[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4664":3,"related-4664":79},{"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":54,"slug":57,"view_count":22,"doi":58,"paper":59,"created_at":78},4664,"Realising the potential of ecological digital twins as decision support tools","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolmodel.2026.111867","Ecological digital twins are continually updating models used for decision-making. Current progress in ecological digital twinning has focused on overcoming the technological and modelling challenges to create a continually updating system. We argue that attention should now also focus on effectively integrating digital twins into decision-making systems to inform timely action. This requires the direct inclusion of end users and greater focus on the socio-political context. We outline challenges faced in developing such ‘translational’ digital twins from our own experience and translate recommendations for best practice from the transdisciplinary research literature. We champion co-creation as a powerful approach for future translational digital twin projects, especially in larger and more complex research teams.","生态数字孪生（ecological digital twins）是持续更新的模型，用于决策支持。当前生态数字孪生领域的进展主要集中于克服技术与建模方面的挑战，以构建一个持续更新的系统。我们认为，如今还应关注如何将数字孪生有效整合到决策系统中，以支持及时行动。这要求直接纳入终端用户，并更加重视社会政治背景。我们根据自身经验概述了开发此类“转化型”数字孪生所面临的挑战，并从跨学科研究文献中提炼出最佳实践建议。我们倡导将共同创造（co-creation）作为未来转化型数字孪生项目的有力方法，尤其是在规模更大、更为复杂的研究团队中。",null,"Ecological Modelling","2026-10-09T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,18,14,8,1,"核心期刊论文，提出生态数字孪生从技术构建转向决策支持与共同创造，方法视角新颖，对智慧农业决策系统建设有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","决策支持","生态数字孪生",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":53},[38,45],{"code":39,"score":13,"supported":40,"title":41,"abstract":43,"tags":44},"I2",true,[42],"decision support",[],[28,29],{"code":46,"score":47,"supported":40,"title":48,"abstract":49,"tags":52},"I1",7,[],[50,51],"数字孪生","digital twin",[27],"农业相关性需要复核",[55,56],"生态数字孪生 决策支持","Ecological Modelling 数字孪生","生态数字孪生决策支持-4664","10.1016\u002Fj.ecolmodel.2026.111867",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":68,"oa_url":69,"card":70,"direction":76,"ingested_from":77},"W7221174270",[62,65],{"name":63,"orcid":64},"Anna M. Davison","https:\u002F\u002Forcid.org\u002F0000-0003-4249-3424",{"name":66,"orcid":67},"Koen de Koning","https:\u002F\u002Forcid.org\u002F0000-0002-2586-0184",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0304380026003947\u002Fpdf",{"tldr":71,"method":72,"finding":73,"direction":74,"opportunity":75},"论文主张生态数字孪生应转向决策支持，通过共创整合终端用户与社会政治背景。","基于跨学科研究经验，提出共创方法及最佳实践建议。","生态数字孪生需从技术开发转向决策整合，共创是关键路径。","农业人工智能与决策模型","可研究农业数字孪生中终端用户参与机制及社会政治因素对决策采纳的影响。","智慧农业 \u002F 农业物联网","openalex","2026-10-11T23:30:13.457966Z",{"total":80,"page":22,"page_size":80,"items":81},6,[82,158,233,295,358,413],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":94,"tags":96,"primary_topic":99,"secondary_topics":105,"classification":106,"search_phrases":140,"slug":143,"view_count":22,"doi":144,"paper":145,"created_at":157},4799,"Applications of Neutrosophic Topology in the Indian Context: A Review and Emerging Research Directions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23318355","AbstractNeutrosophic topology is an extension of classical topology designed to represent informationcharacterized by truth, indeterminacy, and falsity simultaneously. Unlike classical mathematicalmodels, which generally assume that an element either belongs or does not belong