[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4665":3,"related-4665":82},{"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":22,"primary_topic":9,"secondary_topics":27,"classification":28,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":81},4665,"AI-ENABLED BLOCKCHAIN ARCHITECTURE FOR TRUSTED IOT DATA MANAGEMENT ACROSS THE CHEMICAL LIFECYCLE: A REFERENCE FRAMEWORK FOR SAUDI ARABIA'S CHEMICAL INDUSTRY","https:\u002F\u002Fdoi.org\u002F10.18623\u002Frvd.v23.8485","Chemical manufacturers and distributors are increasingly dependent upon a steady stream of data from sensors, lab systems, logistics platforms and connected assets. However, when records are fragmented, unverifiable, or difficult to exchange across organisational boundaries, lifecycle decisions remain exposed to avoidable uncertainty. This review examines the integration of artificial intelligence, blockchain, and the Industrial Internet of Things within a trusted data architecture for the chemical lifecycle in Saudi Arabia. A structured integrative review synthesised peer-reviewed studies mainly published between 2020 and 2025 on industrial IoT security, edge intelligence, blockchain provenance, federated learning, digital twins, smart-contract governance, and supply chain traceability. The evidence indicates that no single technology resolves the trust problem in isolation. Artificial intelligence can spot anomalous or implausible process data, but its outputs inherit the weaknesses of the data pipeline. Blockchain can preserve event provenance and enforce multi-party rules, but high-frequency telemetry should not be directly stored on-chain. The IoT offers operational visibility but the attack surface is expanded by heterogeneous devices, legacy protocols, and weak identities. The review therefore develops a layered architecture where raw telemetry resides in governed off-chain stores, critical lifecycle events are hashed and signed to a permissioned consortium ledger, and edge or federated intelligence provides validation, anomaly detection, predictive functions, and decision support. The proposed design offers auditable event commitments that tie feedstock receipt, processing, quality release, storage, transport, downstream handoff and recovery. For organisations operating in Saudi Arabia's chemical industry, identity governance, semantic interoperability, latency-aware partitioning, privacy-preserving analytics, accountable smart contracts, and phased integration with existing operational technology are central implementation priorities. The review concludes that trusted lifecycle management is less about maximising blockchain usage and more about allocating a narrow, verifiable role for each technology within an end-to-end governance model.","化工制造商与分销商日益依赖来自传感器、实验室系统、物流平台及互联资产的稳定数据流。然而，当记录碎片化、不可验证或难以跨组织边界交换时，生命周期决策仍面临本可避免的不确定性。本综述考察了在沙特阿拉伯化工生命周期可信数据架构中，人工智能、区块链与工业物联网的集成。一项结构化整合综述综合了主要发表于2020年至2025年间的同行评审研究，涵盖工业物联网安全、边缘智能、区块链溯源、联邦学习、数字孪生、智能合约治理及供应链可追溯性。证据表明，没有任何单一技术能够孤立地解决信任问题。人工智能可以识别异常或不可信的过程数据，但其输出继承了数据管道的弱点。区块链可以保存事件溯源并执行多方规则，但高频遥测数据不应直接存储在链上。物联网提供了运营可见性，但异构设备、遗留协议和薄弱身份扩大了攻击面。因此，本综述提出了一种分层架构：原始遥测数据驻留在受治理的链下存储中，关键生命周期事件经哈希处理并签名后写入许可型联盟账本，边缘或联邦智能提供验证、异常检测、预测功能及决策支持。所提出的设计提供了可审计的事件承诺，将原料接收、加工、质量放行、储存、运输、下游交接及回收环节串联起来。对于在沙特阿拉伯化工行业运营的组织而言，身份治理、语义互操作性、延迟感知分区、隐私保护分析、可问责智能合约以及与现有运营技术的分阶段集成是核心实施优先事项。