[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1378":3,"related-1378":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":55,"slug":58,"view_count":22,"doi":59,"paper":60,"created_at":85},1378,"Assessing social stress in Nile tilapia through AI behavioral analysis and biosensor integration","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112348","Assessing social stress in Nile tilapia through AI behavioral analysis and biosensor integration。Computers and Electronics in Agriculture","通过人工智能行为分析与生物传感器集成评估尼罗罗非鱼的社会应激。",null,"Computers and Electronics in Agriculture","2026-09-01T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,15,16,13,7,1,"AI结合生物传感器评估罗非鱼应激，方法新颖，对精准水产养殖有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","水产养殖","动物行为分析",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":53,"reason":54},[38,43,48],{"code":39,"score":22,"supported":14,"title":40,"abstract":41,"tags":42},"D",[],[],[29],{"code":44,"score":22,"supported":14,"title":45,"abstract":46,"tags":47},"I1",[],[],[27],{"code":49,"score":22,"supported":14,"title":50,"abstract":51,"tags":52},"I2",[],[],[28],true,"缺少足够的主题证据",[56,57],"农业人工智能 动物行为分析 智慧农业 水产养殖","农业人工智能 动物行为分析","农业人工智能动物行为分析智慧农业水产养殖-1378","10.1016\u002Fj.compag.2026.112348",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":77,"oa_url":9,"card":78,"direction":82,"ingested_from":84},"W7204953967",[63,65,68,70,72,75],{"name":64,"orcid":9},"Yusuke Horiguchi",{"name":66,"orcid":67},"Haiyun Wu","https:\u002F\u002Forcid.org\u002F0000-0003-4898-1621",{"name":69,"orcid":9},"Masataka Murata",{"name":71,"orcid":9},"Haruto Matsumoto",{"name":73,"orcid":74},"Hitoshi Ohnuki","https:\u002F\u002Forcid.org\u002F0000-0002-6409-0629",{"name":76,"orcid":9},"Hideaki Endo",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"结合AI行为分析与生物传感器评估尼罗罗非鱼的社会应激。","AI行为分析、生物传感器集成。","该方法可有效评估鱼类社会应激。","智慧农业 \u002F 农业物联网","可探索将AI行为分析与生物传感器结合用于其他水产养殖物种的应激监测。","openalex","2026-09-02T23:30:02.513843Z",{"total":87,"page":22,"page_size":87,"items":88},6,[89,162,218,288,354,421],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":10,"source_url":92,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":103,"tags":105,"primary_topic":9,"secondary_topics":108,"classification":109,"search_phrases":124,"slug":127,"view_count":101,"doi":128,"paper":129,"created_at":161},3984,"Occlusion-Robust fish individual identification: a multi-model video recognition framework for complex underwater environments","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112486","Occlusion-Robust fish individual identification: a multi-model video recognition framework for complex underwater environments。Computers and Electronics in Agriculture","遮挡鲁棒的鱼类个体识别：面向复杂水下环境的多模型视频识别框架","2026-10-01T00:00:00Z",70,{"impact":17,"substance":98,"depth":99,"authority":100,"freshness":101,"relevant":22,"comment":102},18,17,14,9,"发表于农业信息领域核心期刊的鱼类个体识别方法论文，方法新颖且面向复杂水下环境，但属细分技术进展，公共影响有限。",[104],{"name":10,"url":92},[27,28,29,106,107],"鱼类识别","视频识别",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":110},{"candidates":111,"agricultural_evidence":53,"reason":54},[112,116,120],{"code":39,"score":22,"supported":14,"title":113,"abstract":114,"tags":115},[],[],[29],{"code":44,"score":22,"supported":14,"title":117,"abstract":118,"tags":119},[],[],[27],{"code":49,"score":22,"supported":14,"title":121,"abstract":122,"tags":123},[],[],[28],[125,126],"鱼类个体识别 水下视频","遮挡鲁棒 鱼类识别","鱼类个体识别水下视频-3984","10.1016\u002Fj.compag.2026.112486",{"doi":128,"openalex_id":130,"authors":131,"venue":10,"cited_by_count":77,"oa_url":9,"card":155,"direction":159,"ingested_from":84},"W7215129880",[132,134,137,140,142,144,146,148,150,152],{"name":133,"orcid":9},"Xuelan