活动的平台，持续展示南海特色产品。当天，顾耀辉在广东香良食品科技有限公司，详细了解免浆黑鱼片、液氮鲈鱼柳、液氮三去鲈鱼等水产制品的生产过程；在广东鱼之渔水产有限公司，深入了解水产加工智能化工厂并与企业相关负责人进行了交流。分鱼机器人、去鳞机器人、去鳃机器人、清内脏
本文作者：佛山新闻
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Review
. 2026 May;25(3):e70462.
doi: 10.1111/1541-4337.70462.
AI-Driven Farm-To-Fork Biofilm Detection and Control in Aquatic Foods: From Industry 4.0 to Industry 5.0
Md Ashikur Rahman 1 2 , Shirin Akter 1 3 , Md Ashrafudoulla 4 5 , Ha Rim Lee 6 , Gaeul Lee 6 , Alina Ghimire 6 , Soo-Jin Jung 6 7 , Hye-Jeong Yoon 1 , Sang-Do Ha 1 6 7
Affiliations
1 Food Safety and Regulatory Science, Chung-Ang University, Anseong-Si, Republic of Korea.
2 Bangladesh Fisheries Research Institute, Mymensingh, Bangladesh.
3 Department of Fisheries and Marine Bioscience, Gopalganj Science and Te
2026
Bonnie Waycott
New solutions are helping fisheries deliver higher-quality products with greater efficiency
Shinkei Systems’ Poseidon is about the size of a common household refrigerator and sits on fishing boat decks. Photo courtesy of Shinkei Systems.
Technology and artificial intelligence (AI) are taking hold across fisheries, improving operations and benefiting consumers. Smarter sorting, real-time freshness monitoring and digital traceability each ensure that seafood reaches customers in better condition with greater consistency and transparency. The result is a more trustworthy sup
Comprehensive Reviews in Food Science and Food Safety
Volume 25, Issue 3 e70462
COMPREHENSIVE REVIEW
AI-Driven Farm-To-Fork Biofilm Detection and Control in Aquatic Foods: From Industry 4.0 to Industry 5.0
Md. Ashikur Rahman,
Md. Ashikur Rahman
orcid.org/0000-0002-0359-6239
Food Safety and Regulatory Science, Chung-Ang University, Anseong-Si, Republic of Korea
Bangladesh Fisheries Research Institute, Mymensingh, Bangladesh
Contribution: Conceptualization, Methodology, Visualization, Writing – original draft, Formal analysis, Software
Shirin Akter ,
Shirin Akter
orcid.org/0000-0003-1543-4832
F
原文链接：https://ift.onlinelibrary.wiley.com/doi/abs/10.1111/1541-4337.70462
品企业搭建专属数智化管理体系，既降低中小企业数字化转型门槛，又满足头部企业全流程精细化管控需求，让科技赋能食安落地更贴合产业实际。 在生产管控智能化层面，质臻科技打造的智慧监管平台，融合物联网传感、AI智能识别技术，实现对食品生产环节的实时数据采集与风险预警。从水产加工车间的温湿度精准把控、操作流程规范识别，到餐饮后厨的“明厨亮灶”智能监控，平台可对各类不规范操作、环境指标异常等问题进行秒级识别、
本文作者：中国食品报
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od processing plants, from machine-vision grading and defect detection to yield optimization, demand forecasting, and predictive maintenance. These tools help processors reduce trimming loss, improve consistency, monitor hygiene risks, and better match production with retail and foodservice demand.
AI is also increasingly relevant to traceability and compliance. FDA’s Food Traceability Rule under FSMA Section 204 and EU controls on illegal, unreported, and unregulated fishing are pushing companies toward stronger digital recordkeeping, where AI can flag anomalies in sourcing, labeling, tempera
原文链接：https://www.giiresearch.com/report/ires2086272-processed-seafood-market-by-product-type-form.html
Project Poster IASAI – Image Analytics for Seafood machinery using AI
Creators
Di Nicola, Luca (Project leader) 1
1. GO INFOTEAM
Contributors
Researcher:
Mercuri, Francesco 1
1. DAIMON Team @CNR
Description
Image Analytics for Seafood machinery using AI
Recognition of fish species by means of AI systems through the image of the products
The project aims to develop a machine learning system for the recognition of fish species.
In the field of intelligent manufacturing, the application context is that of technological innovation in the agri-food industry, in the fishing and aquaculture supply ch