[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-353":3,"related-353":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":20,"doi":8,"paper":8,"created_at":67},353,"全国首创AI养鱼模式落地临平——浙江省农科院AI大模型让加州鲈鱼和鳜鱼成活率提升10倍","https:\u002F\u002Fwww.hi-lp.cn\u002Ftt\u002F202608\u002Ft20260806_7605656.shtml","由浙江省农科院在全国首创的AI养鱼模式落地杭州临平,依托多维度智能传感设备与AI大模型精准调控,让加州鲈鱼和鳜鱼始终处于生长'舒适区',将幼鱼成活率提升10倍,同时能耗下降50%以上、人工成本缩减超80%。基地标准化养殖池6米直径单茬产量可达3000斤以上,13米直径单茬产量最高可达16000斤,全年可养殖三茬以上。省农科院农业人工智能创新中心主任孔德栋介绍,全流程嵌入AI后,95%的核心环节实现无人值守。这套方案已复制推广至江苏等地,未来三年计划在全国打造25个AI智慧渔业工厂。",null,"不用“靠天养鱼”，全程AI值守、智能管护，临平渔业养殖迎来数智化变革。日前，由浙江省农科院在全国首创的AI养鱼模式落地临平，依托多维度智能传感设备与AI大模型精准调控，让养殖的加州鲈鱼和鳜鱼始终处于生长“舒适区”，将幼鱼成活率提升10倍，同时能耗下降50%以上、人工成本缩减超80%，为现代水产养殖转型升级提供了全新范式。\n\n在2026年智慧农业创新大赛暨雄安智慧农业技术应用大赛中，杭州智骏高技科技有限公司在鱼群智能检测设备赛道中脱颖而出，荣获全国第一名，其“AI驱动型数智无人渔业工厂”项目已落地运河街道东新村。\n\n走进位于运河街道的“智物嘉渔”基地车间，一排排直径6米或13米的标准化圆形养殖池里，鱼儿们四处游弋、状态极佳。它们所处的池子内里布满科技：池底的智能吸污机器人实时监测水质，水下摄像头监控鱼儿状态，顶部投喂视觉传感器观测鱼儿摄食活跃度，再加上巡检机器人定期巡查，所有数据传输到云上专家系统，由AI统一调度，反向控制水循环和投喂节奏，以全方位智能监护，养出品质好鱼。\n\n省农科院农业人工智能创新中心主任孔德栋介绍，全流程嵌入AI后，95%的核心环节实现无人值守。“打个比方，原来我们10000平方米的工厂，需要6到7个技术人员，目前基本上只需要1个人，人工与能耗成本大大降低。而且AI精准调控让养殖水质持续优化、投喂科学高效，鱼儿的生存环境稳定，全程无需添加任何抗生素，鱼的安全和品质都有了坚实保障。”\n\n这一成效的取得并非一蹴而就。传统户外养鱼受天气影响大、成本高，幼鱼成活率低，且易对环境造成污染，以此为出发点，杭州智骏高技科技有限公司联合浙江省农业科学院、浙江大学、中大物通商贸集团等单位开展科技攻关，基于十余年的技术、算法与数据积累，开发训练出精通水质管控、鱼类生长规律与养殖管理的AI算法模型。\n\n孔德栋介绍，基地标准化养殖池已形成稳定产能，6米直径养殖池单茬产量可达3000斤以上，13米直径养殖池单茬产量最高可达16000斤。依托恒温恒质的智能化养殖环境，基地全年可养殖三茬以上，打破季节限制，实现稳产、高产，精准对接市场供应需求。从鱼卵孵化到成鱼出栏，AI全程护航鱼类全生命周期生长，让传统渔业彻底告别“靠天吃饭”，迈入“数智丰收”的全新阶段。\n\n作为现代农业的重要赛道，我国渔业经济市场潜力巨大，目前，全国渔业经济总产值已达3.4万亿元，水产养殖产量超6300万吨，市场空间巨大。眼下，诞生于临平的这套数智方案，已完成模式验证，并迅速复制推广至江苏等地。\n\n“临平示范基地一期已建成投运，今年将完成二期建设。未来三年，我们计划在全国打造约25个AI智慧渔业工厂基地。”除了把“盘子铺开”，孔德栋说，团队还将持续深耕，培育更多新成果，“后续将采用智慧渔业工厂结合标准化工程塘的模式，实现联农带农，探索高质量共富新路径。”","临平新闻网 2026-08-06","2026-08-06T00:00:00Z","报道",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},25,20,15,8,4,1,"全国首创AI养鱼模式，成活率提升10倍，具有产业级影响，信息详实，但信源为地方媒体且时效性稍差。",[25],{"name":10,"url":6},[27,28,29,30],"浙江省农科院","智慧渔业","AI养鱼","水产养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,58],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},14,true,[48],"大模型",[50,48],"农业人工智能",[],{"code":53,"score":21,"supported":46,"title":54,"abstract":55,"tags":57},"D",[],[56],"渔业",[30],{"code":59,"score":21,"supported":46,"title":60,"abstract":61,"tags":62},"D5",[],[28],[28],[64,65],"浙江省农科院 智慧渔业 水产养殖 AI养鱼","浙江省农科院 智慧渔业","浙江省农科院智慧渔业水产养殖AI养鱼-353","2026-08-10T23:54:42.450643Z",{"total":69,"page":22,"page_size":69,"items":70},6,[71,145,209,274,341,392],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":87,"tags":89,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":119,"slug":122,"view_count":123,"doi":124,"paper":125,"created_at":144},3972,"Interpretable machine learning reveals threshold effects of blue mussel (Mytilus edulis) fouling on the hydrodynamic response of the marine aquaculture netting","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112492","Interpretable machine learning reveals threshold effects of blue mussel (Mytilus edulis) fouling on the hydrodynamic response of the marine aquaculture netting。