本综述得出结论：可信生命周期管理与其说在于最大化区块链的使用，不如说在于在端到端治理模型中为每种技术分配狭窄且可验证的角色。",null,"Veredas do Direito Direito Ambiental e Desenvolvimento Sustentável","2026-10-08T00:00:00Z","论文",25,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为沙特化工行业AI+区块链+IIoT可信数据架构，属工业物联网与供应链治理范畴，与三农、农业信息化、智慧农业无直接关联，相关性门槛未通过。",[19,20],{"name":10,"url":6},{"name":10,"url":21},"https:\u002F\u002Fdoi.org\u002F10.18623\u002Frvd.v23.8591",[23,24,25,26],"区块链溯源","工业物联网","化工供应链","可信数据架构",[],{"status":29,"locked":14,"version":30,"method":31,"evidence":32},"needs_review","2026-10-22.2","rules",{"candidates":33,"agricultural_evidence":14,"reason":59},[34,44,52],{"code":35,"score":36,"supported":37,"title":38,"abstract":40,"tags":43},"I5",17,true,[39],"IoT",[41,42,39],"物联网","internet of things",[],{"code":45,"score":46,"supported":37,"title":47,"abstract":48,"tags":51},"I2",6,[],[49,50],"artificial intelligence","decision support",[],{"code":53,"score":54,"supported":37,"title":55,"abstract":56,"tags":58},"I1",3,[],[57],"数字孪生",[],"农业相关性需要复核",[61,62],"沙特阿拉伯 化工 区块链","工业物联网 可信数据 架构","沙特阿拉伯化工区块链-4665",1,"10.18623\u002Frvd.v23.8485",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":15,"oa_url":72,"card":73,"direction":79,"ingested_from":80},"W7221140099",[69],{"name":70,"orcid":71},"Sathya Narayanan Lakshmi Kantham","https:\u002F\u002Forcid.org\u002F0009-0002-3197-8653","https:\u002F\u002Frevista.domhelder.edu.br\u002Findex.php\u002Fveredas\u002Farticle\u002Fdownload\u002F8485\u002F29644",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"综述AI、区块链与工业物联网融合架构，实现化工全生命周期可信数据管理。","2020-2025年文献整合综述，分析边缘智能、联邦学习、数字孪生与智能合约。","单一技术无法解决信任问题，需分层架构：链下存储原始数据、链上哈希关键事件。","其他","可将该可信数据架构迁移至农产品全链条溯源，研究边缘智能与轻量区块链的协同治理。","智慧农业 \u002F 农业物联网","openalex","2026-10-11T23:30:14.655187Z",{"total":46,"page":64,"page_size":46,"items":83},[84,177,250,348,398,466],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":9,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":98,"tags":102,"primary_topic":107,"secondary_topics":113,"classification":119,"search_phrases":154,"slug":157,"view_count":15,"doi":158,"paper":159,"created_at":176},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",73,{"impact":94,"substance":95,"depth":36,"authority":96,"freshness":15,"relevant":64,"comment":97},22,21,13,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[99,100],{"name":90,"url":87},{"name":90,"url":101},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[103,104,105,23,106],"智慧农业","农业人工智能","农业物联网","精准灌溉",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[114],{"code":35,"slug":115,"name":116,"path":117,"parent_code":118},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":120,"locked":14,"version":30,"method":31,"evidence":121},"classified",{"candidates":122,"agricultural_evidence":37},[123,129,134,139,145,150],{"code":108,"score":124,"supported":37,"title":125,"abstract":126,"tags":128},15,[109],[127,109],"灌溉",[106],{"code":35,"score":130,"supported":37,"title":131,"abstract":132,"tags":133},10,[],[41,42,39],[105],{"code":45,"score":135,"supported":37,"title":136,"abstract":137,"tags":138},4,[],[49],[104],{"code":140,"score":54,"supported":37,"title":141,"abstract":142,"tags":144},"G2",[],[143],"小农户",[],{"code":146,"score":64,"supported":14,"title":147,"abstract":148,"tags":149},"E4",[],[],[23],{"code":53,"score":64,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[103],[155,156],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":158,"openalex_id":160,"authors":161,"venue":90,"cited_by_count":15,"oa_url":87,"card":170,"direction":79,"ingested_from":80},"W7223034914",[162,165,167],{"name":163,"orcid":164},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":166,"orcid":9},"Dr.T.Baskar",{"name":168,"orcid":169},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":171,"method":172,"finding":173,"direction":174,"opportunity":175},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":130,"is_selected":14,"score":185,"score_detail":186,"sources":191,"tags":193,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":233,"slug":236,"view_count":54,"doi":237,"paper":238,"created_at":249},3889,"FROM