[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4882":3,"related-4882":70},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":46,"slug":49,"view_count":15,"doi":50,"paper":51,"created_at":69},4882,"A systematic review of digital twins as decision support systems: a classification framework and identification of organisational constraints","https:\u002F\u002Fdoi.org\u002F10.31224\u002F8420","Digital twins (DTs) are increasingly framed as decision support systems (DSSs), yet no framework has systematically examined how DT configuration shapes the decision support they provide. This study presents a systematic literature review of 299 studies from four databases, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to examine how DT configuration influences decision support and value realisation. We develop a four-dimensional classification framework comprising abstraction level, DSS mechanism, decision level, and value type, and statistically test the relationships between these dimensions. The results show that DTs support decisions through four distinct mechanisms that differ in how information flows and how involved the decision maker remains. Model-driven DTs, which compute answers through embedded models, take on more of the reasoning themselves, whereas knowledge-driven DTs, which let users explore scenarios, keep decision makers actively involved. Whether a DT delivers value depends on organisational conditions as much as on its technical capability. Fully automated decisions are rare and occur mainly in model-driven DTs, suggesting that governance and accountability, rather than technology, limit automation. Model-driven DTs are also seldom used for long-term strategic decisions, as are DTs that represent only a single component rather than a wider system or enterprise. These patterns have been stable over time and do not appear to be driven by the uneven spread of studies across industries. Overall, the findings indicate that DT value depends on alignment with organisational decision contexts rather than technical sophistication alone. This paper highlights organisational and governance factors that cannot be captured by purely technical analyses.","数字孪生(DTs)日益被定位为决策支持系统(DSSs)，然而尚无框架系统性地考察数字孪生配置如何塑造其提供的决策支持。本研究对来自四个数据库的299项研究进行了系统性文献综述，采用系统综述和荟萃分析首选报告条目(PRISMA)来考察数字孪生配置如何影响决策支持与价值实现。我们构建了一个包含抽象层次、决策支持系统机制、决策层次和价值类型四个维度的分类框架，并对这些维度之间的关系进行了统计检验。结果表明，数字孪生通过四种不同的机制支持决策，这些机制在信息流动方式以及决策者的参与程度上存在差异。模型驱动的数字孪生通过内嵌模型计算答案，自身承担了更多推理工作；而知识驱动的数字孪生允许用户探索情景，使决策者保持积极参与。数字孪生能否创造价值，既取决于其技术能力，也取决于组织条件。完全自动化的决策较为罕见，且主要出现在模型驱动的数字孪生中，这表明限制自动化的是治理与问责机制，而非技术本身。模型驱动的数字孪生也很少用于长期战略决策，仅表征单一组件而非更广泛系统或企业的数字孪生同样如此。这些模式随时间保持稳定，且似乎并非由各行业研究分布不均所驱动。总体而言，研究结果表明，数字孪生的价值取决于其与组织决策情境的匹配，而非仅取决于技术先进性。本文强调了纯技术分析无法捕捉的组织与治理因素。",null,"OpenAlex","2026-10-12T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"数字孪生决策支持的系统综述属通用工业管理研究，未涉及三农或农业信息化场景，与平台主题不相关。",[19],{"name":10,"url":6},[21,22,23],"数字孪生","决策支持系统","数字化转型",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":45},[31,39],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":38},"I2",12,true,[36],"decision support",[36],[22],{"code":40,"score":41,"supported":34,"title":42,"abstract":43,"tags":44},"I1",5,[],[21],[23,21],"农业相关性需要复核",[47,48],"决策支持系统 数字化转型 数字孪生","决策支持系统 数字化转型","决策支持系统数字化转型数字孪生-4882","10.31224\u002F8420",{"doi":50,"openalex_id":52,"authors":53,"venue":9,"cited_by_count":15,"oa_url":60,"card":61,"direction":67,"ingested_from":68},"W7221510147",[54,57],{"name":55,"orcid":56},"Hoong Hao Yap","https:\u002F\u002Forcid.org\u002F0009-0007-0471-4253",{"name":58,"orcid":59},"Karel