Computers and Electronics in Agriculture","可解释机器学习揭示蓝贻贝（Mytilus edulis）污损对海水养殖网箱水动力响应的阈值效应。","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",74,{"impact":82,"substance":83,"depth":84,"authority":45,"freshness":85,"relevant":22,"comment":86},12,21,18,9,"可解释机器学习揭示贻贝污损对养殖网衣水动力响应的阈值效应，方法新颖、结论可靠，对深远海养殖装备运维有参考价值，但属细分领域研究，影响面有限。",[88],{"name":77,"url":74},[90,91,30,92,93],"智慧农业","机器学习","海洋牧场","养殖装备",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":46},[99,104,109,114],{"code":32,"score":19,"supported":46,"title":100,"abstract":102,"tags":103},[101],"machine learning",[91,101],[91],{"code":53,"score":82,"supported":46,"title":105,"abstract":107,"tags":108},[106],"aquaculture",[106],[30],{"code":110,"score":22,"supported":14,"title":111,"abstract":112,"tags":113},"D2",[],[],[92],{"code":115,"score":22,"supported":14,"title":116,"abstract":117,"tags":118},"I1",[],[],[90],[120,121],"紫贻贝 污损 养殖网箱","水产养殖网衣 水动力","紫贻贝污损养殖网箱-3972",2,"10.1016\u002Fj.compag.2026.112492",{"doi":124,"openalex_id":126,"authors":127,"venue":77,"cited_by_count":136,"oa_url":8,"card":137,"direction":141,"ingested_from":143},"W7215081072",[128,130,133],{"name":129,"orcid":8},"Ming-Shan Sun",{"name":131,"orcid":132},"Chun-Wei Bi","https:\u002F\u002Forcid.org\u002F0000-0002-5306-7976",{"name":134,"orcid":135},"Shubin Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-7985-4898",0,{"tldr":138,"method":139,"finding":140,"direction":141,"opportunity":142},"用可解释机器学习揭示蓝贻贝污损对海水养殖网箱水动力响应的阈值效应。","可解释机器学习模型，分析网衣污损程度与水动力响应数据。","蓝贻贝污损对网衣水动力响应存在阈值效应，超过阈值后影响显著加剧。","智慧农业 \u002F 农业物联网","可延伸至不同污损生物与网衣材料下的阈值建模，支撑智能清洗决策与网箱安全预警。","openalex","2026-10-02T23:30:02.311269Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":8,"source_name":151,"source_url":148,"published_at":152,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":153,"score_detail":154,"sources":158,"tags":160,"primary_topic":163,"secondary_topics":164,"classification":165,"search_phrases":182,"slug":185,"view_count":186,"doi":187,"paper":188,"created_at":208},2913,"A quantum-inspired multi-objective learning framework for real-time sustainable aquaculture water quality prediction","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827991","Management of sustainable aquaculture necessitates accurate prediction of water quality parameters, since environmental variability is an important factor in determining aquatic productivity and ecological stability. Traditional machine learning systems are usually limited by inefficient parameter optimization, feature overlap, and limited adaptability to non-linear and time-varying environmental trends. To address these issues, a