to a set,neutrosophic approaches permit the coexistence of partial truth, uncertainty, and contradiction. Thisfeature makes neutrosophic topology potentially useful for modelling complex systems in whichinformation is incomplete, inconsistent, imprecise, or dynamically changing. India, with its largeand heterogeneous population, geographically diverse environment, rapidly expanding digitalinfrastructure, and complex decision-making systems, provides several important contexts in whichsuch mathematical frameworks may be investigated. This article reviews the development ofneutrosophic topology and examines its potential and emerging applications in the Indian context.Particular attention is given to geographical information systems, medical diagnosis, data mining,decision-making, smart-city management, environmental monitoring, agricultural informationsystems, transportation networks, and information systems. Existing Indian contributionsdemonstrate that neutrosophic topological structures are increasingly being studied in connectionwith data analysis and decision problems. The article also identifies limitations in current research,particularly the gap between theoretical topological constructions and validated real-world datasets.Finally, several future research directions are proposed, including integration with geographicinformation systems, artificial intelligence, Internet-of-Things data, remote sensing, healthcareanalytics, and multi-criteria decision-making. The study concludes that neutrosophic topology offersa promising mathematical framework for representing uncertainty and inconsistency in Indian socio-technical and environmental systems, provided that future research emphasizes empirical validation and computational implementation. Keywords: Neutrosophic topology; neutrosophic sets; uncertainty; indeterminacy; fuzzy topology; GIS; decision making; data mining; India; mathematical modelling.","摘要：中智拓扑学（Neutrosophic topology）是经典拓扑学的扩展，旨在同时表示由真、不确定和假所刻画的信息。与通常假设元素要么属于某集合、要么不属于某集合的经典数学模型不同，中智方法允许部分真、不确定性和矛盾同时存在。这一特性使中智拓扑学在建模信息不完整、不一致、不精确或动态变化的复杂系统方面具有潜在价值。印度拥有庞大且异质的人口、地理多样的环境、快速扩展的数字基础设施以及复杂的决策系统，为研究此类数学框架提供了若干重要场景。本文综述了中智拓扑学的发展，并考察其在印度语境下的潜力和新兴应用。特别关注地理信息系统、医学诊断、数据挖掘、决策、智慧城市管理、环境监测、农业信息系统、交通网络和信息系统等领域。印度已有的研究成果表明，中智拓扑结构正日益被用于数据分析和决策问题的研究。本文还指出了当前研究的局限性，尤其是理论拓扑构造与经过验证的现实世界数据集之间的差距。最后，提出了若干未来研究方向，包括与地理信息系统、人工智能、物联网数据、遥感、医疗分析和多准则决策的整合。研究认为，中智拓扑学为表示印度社会技术系统和环境系统中的不确定性与不一致性提供了一个有前景的数学框架，前提是未来研究应重视实证验证和计算实现。关键词：中智拓扑学；中智集；不确定性；不确定；模糊拓扑学；地理信息系统；决策；数据挖掘；印度；数学建模。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-12T00:00:00Z",57,{"impact":21,"substance":20,"depth":92,"authority":17,"freshness":21,"relevant":22,"comment":93},15,"综述性论文，将中智拓扑引入印度农业信息系统、遥感与决策场景，理论价值明确但缺乏实证数据，属细分领域参考。",[95],{"name":88,"url":85},[27,28,97,98,29],"农业信息化","遥感",{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":107,"locked":14,"version":34,"method":35,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":40},[110,115,122,128,135],{"code":100,"score":47,"supported":40,"title":111,"abstract":112,"tags":114},[],[98,113],"remote sensing",[98],{"code":116,"score":80,"supported":40,"title":117,"abstract":118,"tags":121},"I4",[],[119,120],"地理信息","GIS",[],{"code":39,"score":123,"supported":40,"title":124,"abstract":125,"tags":127},5,[],[126],"artificial intelligence",[28,29],{"code":129,"score":130,"supported":40,"title":131,"abstract":132,"tags":134},"I5",3,[],[133],"物联网",[],{"code":46,"score":136,"supported":14,"title":137,"abstract":138,"tags":139},2,[],[],[97,27],[141,142],"中智拓扑 农业信息系统","农业人工智能 