FARM DATA TO MARKET VALUE: A SYSTEMATİC REVİEW OF PRECİSİON AGRİCULTURE TECHNOLOGİES AND DATA-DRİVEN MARKETİNG STRATEGİES","https:\u002F\u002Fdoi.org\u002F10.62425\u002Fdssh.1978979","This study examines, at a conceptual level, the potential contributions of the multi-layered data ecosystem generated by precision agriculture technologies to agricultural market strategies, drawing on existing literature. The study adopts a systematic review approach conducted under the guidance of a PRISMA flow diagram, with a staged elimination process narrowing 340 titles to 29 included sources, thereby establishing a transparent methodological foundation. The study's primary theoretical contribution is the Farm Data-to-Market Value Transformation Model (FDMV), which—for the first time in the precision agriculture literature—systematically bridges the technical focus of precision agriculture research with the consumer behaviour focus of marketing scholarship. The model integrates Akerlof's (1970) information asymmetry theory, Verlegh and Steenkamp's (1999) country-of-origin effect findings, Nuttavuthisit and Thøgersen's (2017) trust economy framework, and Kotler and Armstrong's (2016) value-based marketing perspective within a single four-stage conceptual structure, presenting this theoretical synthesis as an original contribution absent from existing precision agriculture literature. Findings indicate that IoT sensors, satellite imaging, unmanned aerial vehicles (UAVs), blockchain-based traceability systems, and decision support tools may exert significant influence on quality-based pricing, market segmentation, supply chain coordination, and consumer trust mechanisms. Assessment with specific reference to Türkiye highlights that structural barriers—including land fragmentation, digital literacy gaps, and the absence of data standardisation—are difficult to overcome without integrated policy intervention, while the country's agro-ecological diversity and geographical indication portfolio constitute a favourable foundation for data-driven market differentiation.","本研究在概念层面探讨了精准农业技术所生成的多层数据生态系统对农业市场策略的潜在贡献，并基于现有文献展开分析。研究采用系统综述方法，在PRISMA流程图指导下进行，通过分阶段筛选将340篇文献缩减至29篇纳入来源，从而建立了透明的方法论基础。本研究的主要理论贡献是提出了农场数据到市场价值转化模型（Farm Data-to-Market Value Transformation Model, FDMV），该模型首次在精准农业文献中系统性地将精准农业研究的技术焦点与营销学术界的消费者行为焦点相衔接。该模型将Akerlof（1970）的信息不对称理论、Verlegh和Steenkamp（1999）的原产国效应研究发现、Nuttavuthisit和Thøgersen（2017）的信任经济框架以及Kotler和Armstrong（2016）的价值导向营销视角整合于一个四阶段概念结构之中，并将这一理论综合呈现为现有精准农业文献中所缺失的原创性贡献。研究结果表明，物联网传感器、卫星成像、无人机（UAV）、基于区块链的可追溯系统以及决策支持工具可能对基于质量的定价、市场细分、供应链协调和消费者信任机制产生显著影响。以土耳其为具体参照的评估表明，土地细碎化、数字素养差距和数据标准化缺失等结构性障碍若无综合性政策干预则难以克服，而该国农业生态多样性和地理标志组合则构成了数据驱动市场差异化的有利基础。","Dynamics in Social Sciences and Humanities","2026-09-30T00:00:00Z",71,{"impact":187,"substance":188,"depth":36,"authority":96,"freshness":189,"relevant":64,"comment":190},12,20,9,"系统综述提出FDMV模型，将精准农业数据与市场价值衔接，理论增量明确，但属概念性研究、缺乏实证数据，产业落地影响有限。",[192],{"name":183,"url":180},[103,194,23,195,196],"精准农业","农业数据","农产品营销",{"code":35,"slug":115,"name":116,"path":117,"parent_code":118},[],{"status":120,"locked":14,"version":30,"method":31,"evidence":200},{"candidates":201,"agricultural_evidence":37},[202,208,212,218,224,228],{"code":53,"score":203,"supported":37,"title":204,"abstract":205,"tags":207},8,[],[194,206],"precision agriculture",[103,194],{"code":35,"score":46,"supported":37,"title":209,"abstract":210,"tags":211},[],[41,39],[],{"code":213,"score":54,"supported":37,"title":214,"abstract":215,"tags":217},"E5",[],[216],"地理标志",[],{"code":219,"score":54,"supported":37,"title":220,"abstract":221,"tags":223},"H6",[],[222],"农业生态",[],{"code":45,"score":54,"supported":37,"title":225,"abstract":226,"tags":227},[],[50],[],{"code":229,"score":64,"supported":14,"title":230,"abstract":231,"tags":232},"I6",[],[],[195],[234,235],"精准农业 