[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1820":3,"related-1820":88},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":21,"doi":65,"paper":66,"created_at":87},1820,"Numerical simulation of water dynamics and wastewater treatment effect in spiral substrate for recirculating aquaculture systems of Apostichopus japonicus","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112373","Numerical simulation of water dynamics and wastewater treatment effect in spiral substrate for recirculating aquaculture systems of Apostichopus japonicus。Computers and Electronics in Agriculture","刺参循环水养殖系统中螺旋基质的水动力学与废水处理效果数值模拟。《计算机与电子在农业中的应用》",null,"Computers and Electronics in Agriculture","2026-09-06T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,14,2,1,"针对刺参循环水养殖螺旋基质的数值模拟研究，方法新颖，但影响范围限于细分领域，时效性较低。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","水产养殖","数值模拟","循环水系统",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"D1","pond-factory-aquaculture","池塘与工厂化养殖","\u002Ftopic\u002Faquaculture\u002Fpond-factory-aquaculture","D",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,51,56],{"code":32,"score":20,"supported":45,"title":46,"abstract":48,"tags":50},true,[47],"recirculating aquaculture",[49,47],"循环水养殖",[],{"code":36,"score":17,"supported":45,"title":52,"abstract":54,"tags":55},[53],"aquaculture",[53],[28],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I1",[],[],[27],[62,63],"循环水系统 数值模拟 智慧农业 水产养殖","循环水系统 数值模拟","循环水系统数值模拟智慧农业水产养殖-1820","10.1016\u002Fj.compag.2026.112373",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":79,"oa_url":9,"card":80,"direction":84,"ingested_from":86},"W7210275851",[69,71,73,75,77],{"name":70,"orcid":9},"Chenyu Song",{"name":72,"orcid":9},"Yijing Zhou",{"name":74,"orcid":9},"Ruiguang Dong",{"name":76,"orcid":9},"Xiefa Song",{"name":78,"orcid":9},"Meng Li",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"模拟循环水养殖系统中螺旋基质的流场与净水效果，优化刺参养殖。","数值模拟（CFD）分析水动力与废水处理效果。","螺旋基质可改善水流分布并增强废水处理能力。","智慧农业 \u002F 农业物联网","可延伸研究不同基质的流固耦合优化，结合物联网实时监控水质，实现智能调控。","openalex","2026-09-07T23:30:01.869886Z",{"total":89,"page":22,"page_size":89,"items":90},6,[91,166,237,276,356,422],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":105,"tags":107,"primary_topic":110,"secondary_topics":111,"classification":118,"search_phrases":141,"slug":144,"view_count":145,"doi":146,"paper":147,"created_at":165},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",78,{"impact":18,"substance":101,"depth":18,"authority":102,"freshness":103,"relevant":22,"comment":104},21,13,8,"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。",[106],{"name":97,"url":94},[27,108,109,28,49],"农业人工智能","大语言模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[112],{"code":113,"slug":114,"name":115,"path":116,"parent_code":117},"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":119},{"candidates":120,"agricultural_evidence":45},[121,127,131,137],{"code":36,"score":122,"supported":45,"title":123,"abstract":124,"tags":126},15,[53],[125,53],"水产",[28],{"code":32,"score":20,"supported":45,"title":128,"abstract":129,"tags":130},[47],[49,47],[],{"code":113,"score":102,"supported":45,"title":132,"abstract":134,"tags":136},[133],"decision