农业信息化 决策支持 智慧农业","中智拓扑农业信息系统-4799","10.5281\u002Fzenodo.23318355",{"doi":144,"openalex_id":146,"authors":147,"venue":88,"cited_by_count":68,"oa_url":85,"card":152,"direction":76,"ingested_from":77},"W7221514584",[148,150],{"name":149,"orcid":9},"N. Raksha Ben",{"name":151,"orcid":9},"G. Helen Rajapushpam",{"tldr":153,"method":154,"finding":155,"direction":74,"opportunity":156},"综述中智拓扑在印度多领域的应用潜力，并指出理论与实证数据脱节的局限。","文献综述，梳理中智拓扑在GIS、决策、农业信息系统等场景的应用。","中智拓扑能刻画不确定性，但缺乏真实数据验证与计算实现。","可将中智拓扑与农业物联网、遥感数据结合，构建可验证的不确定性决策模型。","2026-10-13T23:30:08.455353Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":9,"source_name":164,"source_url":161,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":174,"secondary_topics":178,"classification":179,"search_phrases":207,"slug":210,"view_count":136,"doi":211,"paper":212,"created_at":232},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":17,"substance":19,"depth":168,"authority":13,"freshness":21,"relevant":22,"comment":169},16,"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。",[171],{"name":164,"url":161},[27,28,133,29,173],"人机交互",{"code":39,"slug":175,"name":176,"path":177,"parent_code":104},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":107,"locked":14,"version":34,"method":35,"evidence":180},{"candidates":181,"agricultural_evidence":40},[182,189,195,200],{"code":39,"score":183,"supported":40,"title":184,"abstract":186,"tags":188},27,[185,42],"machine learning",[187,185,42],"机器学习",[28,29],{"code":46,"score":13,"supported":40,"title":190,"abstract":191,"tags":194},[],[27,192,193],"精准农业","precision agriculture",[27],{"code":129,"score":47,"supported":40,"title":196,"abstract":197,"tags":199},[],[133,198],"IoT",[133],{"code":201,"score":80,"supported":40,"title":202,"abstract":203,"tags":206},"H3",[],[204,205],"灌溉","irrigation",[],[208,209],"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":211,"openalex_id":213,"authors":214,"venue":164,"cited_by_count":68,"oa_url":161,"card":227,"direction":76,"ingested_from":77},"W7214922384",[215,218,220,222,224],{"name":216,"orcid":217},"Adam Faturahman","https:\u002F\u002Forcid.org\u002F0000-0001-9727-9092",{"name":219,"orcid":9},"James Williams",{"name":221,"orcid":9},"Evelyn Anderson",{"name":223,"orcid":9},"Goh Yao",{"name":225,"orcid":226},"Muhammad Rifat Syauqy","https:\u002F\u002Forcid.org\u002F0009-0001-4582-5693",{"tldr":228,"method":229,"finding":230,"direction":76,"opportunity":231},"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":234,"title":235,"url":236,"summary":237,"summary_zh":9,"content":9,"source_name":238,"source_url":236,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":244,"tags":246,"primary_topic":249,"secondary_topics":255,"classification":257,"search_phrases":278,"slug":281,"view_count":130,"doi":282,"paper":283,"created_at":294},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":21,"substance":20,"depth":92,"authority":80,"freshness":242,"relevant":22,"comment":243},9,"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。",[245],{"name":238,"url":236},[27,28,29,247,248],"遥感监测","小麦产量",{"code":250,"slug":251,"name":252,"path":253,"parent_code":254},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[256],{"code":39,"slug":175,"name":176,"path":177,"parent_code":104},{"status":107,"locked":14,"version":34,"method":35,"evidence":258},{"candidates":259,"agricultural_evidence":40},[260,265,270,274],{"code":39,"score":261,"supported":40,"title":262,"abstract":263,"tags":264},13,[42],[42],[28,29],{"code":250,"score":266,"supported":40,"title":267