Liang",{"name":135,"orcid":136},"Junfeng Wu","https:\u002F\u002Forcid.org\u002F0009-0007-4597-633X",{"name":138,"orcid":139},"Yida Sha","https:\u002F\u002Forcid.org\u002F0009-0001-5878-2258",{"name":141,"orcid":9},"Miao Chen",{"name":143,"orcid":9},"Peihua Wu",{"name":145,"orcid":9},"Saibei Zheng",{"name":147,"orcid":9},"程少江",{"name":149,"orcid":9},"Yizhi Zhou",{"name":151,"orcid":9},"Yongzhi Xie",{"name":153,"orcid":154},"Hong Yu","https:\u002F\u002Forcid.org\u002F0000-0003-2248-5056",{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"提出多模型视频识别框架，实现复杂水下遮挡环境中的鱼类个体识别。","多模型视频识别框架，结合遮挡鲁棒特征学习，处理水下视频数据。","该框架在遮挡条件下仍能准确识别鱼类个体，优于现有方法。","农业人工智能与决策模型","可探索轻量化模型与边缘部署，用于水产养殖实时个体监测与行为分析。","2026-10-02T23:30:03.681674Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":10,"source_url":165,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":168,"score_detail":169,"sources":172,"tags":174,"primary_topic":9,"secondary_topics":177,"classification":178,"search_phrases":194,"slug":197,"view_count":198,"doi":199,"paper":200,"created_at":217},3978,"Multimodal spatiotemporal perception fusion: An intelligent classification system for fish stress using iterated dilated convolutional graph temporal neural network","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112483","Multimodal spatiotemporal perception fusion: An intelligent classification system for fish stress using iterated dilated convolutional graph temporal neural network。Computers and Electronics in Agriculture","多模态时空感知融合：基于迭代空洞卷积图时序神经网络的鱼类应激智能分类系统。",75,{"impact":100,"substance":170,"depth":98,"authority":100,"freshness":101,"relevant":22,"comment":171},20,"核心期刊新论文，提出多模态时空感知融合与图时序网络用于鱼类应激智能分类，方法新颖但属细分领域技术进展，公共影响有限。",[173],{"name":10,"url":165},[27,28,175,29,176],"多模态感知","鱼类应激",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":179},{"candidates":180,"agricultural_evidence":53,"reason":54},[181,186,190],{"code":49,"score":182,"supported":14,"title":183,"abstract":184,"tags":185},2,[],[],[28,175],{"code":39,"score":22,"supported":14,"title":187,"abstract":188,"tags":189},[],[],[29],{"code":44,"score":22,"supported":14,"title":191,"abstract":192,"tags":193},[],[],[27],[195,196],"鱼类应激 智能分类 多模态","Computers and Electronics in Agriculture 鱼类应激","鱼类应激智能分类多模态-3978",4,"10.1016\u002Fj.compag.2026.112483",{"doi":199,"openalex_id":201,"authors":202,"venue":10,"cited_by_count":77,"oa_url":9,"card":212,"direction":159,"ingested_from":84},"W7215107404",[203,205,207,210],{"name":204,"orcid":9},"Wentao Huang",{"name":206,"orcid":9},"Haoyu Wen",{"name":208,"orcid":209},"Yingsheng Li","https:\u002F\u002Forcid.org\u002F0009-0003-9287-1259",{"name":211,"orcid":9},"Xiaoshuan Zhang",{"tldr":213,"method":214,"finding":215,"direction":159,"opportunity":216},"提出多模态时空感知融合系统，用迭代膨胀卷积图时序网络分类鱼类应激状态。","多模态时空感知融合与迭代膨胀卷积图时序神经网络。","该网络能有效融合多模态时空信息，提升鱼类应激分类准确率。","可探索多模态感知融合在养殖鱼类健康监测与精准投喂中的实时决策应用。","2026-10-02T23:30:02.938961Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":9,"source_name":224,"source_url":221,"published_at":225,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":229,"tags":231,"primary_topic":234,"secondary_topics":239,"classification":240,"search_phrases":261,"slug":264,"view_count":265,"doi":266,"paper":267,"created_at":287},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":17,"substance":170,"depth":99,"authority":20,"freshness":101,"relevant":22,"comment":228},"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。",[230],{"name":224,"url":221},[27,28,29,232,233],"多目标优化","水质预测",{"code":49,"slug":235,"name":236,"path":237,"parent_code":238},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":241,"locked":14,"version":34,"method":35,"evidence":242},"classified",{"candidates":243,"agricultural_evidence":53},[244,250,257],{"code":39,"score":18,"supported":53,"title":245,"abstract":247,"tags":249},[246],"aquaculture",[248,246],"水产",[29],{"code":49,"score":13,"supported":53,"title":251,"abstract":252,"tags":256},[],[253,254,255],"机器学习","machine