Kruger","https:\u002F\u002Forcid.org\u002F0000-0003-0932-2850","https:\u002F\u002Fengrxiv.org\u002Fpreprint\u002Fdownload\u002F8420\u002F13792\u002F12005",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"系统综述299项研究，构建数字孪生作为决策支持系统的四维分类框架并识别组织约束。","PRISMA系统综述299篇文献，四维分类框架与统计检验。","数字孪生价值取决于组织决策情境匹配，而非技术先进性；全自动决策罕见。","农业人工智能与决策模型","农业数字孪生决策支持的组织与治理约束研究尚缺，可探索农户采纳与责任机制。","智慧农业 \u002F 农业物联网","openalex","2026-10-14T23:30:10.302822Z",{"total":71,"page":72,"page_size":71,"items":73},6,1,[74,157,198,282,361,413],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":9,"content":9,"source_name":79,"source_url":77,"published_at":80,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":88,"tags":90,"primary_topic":93,"secondary_topics":98,"classification":99,"search_phrases":119,"slug":122,"view_count":123,"doi":124,"paper":125,"created_at":156},1273,"OurMED D6.3 GoAigua Digital Twins Tool at the Farm, Basin and MED Scale. Version 1.2","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22142119","This deliverable, D6.3 GoAigua Digital Twins Tool at the plot, basin and MED scale, presents the development and integration of the OurMED Digital Twin framework implemented within WP6 - Digital Twins and Decision Support Systems. The framework combines EO products, in situ observations, modelling outputs, citizen-science contributions, and smart agriculture services into interoperable digital platforms supporting near real-time monitoring, forecasting, environmental assessment, and decision-making.","Zenodo (CERN European Organization for Nuclear Research)","2026-08-28T00:00:00Z",69,{"impact":83,"substance":84,"depth":85,"authority":33,"freshness":86,"relevant":72,"comment":87},15,20,18,4,"欧盟项目交付物，整合多源数据构建农业数字孪生框架，具行业参考价值，但时效性较低。",[89],{"name":79,"url":77},[91,92,21,22],"智慧农业","农业遥感",{"code":32,"slug":94,"name":95,"path":96,"parent_code":97},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":100,"locked":14,"version":27,"method":28,"evidence":101},"classified",{"candidates":102,"agricultural_evidence":34},[103,110,114],{"code":40,"score":104,"supported":34,"title":105,"abstract":106,"tags":109},8,[],[107,108],"smart agriculture","digital twin",[21,91],{"code":32,"score":86,"supported":34,"title":111,"abstract":112,"tags":113},[],[36],[22],{"code":115,"score":72,"supported":14,"title":116,"abstract":117,"tags":118},"I3",[],[],[92],[120,121],"决策支持系统 农业遥感 数字孪生 智慧农业","决策支持系统 农业遥感","决策支持系统农业遥感数字孪生智慧农业-1273",2,"10.5281\u002Fzenodo.22142119",{"doi":124,"openalex_id":126,"authors":127,"venue":79,"cited_by_count":15,"oa_url":77,"card":151,"direction":67,"ingested_from":68},"W7204518076",[128,130,133,136,139,142,145,148],{"name":129,"orcid":9},"Isabel Augscheller",{"name":131,"orcid":132},"Maximilian Schwarz","https:\u002F\u002Forcid.org\u002F0000-0001-8059-5697",{"name":134,"orcid":135},"Patrícia Lourenço","https:\u002F\u002Forcid.org\u002F0000-0002-4901-0934",{"name":137,"orcid":138},"Ainhoa Maté Marín","https:\u002F\u002Forcid.org\u002F0000-0002-1125-6381",{"name":140,"orcid":141},"Anis Guelmami","https:\u002F\u002Forcid.org\u002F0000-0002-3906-6424",{"name":143,"orcid":144},"Nina Bègue","https:\u002F\u002Forcid.org\u002F0009-0000-1749-2177",{"name":146,"orcid":147},"Amir Rouhani","https:\u002F\u002Forcid.org\u002F0000-0002-7976-3558",{"name":149,"orcid":150},"Seifeddine Jomaa","https:\u002F\u002Forcid.org\u002F0000-0003-4782-9468",{"tldr":152,"method":153,"finding":154,"direction":67,"opportunity":155},"开发了跨地块、流域和地中海尺度的农业数字孪生工具，集成多源数据支持实时监测与决策。","集成EO产品、实地观测、模型输出、公民科学和智慧农业服务，构建互操作数字平台。","数字孪生框架支持近实时监测、预报、环境评估和决策，覆盖多尺度。","可探索数字孪生与具体作物模型耦合，提升地块尺度精准管理，并验证跨尺度可扩展性。","2026-09-01T04:03:13.243484Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":165,"sources":167,"tags":169,"primary_topic":9,"secondary_topics":172,"classification":173,"search_phrases":180,"slug":183,"view_count":15,"doi":184,"paper":185,"created_at":197},5492,"Implementasi Metode VIsekriterijumsko KOmpromisno