support",[135],"large language model",[108,109],{"code":57,"score":22,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[27],[142,143],"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",4,"10.1016\u002Fj.atech.2026.102554",{"doi":146,"openalex_id":148,"authors":149,"venue":97,"cited_by_count":79,"oa_url":158,"card":159,"direction":84,"ingested_from":86},"W7167492172",[150,153,155],{"name":151,"orcid":152},"Shai Avraham Shaked","https:\u002F\u002Forcid.org\u002F0000-0003-1995-6419",{"name":154,"orcid":9},"Assaf Shechter",{"name":156,"orcid":157},"Amir Sagi","https:\u002F\u002Forcid.org\u002F0000-0002-4229-1059","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007793\u002Fpdf",{"tldr":160,"method":161,"finding":162,"direction":163,"opportunity":164},"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":167,"title":168,"url":169,"summary":170,"summary_zh":9,"content":171,"source_name":172,"source_url":9,"published_at":173,"category":174,"cover_url":9,"hotness":175,"is_selected":14,"score":176,"score_detail":177,"sources":181,"tags":186,"primary_topic":192,"secondary_topics":193,"classification":194,"search_phrases":232,"slug":235,"view_count":22,"doi":9,"paper":9,"created_at":236},1679,"浙江省农科院：探索水产养殖尾水循环利用新路径——让\"废\"水变\"肥\"水","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681623657952592424","9 月 4 日，浙江省农业科学院联合当地企业和农业部门在嘉兴嘉善西塘召开循环水养殖系统（RAS）设施渔业养殖尾水资源化利用技术现场考察交流会。团队给出新解法：不新建专门的尾水处理设施，而是利用周边现成的稻田\"吃掉\"尾水。实测数据印证成效：尾水排放量削减 70% 以上，处理成本从每吨 1 元降至 0.4 元；每公顷水稻年增产约 650 公斤，化肥用量减少 15%-30%。","## 省农科院探索水产养殖尾水循环利用新路径，让“废”水变“肥”水\n\n2026-09-04 18:23·[浙江日报](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAATrbOByn_34fAjCz7FVgDhykDJtg-bDdUW1ZdzpQq3xc\u002F?source=tuwen_detail)\n\n潮新闻 记者 谢丹颖 通讯员 王语嫣\n\n9月4日，浙江省农业科学院联合当地企业和农业部门在嘉兴嘉善西塘召开循环水养殖系统（RAS）设施渔业养殖尾水资源化利用技术现场考察交流会，科研院所与产业一线的目光聚到同一道题上：如何将工厂化循环水养殖尾水用起来？\n\n走进“浙农·商达共富渔仓”养殖车间，鱼池整齐排列，大西洋鲑、鳗鱼、墨瑞鳕在循环水系统中自在游弋。项目总投资6.25亿元，养殖车间仅占15亩，年产商品鱼可达100万斤，亩产效益远超传统池塘养殖。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F64a37975cede485481609aaf887a3485~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171546&x-signature=IUCpqhO%2BkqYI0%2B18pHzQ3xRU%2BHQ%3D)\n嘉兴嘉善西塘大西洋鲑育苗车间（受访者供图）\n\n养鱼密度大了，尾水处理就成了难题：每天车间排放的300吨尾水中，大部分经处理后循环利用，但仍有一部分需要持续外排，如何让这部分水达标入河？省农科院团队给出新解法：不新建专门的尾水处理设施，而是利用周边现成的稻田“吃掉”尾水。根据尾水排放量和稻田消纳能力，团队设计了稻鱼共生、水稻-绿肥轮作、水稻-中药材等不同种植模式，并搭配净水菌剂和氮磷强化去除技术，保障灌溉用水安全和清水入河。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fc93f97b3cd6a7c299f0cc9284b8e14d0~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171546&x-signature=g0emu%2BChqWkjCtMvmZYk4E90ObM%3D)\n嘉兴嘉善西塘“浙农·商达共富渔仓”稻田现场（受访者供图）\n\n“我们利用稻田的需水和灌溉特征，通过水肥等参数优化，把稻田变成一个天然的‘净化器’。”省农科院环境资源与土壤肥料研究所研究员李华介绍，原理并不复杂：养殖尾水通过沟渠或管道引入稻田，水稻根系和土壤微生物就会像一张“过滤网”，将水中残留的氮、磷等养分吸收转化——水干净了、化肥也省了不少，一举两得。\n\n实测数据印证成效：尾水排放量削减70%以上，处理成本从每吨1元降至0.4元；每公顷水稻年增产约650公斤，化肥用量减少15%-30%，成本节省近2000元，每公顷稻鱼收益达1.8万元，土壤固碳能力提升2-3吨。\n\n“让尾水从污染源变成养分源，既降低了治污成本，又肥沃了稻田。”李华说，下一步，团队将推动该技术模式在长三角复制推广。