[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1756":3,"related-1756":82},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":81},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",null,"Biosystems Engineering","2026-09-05T00:00:00Z","论文",10,false,67,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,20,18,12,2,1,"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。",[24],{"name":9,"url":6},[26,27,28],"智慧农业","水产养殖","决策支持",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,54],{"code":43,"score":19,"supported":44,"title":45,"abstract":47,"tags":48},"D",true,[46],"aquaculture",[46],[27],{"code":30,"score":19,"supported":44,"title":50,"abstract":52,"tags":53},[51],"decision support",[51],[28],{"code":55,"score":21,"supported":13,"title":56,"abstract":57,"tags":58},"I1",[],[],[26],[60,61],"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",3,"10.1016\u002Fj.biosystemseng.2026.104587",{"doi":64,"openalex_id":66,"authors":67,"venue":9,"cited_by_count":79,"oa_url":8,"card":8,"direction":8,"ingested_from":80},"W7208811750",[68,70,72,75,77],{"name":69,"orcid":8},"Haixiang Zhao",{"name":71,"orcid":8},"Zuqiang Liu",{"name":73,"orcid":74},"Zhihong Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4821-6751",{"name":76,"orcid":8},"Weimin Yu",{"name":78,"orcid":8},"Xian Li",0,"openalex","2026-09-06T23:30:06.005522Z",{"total":83,"page":21,"page_size":83,"items":84},6,[85,151,226,289,363,421],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":8,"source_name":91,"source_url":88,"published_at":92,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":93,"score_detail":94,"sources":99,"tags":101,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":128,"slug":131,"view_count":20,"doi":132,"paper":133,"created_at":150},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",74,{"impact":19,"substance":95,"depth":18,"authority":96,"freshness":97,"relevant":21,"comment":98},21,14,9,"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。",[100],{"name":91,"url":88},[26,102,27,103,104],"机器学习","海洋牧场","养殖装备",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":108},{"candidates":109,"agricultural_evidence":44},[110,115,119,124],{"code":30,"score":16,"supported":44,"title":111,"abstract":113,"tags":114},[112],"machine learning",[102,112],[102],{"code":43,"score":19,"supported":44,"title":116,"abstract":117,"tags":118},[46],[46],[27],{"code":120,"score":21,"supported":13,"title":121,"abstract":122,"tags":123},"D2",[],[],[103],{"code":55,"score":21,"supported":13,"title":125,"abstract":126,"tags":127},[],[],[26],[129,130],"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","10.1016\u002Fj.compag.2026.112492",{"doi":132,"openalex_id":134,"authors":135,"venue":91,"cited_by_count":79,"oa_url":8,"card":144,"direction":148,"ingested_from":80},"W7215081072",[136,138,141],{"name":137,"orcid":8},"Ming-Shan Sun",{"name":139,"orcid":140},"Chun-Wei Bi","https:\u002F\u002Forcid.org\u002F0000-0002-5306-7976",{"name":142,"orcid":143},"Shubin Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-7985-4898",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"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。","2026-10-02T23:30:02.311269Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":157,"source_url":154,"published_at":158,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":159,"score_detail":160,"sources":164,"tags":166,"primary_topic":170,"secondary_topics":171,"classification":172,"search_phrases":200,"slug":203,"view_count":20,"doi":204,"paper":205,"created_at":225},3881,"A Human-Centered Machine Learning Dashboard for Real-Time Smart Farming Decision Support","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fshem.v2i2.302","The