new concept is proposed, which is called Quantum-Inspired Aquaculture Optimization Network (Q-AQUAOptNet). To improve the process of feature selection, hyperparameter optimization, and model stability before temporal prediction, the proposed architecture combines sustainability index modeling and quantum-inspired multi-objective evolutionary optimization. The optimization approach balances exploration and exploitation, enhancing the learning ability of the prediction model. Implementation of the framework was done through Python-based simulation tools in preprocessing, optimization, and performance evaluation. The created system had a Root Mean Square Error of 0.24, a Mean Absolute Error of 0.21, and an R 2 of 0.97, which means that it has effective predictive power and a great ability to explain the variance. Integrating evolutionary optimization and temporal learning improves generalization performance and reduces prediction uncertainty. The findings demonstrate that Q-AQUAOptNet provides effective predictive performance within a simulation-based framework and shows potential as a sustainability-oriented intelligent water-quality monitoring and decision support system for aquaculture applications.","可持续水产养殖管理需要准确预测水质参数，因为环境变异性是决定水生生产力和生态稳定性的重要因素。传统机器学习系统通常受限于参数优化效率低、特征重叠以及对非线性和时变环境趋势的适应能力有限。为解决这些问题，提出了一种新概念，称为量子启发水产养殖优化网络（Quantum-Inspired Aquaculture Optimization Network，Q-AQUAOptNet）。为在时间预测之前改进特征选择、超参数优化和模型稳定性，所提出的架构结合了可持续性指数建模和量子启发多目标进化优化。该优化方法平衡了探索与利用，增强了预测模型的学习能力。该框架的实现通过基于Python的仿真工具完成，涵盖预处理、优化和性能评估。所构建系统的均方根误差为0.24，平均绝对误差为0.21，R²为0.97，这意味着其具有有效的预测能力和较强的方差解释能力。将进化优化与时间学习相结合，提高了泛化性能并降低了预测不确定性。研究结果表明，Q-AQUAOptNet在基于仿真的框架内提供了有效的预测性能，并显示出作为面向可持续性的智能水质监测与决策支持系统应用于水产养殖的潜力。","Frontiers in Sustainable Food Systems","2026-09-18T00:00:00Z",71,{"impact":82,"substance":18,"depth":155,"authority":156,"freshness":85,"relevant":22,"comment":157},17,13,"提出量子启发多目标优化网络用于水产养殖水质预测，仿真指标较优，属智慧渔业细分领域的方法学进展，但尚处仿真阶段、缺乏真实场景验证。",[159],{"name":151,"url":148},[90,50,30,161,162],"多目标优化","水质预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":166},{"candidates":167,"agricultural_evidence":46},[168,173,178],{"code":53,"score":19,"supported":46,"title":169,"abstract":170,"tags":172},[106],[171,106],"水产",[30],{"code":32,"score":13,"supported":46,"title":174,"abstract":175,"tags":177},[],[91,101,176],"decision support",[50],{"code":115,"score":22,"supported":14,"title":179,"abstract":180,"tags":181},[],[],[90],[183,184],"Q-AQUAOptNet 水产养殖 水质预测","农业人工智能 多目标优化 智慧农业 水产养殖","Q-AQUAOptNet水产养殖水质预测-2913",3,"10.3389\u002Ffsufs.2026.1827991",{"doi":187,"openalex_id":189,"authors":190,"venue":151,"cited_by_count":136,"oa_url":148,"card":203,"direction":141,"ingested_from":143},"W7213532503",[191,194,197,199,201],{"name":192,"orcid":193},"Abdel‐Haleem Abdel‐Aty","https:\u002F\u002Forcid.org\u002F0000-0002-6763-2569",{"name":195,"orcid":196},"Ali