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F49d2f604ac40e946bd9c934fe73c1736~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789171546&x-signature=V7K5LSgxmv7JqKMXjCrr2urs0BI%3D)\n省农科院团队探索水产养殖尾水循环利用展示板（受访者供图）\n\n“转载请注明出处”","浙江日报","2026-09-04T00:00:00Z","报道",25,75,{"impact":18,"substance":178,"depth":19,"authority":13,"freshness":179,"relevant":22,"comment":180},22,9,"报道浙江省农科院在设施渔业尾水资源化利用上的创新实践，数据详实，具有推广价值。",[182,183],{"name":172,"url":169},{"name":184,"url":185},"今日头条 浙江日报 2026-09-04","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681623657952592424\u002F",[187,188,189,190,191],"浙江","循环农业","尾水利用","设施渔业","稻渔综合种养",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":195},{"candidates":196,"agricultural_evidence":45},[197,203,208,215,222,227],{"code":36,"score":198,"supported":45,"title":199,"abstract":200,"tags":202},11,[125],[201],"渔业",[],{"code":32,"score":204,"supported":45,"title":205,"abstract":206,"tags":207},7,[],[190,49],[190],{"code":209,"score":89,"supported":45,"title":210,"abstract":211,"tags":214},"A1",[],[212,213],"水稻","稻田",[],{"code":216,"score":217,"supported":45,"title":218,"abstract":219,"tags":221},"H3",3,[],[220],"水资源",[],{"code":223,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},"D3",[],[],[191],{"code":228,"score":22,"supported":14,"title":229,"abstract":230,"tags":231},"H8",[],[],[188],[233,234],"稻渔综合种养 尾水利用 循环农业 设施渔业","稻渔综合种养 尾水利用","稻渔综合种养尾水利用循环农业设施渔业-1679","2026-09-05T00:04:38.120518Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":9,"content":242,"source_name":243,"source_url":9,"published_at":244,"category":174,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":249,"tags":251,"primary_topic":257,"secondary_topics":258,"classification":259,"search_phrases":271,"slug":274,"view_count":217,"doi":9,"paper":9,"created_at":275},986,"浙江大学等入选浙江省2026年农业主导品种和主推技术 现代农学院3项成果登榜","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F500930.html","近日浙江省2026年农业主导品种和主推技术名单正式公布。现代农学院主持培育的缢蛏『甬乐2号』、参与培育的浙贝母『浙贝3号』，以及主导研发优化的『贝类平面流中间培育技术』成功入选。缢蛏『甬乐2号』由董迎辉教授团队培育，在氨氮胁迫条件下存活率较未经选育群体提高42.23%，14月龄体重提高41.15%；浙贝母『浙贝3号』由王忠华教授团队参与培育，繁殖系数提升40%，亩产超1000kg，种植过程可减少用药1-2次；贝类平面流中间培育技术由何琳教授团队研发优化，构建循环水养殖体系，缢蛏苗种中培产量达8 kg\u002Fm²~15 kg\u002Fm²，每平方米效益超1000元。","由何琳教授团队研发优化的贝类平面流中间培育技术，是贝类养殖模式转型升级的“关键抓手”。该技术突破传统滩涂贝类苗种培育受自然环境影响大、成活率低、生长周期长的难题，通过构建循环水养殖体系，让“静水”池塘实现“流水”养贝，贝苗滤食循环流水中的单细胞藻类与有机碎屑，滤食后的清水回流塘中，实现水质净化与饵料循环利用。技术优势尤为突出：贝苗生长速度快、病害发生率低，周年可开展2-3批次贝类苗种中培，缢蛏苗种中培产量达8 kg\u002Fm²~15 kg\u002Fm²，每平方米效益超1000元；同时配套对虾养殖尾水利用，让尾水氮元素降低86%，达到二级排放标准以上，实现生态效益与经济效益双赢。目前我院已制定该技术的宁波市地方标准，在宁海、三门、奉化等多地示范应用，新增平面流系统面积2.6万平方米，成为推动贝类养殖规模化、标准化、生态化发展的重要技术支撑。2026年3项成果的再度登榜，展现学院始终坚持以科技创新为引领，聚焦农业产业发展中的关键问题，持续推进优良品种培育和先进技术研发，加快推动科技成果从实验室走向生产一线、从科研成果转化为现实生产力。未来，学院将继续立足学科特色，深化产学研协同创新，推动更多优质品种和先进技术落地推广，培育形成更多具有行业影响力和区域带动力的标志性成果，为农业高质量发展和乡村全面振兴贡献更多智慧和力量。","农业信息网 2026-08-25 · 2026-08-25","2026-08-25T00:00:00Z",70,{"impact":18,"substance":247,"depth":122,"authority":13,"freshness":204,"relevant":22,"comment":248},20,"省级农业技术推广成果，含具体数据与地方标准，但信源为学院宣传稿，深度一般。",[250],{"name":243,"url":240},[187,252,253,254,255,256],"主导品种","主推技术","缢蛏甬乐2号","浙贝3号","平面流中间培育",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":260},{"candidates":261,"agricultural_evidence":45},[262,266],{"code":32,"score":217,"supported":45,"title":263,"abstract":264,"tags":265},[],[49],[],{"code":267,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},"F1",[],[],[252,253],[272,273],"平面流中间培育 