integration of machine learning (ML) into precision agriculture has advanced predictive accuracy for tasks such as crop monitoring, irrigation scheduling, and yield estimation. However, many existing IoT-based smart farming systems remain technically driven, offering limited attention to how farmers and field operators actually interact with and interpret model outputs. This gap often results in low adoption rates, as complex predictions fail to translate into actionable, trustworthy decisions for end users. This study proposes a human-centered machine learning dashboard designed to bridge predictive modeling with real-time, farmer-friendly decision support. The system integrates IoT sensor data streams with ML-based prediction modules, presented through an interactive, usability-tested interface that emphasizes clarity, accessibility, and interpretability over raw technical complexity. A working prototype was developed and evaluated using both quantitative usability metrics (e.g., task completion time, System Usability Scale scores) and qualitative feedback from target users to assess the dashboard's effectiveness in supporting field-level decisions. The proposed system architecture, dashboard interface, and underlying ML pipeline are implemented as open and reproducible source code, enabling replication and adaptation across similar smart farming contexts. Results indicate that the human-centered design significantly improves users' comprehension of model outputs and their confidence in acting on system recommendations, without compromising predictive performance. This research contributes a practical framework for designing machine intelligence systems that prioritize end-user experience alongside technical accuracy, offering implications for the broader development of human-centered AI in agricultural and IoT-driven decision support environments.","机器学习（ML）在精准农业中的融合提升了作物监测、灌溉调度和产量估算等任务的预测准确性。然而，许多现有的基于物联网的智慧农业系统仍然以技术驱动为主，对农民和田间操作人员实际如何与模型输出交互并加以解读关注有限。这一差距往往导致采用率偏低，因为复杂的预测无法转化为最终用户可操作、可信赖的决策。本研究提出了一种以人为中心的机器学习仪表盘，旨在将预测建模与实时、面向农民的决策支持相衔接。该系统将物联网传感器数据流与基于机器学习的预测模块相集成，并通过一个经过可用性测试的交互式界面呈现，强调清晰性、可及性和可解释性，而非原始的技术复杂性。我们开发了一个可运行的原型，并使用定量可用性指标（如任务完成时间、系统可用性量表评分）和来自目标用户的定性反馈进行评估，以考察该仪表盘在支持田间决策方面的有效性。所提出的系统架构、仪表盘界面和底层机器学习流水线均以开放且可复现的源代码形式实现，便于在类似的智慧农业场景中复制和适配。结果表明，以人为中心的设计显著提升了用户对模型输出的理解以及依据系统建议采取行动的信心，同时未损害预测性能。本研究为设计在技术准确性的同时优先考虑最终用户体验的机器智能系统提供了一个实用框架，对农业及物联网驱动的决策支持环境中以人为中心的人工智能的更广泛发展具有启示意义。","Smart Human-centered Emerging Research in Machine Intelligence","2026-09-30T00:00:00Z",64,{"impact":19,"substance":18,"depth":161,"authority":12,"freshness":162,"relevant":21,"comment":163},16,8,"提出以人为本的机器学习仪表盘，兼顾可用性与预测性能，并开源实现，对智慧农业落地有参考价值。",[165],{"name":157,"url":154},[26,167,168,28,169],"农业人工智能","物联网","人机交互",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":173},{"candidates":174,"agricultural_evidence":44},[175,180,186,193],{"code":30,"score":176,"supported":44,"title":177,"abstract":178,"tags":179},27,[112,51],[102,112,51],[167,28],{"code":55,"score":12,"supported":44,"title":181,"abstract":182,"tags":185},[],[26,183,184],"精准农业","precision agriculture",[26],{"code":187,"score":188,"supported":44,"title":189,"abstract":190,"tags":192},"I5",7,[],[168,191],"IoT",[168],{"code":194,"score":83,"supported":44,"title":195,"abstract":196,"tags":199},"H3",[],[197,198],"灌溉","irrigation",[],[201,202],"农业人工智能 人机交互 决策支持 智慧农业","农业人工智能 人机交互","农业人工智能人机交互决策支持智慧农业-3881","10.68012\u002Fshem.v2i2.302",{"doi":204,"openalex_id":206,"authors":207,"venue":157,"cited_by_count":79,"oa_url":154,"card":220,"direction":148,"ingested_from":80},"W7214922384",[208,211,213,215,217],{"name":209,"orcid":210},"Adam