Jaber Almalki","https:\u002F\u002Forcid.org\u002F0009-0004-1359-2612",{"name":198,"orcid":8},"Sara A. Ghorashi",{"name":200,"orcid":8},"Betty Wan Niu Voon",{"name":202,"orcid":8},"Mohamed Hafez",{"tldr":204,"method":205,"finding":206,"direction":141,"opportunity":207},"提出量子启发多目标优化网络Q-AQUAOptNet，实现水产养殖水质实时预测。","量子启发多目标进化优化结合时间预测与可持续指数建模，Python仿真。","RMSE 0.24、MAE 0.21、R² 0.97，预测精度高且泛化好。","可探索真实养殖场部署与多源传感器融合，验证量子启发优化在边缘端的实时性。","2026-09-19T23:30:07.985707Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":8,"source_name":77,"source_url":212,"published_at":215,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":219,"tags":221,"primary_topic":224,"secondary_topics":225,"classification":226,"search_phrases":242,"slug":245,"view_count":21,"doi":246,"paper":247,"created_at":273},2281,"Artificial intelligence-enabled aquacultural engineering systems: A design-oriented review of sensing, modelling, and deployment architectures","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112353","Artificial intelligence-enabled aquacultural engineering systems: A design-oriented review of sensing, modelling, and deployment architectures。Computers and Electronics in Agriculture","人工智能赋能的水产养殖工程系统：面向设计的传感、建模与部署架构综述。农业中的计算机与电子学","2026-09-11T00:00:00Z",78,{"impact":84,"substance":18,"depth":84,"authority":45,"freshness":20,"relevant":22,"comment":218},"核心期刊发表的水产养殖AI工程系统设计综述，涵盖感知、建模与部署架构，对智慧渔业技术路线有参考价值，但属综述类论文，产业落地影响有限。",[220],{"name":77,"url":212},[90,50,30,222,223],"智能传感","数字渔业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":227},{"candidates":228,"agricultural_evidence":46},[229,234,238],{"code":32,"score":82,"supported":46,"title":230,"abstract":232,"tags":233},[231],"artificial intelligence",[231],[50],{"code":53,"score":21,"supported":46,"title":235,"abstract":236,"tags":237},[],[171],[30],{"code":115,"score":22,"supported":14,"title":239,"abstract":240,"tags":241},[],[],[90],[243,244],"农业人工智能 数字渔业 智慧农业 智能传感","农业人工智能 数字渔业","农业人工智能数字渔业智慧农业智能传感-2281","10.1016\u002Fj.compag.2026.112353",{"doi":246,"openalex_id":248,"authors":249,"venue":77,"cited_by_count":136,"oa_url":212,"card":268,"direction":141,"ingested_from":143},"W7212232512",[250,253,256,259,262,265],{"name":251,"orcid":252},"V. Ravi Sankar","https:\u002F\u002Forcid.org\u002F0000-0002-8580-2920",{"name":254,"orcid":255},"Alzayat Saleh","https:\u002F\u002Forcid.org\u002F0000-0001-6973-019X",{"name":257,"orcid":258},"Armin Ehrampoosh","https:\u002F\u002Forcid.org\u002F0000-0002-5482-3454",{"name":260,"orcid":261},"Phoebe Arbon","https:\u002F\u002Forcid.org\u002F0000-0003-0171-6302",{"name":263,"orcid":264},"Dean R. Jerry","https:\u002F\u002Forcid.org\u002F0000-0003-3735-1798",{"name":266,"orcid":267},"Mostafa Rahimi