[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5492":3,"related-5492":60},{"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":40,"slug":43,"view_count":15,"doi":44,"paper":45,"created_at":59},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。",null,"Komputika Jurnal Sistem Komputer","2026-10-20T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"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。",[19],{"name":10,"url":6},[21,22,23],"决策支持系统","多准则决策","发育迟缓",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":39},[31],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":38},"I2",4,true,[],[37],"decision support",[21],"农业相关性需要复核",[41,42],"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":44,"openalex_id":46,"authors":47,"venue":10,"cited_by_count":15,"oa_url":50,"card":51,"direction":57,"ingested_from":58},"W7164556015",[48],{"name":49,"orcid":9},"Nurul Ilma Hasana Kunio","https:\u002F\u002Fojs.unikom.ac.id\u002Findex.php\u002Fkomputika\u002Farticle\u002Fdownload\u002F17784\u002F6123",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"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。","农业人工智能与决策模型","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","openalex","2026-10-21T23:30:33.871610Z",{"total":61,"page":62,"page_size":61,"items":63},6,1,[64,153,225,274,331,380],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":69,"content":9,"source_name":70,"source_url":67,"published_at":71,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":79,"tags":81,"primary_topic":86,"secondary_topics":91,"classification":92,"search_phrases":135,"slug":138,"view_count":15,"doi":139,"paper":140,"created_at":152},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":74,"substance":75,"depth":76,"authority":77,"freshness":74,"relevant":62,"comment":78},8,16,15,3,"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。",[80],{"name":70,"url":67},[21,82,83,84,85],"遥感","GIS","水稻种植","土地评价",{"code":87,"slug":88,"name":89,"path":90,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":93,"locked":14,"version":27,"method":28,"evidence":94},"partial",{"candidates":95,"agricultural_evidence":34,"reason":134},[96,101,106,115,122,129],{"code":32,"score":97,"supported":34,"title":98,"abstract":99,"tags":100},12,[37],[37],[21],{"code":102,"score":97,"supported":34,"title":103,"abstract":104,"tags":105},"I4",[83],[83],[83],{"code":107,"score":13,"supported":34,"title":108,"abstract":109,"tags":114},"A1",[],[110,111,112,113],"水稻","稻田","rice","paddy",[84],{"code":116,"score":61,"supported":34,"title":117,"abstract":118,"tags":121},"A8",[],[119,120],"粮食安全","food security",[],{"code":123,"score":61,"supported":34,"title":124,"abstract":125,"tags":128},"H2",[],[126,127],"土壤","soil",[],{"code":130,"score":62,"supported":14,"title":131,"abstract":132,"tags":133},"I3",[],[],[82],"一级领域明确，二级方向需要细分",[136,137],"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":139,"openalex_id":141,"authors":142,"venue":70,"cited_by_count":15,"oa_url":67,"card":146,"direction":55,"ingested_from":58},"W7222963628",[143],{"name":144,"orcid":145},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":147,"method":148,"finding":149,"direction":150,"opportunity":151},"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":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":166,"tags":168,"primary_topic":173,"secondary_topics":177,"classification":178,"search_phrases":210,"slug":213,"view_count":15,"doi":214,"paper":215,"created_at":224},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":74,"substance":163,"depth":97,"authority":61,"freshness":164,"relevant":62,"comment":165},14,9,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[167],{"name":159,"url":156},[169,170,21,171,172],"智慧农业","农业人工智能","病害识别","灌溉管理",{"code":32,"slug":174,"name":175,"path":176,"parent_code":87},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":179,"locked":14,"version":27,"method":28,"evidence":180},"classified",{"candidates":181,"agricultural_evidence":34},[182,187,195,199,205],{"