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In this case study, we report a four-month observation using a large language model (LLM) that – collaboratively with human expertise – analyzed and resolved complex challenges in a Macrobrachium rosenbergii RAS hatchery. Unacceptable larval and post-larval mortality prompted the integration into hatchery management of an AI decision-support system as a strategic management partner, enabling exploration across chemical, biological, physical, engineering, and behavioral domains. Key interventions suggested by the AI agent included mineral balance recalibration, microbial load diagnostics, behavioral pattern decoding, and lighting and flow engineering. Outcomes were evaluated in terms of a reduction in larval mortality and improved rates of larval metamorphosis to post larvae. Central to the process was the presence of a guiding human entity, steering AI's analytical power through deliberate questioning and contextual framing. This case study suggests that AI has the potential to improve intensive aquaculture systems. However, the tendency of AI agents to oversimplify complex systems requires the direction and guidance of a human expert to lead AI-human conversations. The adoption of LLMs in RAS-based aquaculture, bridging the gaps between raw data and actionable insights, has the potential to drive both the efficiency and the long-term sustainability of the aquaculture industry.","水产养殖可持续和负责任生产的需要，要求创新性地实施循环水养殖系统（RAS），该系统极为复杂，需要整合各种科学和技术领域以实现理想的生产力。在本案例研究中，我们报告了一项为期四个月的观察，使用大型语言模型（LLM）与人类专业知识协作，分析和解决了罗氏沼虾（Macrobrachium rosenbergii）RAS孵化场中的复杂挑战。不可接受的幼体和后期幼体死亡率促使将AI决策支持系统作为战略管理伙伴纳入孵化场管理，从而能够在化学、生物、物理、工程和行为领域进行探索。AI代理建议的关键干预措施包括矿物质平衡重新校准、微生物负荷诊断、行为模式解码以及光照和水流工程。结果通过幼体死亡率的降低和幼体变态为后期幼体的比率提高来评估。该过程的核心是有一个指导性的人类实体，通过有意的提问和情境构建来引导AI的分析能力。本案例研究表明，AI有潜力改善集约化水产养殖系统。然而，AI代理倾向于过度简化复杂系统，需要人类专家的指导和引导来主导AI与人类的对话。在基于RAS的水产养殖中采用LLM，弥合原始数据与可操作见解之间的差距，有潜力推动水产养殖行业的效率和长期可持续性。","Smart Agricultural Technology","2026-09-07T00:00:00Z","论文",false,78,{"impact":361,"substance":584,"depth":361,"authority":585,"freshness":173,"relevant":455,"comment":586},21,13,"核心期刊发表的LLM辅助RAS对虾育苗决策案例，方法新颖、结论有实证支撑，对智慧水产养殖具参考价值，值得进入每日精选。",[588],{"name":578,"url":575},[590,591,592,593,594],"智慧农业","农业人工智能","大语言模型","水产养殖","循环水养殖",{"code":395,"slug":396,"name":397,"path":398,"parent_code":388},[597],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":605},[603,610,615,621],{"code":388,"score":604,"supported":605,"title":606,"abstract":607,"tags":609},15,true,[389],[608,389],"水产",[593],{"code":395,"score":345,"supported":605,"title":611,"abstract":613,"tags":614},[612],"recirculating aquaculture",[594,612],[],{"code":24,"score":585,"supported":605,"title":616,"abstract":618,"tags":620},[617],"decision support",[619],"large language model",[591,592],{"code":16,"score":455,"supported":581,"title":622,"abstract":623,"tags":624},[],[],[590],[626,627],"农业人工智能 大语言模型 循环水养殖 智慧农业","农业人工智能 大语言模型","农业人工智能大语言模型循环水养殖智慧农业-2020","10.1016\u002Fj.atech.2026.102554",{"doi":629,"openalex_id":631,"authors":632,"venue":578,"cited_by_count":424,"oa_url":641,"card":642,"direction":648,"ingested_from":649},"W7167492172",[633,636,638],{"name":634,"orcid":635},"Shai Avraham Shaked","https:\u002F\u002Forcid.org\u002F0000-0003-1995-6419",{"name":637,"orcid":11},"Assaf Shechter",{"name":639,"orcid":640},"Amir Sagi","https:\u002F\u002Forcid.org\u002F0000-0002-4229-1059","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007793\u002Fpdf",{"tldr":643,"method":644,"finding":645,"direction":646,"opportunity":647},"用大语言模型辅助人类专家，解决罗氏沼虾RAS育苗中幼体高死亡率问题。","四个月案例观察，LLM与人类专家协作分析化学、生物、工程等多域数据。","AI建议的矿物质平衡、微生物诊断等干预降低了幼体死亡率并提高变态率。","农业人工智能与决策模型","可研究LLM在RAS多参数耦合决策中的可解释性与人机协同机制，避免过度简化。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:03.364805Z",{"technologies":652,"keywords":657},[653,655,656],{"name":654,"count":455},"人工智能",{"name":591,"count":455},{"name":592,"count":455},[658,659,662,663,665,667,669,671,673,675,677,679],{"name":593,"count":385},{"name":660,"count":661},"种业振兴",7,{"name":590,"count":408},{"name":664,"count":400},"海洋牧场",{"name":429,"count":666},3,{"name":668,"count":666},"深远海养殖",{"name":670,"count":666},"稻渔综合种养",{"name":672,"count":69},"十五五",{"name":674,"count":69},"国际合作",{"name":676,"count":69},"增殖放流",{"name":678,"count":69},"渔业资源",{"name":680,"count":69},"生态保护"]