数据驱动 营销","FDMV 模型 农业数据","精准农业数据驱动营销-3889","10.62425\u002Fdssh.1978979",{"doi":237,"openalex_id":239,"authors":240,"venue":183,"cited_by_count":15,"oa_url":180,"card":244,"direction":79,"ingested_from":80},"W7214924809",[241],{"name":242,"orcid":243},"Bülent Şen","https:\u002F\u002Forcid.org\u002F0000-0001-8561-8173",{"tldr":245,"method":246,"finding":247,"direction":174,"opportunity":248},"系统综述精准农业数据生态如何转化为市场价值，提出FDMV模型。","PRISMA系统综述，340篇筛至29篇，整合多理论构建四阶段模型。","物联网、遥感、区块链等可影响定价、细分、供应链协调与消费者信任。","可实证检验FDMV模型，并研究土地碎片化与数据标准化对数据驱动营销的制约。","2026-10-01T23:30:14.585584Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":130,"is_selected":14,"score":258,"score_detail":259,"sources":264,"tags":266,"primary_topic":270,"secondary_topics":271,"classification":272,"search_phrases":317,"slug":320,"view_count":54,"doi":321,"paper":322,"created_at":347},2775,"Digital-enabled life cycle thinking for sustainable food systems: aligning with sustainable development goals","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-63273-w","The increasing complexity of global food systems, coupled with climate change, resource scarcity, and socio-economic pressures, necessitates advanced sustainability assessment approaches. While life cycle thinking (LCT) provides a comprehensive framework, its practical application is constrained by data limitations, methodological uncertainty, and the limited ability of conventional models to capture dynamic operational conditions. This study proposes a digitally integrated LCT framework that combines artificial intelligence (AI), blockchain, internet of things (IoT), and remote sensing to enhance data accuracy, transparency, and real-time decision-making in food supply chains. A key contribution of this research is the introduction of two novel constructs, the digital trust index (DTI) and proof-of-climate impact (PCI) which quantify stakeholder trust and provide verifiable evidence linking digital interventions to measurable environmental outcomes. The DTI is conceptualized as a composite measure of stakeholder trust, integrating both digital infrastructure capabilities and governance-based trust mechanisms. Scenario analysis demonstrates that integrated digital and governance systems can achieve up to 34% climate impact reduction (CIR), while Monte Carlo simulation results indicate an average CIR of approximately 20%, confirming the robustness of the framework under uncertainty. The study reveals that SDG 12 (23%) and SDG 9 (20%) dominate digital LCT research in food systems, followed by SDG 2 and SDG 13, while biodiversity-focused goals (SDG 14 & SDG 15) receive less attention. The proposed SDG oriented framework integrates digital technologies across LCT phases, enabling real-time and transparent sustainability assessment while enhancing the monitoring of biodiversity impacts. The findings highlight that digitalization, when coupled with governance and sustainable development goal-oriented metrics, enables more transparent, adaptive, and impact-driven sustainability assessment in food systems.","全球粮食系统日益复杂，加之气候变化、资源稀缺和社会经济压力，迫切需要先进的可持续性评估方法。尽管生命周期思维（LCT）提供了全面的框架，但其实际应用受到数据限制、方法学不确定性以及传统模型捕捉动态运行条件能力有限的制约。本研究提出了一种数字化集成的LCT框架，将人工智能（AI）、区块链、物联网（IoT）和遥感相结合，以提升食品供应链中的数据准确性、透明度和实时决策能力。本研究的一项关键贡献是引入了两个新颖构念——数字信任指数（DTI）和气候影响证明（PCI），用以量化利益相关者信任，并提供可验证的证据，将数字干预与可衡量的环境成果相联系。DTI被概念化为利益相关者信任的综合度量，整合了数字基础设施能力和基于治理的信任机制。情景分析表明，集成的数字与治理系统可实现高达34%的气候影响减排（CIR），而蒙特卡洛模拟结果显示平均CIR约为20%，证实了该框架在不确定性下的稳健性。