learning","decision support",[28],{"code":44,"score":22,"supported":14,"title":258,"abstract":259,"tags":260},[],[],[27],[262,263],"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":266,"openalex_id":268,"authors":269,"venue":224,"cited_by_count":77,"oa_url":221,"card":282,"direction":82,"ingested_from":84},"W7213532503",[270,273,276,278,280],{"name":271,"orcid":272},"Abdel‐Haleem Abdel‐Aty","https:\u002F\u002Forcid.org\u002F0000-0002-6763-2569",{"name":274,"orcid":275},"Ali Jaber Almalki","https:\u002F\u002Forcid.org\u002F0009-0004-1359-2612",{"name":277,"orcid":9},"Sara A. Ghorashi",{"name":279,"orcid":9},"Betty Wan Niu Voon",{"name":281,"orcid":9},"Mohamed Hafez",{"tldr":283,"method":284,"finding":285,"direction":82,"opportunity":286},"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":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":10,"source_url":291,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":306,"search_phrases":322,"slug":325,"view_count":198,"doi":326,"paper":327,"created_at":353},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":98,"substance":170,"depth":98,"authority":100,"freshness":297,"relevant":22,"comment":298},8,"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。",[300],{"name":10,"url":291},[27,28,29,302,303],"智能传感","数字渔业",{"code":49,"slug":235,"name":236,"path":237,"parent_code":238},[],{"status":241,"locked":14,"version":34,"method":35,"evidence":307},{"candidates":308,"agricultural_evidence":53},[309,314,318],{"code":49,"score":17,"supported":53,"title":310,"abstract":312,"tags":313},[311],"artificial intelligence",[311],[28],{"code":39,"score":198,"supported":53,"title":315,"abstract":316,"tags":317},[],[248],[29],{"code":44,"score":22,"supported":14,"title":319,"abstract":320,"tags":321},[],[],[27],[323,324],"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":326,"openalex_id":328,"authors":329,"venue":10,"cited_by_count":77,"oa_url":291,"card":348,"direction":82,"ingested_from":84},"W7212232512",[330,333,336,339,342,345],{"name":331,"orcid":332},"V. Ravi Sankar","https:\u002F\u002Forcid.org\u002F0000-0002-8580-2920",{"name":334,"orcid":335},"Alzayat Saleh","https:\u002F\u002Forcid.org\u002F0000-0001-6973-019X",{"name":337,"orcid":338},"Armin Ehrampoosh","https:\u002F\u002Forcid.org\u002F0000-0002-5482-3454",{"name":340,"orcid":341},"Phoebe Arbon","https:\u002F\u002Forcid.org\u002F0000-0003-0171-6302",{"name":343,"orcid":344},"Dean R. Jerry","https:\u002F\u002Forcid.org\u002F0000-0003-3735-1798",{"name":346,"orcid":347},"Mostafa Rahimi Azghadi","https:\u002F\u002Forcid.org\u002F0000-0001-7975-3985",{"tldr":349,"method":350,"finding":351,"direction":82,"opportunity":352},"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":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":362,"sources":365,"tags":367,"primary_topic":370,"secondary_topics":375,"classification":377,"search_phrases":398,"slug":401,"view_count":198,"doi":402,"paper":403,"created_at":420},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":98,"substance":363,"depth":98,"authority":20,"freshness":297,"relevant":22,"comment":364},21,"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。",[366],{"name":360,"url":357},[27,28,368,29,369],"大语言模型","循环水养殖",{"code":371,"slug":372,"name":373,"path":374,"parent_code":39},"D1","pond-factory-aquaculture","池塘与工厂化养殖","\u002Ftopic\u002Faquaculture\u002Fpond-factory-aquaculture",[376],{"code":49,"slug":235,"name":236,"path":237,"parent_code":238},{"status":241,"locked":14,"version":34,"method":35,"evidence":378},{"candidates":379,"agricultural_evidence":53},[380,384,389,394],{"code":39,"score":18,"supported":53,"title":381,"abstract":382,"tags":383},[246],[248,246],[29],{"code":371,"score":100,"supported":53,"title":385,"abstract":387,"tags":388},[386],"recirculating