Azghadi","https:\u002F\u002Forcid.org\u002F0000-0001-7975-3985",{"tldr":269,"method":270,"finding":271,"direction":141,"opportunity":272},"综述AI赋能水产养殖工程系统，从感知、建模到部署架构的设计视角。","设计导向综述，梳理传感器、AI建模与部署架构。","提出面向水产养殖的AI工程系统设计框架与架构分类。","可针对水产养殖场景的轻量化边缘AI与多模态传感融合部署开展实证研究。","2026-09-13T23:30:01.790828Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":8,"source_name":280,"source_url":277,"published_at":281,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":216,"score_detail":282,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":294,"classification":296,"search_phrases":317,"slug":320,"view_count":21,"doi":321,"paper":322,"created_at":340},2020,"Decision support in recirculating aquaculture systems (RAS): A case study of a human–AI interface in prawn hatchery operation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102554","The need for sustainable and responsible production in aquaculture calls for innovative implementation of recirculating aquaculture systems (RAS), which are extraordinarily complex, requiring the integration of various fields of science and technology to reach the desired productivity. 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",{"impact":84,"substance":83,"depth":84,"authority":156,"freshness":20,"relevant":22,"comment":283},"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。",[285],{"name":280,"url":277},[90,50,287,30,288],"大语言模型","循环水养殖",{"code":290,"slug":291,"name":292,"path":293,"parent_code":53},"D1","pond-factory-aquaculture","池塘与工厂化养殖","\u002Ftopic\u002Faquaculture\u002Fpond-factory-aquaculture",[295],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":297},{"candidates":298,"agricultural_evidence":46},[299,303,308,313],{"code":53,"score":19,"supported":46,"title":300,"abstract":301,"tags":302},[106],[171,106],[30],{"code":290,"score":45,"supported":46,"title":304,"abstract":306,"tags":307},[305],"recirculating aquaculture",[288,305],[],{"code":32,"score":156,"supported":46,"title":309,"abstract":310,"tags":312},[176],[311],"large language model",[50,287],{"code":115,"score":22,"supported":14,"title":314,"abstract":315,"tags":316},[],[],[90],[318,319],"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":321,"openalex_id":323,"authors":324,"venue":280,"cited_by_count":136,"oa_url":333,"card":334,"direction":141,"ingested_from":143},"W7167492172",[325,328,330],{"name":326,"orcid":327},"Shai Avraham Shaked","https:\u002F\u002Forcid.org\u002F0000-0003-1995-6419",{"name":329,"orcid":8},"Assaf Shechter",{"name":331,"orcid":332},"Amir Sagi","https:\u002F\u002Forcid.org\u002F0000-0002-4229-1059","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007793\u002Fpdf",{"tldr":335,"method":336,"finding":337,"direction":338,"opportunity":339},"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。","2026-09-10T23:30:03.364805Z",{"id":342,"title":343,"url":344,"summary":345,"summary_zh":8,"content":8,"source_name":346,"source_url":344,"published_at":347,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":348,"score_detail":349,"sources":351,"tags":353,"primary_topic":355,"secondary_topics":356,"classification":357,"search_phrases":372,"slug":375,"view_count":186,"doi":376,"paper":377,"created_at":391},1756,"From