研究揭示，SDG 12（23%）和SDG 9（20%）在食品系统中的数字LCT研究中占主导地位，其次是SDG 2和SDG 13，而聚焦生物多样性的目标（SDG 14和SDG 15）受到的关注较少。所提出的面向SDG的框架将数字技术整合到LCT各阶段，实现实时、透明的可持续性评估，同时增强对生物多样性影响的监测。研究结果表明，数字化在与治理和面向可持续发展目标的指标相结合时，能够在食品系统中实现更透明、更具适应性和以影响为导向的可持续性评估。","Scientific Reports","2026-09-15T00:00:00Z",86,{"impact":94,"substance":260,"depth":261,"authority":262,"freshness":203,"relevant":64,"comment":263},23,19,14,"提出数字信任指数与气候影响证明等新构念，量化数字技术对食物系统可持续性的减排贡献，方法新颖、结论可靠，对农业信息化与绿色转型具有较强参考价值。",[265],{"name":256,"url":253},[103,104,267,268,23,269],"农业遥感","可持续发展","食物系统",{"code":35,"slug":115,"name":116,"path":117,"parent_code":118},[],{"status":120,"locked":14,"version":30,"method":31,"evidence":273},{"candidates":274,"agricultural_evidence":37},[275,279,287,293,300,304,309,313],{"code":35,"score":189,"supported":37,"title":276,"abstract":277,"tags":278},[],[41,42,39],[],{"code":280,"score":281,"supported":37,"title":282,"abstract":283,"tags":286},"I3",7,[],[284,285],"遥感","remote sensing",[267],{"code":219,"score":46,"supported":37,"title":288,"abstract":289,"tags":292},[],[290,291],"生物多样性","biodiversity",[],{"code":294,"score":46,"supported":37,"title":295,"abstract":296,"tags":299},"H7",[],[297,298],"气候变化","climate change",[],{"code":45,"score":135,"supported":37,"title":301,"abstract":302,"tags":303},[],[49],[104],{"code":305,"score":64,"supported":14,"title":306,"abstract":307,"tags":308},"E2",[],[],[269],{"code":146,"score":64,"supported":14,"title":310,"abstract":311,"tags":312},[],[],[23],{"code":53,"score":64,"supported":14,"title":314,"abstract":315,"tags":316},[],[],[103],[318,319],"农业人工智能 区块链溯源 可持续发展 农业遥感","农业人工智能 区块链溯源","农业人工智能区块链溯源可持续发展农业遥感-2775","10.1038\u002Fs41598-026-63273-w",{"doi":321,"openalex_id":323,"authors":324,"venue":256,"cited_by_count":15,"oa_url":340,"card":341,"direction":79,"ingested_from":80},"W7213239181",[325,327,330,332,334,337],{"name":326,"orcid":9},"S. U. Parvathy",{"name":328,"orcid":329},"Vysakh Kani Kolil","https:\u002F\u002Forcid.org\u002F0000-0003-2035-3439",{"name":331,"orcid":9},"Raghu Raman",{"name":333,"orcid":9},"Nripendra P. Rana",{"name":335,"orcid":336},"Sasangan Ramanathan","https:\u002F\u002Forcid.org\u002F0000-0002-7459-0934",{"name":338,"orcid":339},"Krishnashree Achuthan","https:\u002F\u002Forcid.org\u002F0000-0003-2618-0882","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63273-w_reference.pdf",{"tldr":342,"method":343,"finding":344,"direction":345,"opportunity":346},"提出融合AI、区块链、物联网与遥感的数字生命周期思维框架，用于食品系统可持续性评估。","整合AI、区块链、IoT与遥感，构建数字信任指数和气候影响证明，用情景分析与蒙特","数字与治理结合可使气候影响降低最高34%，平均约20%；SDG12和SDG9主导相关研究。","农业绿色发展与碳","可探索数字信任指数与气候影响证明在农业食品供应链中的实证验证及生物多样性目标监测。","2026-09-17T23:30:15.308110Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":9,"content":353,"source_name":354,"source_url":9,"published_at":355,"category":356,"cover_url":9,"hotness":130,"is_selected":14,"score":357,"score_detail":358,"sources":362,"tags":364,"primary_topic":369,"secondary_topics":375,"classification":376,"search_phrases":393,"slug":396,"view_count":385,"doi":9,"paper":9,"created_at":397},2262,"上海\"申农链\"区块链溯源系统累计生成溯源码超1100万个","https:\u002F\u002Fpaper.xinmin.cn\u002Fhtml\u002Fxmwb\u002F2026-09-08\u002F4\u002F247241.html","上海\"申农链\"溯源系统已有30个品类、206家规模化生产主体完成上链,累计生成溯源码超1100万个。南汇8424西瓜、外冈大米等实现\"一瓜一码\"\"一物一码\"管理。今年上半年全市规模化主体开具承诺达标合格证777.63万张,守护65.11万吨地产农产品带证上市。","上海地产农产品有可追溯“身份证”——“申农链”溯源系统已累计生成溯源码超1100万个；此外，今年上半年，全市规模化主体开具承诺达标合格证777.63万张，守护65.11万吨地产农产品带证上市。这是本报记者昨天从2026上海市食品安全宣传周市农业农村委主题日活动上获得的讯息。\n\n食品安全，一头连着农业生产端，一头连着千家万户餐桌。近年来，上海坚持“产管并重、严优并举”，不断夯实从田间到餐桌的全链条监管基础，努力实现地产农产品安全可追溯、品质看得见、监管有力度。\n\n区块链赋码：为农产品盖上“防伪”印章\n\n你知道吗？上海地产优质农产品有一个特殊的溯源码：“申农码”。这相当于一张农产品的“区块链身份证”。