aquaculture",[369,386],[],{"code":49,"score":20,"supported":53,"title":390,"abstract":391,"tags":393},[255],[392],"large language model",[28,368],{"code":44,"score":22,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[27],[399,400],"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":402,"openalex_id":404,"authors":405,"venue":360,"cited_by_count":77,"oa_url":414,"card":415,"direction":82,"ingested_from":84},"W7167492172",[406,409,411],{"name":407,"orcid":408},"Shai Avraham Shaked","https:\u002F\u002Forcid.org\u002F0000-0003-1995-6419",{"name":410,"orcid":9},"Assaf Shechter",{"name":412,"orcid":413},"Amir Sagi","https:\u002F\u002Forcid.org\u002F0000-0002-4229-1059","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007793\u002Fpdf",{"tldr":416,"method":417,"finding":418,"direction":159,"opportunity":419},"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。","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":422,"title":423,"url":424,"summary":425,"summary_zh":9,"content":9,"source_name":426,"source_url":9,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":428,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":440,"classification":441,"search_phrases":471,"slug":474,"view_count":265,"doi":9,"paper":475,"created_at":483},1504,"Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring","https:\u002F\u002Fwww.journaljsrr.com\u002Findex.php\u002FJSRR\u002Farticle\u002Fview\u002F4484","研究构建了集成物联网传感器、无人机、深度学习与 Web\u002F移动应用的农业 AI 系统，用于作物与水产健康监测。数据集包括 7 万张图像与 3 个月的物联网传感器数据，按 80:10:10 划分训练\u002F验证\u002F测试。模型结果：YOLOv8 害虫识别准确率 92.1%，CNN 病害识别准确率 93.4%，虾类识别 88.4%；整体系统准确率 97%，传感器预测 RMSE 为 1.2。结论指出 AI、物联网与无人机技术可有效检测生物与非生物胁迫，对可持续农业系统形成支撑。原文标题：Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring。","Journal of Scientific Research and Reports 32(9) 468-478","2026-09-02T00:00:00Z",69,{"impact":98,"substance":170,"depth":99,"authority":17,"freshness":182,"relevant":22,"comment":430},"研究集成物联网、无人机与深度学习，实现作物和水产健康监测，数据详实，模型准确率高，对智慧农业有实质参考价值。",[432],{"name":426,"url":424},[27,434,28,435,29],"无人机","物联网",{"code":238,"slug":437,"name":438,"path":439,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":442,"locked":14,"version":34,"method":35,"evidence":443},"partial",{"candidates":444,"agricultural_evidence":53,"reason":470},[445,450,456,460,466],{"code":49,"score":18,"supported":53,"title":446,"abstract":447,"tags":449},[311],[448,311],"深度学习",[28],{"code":451,"score":18,"supported":53,"title":452,"abstract":454,"tags":455},"I5",[453],"internet of things",[435,453],[435],{"code":39,"score":198,"supported":53,"title":457,"abstract":458,"tags":459},[],[248],[29],{"code":461,"score":265,"supported":53,"title":462,"abstract":463,"tags":465},"H8",[],[464],"可持续农业",[],{"code":44,"score":22,"supported":14,"title":467,"abstract":468,"tags":469},[],[],[27],"一级领域明确，二级方向需要细分",[472,473],"农业人工智能 智慧农业 水产养殖 无人机","农业人工智能 智慧农业","农业人工智能智慧农业水产养殖无人机-1504",{"doi":9,"openalex_id":9,"authors":476,"venue":9,"cited_by_count":77,"oa_url":9,"card":477,"direction":82,"ingested_from":482},[],{"tldr":478,"method":479,"finding":480,"direction":82,"opportunity":481},"构建集成物联网、无人机与深度学习的农业AI系统，实现作物与水产健康监测。","集成物联网传感器、无人机、YOLOv8与CNN，使用7万图像及3个月传感器数据训","系统准确率97%，害虫识别92.1%，病害93.4%，虾类88.4%，传感器预测RMSE1.2。","可探索多源数据融合与边缘计算，提升实时监测精度，并扩展至更多水产种类与复杂环境。","agent","2026-09-03T00:06:44.772165Z"]