code":32,"score":183,"supported":34,"title":184,"abstract":185,"tags":186},11,[37],[],[170,21,171],{"code":188,"score":189,"supported":34,"title":190,"abstract":191,"tags":194},"H3",7,[],[192,193],"灌溉","irrigation",[172],{"code":123,"score":61,"supported":34,"title":196,"abstract":197,"tags":198},[],[126,127],[],{"code":200,"score":77,"supported":34,"title":201,"abstract":202,"tags":204},"F3",[],[203],"肥料",[],{"code":206,"score":62,"supported":14,"title":207,"abstract":208,"tags":209},"I1",[],[],[169],[211,212],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":214,"openalex_id":216,"authors":217,"venue":159,"cited_by_count":15,"oa_url":156,"card":218,"direction":223,"ingested_from":58},"W7222785888",[],{"tldr":219,"method":220,"finding":221,"direction":55,"opportunity":222},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","智慧农业 \u002F 农业物联网","2026-10-19T23:30:08.043132Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":230,"content":9,"source_name":231,"source_url":228,"published_at":232,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":233,"sources":235,"tags":237,"primary_topic":9,"secondary_topics":239,"classification":240,"search_phrases":251,"slug":254,"view_count":62,"doi":255,"paper":256,"created_at":273},5151,"XAI and its Applications in Decision Support System","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85188","Artificial Intelligence (AI) has transformed decision-making across various industries by enabling systems to analyse large volumes of data and provide intelligent recommendations. However, many modern AI models, particularly deep learning algorithms, function as \"black boxes,\" making it difficult for users to understand how decisions are generated. This lack of transparency limits the adoption of AI in critical domains such as healthcare, finance, law, and business. Explainable Artificial Intelligence (XAI) has emerged as a promising solution that improves transparency, interpretability, and trust in AI systems. Decision Support Systems (DSS) assist decision-makers by combining data, analytical models, and domain knowledge. Integrating XAI into DSS enables users to understand AI-generated recommendations, thereby improving confidence and accountability. This review paper explores the concepts of XAI, different explanation techniques, and their applications in DSS. The paper also discusses recent research, challenges, limitations, and future research directions","人工智能（Artificial Intelligence, AI）通过使系统能够分析大量数据并提供智能建议，已经改变了各行各业的决策方式。然而，许多现代AI模型，尤其是深度学习算法，作为“黑箱”运作，使用户难以理解决策是如何生成的。这种透明度的缺乏限制了AI在医疗、金融、法律和商业等关键领域的应用。可解释人工智能（Explainable Artificial Intelligence, XAI）作为一种有前景的解决方案应运而生，旨在提高AI系统的透明度、可解释性和信任度。决策支持系统（Decision Support Systems, DSS）通过结合数据、分析模型和领域知识来辅助决策者。将XAI集成到DSS中，使用户能够理解AI生成的建议，从而提高信心和问责性。本文综述探讨了XAI的概念、不同的解释技术及其在DSS中的应用。本文还讨论了近期研究、挑战、局限性以及未来研究方向。","International Journal for Research in Applied Science and Engineering Technology","2026-10-16T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":234},"该文为通用XAI与决策支持系统综述，未涉及农业场景，与三农及农业信息化无直接关联，不建议进入每日精选。",[236],{"name":231,"url":228},[170,238,21],"可解释AI",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":241},{"candidates":242,"agricultural_evidence":14,"reason":39},[243],{"code":32,"score":244,"supported":34,"title":245,"abstract":246,"tags":250},20,[37],[247,248,249,37],"深度学习","deep learning","artificial intelligence",[170,21,238],[252,253],"农业人工智能 决策支持系统 可解释AI","农业人工智能 决策支持系统","农业人工智能决策支持系统可解释AI-5151","10.22214\u002Fijraset.2026.85188",{"doi":255,"openalex_id":257,"authors":258,"venue":231,"cited_by_count":15,"oa_url":228,"card":268,"direction":223,"ingested_from":58},"W7222727160",[259,261,263,266],{"name":260,"orcid":9},"Gitin Garg",{"name":262,"orcid":9},"Kiya Jain",{"name":264,"orcid":265},"Vishal Shrivastava","https:\u002F\u002Forcid.org\u002F0000-0002-8353-2752",{"name":267,"orcid":9},"Akhil