通过“随申办”扫一扫，消费者可以看到产品信息、合格证信息、溯源主体、安全监管、生产档案等，基于区块链技术实现可信追溯、不能篡改，“申农码”相当于为农产品盖上了“防伪”印章。\n\n近年来，上海积极做强“申农链”溯源系统。2024年，南汇8424西瓜、马陆葡萄、奉贤黄桃、光明大米、白鹤草莓等14款上海地产优质品牌农产品上链。到目前，已有30个品类、206家规模化生产主体完成上链，累计生成溯源码超1100万个。\n\n比如，市民喜爱的南汇8424西瓜已实现“一瓜一码”管理。从选种育苗、田间水肥管控、病虫害绿色防控、农残检测，一直到采摘上市，整套农事数据全部上链存证。消费者扫码就可以查阅产地地块、检测报告、采收时间，有效甄别市面上假冒的南汇8424西瓜。\n\n再以外冈大米为例，为了实现农产品的区块链溯源应用，从稻谷收割开始，生产基地会严格记录相关数据，确保了大米从收割、烘干、碾压、生产加工到销售的每一个环节都公开透明。消费者只需扫描包装上的溯源码，就可查看大米的“前世今生”。\n\n合格证护航：覆盖田间到市场关键链路\n\n农产品安全，根在田间、重在源头。今年2月1日起，《农产品质量安全承诺达标合格证管理办法》（以下简称《办法》）正式施行。一张小巧的农产品质量安全承诺达标合格证（以下简称合格证），覆盖了农产品从源头管控到消费端交付的全流程安全链路，与市民日常饮食健康息息相关。\n\n按《办法》要求，目前主要对蔬菜（含人工种植的食用菌）、水果、茶鲜叶、活畜禽、禽蛋、养殖水产品等实施合格证管理。规模化主体（生产企业、合作社）按生产批次开具合格证，小农户则鼓励检测后开证。\n\n据悉，上海9个涉农区已建成的105个镇级农产品质量安全监管站内，均已配套设立合格证服务点，实现快检、赋码、开证“一站式”服务，确保合格证真实可溯，让零散种养主体也能牢牢守住质量安全红线。\n\n市农业农村委相关负责人介绍，上半年全市规模化主体开具承诺达标合格证777.63万张，守护65.11万吨地产农产品带证上市。\n\n值得一提的是，今年8月11日起至12月，上海市农业农村、市场监管部门联合开展农产品质量安全承诺达标合格证监管执法工作，确保本市农产品“源头可溯、去向可追”。 本报记者 杨洁","新民晚报","2026-09-07T16:00:00Z","报道",69,{"impact":359,"substance":188,"depth":360,"authority":189,"freshness":46,"relevant":64,"comment":361},18,16,"上海申农链上链品类与溯源码量、合格证开具数据均为新披露，区块链溯源与合格证制度结合具行业示范价值，值得进入每日精选。",[363],{"name":354,"url":351},[365,366,23,367,368],"数字乡村","农产品质量安全","承诺达标合格证","智慧监管",{"code":370,"slug":371,"name":372,"path":373,"parent_code":374},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":120,"locked":14,"version":30,"method":31,"evidence":377},{"candidates":378,"agricultural_evidence":37},[379,384,389],{"code":370,"score":54,"supported":37,"title":380,"abstract":381,"tags":383},[],[382],"西瓜",[],{"code":146,"score":385,"supported":14,"title":386,"abstract":387,"tags":388},2,[],[],[366,23],{"code":229,"score":64,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[365],[394,395],"农产品质量安全 承诺达标合格证 区块链溯源 数字乡村","农产品质量安全 承诺达标合格证","农产品质量安全承诺达标合格证区块链溯源数字乡村-2262","2026-09-13T00:04:03.386926Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":405,"category":12,"cover_url":9,"hotness":130,"is_selected":14,"score":406,"score_detail":407,"sources":409,"tags":411,"primary_topic":415,"secondary_topics":419,"classification":420,"search_phrases":437,"slug":440,"view_count":135,"doi":441,"paper":442,"created_at":465},2147,"From Farm to Fork: Integrating Smart Farming Data with Isotopic and Spectroscopic Analysis for Food Authentication and Traceability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fpr14182884","Ensuring food authenticity, traceability, and quality has become a critical challenge in increasingly complex and globalized food supply chains. Conventional post-harvest analytical approaches, while powerful, often operate in isolation and fail to fully capture the influence of pre-harvest conditions on food composition. In parallel, the emergence of smart farming technologies has enabled the collection of high-resolution environmental and agronomic data, offering new opportunities to establish baseline signatures linked to geographical origin and production practices. This review explores the integration of pre-harvest data from precision agriculture with advanced post-harvest analytical techniques, focusing on spectroscopic and isotopic methods for food authentication. Recent advances in vibrational spectroscopy, including near- and mid-infrared, Fourier-transform infrared, and Raman techniques, alongside complementary methods such as nuclear magnetic resonance and fluorescence spectroscopy, have enabled rapid and non-destructive food fingerprinting. In parallel, isotope ratio mass spectrometry and compound-specific isotope analysis provide robust markers of origin, climate conditions, and agricultural inputs through the analysis of stable isotopes of carbon, hydrogen, oxygen, nitrogen, and