缢蛏甬乐2号 主导品种 主推技术","平面流中间培育 缢蛏甬乐2号","平面流中间培育缢蛏甬乐2号主导品种主推技术-986","2026-08-27T00:05:18.791853Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":284,"sources":287,"tags":289,"primary_topic":293,"secondary_topics":298,"classification":299,"search_phrases":334,"slug":337,"view_count":79,"doi":9,"paper":338,"created_at":355},4890,"ระบบพยากรณและเฝาระวงคณภาพนำอจฉรยะตามดชนคณภาพนำ ดวยเทคนคการเรยนรของเครองและอนเทอรเนตของสรรพสง","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FJSSE\u002Farticle\u002Fview\u002F289948","This research utilizes Internet of Things (IoT) technology and Machine Learning (ML) to develop a prototype for a smart water quality forecasting and monitoring system. The study focuses on analyzing the Reduced Water Quality Index (rWQI) based on water temperature, pH, and Total Dissolved Solids (TDS). To forecast water quality 1 to 6 hours in advance, the performance of four predictive models was evaluated and compared: Extremely Randomized Trees (ET), Random Forest (RF), Simple Linear Regression (LR), and Multiple Linear Regression (MLR). The efficiency of these algorithms was validated using four key evaluation metrics: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE). The experimental results indicated that the predictive performance of the models varied significantly depending on the forecasting horizon. In the short-term horizon (1–3 hours ahead), the RF model achieved the highest performance, with an R² of 0.566 and an MAE of 5.215 in the first hour. Conversely, for long-term forecasting (4–6 hours ahead), all models exhibited a significant drop in performance (R² \u003C 0), yielding higher prediction errors than the Mean Baseline. This long-term limitation stems from high water quality volatility in real-world environments combined with error accumulation in multi-step ahead forecasting. In conclusion, these findings emphasize that aligning model selection with the specific forecasting horizon is critical for enhancing system efficiency and stability. The developed model demonstrates high accuracy and suitability for short-term early warnings within 3 hours. This knowledge enables operators to plan water quality management with precision and speed, thereby reducing operational costs and the risk of aquatic livestock loss. Furthermore, this system serves as a technological prototype in digital agriculture, elevating conventional water quality monitoring into proactive management through intelligent technology.","本研究利用物联网（IoT）技术与机器学习（ML），开发了一套智能水质预测与监测系统原型。研究聚焦于基于水温、pH值和总溶解固体（TDS）的简化水质指数（rWQI）分析。为提前1至6小时预测水质，本研究评估并比较了四种预测模型的性能：极端随机树（ET）、随机森林（RF）、简单线性回归（LR）和多元线性回归（MLR）。采用四项关键评价指标验证了这些算法的有效性：决定系数（R²）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和对称平均绝对百分比误差（sMAPE）。实验结果表明，各模型的预测性能因预测时间跨度不同而存在显著差异。在短期预测（提前1至3小时）中，RF模型表现最佳，第一小时的R²为0.566，MAE为5.215。相反，在长期预测（提前4至6小时）中，所有模型性能均显著下降（R² \u003C 0），预测误差高于均值基线。这一长期局限性源于真实环境中水质的高波动性以及多步超前预测中的误差累积。综上所述，这些发现强调，将模型选择与特定预测时间跨度相匹配对于提升系统效率与稳定性至关重要。所开发的模型具有较高精度，适用于3小时内的短期预警。这一认识使操作人员能够精准、快速地规划水质管理，从而降低运营成本和水产养殖损失风险。