Rangiranje dalam Sistem Pendukung Keputusan Identifikasi Keluarga Berisiko Stunting","https:\u002F\u002Fdoi.org\u002F10.34010\u002Fkomputika.v15i1.17784","Stunting remains a significant chronic nutritional problem in Palembang City, where the identification and management of families at risk of stunting are still conducted manually, potentially leading to inaccuracies and delays in decision-making. This condition results in suboptimal interventions for the targeted families. This study utilizes a dataset comprising 400 families obtained from the Palembang City Office of Population and Family Planning (DPPKB) in 2024 as the basis for developing a Decision Support System (DSS). The method applied is VIse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), one of the approaches in Multi-Criteria Decision Making (MCDM) that can objectively integrate various risk-determining factors. The main criteria used include sources of drinking water, sanitation facilities, categories of couples of childbearing age, and participation in the Family Planning program. The results show that the family coded E obtained the highest index value of 1.00, categorized as high stunting risk, while the family coded B scored 0.00, indicating a low-risk level. The developed system is capable of providing more accurate and objective rankings of at-risk families, thereby assisting the DPPKB in determining precise intervention priorities. Thus, the implementation of the VIKOR method in this decision support system makes an important contribution to enhancing the effectiveness of stunting reduction acceleration programs in Palembang City. The contribution of this research lies in the application of the VIKOR method to support data-driven policies for accelerating stunting reduction in Palembang City.","发育迟缓仍然是巨港市一个重要的慢性营养问题，该市对发育迟缓风险家庭的识别和管理仍以人工方式进行，可能导致不准确和决策延迟。这种情况导致对目标家庭的干预效果不佳。本研究利用2024年从巨港市人口与计划生育局（DPPKB）获取的包含400个家庭的数据集，作为开发决策支持系统（DSS）的基础。所采用的方法是VIKOR法（多准则优化与妥协解，VIse Kriterijumska Optimizacija Kompromisno Resenje），这是多准则决策（MCDM）中的一种方法，能够客观地整合各种风险决定因素。所使用的主要准则包括饮用水来源、卫生设施、育龄夫妇类别以及计划生育项目参与情况。结果表明，编码为E的家庭获得了最高指数值1.00，被归类为高发育迟缓风险，而编码为B的家庭得分为0.00，表明为低风险水平。所开发的系统能够对风险家庭进行更准确、更客观的排序，从而协助DPPKB确定精准的干预优先次序。因此，在该决策支持系统中实施VIKOR方法对提升巨港市加速降低发育迟缓项目的有效性做出了重要贡献。本研究的贡献在于应用VIKOR方法支持巨港市加速降低发育迟缓的数据驱动政策。","Komputika Jurnal Sistem Komputer","2026-10-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":166},"该研究聚焦印尼巨港市发育迟缓风险家庭的决策支持系统，属于公共卫生信息化范畴，与三农、农业信息化、数字乡村等主题无直接关联，不建议进入每日精选。",[168],{"name":163,"url":160},[22,170,171],"多准则决策","发育迟缓",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":174},{"candidates":175,"agricultural_evidence":14,"reason":45},[176],{"code":32,"score":86,"supported":34,"title":177,"abstract":178,"tags":179},[],[36],[22],[181,182],"巨港 发育迟缓 风险家庭","VIKOR 决策支持系统","巨港发育迟缓风险家庭-5492","10.34010\u002Fkomputika.v15i1.17784",{"doi":184,"openalex_id":186,"authors":187,"venue":163,"cited_by_count":15,"oa_url":190,"card":191,"direction":196,"ingested_from":68},"W7164556015",[188],{"name":189,"orcid":9},"Nurul Ilma Hasana Kunio","https:\u002F\u002Fojs.unikom.ac.id\u002Findex.php\u002Fkomputika\u002Farticle\u002Fdownload\u002F17784\u002F6123",{"tldr":192,"method":193,"finding":194,"direction":65,"opportunity":195},"用VIKOR多准则决策法构建决策支持系统，识别巨港市高风险发育迟缓家庭。","基于400户家庭数据，采用VIKOR多准则决策方法，以饮水、卫生等为准则。","系统能更准确客观地对风险家庭排序，辅助确定精准干预优先级。","可将VIKOR等MCDM方法扩展至乡村健康与农业信息化融合的多指标决策场景。","数字乡村与农业信息化","2026-10-21T23:30:33.871610Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":206,"score_detail":207,"sources":211,"tags":213,"primary_topic":218,"secondary_topics":222,"classification":223,"search_phrases":264,"slug":267,"view_count":15,"doi":268,"paper":269,"created_at":281},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":104,"substance":208,"depth":83,"authority":209,"freshness":104,"relevant":72,"comment":210},16,3,"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[212],{"name":204,"url":201},[22,214,215,216,217],"遥感","GIS","水稻种植","土地评价",{"code":97,"slug":219,"name":220,"path":221,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":224,"locked":14,"version":27,"method":28,"evidence":225},"partial",{"candidates":226,"agricultural_evidence":34,"reason":263},[227,231,236,245,252,259],{"code":32,"score":33,"supported":34,"title":228,"abstract":229,"tags":230},[36],[36],[22],{"code":232,"score":33,"supported":34,"title":233,"abstract":234,"tags":235},"I4",[215],[215],[215],{"code":237,"score":13,"supported":34,"title":238,"abstract":239,"tags":244},"A1",[],[240,241,242,243],"水稻","稻田","rice","paddy",[216],{"code":246,"score":71,"supported":34,"title":247,"abstract":248,"tags":251},"A8",[],[249,250],"粮食安全","food security",[],{"code":253,"score":71,"supported":34,"title":254,"abstract":255,"tags":258},"H2",[],[256,257],"土壤","soil",[],{"code":115,"score":72,"supported":14,"title":260,"abstract":261,"tags":262},[],[],[214],"一级领域明确，二级方向需要细分",[265,266],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":268,"openalex_id":270,"authors":271,"venue":204,"cited_by_count":15,"oa_url":201,"card":275,"direction":65,"ingested_from":68},"W7222963628",[272],{"name":273,"orcid":274},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":276,"method":277,"finding":278,"direction":279,"opportunity":280},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","农业遥感与作物表型","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":9,"source_name":288,"source_url":285,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":295,"tags":297,"primary_topic":300,"secondary_topics":303,"classification":304,"search_phrases":343,"slug":346,"view_count":15,"doi":347,"paper":348,"created_at":360},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development",80,{"impact":85,"substance":291,"depth":85,"authority":292,"freshness":293,"relevant":72,"comment":294},22,13,9,"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[296],{"name":288,"url":285},[91,298,240,21,299],"农业人工智能","病虫害预警",{"code":237,"slug":242,"name":240,"path":301,"parent_code":302},"\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":100,"locked":14,"version":27,"method":28,"evidence":305},{"candidates":306,"agricultural_evidence":34},[307,314,318,325,332,339],{"code":40,"score":308,"supported":34,"title":309,"abstract":310,"tags":313},19,[108],[311,21,312,108],"精准农业","precision agriculture",[21,91],{"code":237,"score":85,"supported":34,"title":315,"abstract":316,"tags":317},[243],[240,241,242,243],[240],{"code":319,"score":71,"supported":34,"title":320,"abstract":321,"tags":324},"F4",[],[322,323],"农药","pesticide",[],{"code":326,"score":71,"supported":34,"title":327,"abstract":328,"tags":331},"H3",[],[329,330],"灌溉","irrigation",[],{"code":333,"score":71,"supported":34,"title":334,"abstract":335,"tags":338},"I5",[],[336,337],"物联网","IoT",[],{"code":32,"score":86,"supported":34,"title":340,"abstract":341,"tags":342},[],[36],[298],[344,345],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":347,"openalex_id":349,"authors":350,"venue":288,"cited_by_count":15,"oa_url":9,"card":355,"direction":67,"ingested_from":68},"W7222969403",[351,353],{"name":352,"orcid":9},"Jyoti Rani",{"name":354,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":356,"method":357,"finding":358,"direction":65,"opportunity":359},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","2026-10-20T23:30:12.636299Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":79,"source_url":364,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":9,"secondary_topics":375,"classification":376,"search_phrases":397,"slug":400,"view_count":15,"doi":401,"paper":402,"created_at":412},5317,"PLANT-GPK