,"abstract":268,"tags":269},11,[251],[251],[],{"code":46,"score":22,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[27],{"code":100,"score":22,"supported":14,"title":275,"abstract":276,"tags":277},[],[],[247],[279,280],"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":282,"openalex_id":284,"authors":285,"venue":238,"cited_by_count":68,"oa_url":236,"card":9,"direction":74,"ingested_from":77},"W7214624863",[286,289,292],{"name":287,"orcid":288},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":290,"orcid":291},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":293,"orcid":9},"Asel Kartanova","2026-09-29T23:31:02.997580Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":9,"content":9,"source_name":300,"source_url":9,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":306,"tags":308,"primary_topic":311,"secondary_topics":316,"classification":317,"search_phrases":345,"slug":348,"view_count":130,"doi":9,"paper":349,"created_at":357},3247,"From Phenotyping to Supervised Agentic Decision Support: A Review of Sensing and Artificial Intelligence for Greenhouse Strawberry Cultivation（从表型到监督智能体决策支持：温室草莓栽培的传感与人工智能综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37426142","MDPI Horticulturae 12(7) 765 发表综述：温室草莓栽培越来越依赖传感技术、人工智能和决策支持基础设施，但其在园艺上的价值取决于异构测量能否转化为生物学上有意义的作物状态和实际管理决策。本综述综合草莓表型、多模态传感、AI作物状态解释和监督智能体协调，提出表型到行动的温室草莓栽培框架。综述研究表明，通过成像、光谱、环境、根区和建模方法在测量和解释营养、生殖、果实品质、压力和环境作物状态方面取得实质性进展。然而，大部分文献仍强调测量精度、模型性能或基础设施能力，而较少研究验证AI输出是否改善作物响应、管理决策、工作流、资源使用或生产结果。综述区分数据采集传感技术与AI解释预测方法，并研究作物状态信息如何与实际温室决策相连接。","MDPI Horticulturae","2026-09-21T00:00:00Z",77,{"impact":19,"substance":18,"depth":304,"authority":261,"freshness":242,"relevant":22,"comment":305},17,"系统梳理温室草莓从表型感知到智能体决策支持的研究进展，指出AI输出缺乏生产结果验证这一关键缺口，对智慧园艺研究有参考价值。",[307],{"name":300,"url":298},[27,28,29,309,310],"温室草莓","多模态传感",{"code":312,"slug":313,"name":314,"path":315,"parent_code":9},"B","horticulture","园艺作物","\u002Ftopic\u002Fhorticulture",[],{"status":318,"locked":14,"version":34,"method":35,"evidence":319},"partial",{"candidates":320,"agricultural_evidence":40,"reason":344},[321,329,336,340],{"code":322,"score":323,"supported":40,"title":324,"abstract":327,"tags":328},"B1",19,[325,326],"草莓","strawberry",[325],[],{"code":330,"score":323,"supported":40,"title":331,"abstract":334,"tags":335},"B7",[332,333],"温室","greenhouse",[332],[],{"code":39,"score":19,"supported":40,"title":337,"abstract":338,"tags":339},[126,42],[],[28,29],{"code":46,"score":22,"supported":14,"title":341,"abstract":342,"tags":343},[],[],[27],"一级领域明确，二级方向需要细分",[346,347],"温室草莓 人工智能 决策支持","草莓表型 多模态传感","温室草莓人工智能决策支持-3247",{"doi":9,"openalex_id":9,"authors":350,"venue":9,"cited_by_count":68,"oa_url":9,"card":351,"direction":76,"ingested_from":356},[],{"tldr":352,"method":353,"finding":354,"direction":76,"opportunity":355},"综述温室草莓从表型感知到监督智能体决策支持的传感与AI技术，提出表型到行动框架。","综合草莓表型、多模态传感、AI状态解释与监督智能体协调的文献综述。","感知与AI解释进展显著，但少有研究验证AI输出是否真正改善作物响应与管理决策。","可研究AI决策输出对温室草莓实际管理、资源利用与产量结果的闭环验证与效果评估。","agent","2026-09-23T00:04:33.259764Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":9,"content":9,"source_name":363,"source_url":9,"published_at":364,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":365,"score_detail":366,"sources":369,"tags":371,"primary_topic":373,"secondary_topics":374,"classification":375,"search_phrases":400,"slug":403,"view_count":136,"doi":9,"paper":404,"created_at":412},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":19,"substance":367,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":368},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