此外，该系统可作为数字农业中的技术原型，通过智能技术将传统水质监测提升为主动管理。","Journal of Science and Science Education (JSSE)","2026-10-12T00:00:00Z",{"impact":17,"substance":247,"depth":285,"authority":102,"freshness":103,"relevant":22,"comment":286},17,"论文将物联网与机器学习结合用于水产养殖水质短期预警，方法对比扎实、结论明确，但属细分技术原型，影响范围有限。",[288],{"name":282,"url":279},[27,290,291,28,292],"机器学习","物联网","水质监测",{"code":294,"slug":295,"name":296,"path":297,"parent_code":117},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":300},{"candidates":301,"agricultural_evidence":45},[302,308,314,319,323,329],{"code":294,"score":13,"supported":45,"title":303,"abstract":304,"tags":307},[],[291,305,306],"internet of things","IoT",[291],{"code":57,"score":204,"supported":45,"title":309,"abstract":310,"tags":313},[],[311,312],"数字农业","digital agriculture",[27],{"code":113,"score":204,"supported":45,"title":315,"abstract":316,"tags":318},[],[290,317],"machine learning",[290],{"code":36,"score":145,"supported":45,"title":320,"abstract":321,"tags":322},[],[125],[28],{"code":324,"score":217,"supported":45,"title":325,"abstract":326,"tags":328},"C",[],[327],"livestock",[],{"code":330,"score":22,"supported":14,"title":331,"abstract":332,"tags":333},"D4",[],[],[292],[335,336],"物联网 机器学习 水质预测","rWQI 水质监测 水产养殖","物联网机器学习水质预测-4890",{"doi":9,"openalex_id":339,"authors":340,"venue":282,"cited_by_count":79,"oa_url":279,"card":350,"direction":84,"ingested_from":86},"W7221732805",[341,343,345,347],{"name":342,"orcid":9},"Wanarase Sinhashthita",{"name":344,"orcid":9},"Tossaporn Alherbe",{"name":346,"orcid":9},"Kamol Chareonsri",{"name":348,"orcid":349},"Chayaporn Kaensar","https:\u002F\u002Forcid.org\u002F0009-0005-3330-8532",{"tldr":351,"method":352,"finding":353,"direction":84,"opportunity":354},"利用物联网与机器学习构建水质预测监测系统，提前1-6小时预测rWQI。","基于水温、pH、TDS数据，比较ET、RF、LR、MLR四种模型预测性能。","短期（1-3小时）RF最优，长期（4-6小时）所有模型性能显著下降。","可探索融合时序深度学习与误差校正，提升长期水质预测精度与鲁棒性。","2026-10-14T23:30:13.314647Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":10,"source_url":359,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":363,"sources":365,"tags":367,"primary_topic":9,"secondary_topics":371,"classification":372,"search_phrases":389,"slug":392,"view_count":217,"doi":393,"paper":394,"created_at":421},4043,"Completeness-aware underwater weight estimation of free-swimming sturgeon using binocular 3D reconstruction","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112499","Completeness-aware underwater weight estimation of free-swimming sturgeon using binocular 3D reconstruction。Computers and Electronics in Agriculture","基于双目三维重建的自由游动鲟鱼完整性感知水下体重估计。《计算机与电子农业》","2026-10-01T00:00:00Z",{"impact":18,"substance":101,"depth":285,"authority":20,"freshness":103,"relevant":22,"comment":364},"核心期刊论文，提出完整性感知的双目三维重建方法实现自由游动鲟鱼无接触估重，方法新颖且对水产养殖智能化有实用价值，值得入选。",[366],{"name":10,"url":359},[27,368,28,369,370],"计算机视觉","三维重建","鱼类表型",[],{"status":373,"locked":14,"version":40,"method":41,"evidence":374},"unclassified",{"candidates":375,"agricultural_evidence":45,"reason":388},[376,380,384],{"code":113,"score":21,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[369,368],{"code":36,"score":22,"supported":14,"title":381,"abstract":382,"tags":383},[],[],[28],{"code":57,"score":22,"supported":14,"title":385,"abstract":386,"tags":387},[],[],[27],"缺少足够的主题证据",[390,391],"双目三维重建 