v4.0: A Unified Time-Discovery Framework for Multi-Stage Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460911","This paper presents a comprehensive extension to the PLANT-GPK framework, demonstrating its robust capability to analyze plants across entirely different developmental stages from single images. By formulating an inverse problem that simultaneously optimizes physiological parameters and the hidden time dimension (t), the model successfully discovered the exact age of two distinct wheat samples: a vegetative (green) stage and a senescent (golden) stage. The framework bridges computer vision, spatial heterogeneity analysis, and ODE-based physics to provide a complete digital twin capable of forecasting future harvest windows regardless of the plant's current state.","本文对PLANT-GPK框架进行了全面扩展，展示了其仅凭单张图像即可分析处于完全不同发育阶段植物的稳健能力。通过构建一个同时优化生理参数与隐藏时间维度（t）的反问题，该模型成功发现了两个不同小麦样本的精确年龄：一个处于营养期（绿色阶段），另一个处于衰老期（金色阶段）。该框架融合了计算机视觉、空间异质性分析以及基于常微分方程（ODE）的物理学，提供了一个完整的数字孪生体，无论植物当前处于何种状态，均能预测未来的收获窗口。",81,{"impact":85,"substance":291,"depth":85,"authority":292,"freshness":13,"relevant":72,"comment":369},"该研究提出统一时间发现框架，可从单幅图像反演植物生育阶段并预测收获窗口，方法新颖且具数字孪生应用潜力，值得进入每日精选。",[371],{"name":79,"url":364},[91,298,373,21,374],"小麦","植物表型",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":377},{"candidates":378,"agricultural_evidence":34,"reason":396},[379,383,390],{"code":40,"score":104,"supported":34,"title":380,"abstract":381,"tags":382},[],[21,108],[21,91],{"code":384,"score":385,"supported":34,"title":386,"abstract":387,"tags":389},"A2",7,[],[373,388],"wheat",[373],{"code":32,"score":385,"supported":34,"title":391,"abstract":392,"tags":395},[],[393,394],"计算机视觉","computer vision",[298],"多个产业证据相当，需要确认主归属",[398,399],"PLANT-GPK 植物表型","小麦 数字孪生 生育期","PLANT-GPK植物表型-5317","10.5281\u002Fzenodo.23460911",{"doi":401,"openalex_id":403,"authors":404,"venue":79,"cited_by_count":15,"oa_url":364,"card":407,"direction":279,"ingested_from":68},"W7222895088",[405],{"name":406,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":408,"method":409,"finding":410,"direction":279,"opportunity":411},"扩展PLANT-GPK框架，从单张图像反演植物生理参数与隐藏时间维度，实现跨发育阶段表型分析。","基于ODE物理模型的反问题优化，联合估计生理参数与时间t，用两个小麦样本验证。","模型成功发现营养期与衰老期小麦的精确年龄，可预测收获窗口。","单图跨阶段时间反演可延伸至多作物多生育期数字孪生与收获期预测的通用化研究。","2026-10-19T23:30:19.498798Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":9,"source_name":79,"source_url":416,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":419,"score_detail":420,"sources":422,"tags":424,"primary_topic":425,"secondary_topics":429,"classification":430,"search_phrases":441,"slug":443,"view_count":15,"doi":444,"paper":445,"created_at":454},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。",79,{"impact":85,"substance":291,"depth":85,"authority":33,"freshness":293,"relevant":72,"comment":421},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[423],{"name":79,"url":416},[91,298,21,311,374],{"code":40,"slug":426,"name":427,"path":428,"parent_code":97},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":100,"locked":14,"version":27,"method":28,"evidence":431},{"candidates":432,"agricultural_evidence":34},[433,437],{"code":40,"score":84,"supported":34,"title":434,"abstract":435,"tags":436},[108],[311,21,312],[21,91,311],{"code":32,"score":72,"supported":14,"title":438,"abstract":439,"tags":440},[],[],[298],[398,442],"空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":444,"openalex_id":446,"authors":447,"venue":79,"cited_by_count":15,"oa_url":416,"card":449,"direction":279,"ingested_from":68},"W7222885652",[448],{"name":406,"orcid":9},{"tldr":450,"method":451,"finding":452,"direction":279,"opportunity":453},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z"]