Faturahman","https:\u002F\u002Forcid.org\u002F0000-0001-9727-9092",{"name":212,"orcid":8},"James Williams",{"name":214,"orcid":8},"Evelyn Anderson",{"name":216,"orcid":8},"Goh Yao",{"name":218,"orcid":219},"Muhammad Rifat Syauqy","https:\u002F\u002Forcid.org\u002F0009-0001-4582-5693",{"tldr":221,"method":222,"finding":223,"direction":148,"opportunity":224},"提出以人为中心的机器学习仪表盘，融合物联网与预测模型，为农户提供实时可解释的决策支持。","集成物联网传感器数据流与机器学习预测模块，开发交互式仪表盘，并用SUS量表和用户","以人为中心的设计显著提升用户对模型输出的理解与采纳信心，且不牺牲预测性能。","可探索不同作物、地区和文化背景下农户对可解释AI界面的差异化需求与长期采纳效果。","2026-10-01T23:30:09.481397Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":8,"content":8,"source_name":231,"source_url":229,"published_at":232,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":233,"score_detail":234,"sources":236,"tags":238,"primary_topic":241,"secondary_topics":247,"classification":249,"search_phrases":271,"slug":274,"view_count":63,"doi":275,"paper":276,"created_at":288},3807,"Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10698325\u002Fv1","Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift。Research Square","Research Square","2026-09-28T00:00:00Z",52,{"impact":162,"substance":96,"depth":16,"authority":83,"freshness":97,"relevant":21,"comment":235},"预印本论文，聚焦小麦产量决策支持系统的可靠性审计与时空迁移问题，方法有一定新意但尚未经同行评审，公共影响有限。",[237],{"name":231,"url":229},[26,167,28,239,240],"遥感监测","小麦产量",{"code":242,"slug":243,"name":244,"path":245,"parent_code":246},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[248],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":250},{"candidates":251,"agricultural_evidence":44},[252,257,262,266],{"code":30,"score":253,"supported":44,"title":254,"abstract":255,"tags":256},13,[51],[51],[167,28],{"code":242,"score":258,"supported":44,"title":259,"abstract":260,"tags":261},11,[243],[243],[],{"code":55,"score":21,"supported":13,"title":263,"abstract":264,"tags":265},[],[],[26],{"code":267,"score":21,"supported":13,"title":268,"abstract":269,"tags":270},"I3",[],[],[239],[272,273],"小麦产量 决策支持 遥感","Research Square 小麦 产量预测","小麦产量决策支持遥感-3807","10.21203\u002Frs.3.rs-10698325\u002Fv1",{"doi":275,"openalex_id":277,"authors":278,"venue":231,"cited_by_count":79,"oa_url":229,"card":8,"direction":287,"ingested_from":80},"W7214624863",[279,282,285],{"name":280,"orcid":281},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":283,"orcid":284},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":286,"orcid":8},"Asel Kartanova","农业人工智能与决策模型","2026-09-29T23:31:02.997580Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":8,"source_name":295,"source_url":292,"published_at":232,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":296,"sources":298,"tags":300,"primary_topic":304,"secondary_topics":305,"classification":306,"search_phrases":322,"slug":325,"view_count":20,"doi":326,"paper":327,"created_at":362},3806,"Data Management in Agricultural Enterprises: A Framework for Data-Driven Decision Support","https:\u002F\u002Fdoi.org\u002F10.7160\u002Faol.2026.180305","The growing digitalisation of agriculture is leading to a rapid increase in the volume of data produced by agricultural enterprises; however, its practical use is often limited by fragmented data sources, varying data quality, insufficient metadata, and inconsistent storage and management practices. The aim of this article is to propose a practical framework for the systematic management of data in agricultural enterprises to support its long-term availability, reuse, and integration into decision-making processes. The proposed approach is based on four interlinked steps: identifying data needs and issues, creating and recording a data inventory, drawing up a Data Management Plan (DMP), and implementing