鲟鱼 体重估算","水下鱼类 无损测重","双目三维重建鲟鱼体重估算-4043","10.1016\u002Fj.compag.2026.112499",{"doi":393,"openalex_id":395,"authors":396,"venue":10,"cited_by_count":79,"oa_url":9,"card":416,"direction":163,"ingested_from":86},"W7216402348",[397,400,402,405,408,411,413],{"name":398,"orcid":399},"Sitao Liu","https:\u002F\u002Forcid.org\u002F0009-0002-5683-9860",{"name":401,"orcid":9},"刘健晖",{"name":403,"orcid":404},"Henghui Mo","https:\u002F\u002Forcid.org\u002F0009-0000-6815-2738",{"name":406,"orcid":407},"Jiaxuan Yu","https:\u002F\u002Forcid.org\u002F0009-0004-4050-7444",{"name":409,"orcid":410},"Guangxu Wang","https:\u002F\u002Forcid.org\u002F0009-0001-6595-0678",{"name":412,"orcid":9},"Pan Zhang",{"name":414,"orcid":415},"Daoliang Li","https:\u002F\u002Forcid.org\u002F0000-0002-1842-419X",{"tldr":417,"method":418,"finding":419,"direction":163,"opportunity":420},"提出完整性感知的双目3D重建方法，实现自由游动鲟鱼的水下体重估计。","双目立体视觉3D重建，结合完整性感知策略处理遮挡与缺失数据。","该方法能在自由游动条件下较准确估计鲟鱼体重，克服水下遮挡影响。","可迁移至其他水产养殖物种的無接触体重估计，并融合多模态数据提升鲁棒性。","2026-10-03T23:30:01.894880Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":10,"source_url":425,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":428,"sources":430,"tags":432,"primary_topic":9,"secondary_topics":435,"classification":436,"search_phrases":451,"slug":454,"view_count":179,"doi":455,"paper":456,"created_at":487},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",{"impact":17,"substance":18,"depth":285,"authority":20,"freshness":179,"relevant":22,"comment":429},"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。",[431],{"name":10,"url":425},[27,108,28,433,434],"鱼类识别","视频识别",[],{"status":373,"locked":14,"version":40,"method":41,"evidence":437},{"candidates":438,"agricultural_evidence":45,"reason":388},[439,443,447],{"code":36,"score":22,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[28],{"code":57,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[27],{"code":113,"score":22,"supported":14,"title":448,"abstract":449,"tags":450},[],[],[108],[452,453],"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":455,"openalex_id":457,"authors":458,"venue":10,"cited_by_count":79,"oa_url":9,"card":482,"direction":163,"ingested_from":86},"W7215129880",[459,461,464,467,469,471,473,475,477,479],{"name":460,"orcid":9},"Xuelan Liang",{"name":462,"orcid":463},"Junfeng Wu","https:\u002F\u002Forcid.org\u002F0009-0007-4597-633X",{"name":465,"orcid":466},"Yida Sha","https:\u002F\u002Forcid.org\u002F0009-0001-5878-2258",{"name":468,"orcid":9},"Miao Chen",{"name":470,"orcid":9},"Peihua Wu",{"name":472,"orcid":9},"Saibei Zheng",{"name":474,"orcid":9},"程少江",{"name":476,"orcid":9},"Yizhi Zhou",{"name":478,"orcid":9},"Yongzhi Xie",{"name":480,"orcid":481},"Hong Yu","https:\u002F\u002Forcid.org\u002F0000-0003-2248-5056",{"tldr":483,"method":484,"finding":485,"direction":163,"opportunity":486},"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。","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"]