Pandey",{"tldr":269,"method":270,"finding":271,"direction":55,"opportunity":272},"综述可解释AI技术及其在决策支持系统中的应用、挑战与未来方向。","文献综述，梳理XAI解释技术与DSS集成应用。","XAI能提升DSS透明度与信任，但应用仍面临挑战与局限。","可将XAI引入农业决策支持系统，研究面向农户的可解释推荐与信任机制。","2026-10-17T23:30:08.767848Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":280,"source_url":281,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":288,"tags":290,"primary_topic":294,"secondary_topics":298,"classification":300,"search_phrases":312,"slug":315,"view_count":62,"doi":316,"paper":317,"created_at":330},4914,"Water Optimizer: A Tool for Decision Support and Policy Analysis","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413426","Water Optimizer is a tool for analyzing alternative water management strategies when the available water supply is limited. It can be used by producers to determine the profit maximizing crops to produce and the optimum amount of water to apply to each crop, given a particular water supply. It can also be used to evaluate the economic consequences of public policies which expand or reduce the amount of irrigation.","水资源优化器是一种在可用供水有限时分析替代水资源管理策略的工具。生产者可用其确定在特定供水量下能实现利润最大化的作物种类及每种作物的最优灌溉水量，也可用于评估扩大或减少灌溉面积的公共政策的经济后果。","Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43","2026-10-13T00:00:00Z",67,{"impact":97,"substance":285,"depth":75,"authority":286,"freshness":74,"relevant":62,"comment":287},18,13,"内布拉斯加大学开发的灌溉决策支持工具，方法实用但属细分领域工具类论文，公共影响有限。",[289],{"name":280,"url":281},[21,291,292,293],"水资源管理","灌溉决策","农业政策分析",{"code":188,"slug":193,"name":295,"path":296,"parent_code":297},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[299],{"code":32,"slug":174,"name":175,"path":176,"parent_code":87},{"status":179,"locked":14,"version":27,"method":28,"evidence":301},{"candidates":302,"agricultural_evidence":34},[303,308],{"code":188,"score":183,"supported":34,"title":304,"abstract":305,"tags":307},[],[192,306,193],"水资源",[291,292],{"code":32,"score":164,"supported":34,"title":309,"abstract":310,"tags":311},[37],[],[21],[313,314],"Water Optimizer 灌溉决策","内布拉斯加大学 水资源管理","WaterOptimizer灌溉决策-4914","10.22004\u002Fag.econ.413426",{"doi":316,"openalex_id":318,"authors":319,"venue":280,"cited_by_count":15,"oa_url":324,"card":325,"direction":55,"ingested_from":58},"W38113320",[320,322],{"name":321,"orcid":9},"Derrel L. Martin",{"name":323,"orcid":9},"Ray Supalla","http:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43",{"tldr":326,"method":327,"finding":328,"direction":55,"opportunity":329},"开发了Water Optimizer工具，在有限供水下优化作物种植与灌溉量以最大化利润。","基于优化模型与决策支持工具，分析不同供水策略及政策的经济影响。","该工具能帮助生产者选择利润最大化作物和最优灌溉量，并评估灌溉政策的经济后果。","可结合实时气象与土壤数据，开发动态灌溉决策模型，并拓展至多目标（经济-环境）优化。","2026-10-14T23:30:55.832240Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":338,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":339,"sources":341,"tags":343,"primary_topic":9,"secondary_topics":346,"classification":347,"search_phrases":359,"slug":362,"view_count":15,"doi":363,"paper":364,"created_at":379},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。","OpenAlex","2026-10-12T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":340},"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。",[342],{"name":337,"url":334},[344,21,345],"数字孪生","数字化转型",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":348},{"candidates":349,"agricultural_evidence":14,"reason":39},[350,354],{"code":32,"score":97,"supported":34,"title":351,"abstract":352,"tags":353},[37],[37],[21],{"code":206,"score":355,"supported":34,"title":356,"abstract":357,"tags":358},5,[],[344],[345,344],[360,361],"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":363,"openalex_id":365,"authors":366,"venue":9,"cited_by_count":15,"oa_url":373,"card":374,"direction":223,"ingested_from":58},"W7221510147",[367,370],{"name":368,"orcid":369},"Hoong Hao Yap","https:\u002F\u002Forcid.org\u002F0009-0007-0471-4253",{"name":371,"orcid":372},"Karel