it in practice. The framework links technical aspects of data management – such as data formats, metadata, quality, integrity, security, backup and archiving – with the definition of responsibilities for individual staff members and farm (agricultural enterprise) management. For practical implementation, two basic approaches to data storage are distinguished: file systems and data platforms or repositories. The more advanced platform-based approach enables the centralisation of heterogeneous data sources, the automation of their transfer and processing, and subsequently the integration of the acquired data with analytical tools and decision-support systems. The proposed framework thus represents a path from the isolated collection of operational data to its systematic management and utilisation as a long-term information source for both operational and strategic decision-making within an agricultural enterprise.","农业日益数字化，导致农业企业产生的数据量迅速增加；然而，其实际应用往往受限于数据来源分散、数据质量参差不齐、元数据不足以及存储和管理实践不一致。本文旨在提出一个实用框架，用于农业企业数据的系统化管理，以支持其长期可用性、再利用以及融入决策过程。所提出的方法基于四个相互关联的步骤：识别数据需求与问题、创建并记录数据清单、制定数据管理计划（Data Management Plan, DMP），以及在实践中实施该计划。该框架将数据管理的技术层面——如数据格式、元数据、质量、完整性、安全性、备份与归档——与个别工作人员及农场（农业企业）管理层的责任界定联系起来。在实践实施方面，区分了两种基本的数据存储方式：文件系统和数据平台或存储库。更为先进的基于平台的方式能够实现异构数据源的集中化、其传输与处理的自动化，并随后将所获取的数据与分析工具及决策支持系统相集成。因此，所提出的框架代表了一条从孤立收集运营数据到系统化管理并将其作为农业企业内部运营和战略决策长期信息源加以利用的路径。","Agris on-line Papers in Economics and Informatics",{"impact":19,"substance":18,"depth":161,"authority":253,"freshness":162,"relevant":21,"comment":297},"核心期刊论文，提出农业企业数据管理四步框架，方法清晰但偏理论，对农业数据治理实践有参考价值。",[299],{"name":295,"url":292},[301,26,302,28,303],"数字农业","数据治理","农业数据管理",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":307},{"candidates":308,"agricultural_evidence":44},[309,313,317],{"code":30,"score":97,"supported":44,"title":310,"abstract":311,"tags":312},[51],[],[28],{"code":55,"score":20,"supported":13,"title":314,"abstract":315,"tags":316},[],[],[301,26],{"code":318,"score":21,"supported":13,"title":319,"abstract":320,"tags":321},"I6",[],[],[302],[323,324],"农业企业 数据管理 框架","农业数据管理计划 DMP","农业企业数据管理框架-3806","10.7160\u002Faol.2026.180305",{"doi":326,"openalex_id":328,"authors":329,"venue":295,"cited_by_count":79,"oa_url":292,"card":356,"direction":287,"ingested_from":80},"W7214583099",[330,333,336,339,342,344,347,349,352,354],{"name":331,"orcid":332},"Michal Stočes","https:\u002F\u002Forcid.org\u002F0000-0001-7128-1071",{"name":334,"orcid":335},"Jan Jarolímek","https:\u002F\u002Forcid.org\u002F0000-0001-7194-3055",{"name":337,"orcid":338},"Irena Benešová","https:\u002F\u002Forcid.org\u002F0000-0002-9381-063X",{"name":340,"orcid":341},"Pavel Šimek","https:\u002F\u002Forcid.org\u002F0000-0001-9244-1476",{"name":343,"orcid":8},"Zdeňka Žáková Kroupová",{"name":345,"orcid":346},"Ludmila Pánková","https:\u002F\u002Forcid.org\u002F0000-0001-8861-4733",{"name":348,"orcid":8},"Jiří Vaněk",{"name":350,"orcid":351},"Eva Kánská","https:\u002F\u002Forcid.org\u002F0000-0001-5249-6405",{"name":353,"orcid":8},"Martin Havránek",{"name":355,"orcid":8},"Anderle Michael",{"tldr":357,"method":358,"finding":359,"direction":360,"opportunity":361},"提出农业企业数据管理框架，实现数据从孤立采集到系统化决策支持。","四步框架：识别需求、数据清单、管理计划、实施；区分文件系统与数据平台。","平台化存储可集中异构数据、自动化处理并集成决策支持系统。","数字乡村与农业信息化","可研究农业数据管理成熟度评估模型及平台落地对决策绩效的实证影响。","2026-09-29T23:31:01.874610Z",{"id":364,"title":365,"url":366,"summary":367,"summary_zh":8,"content":8,"source_name":368,"source_url":8,"published_at":369,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":370,"score_detail":371,"sources":374,"tags":376,"primary_topic":379,"secondary_topics":380,"classification":381,"search_phrases":407,"slug":410,"view_count":20,"doi":8,"paper":411,"created_at":420},3246,"Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）","https:\u002F\u002Fwww.mdpi.com\u002F3043-1204\u002F1\u002F1\u002F4","MDPI AI Precis. Agric. 