。",[370],{"name":363,"url":361},[27,28,98,29,372],"多模态数据",{"code":39,"slug":175,"name":176,"path":177,"parent_code":104},[],{"status":107,"locked":14,"version":34,"method":35,"evidence":376},{"candidates":377,"agricultural_evidence":40},[378,382,386,392,396],{"code":46,"score":18,"supported":40,"title":379,"abstract":380,"tags":381},[192,193],[192],[27],{"code":39,"score":13,"supported":40,"title":383,"abstract":384,"tags":385},[126],[],[28,29],{"code":387,"score":130,"supported":40,"title":388,"abstract":389,"tags":391},"H2",[],[390],"土壤",[],{"code":201,"score":130,"supported":40,"title":393,"abstract":394,"tags":395},[],[204],[],{"code":100,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[98],[401,402],"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":9,"openalex_id":9,"authors":405,"venue":9,"cited_by_count":68,"oa_url":9,"card":406,"direction":410,"ingested_from":356},[],{"tldr":407,"method":408,"finding":409,"direction":410,"opportunity":411},"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。","2026-09-23T00:04:33.172821Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":9,"source_name":419,"source_url":416,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":424,"tags":426,"primary_topic":429,"secondary_topics":430,"classification":431,"search_phrases":448,"slug":451,"view_count":130,"doi":452,"paper":453,"created_at":476},2038,"Trustworthy AutoML indicators for climate-smart agriculture: A sustainability-governance framework for decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101480","Trustworthy AutoML indicators for climate-smart agriculture: A sustainability-governance framework for decision support。Environmental and Sustainability Indicators","面向气候智慧型农业的可信自动机器学习指标：面向决策支持的可持续性治理框架。《环境与可持续性指标》","Environmental and Sustainability Indicators","2026-09-09T00:00:00Z",60,{"impact":21,"substance":168,"depth":92,"authority":261,"freshness":21,"relevant":22,"comment":423},"核心期刊论文，提出面向气候智慧型农业的可信AutoML指标与可持续治理决策框架，方法有新意但属学术探讨，公共影响有限。",[425],{"name":419,"url":416},[27,28,29,427,428],"气候智慧型农业","可持续治理",{"code":39,"slug":175,"name":176,"path":177,"parent_code":104},[],{"status":107,"locked":14,"version":34,"method":35,"evidence":432},{"candidates":433,"agricultural_evidence":40},[434,438,443],{"code":39,"score":168,"supported":40,"title":435,"abstract":436,"tags":437},[42],[187,42],[28,29],{"code":46,"score":17,"supported":40,"title":439,"abstract":441,"tags":442},[440],"smart agriculture",[440],[27],{"code":444,"score":22,"supported":14,"title":445,"abstract":446,"tags":447},"H7",[],[],[427],[449,450],"气候智慧型农业 农业人工智能 可持续治理 决策支持","气候智慧型农业 农业人工智能","气候智慧型农业农业人工智能可持续治理决策支持-2038","10.1016\u002Fj.indic.2026.101480",{"doi":452,"openalex_id":454,"authors":455,"venue":419,"cited_by_count":68,"oa_url":416,"card":471,"direction":76,"ingested_from":77},"W7212009872",[456,459,461,463,465,468],{"name":457,"orcid":458},"Amar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-5567-6521",{"name":460,"orcid":9},"Sukhbir Singh Walia",{"name":462,"orcid":9},"Ajay Singh",{"name":464,"orcid":9},"Shakti Kumar",{"name":466,"orcid":467},"Sahraoui Dhelim","https:\u002F\u002Forcid.org\u002F0000-0002-3620-1395",{"name":469,"orcid":470},"Chatter Singh","https:\u002F\u002Forcid.org\u002F0009-0005-6154-9237",{"tldr":472,"method":473,"finding":474,"direction":74,"opportunity":475},"提出面向气候智慧型农业的可信AutoML指标体系与可持续治理框架，用于决策支持。","构建可信AutoML指标与可持续治理框架，结合环境可持续性指标。","可信AutoML指标可支撑气候智慧型农业的可持续治理决策。","可探索可信AutoML指标在农业碳核算与气候适应决策中的实证验证与落地。","2026-09-10T23:30:09.176914Z"]