Kruger","https:\u002F\u002Forcid.org\u002F0000-0003-0932-2850","https:\u002F\u002Fengrxiv.org\u002Fpreprint\u002Fdownload\u002F8420\u002F13792\u002F12005",{"tldr":375,"method":376,"finding":377,"direction":55,"opportunity":378},"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。","2026-10-14T23:30:10.302822Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":9,"source_name":386,"source_url":383,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":391,"tags":393,"primary_topic":396,"secondary_topics":399,"classification":401,"search_phrases":418,"slug":421,"view_count":15,"doi":422,"paper":423,"created_at":435},4498,"SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23211381","Crop suitability with soil and climatic conditions plays a vital role in increasing productivity of agriculture. This study proposes a smart crop selection model which recommends the suitable crop based on the machine learning techniques with the input parameters of soil pH, soil temperature, soil humidity and rainfall. The data set consisted of 1000 observations and 8 different crop classes and missing values were handled, crop labels were encoded and numerical features were selected. Five-fold cross validation was used to compare the performance of the three classifiers: Random Forest, XGBoost and Support Vector Machine (SVM). Finally, a Gradient-Boosted classification model was trained and evaluated with test-set accuracy, classification metrics, Multiclass ROC analysis and feature importance. The results of the application were 93.60% of the final test accuracy and the model comparison was used to compare the three classification methods. The contribution of four environmental parameters to the crop classification was analyzed by feature-importance analysis. To facilitate practical accessibility, the entire process was carried out using an interactive dashboard developed with Streamlit, allowing for the upload of datasets, evaluation of models, selection of parameters and the generation of predictions in real time for the crops of interest. The developed system is an example of how machine learning can be integrated with an interactive decision support interface to develop a data-driven, crop selection system, relying on simple soil and weather parameters.","作物适宜性与土壤及气候条件对提高农业生产率至关重要。本研究提出了一种智能作物选择模型，该模型基于机器学习技术，以土壤pH值、土壤温度、土壤湿度和降雨量为输入参数，推荐适宜的作物。数据集包含1000个观测值和8个不同作物类别，对缺失值进行了处理，对作物标签进行了编码，并筛选了数值特征。采用五折交叉验证比较了三种分类器——随机森林（Random Forest）、XGBoost和支持向量机（Support Vector Machine, SVM）的性能。最终，训练了一个梯度提升分类模型，并通过测试集准确率、分类指标、多类ROC分析和特征重要性进行了评估。应用结果为最终测试准确率93.60%，并通过模型比较对三种分类方法进行了对比。通过特征重要性分析，探讨了四个环境参数对作物分类的贡献。为便于实际应用，整个流程通过使用Streamlit开发的交互式仪表板实现，支持数据集上传、模型评估、参数选择以及针对感兴趣作物的实时预测生成。所开发的系统展示了如何将机器学习与交互式决策支持界面相结合，构建一个数据驱动的作物选择系统，且仅依赖于简单的土壤和气象参数。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-07T00:00:00Z",69,{"impact":97,"substance":244,"depth":75,"authority":286,"freshness":74,"relevant":62,"comment":390},"基于土壤pH、温湿度与降雨的机器学习选种模型，含93.6%测试精度与Streamlit交互看板，方法完整但属常规应用型研究，产业影响有限。",[392],{"name":386,"url":383},[169,170,21,394,395],"土壤墒情","作物选择",{"code":123,"slug":127,"name":397,"path":398,"parent_code":297},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[400],{"code":32,"slug":174,"name":175,"path":176,"parent_code":87},{"status":179,"locked":14,"version":27,"method":28,"evidence":402},{"candidates":403,"agricultural_evidence":34},[404,408,414],{"code":123,"score":76,"supported":34,"title":405,"abstract":406,"tags":407},[127],[126,127],[394],{"code":32,"score":183,"supported":34,"title":409,"abstract":410,"tags":413},[],[411,412,37],"机器学习","machine learning",[170,21],{"code":206,"score":62,"supported":14,"title":415,"abstract":416,"tags":417},[],[],[169],[419,420],"土壤pH 降雨量 作物选择模型","机器学习 作物推荐 智慧农业","土壤pH降雨量作物选择模型-4498","10.5281\u002Fzenodo.23211381",{"doi":422,"openalex_id":424,"authors":425,"venue":386,"cited_by_count":15,"oa_url":383,"card":430,"direction":223,"ingested_from":58},"W7220991946",[426,428],{"name":427,"orcid":9},"C. SAI KUMAR",{"name":429,"orcid":9},"Prof. G SREENIVASULU",{"tldr":431,"method":432,"finding":433,"direction":55,"opportunity":434},"基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。","1000条8类作物数据，五折交叉验证比较RF、XGBoost、SVM，最终用梯度","梯度提升模型测试准确率达93.60%，特征重要性揭示四参数对选种的贡献。","可引入时序气象与多源土壤数据，提升模型泛化性并适配不同区域作物推荐。","2026-10-09T23:30:12.285129Z"]