1(1) 4 发表系统综述：综述2019-2026年Scopus、Web of Science及补充检索文献，考察精准农业中的应用、多模态数据集成与决策支持。综述表明研究范式从孤立的制图与预测转向集成工作流，整合卫星与无人机影像、田间传感器、气象、土壤与管理记录。报告效益包括产量预测改善、压力与病害更早检测、土壤制图更精细、灌溉与投入品更精准。然而，产量、环境和经济效益仍依赖当地条件和运行验证。核心发现是：地理参考数据本身不能确保真正的地理空间AI；可靠工作流必须解决空间依赖性、尺度不匹配、可迁移性和不确定性。可解释性和融入农场运营对可执行推荐同样重要。采用仍受数据互操作性、连接性、可负担性、隐私和技术能力限制。","MDPI AI in Precision Agriculture","2026-09-22T00:00:00Z",82,{"impact":18,"substance":372,"depth":18,"authority":96,"freshness":12,"relevant":21,"comment":373},22,"系统综述梳理2019-2026年GeoAI在精准农业的集成工作流与落地瓶颈，结论扎实、时效性强，对智慧农业技术路线有参考价值。",[375],{"name":368,"url":366},[26,167,377,28,378],"遥感","多模态数据",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":382},{"candidates":383,"agricultural_evidence":44},[384,388,393,399,403],{"code":55,"score":17,"supported":44,"title":385,"abstract":386,"tags":387},[183,184],[183],[26],{"code":30,"score":12,"supported":44,"title":389,"abstract":391,"tags":392},[390],"artificial intelligence",[],[167,28],{"code":394,"score":63,"supported":44,"title":395,"abstract":396,"tags":398},"H2",[],[397],"土壤",[],{"code":194,"score":63,"supported":44,"title":400,"abstract":401,"tags":402},[],[197],[],{"code":267,"score":21,"supported":13,"title":404,"abstract":405,"tags":406},[],[],[377],[408,409],"GeoAI 精准农业 系统综述","地理空间人工智能 精准农业","GeoAI精准农业系统综述-3246",{"doi":8,"openalex_id":8,"authors":412,"venue":8,"cited_by_count":79,"oa_url":8,"card":413,"direction":417,"ingested_from":419},[],{"tldr":414,"method":415,"finding":416,"direction":417,"opportunity":418},"系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。","系统综述Scopus、Web of Science等文献，分析多模态数据集成与决","地理参考数据不等于地理空间AI，可靠工作流须解决空间依赖、尺度、可迁移性与不确定性。","农业遥感与作物表型","可研究跨尺度空间依赖建模与可迁移性评估，提升模型在不同农场条件下的泛化能力。","agent","2026-09-23T00:04:33.172821Z",{"id":422,"title":423,"url":424,"summary":425,"summary_zh":426,"content":8,"source_name":427,"source_url":424,"published_at":428,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":429,"score_detail":430,"sources":433,"tags":435,"primary_topic":438,"secondary_topics":439,"classification":440,"search_phrases":456,"slug":459,"view_count":63,"doi":460,"paper":461,"created_at":481},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":19,"substance":17,"depth":431,"authority":253,"freshness":97,"relevant":21,"comment":432},17,"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。",[434],{"name":427,"url":424},[26,167,27,436,437],"多目标优化","水质预测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":441},{"candidates":442,"agricultural_evidence":44},[443,448,452],{"code":43,"score":16,"supported":44,"title":444,"abstract":445,"tags":447},[46],[446,46],"水产",[27],{"code":30,"score":12,"supported":44,"title":449,"abstract":450,"tags":451},[],[102,112,51],[167],{"code":55,"score":21,"supported":13,"title":453,"abstract":454,"tags":455},[],[],[26],[457,458],"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","10.3389\u002Ffsufs.2026.1827991",{"doi":460,"openalex_id":462,"authors":463,"venue":427,"cited_by_count":79,"oa_url":424,"card":476,"direction":148,"ingested_from":80},"W7213532503",[464,467,470,472,474],{"name":465,"orcid":466},"Abdel‐Haleem Abdel‐Aty","https:\u002F\u002Forcid.org\u002F0000-0002-6763-2569",{"name":468,"orcid":469},"Ali Jaber Almalki","https:\u002F\u002Forcid.org\u002F0009-0004-1359-2612",{"name":471,"orcid":8},"Sara A. Ghorashi",{"name":473,"orcid":8},"Betty Wan Niu Voon",{"name":475,"orcid":8},"Mohamed Hafez",{"tldr":477,"method":478,"finding":479,"direction":148,"opportunity":480},"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"]