behavioural monitoring to decision support in aquaculture: an interpretable analytical framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104587","From behavioural monitoring to decision support in aquaculture: an interpretable analytical framework。Biosystems Engineering","Biosystems Engineering","2026-09-05T00:00:00Z",67,{"impact":19,"substance":18,"depth":84,"authority":82,"freshness":123,"relevant":22,"comment":350},"论文提出可解释的分析框架，将行为监测转化为决策支持，对水产养殖智能化有实质贡献，但时效性较低。",[352],{"name":346,"url":344},[90,30,354],"决策支持",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":358},{"candidates":359,"agricultural_evidence":46},[360,364,368],{"code":53,"score":82,"supported":46,"title":361,"abstract":362,"tags":363},[106],[106],[30],{"code":32,"score":82,"supported":46,"title":365,"abstract":366,"tags":367},[176],[176],[354],{"code":115,"score":22,"supported":14,"title":369,"abstract":370,"tags":371},[],[],[90],[373,374],"决策支持 智慧农业 水产养殖","决策支持 智慧农业","决策支持智慧农业水产养殖-1756","10.1016\u002Fj.biosystemseng.2026.104587",{"doi":376,"openalex_id":378,"authors":379,"venue":346,"cited_by_count":136,"oa_url":8,"card":8,"direction":8,"ingested_from":143},"W7208811750",[380,382,384,387,389],{"name":381,"orcid":8},"Haixiang Zhao",{"name":383,"orcid":8},"Zuqiang Liu",{"name":385,"orcid":386},"Zhihong Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4821-6751",{"name":388,"orcid":8},"Weimin Yu",{"name":390,"orcid":8},"Xian Li","2026-09-06T23:30:06.005522Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":8,"content":397,"source_name":398,"source_url":8,"published_at":399,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":404,"tags":406,"primary_topic":411,"secondary_topics":412,"classification":413,"search_phrases":429,"slug":432,"view_count":69,"doi":8,"paper":8,"created_at":433},189,"AI引领农业转型升级高研班8月4-6日在杭州开班——浙江省农科院数农所牵头,聚焦人工智能+农业复合人才培养","https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Fxinwen\u002F886\u002F","浙江省人力资源和社会保障厅办公室将浙大城市学院人工智能引领农业数智化转型升级高级研修班列入2026年省级自筹项目,8月4-6日在杭州开班,共60人。研修紧扣人工智能+农业融合创新,覆盖战略解读、智能装备与工厂模式、数智化运营等核心方向,集结浙江省农业科学院、浙江大学等知名专家学者线下授课,并走进全省标杆未来农场现场教学。课程主持人由浙江省农业科学院农业人工智能创新中心主任、数字农业研究所副所长孔德栋研究员担任。","## **一、项目背景**\n\n农业正经历一场深刻的“智慧革命”\n\n国务院“人工智能+”行动与“十五五”规划明确部署，推动人工智能与农业深度融合，大力发展智能农机、数字农业工厂和精准作业装备。浙江省正全力打造高效生态农业强省，以数智化技术全面重塑农业生产、经营与管理体系，对具备“AI+农业”复合能力的高层次人才需求空前迫切。日前，浙江省人力资源和社会保障厅办公室《关于印发2026年度国家级和省级专业技术人才高级研修项目计划的通知》（浙人社办函〔2026〕59号），**我校“人工智能引领农业数智化转型升级”高级研修班列入2026年省级自筹项目。**\n\n**![Image 1](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F000DkVumJjD.png)**\n\n## **二、关于研修**\n\n**研修方式**\n\n研修紧扣**“人工智能+农业”**融合创新，覆盖战略解读、智能装备与工厂模式、数智化运营等核心方向，依托省级 专业技术人才知识更新工程，聚焦“政策—技术—运营”全链条，集结浙江省农业科学院、浙江大学等国内知名专家学者进行线下授课培训，走进全省标杆 未来农场 现场教学，深度专题研讨人工智能落地真实需求等问题。