sulfur. The combination of these analytical approaches with chemometric and machine learning tools facilitates the extraction of meaningful patterns from complex datasets. A central focus of this review is the development of integrated farm-to-fork frameworks that use multi-source data, including field sensor technologies, spectral fingerprints, and isotopic signatures, to enhance traceability and authentication. Applications across a wide range of food systems, including edible oils, beverages, plant-based products, and animal-derived foods, are critically evaluated to highlight the strengths and limitations of current methodologies. Key challenges related to data standardization, system interoperability, cost, portability, miniaturization and regulatory acceptance are discussed, alongside emerging solutions such as artificial intelligence-driven models, digital twins, and blockchain-enabled traceability systems. The review underscores a paradigm shift from reactive testing toward predictive and real-time food authentication systems, driven by the convergence of smart agriculture and advanced analytical chemistry. This integrated approach has the potential to significantly enhance transparency, trust, and sustainability in the global food system.","在日益复杂和全球化的食品供应链中，确保食品真实性、可追溯性和质量已成为一项关键挑战。传统的采后分析方法虽然功能强大，但往往独立运作，无法充分捕捉采前条件对食品成分的影响。与此同时，智慧农业技术的出现使得高分辨率环境和农艺数据的采集成为可能，为建立与地理来源和生产实践相关的基线特征谱提供了新机遇。本综述探讨了精准农业采前数据与先进采后分析技术的整合，重点关注用于食品鉴伪的光谱和同位素方法。振动光谱学的最新进展，包括近红外和中红外、傅里叶变换红外及拉曼技术，以及核磁共振和荧光光谱等互补方法，已使快速、无损的食品指纹分析成为可能。与此同时，同位素比值质谱（IRMS）和化合物特异性同位素分析（CSIA）通过分析碳、氢、氧、氮和硫的稳定同位素，提供了可靠的产地、气候条件和农业投入品标记。将这些分析方法与化学计量学和机器学习工具相结合，有助于从复杂数据集中提取有意义的模式。本综述的核心焦点是开发从农场到餐桌的集成框架，利用多源数据（包括田间传感器技术、光谱指纹和同位素特征）来增强可追溯性和鉴伪能力。本文对食用油、饮料、植物基产品和动物源性食品等广泛食品系统中的应用进行了批判性评估，以突出当前方法的优势和局限性。讨论了与数据标准化、系统互操作性、成本、便携性、小型化和法规接受度相关的关键挑战，以及人工智能驱动模型、数字孪生和区块链赋能可追溯系统等新兴解决方案。本综述强调了由智慧农业与先进分析化学融合所驱动的、从被动检测向预测性和实时食品鉴伪系统的范式转变。这种集成方法有望显著","Processes","2026-09-10T00:00:00Z",79,{"impact":359,"substance":94,"depth":359,"authority":96,"freshness":203,"relevant":64,"comment":408},"综述性论文，系统梳理精准农业数据与同位素\u002F光谱分析融合的农场到餐桌溯源框架，方法学视角有参考价值，但属综述而非原创突破，产业落地仍受成本与标准制约。",[410],{"name":404,"url":401},[103,412,366,23,413,414],"机器学习","光谱检测","食品溯源",{"code":45,"slug":416,"name":417,"path":418,"parent_code":118},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":120,"locked":14,"version":30,"method":31,"evidence":421},{"candidates":422,"agricultural_evidence":37},[423,428,433],{"code":53,"score":130,"supported":37,"title":424,"abstract":425,"tags":427},[],[103,194,57,426,206],"smart agriculture",[103],{"code":45,"score":130,"supported":37,"title":429,"abstract":430,"tags":432},[],[412,431,49],"machine learning",[412],{"code":146,"score":54,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[413,366,23],[438,439],"农产品质量安全 区块链溯源 光谱检测 智慧农业","农产品质量安全 区块链溯源","农产品质量安全区块链溯源光谱检测智慧农业-2147","10.3390\u002Fpr14182884",{"doi":441,"openalex_id":443,"authors":444,"venue":404,"cited_by_count":15,"oa_url":401,"card":460,"direction":79,"ingested_from":80},"W7212176948",[445,448,451,454,457],{"name":446,"orcid":447},"Maria Tarapoulouzi","https:\u002F\u002Forcid.org\u002F0000-0003-0206-4860",{"name":449,"orcid":450},"Jordi Cruz","https:\u002F\u002Forcid.org\u002F0000-0001-8191-8689",{"name":452,"orcid":453},"Yakdiel Rodríguez-Gallo","https:\u002F\u002Forcid.org\u002F0000-0002-5737-6442",{"name":455,"orcid":456},"Guillermo Medina-González","https:\u002F\u002Forcid.org\u002F0000-0002-2630-3400",{"name":458,"orcid":459},"Ioannis