\n\n**研修时间**\n\n2026年**8月4-6日**，共3天\n\n*具体日期以报名后通知为准避开农忙高峰与人工智能大会，确保安心学习\n\n**研修地点**\n\n浙大城市学院（浙江·杭州）\n\n**研修对象**\n\n1、浙江省内人工智能、农业相关领域具有中、高级职称或职业资格的专业技术人员；\n\n2、相关领域经营管理人员；\n\n3、基层一线专业技术骨干；\n\n4、非公有制领域专业技术人员或经营管理人员。\n\n— 共**60**人，额满即止 —\n\n考核要求与学时认定\n\n1.学员在研修期间，应遵守各项规章制度，认真听取专题报告，积极参加研讨和交流。\n\n2.根据学员研修期间到课率、学习交流情况进行考核，考核合格学员可获得继续教育**36学时**，学员可登录浙江省专业技术人员继续教育学时登记管理系统（https:\u002F\u002Fzjjx.rlsbt.zj.gov.cn），在线查询和打印。\n\n## **三、研修内容**\n\n**课程安排**\n\n**![Image 2](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F01ptWAxgko.png)**\n\n**课题主持人—孔德栋**\n\n**简介：**博士、研究员、博士生导师，现任浙江省农业科学院农业人工智能创新中心主任、数字农业研究所副所长、数字农业工厂学科带头人，国际生物过程学会终身会员、浙江省农业机械学会常务理事，全国智慧农业领域知名专家、浙江省“数字工匠”。长期从事数智农业工厂、农业机器人与农业人工智能领域科研与推广，成果丰硕，主持国家级、省部级科研项目20多项，发表学术论文70余篇（其中SCI收录45篇），获国家授权专利60多件，获省部级科技奖励2项；技术成果已推广至全国10多个省区以及全球10余个国家，近5年创造经济效益超6亿元，技术推广工作获3项国家级荣誉。\n\n## **四、培训亮点与收获**\n\n**战略高度，直击政策内核**\n\n深入解读国务院“人工智能+”意见与“十五五”规划，看清智能农业新形态的产业风口与布局逻辑。\n\n**硬核内容，打通“技术—运营”全链**\n\n从数字农业工厂构建、智能农机与全链管控，到高效生态农业数智化运营，层层递进，学理念更学方法。\n\n**标杆现场教学，不走过场**\n\n走进“田立方·临平未来农场”先进基地，带着问题看，对着场景学，让技术与运营路径真实可见。\n\n**直面痛点研讨，带回破局方案**\n\n采用结构化研讨，深挖落地难题，聚焦AI融合的可行场景、商业闭环与风险治理，让思考直接转化为行动策略。\n\n## **五、欢迎报名**\n\n**收费标准**\n\n**1650元\u002F人\u002F期**\n\n（含午餐和晚餐、培训费、资料费等）\n\n住宿费与交通费需自理（住宿可代为预留，优先安排校内酒店）\n\n**报名方式**\n\n扫描下方二维码报名工作人员将及时和您联系\n\n![Image 3](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F002U25NLku4.png)\n\n**报名咨询**\n\n联系人丨严老师\n\n手机丨18668129969\n\n电话丨0571-88283982\n\n（微信同号，请备注高研班+姓名）\n\n![Image 4: 严闫微信](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F00316uYN1pS.png)\n\n## **六、研修环境**\n\n**![Image 5](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F004wSQVEacG.png)**\n\n**![Image 6](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F005TcJXT4c3.png)**\n\n**![Image 7](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F006uLrdV2Qe.png)**\n\n**![Image 8](https:\u002F\u002Fpeixun.hzcu.edu.cn\u002Ffile\u002Ffile_path\u002F0078YRstUi8.png)**\n\n**欢迎浙江省人工智能、农业领域相关专业人员踊跃报名，共赴AI赋能现代农业的智慧之约！**\n\n来源：浙大城市学院继续教育学院公众号\n\n本文由浙大城市学院培训官网整理发布","浙大城市学院继续教育学院 2026-08-01","2026-08-01T01:00:00Z",41,{"impact":82,"substance":20,"depth":20,"authority":69,"freshness":402,"relevant":22,"comment":403},7,"省级农业AI培训通知，内容具体但偏宣传，权威性一般，时效性尚可。",[405],{"name":398,"url":395},[407,27,408,409,410],"数字农业","人工智能+农业","人才培养","高研班",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":414},{"candidates":415,"agricultural_evidence":46},[416,420,424],{"code":115,"score":21,"supported":46,"title":417,"abstract":418,"tags":419},[],[407],[407],{"code":32,"score":21,"supported":46,"title":421,"abstract":422,"tags":423},[],[50],[408],{"code":425,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},"G5",[],[],[409],[430,431],"人工智能+农业 浙江省农科院 人才培养 数字农业","人工智能+农业 浙江省农科院","人工智能+农业浙江省农科院人才培养数字农业-189","2026-08-04T01:15:42.986944Z"]