Pashalidis","https:\u002F\u002Forcid.org\u002F0000-0002-7587-6395",{"tldr":461,"method":462,"finding":463,"direction":79,"opportunity":464},"综述将精准农业采前数据与光谱、同位素分析结合，构建从农场到餐桌的食品溯源与真伪鉴别框架。","整合田间传感器数据、振动光谱、同位素比质谱及化学计量学与机器学习。","多源数据融合可推动食品鉴别从被动检测转向预测性、实时化体系。","可研究田间传感器数据与光谱\u002F同位素指纹的标准化融合模型，并探索数字孪生与区块链在实时溯源中的落地。","2026-09-11T23:30:10.732050Z",{"id":467,"title":468,"url":469,"summary":470,"summary_zh":9,"content":471,"source_name":472,"source_url":9,"published_at":473,"category":356,"cover_url":9,"hotness":130,"is_selected":14,"score":474,"score_detail":475,"sources":477,"tags":479,"primary_topic":483,"secondary_topics":488,"classification":489,"search_phrases":504,"slug":507,"view_count":46,"doi":9,"paper":9,"created_at":508},597,"广西田东县推动芒果产业全链条智慧化转型:智慧农运系统覆盖率 71%","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fgx\u002F202608\u002Ft20260812_8863374.htm","广西田东县林逢镇东养芒果基地建成智慧果园,集成物联网、AI 识别、区块链溯源等技术,无人机单台日均运力 12-15 吨,运输效率提升 3 倍、成本降低 40%。截至目前,全县 63 个千亩以上基地接入智慧农运系统,配备作业无人机超 280 架,覆盖率达 71%。首批 60 名学员通过粤桂协作促就业培训获 CAAC 无人机执照。","盛夏八月，右江河谷暑气蒸腾。田东县林逢镇东养芒果核心智慧示范基地里，种植大户陆秀缎没往山上走，却往屋里钻。她站在果园中控大屏前，指尖轻滑，200亩果园的土壤湿度、虫情预警、长势热力图等信息呈现在屏幕上。\n\n“以前施肥浇水全凭感觉，现在土壤‘喊渴’、虫子‘报警’，手机上都看得清清楚楚。”这位与芒果树打了多年交道的老把式，如今成了“数据控”。\n\n从凭经验种果到靠数据管园，陆秀缎的“屏幕种田”，是田东芒果产业数字化转型的缩影。当物联网、AI识别、区块链溯源等新技术嵌入种植、采收、加工、销售全链条，一颗芒果从枝头到市场，每一步都留下了“数据脚印”。\n\n1智管入园，老把式遇上新算法\n\n上午10时，艳阳高照。陆秀缎坐在自家院子的竹椅上，笑着查看手机里的果园实况，“往年要去果地观察有没有虫咬、长没长病，全靠这双眼睛。现在不用了，手机一目了然”。\n\n笑容背后，是一张在果园里织就的“科技网”：微型气象站、土壤墒情监测点、虫情AI识别设备全域布点；高清摄像头实时回传画面，水肥一体化系统精准补给；空天地一体化监测叠加AI分析，覆盖芒果全生长周期。\n\n技术落地，果农感受最直观。班彩帽边演示边说：“只需选定果树片区，设置水肥配比，山下泵房便会自动输送营养液直达树根。”\n\n数字化管护实惠算得明白：人工减少约一成，农资降了三成，亩均省三成水肥，优质果率攀升，今年产量又比去年高出20%。传统粗放种植每亩纯收入约3800元，智慧果园亩均收益稳定在5000元以上。\n\n种得好，还要运得出。祥周镇百银村芒果种植基地山高坡陡，往年采摘后全靠人工一趟趟往下扛。如今启用无人机点对点直运，单台无人机日均运力可达12—15吨，运输效率提升3倍，成本降低四成，商品果损耗率下降18%。\n\n运力上去了，飞手队伍也在壮大。田东县首批参加粤桂协作促就业培训的60名学员，日前通过考核，拿到了民航局CAAC无人机执照和植保专项技能证书。\n\n截至目前，田东县已有63个千亩以上基地接入智慧农运系统，配备作业无人机超280架，覆盖率达71%。\n\n2码上溯源，好果子卖出好身价\n\n好芒果下山，第一件事是“验明正身”。\n\n在平马镇小龙村芒果标准园打包车间，工人们正给一箱箱芒果贴上二维码标签。“以前最怕假货砸招牌。”田东县小龙村臻芒合作社理事长黄忠伟拿起一箱芒果说：“现在一扫码，承诺达标合格证、产品产地、质量认证、检测报告、主体信息、农事记录全跳出来，从枝头到舌尖，透明得跟玻璃似的。”\n\n这张“数字身份证”背后，是田东县大力推行的产地承诺达标合格证制度。全县35家芒果绿色食品认证经营主体、17.7万亩认证面积，年产绿色认证芒果20万吨，筑牢“百色芒果”地理标志品质防线。\n\n但好果子不该只卖原料价。鲜友食品智能化车间里，芒果排着队“体检”——AI分拣线机器一扫，果型、着色、果径即刻分档，速度是人工的5倍。分级后的果子随即送入智能恒温冷链仓储，保鲜期延长12天，赶早市变成了错峰卖，好果子再也不怕卖不上价。\n\n车间另一头，自动化生产线正把芒果“吃干榨尽”：果肉变果干、果浆、芒果冰棒，一颗芒果衍生出十多种形态。田东县已引进培育精深加工企业5家，建成20多条自动化生产线，年加工鲜果能力超过8万吨。\n\n3云上出圈，新农人闯出新市场\n\n田间车间忙完了，直播间里又热闹起来。\n\n晚8时，平马镇百林村，新农人黄尚善支起三脚架，补光灯把满桌芒果照得透亮。“家人们，这是咱们百色田东的桂七芒，标准的S型身材还带个小翘嘴，核薄肉厚……”他对着镜头侃侃而谈。而谁能想到，这个如今张口就来产品卖点的年轻人，2021年第一次直播时，对着镜头半晌憋不出一句整话。\n\n“近两个月，直播芒果带货营业额近80万元。”黄尚善说，“手机成了新农具，直播成了新农活。我自己就是果农，对着镜头讲得出门道，观众看得明白，买得放心。”\n\n一个人走得快，一群人走得远。为壮大新农人队伍，6月25日，田东县组织50名芒果种植户、电商从业者、合作社负责人及乡村创业者，参加了2026年粤桂协作百色芒果“新农人”传播人才培育活动。系统教学后，学员们又在芒果标准园开展实地直播带货练习，现学现卖。\n\n“2025年，芒果线上销售额突破7.6亿元。”田东县电子商务协会会长蒙天庚说，“今年我们提前对接各大电商平台、直播团队、外地采购商，持续优化销售渠道，完善分级筛选、冷链保鲜、物流配送体系，同时规范果品分级标准和包装标准，销售额预计会有新的突破”。\n\n从枝头到舌尖，从果园到云端，数字技术正让右江河谷的芒果产业焕发出新的生机。","中国农业信息网 2026-08-12 田东数字农业","2026-08-12T00:00:00Z",74,{"impact":359,"substance":94,"depth":359,"authority":130,"freshness":46,"relevant":64,"comment":476},"报道田东县芒果产业全链条智慧化转型，含具体数据与案例，具行业参考价值，但属地方性进展，时效性稍弱。",[478],{"name":472,"url":469},[480,481,482,23],"数字农业","无人机","芒果",{"code":484,"slug":485,"name":486,"path":487,"parent_code":374},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit",[],{"status":120,"locked":14,"version":30,"method":31,"evidence":490},{"candidates":491,"agricultural_evidence":37},[492,496,500],{"code":484,"score":187,"supported":37,"title":493,"abstract":494,"tags":495},[482],[482],[482],{"code":35,"score":54,"supported":37,"title":497,"abstract":498,"tags":499},[],[41],[],{"code":53,"score":64,"supported":14,"title":501,"abstract":502,"tags":503},[],[],[480],[505,506],"区块链溯源 数字农业 无人机 芒果","区块链溯源 数字农业","区块链溯源数字农业无人机芒果-597","2026-08-16T23:55:38.819914Z"]