[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2108":3,"related-2108":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":32,"classification":33,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":66,"created_at":76},2108,"《AI in Agriculture: Transforming the Food System from Data to Decisions to Action》 美国密西西比州立CAST论文","https:\u002F\u002Fpublications.cast-science.org\u002FCAST\u002Fen\u002Farticle\u002Fview\u002F245","密西西比州立大学农业自主研究所所长Thomasson发表CAST评论,系统论述AI正成为美国食物系统的基础技术,深度嵌入田间装备、生产系统、食物加工、供应链与自然资源管理。引入\"物理AI\"概念——能在生物变异性与环境不确定性下感知、推理、行动的AI系统,农业是其最严苛的试验场。生成式AI被视为让先进分析民主化的力量。约束AI负责任采用的核心不是技术,而是制度——数据质量、田间验证、推荐透明度、网络安全与互操作性。",null,"Council for Agricultural Science and Technology (CAST)","2026-09-08T00:00:00Z","论文",10,false,86,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},24,22,18,14,8,1,"CAST权威评论系统提出物理AI概念并指出制度而非技术是负责任采用的核心约束，观点新颖、信息增量大，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30,31],"智慧农业","农业人工智能","数据治理","食物系统","物理AI","生成式AI",[],{"status":34,"locked":13,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":59,"reason":60},[39,44,49,54],{"code":40,"score":21,"supported":13,"title":41,"abstract":42,"tags":43},"E2",[],[],[29],{"code":45,"score":21,"supported":13,"title":46,"abstract":47,"tags":48},"I1",[],[],[26],{"code":50,"score":21,"supported":13,"title":51,"abstract":52,"tags":53},"I2",[],[],[27],{"code":55,"score":21,"supported":13,"title":56,"abstract":57,"tags":58},"I6",[],[],[28],true,"缺少足够的主题证据",[62,63],"农业人工智能 数据治理 智慧农业 食物系统","农业人工智能 数据治理","农业人工智能数据治理智慧农业食物系统-2108",4,{"doi":8,"openalex_id":8,"authors":67,"venue":8,"cited_by_count":68,"oa_url":8,"card":69,"direction":73,"ingested_from":75},[],0,{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"系统论述AI正成为美国食物系统基础技术，提出物理AI概念并强调制度约束。","CAST评论文章，基于田间装备、供应链等应用场景的系统分析。","约束AI负责任采用的核心是制度而非技术，如数据质量与互操作性。","农业人工智能与决策模型","物理AI在生物变异与环境不确定性下的感知推理行动机制，及制度适配研究。","agent","2026-09-11T00:04:22.740798Z",{"total":78,"page":21,"page_size":78,"items":79},6,[80,161,247,341,410,471],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":8,"source_name":86,"source_url":83,"published_at":87,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":94,"tags":96,"primary_topic":99,"secondary_topics":105,"classification":106,"search_phrases":132,"slug":135,"view_count":68,"doi":136,"paper":137,"created_at":160},4663,"Sensor Data Transmission in Smart Agriculture: Challenges, Potentials, and Limitations in the Fourth Industrial Revolution","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsignals7050098","Smart agriculture is a key application domain of the Fourth Industrial Revolution (4IR), where cyber–physical systems, the Internet of Things (IoT), and data-driven analytics converge to improve productivity, sustainability, and resource efficiency. In this context, large volumes of sensor data collected in the field form the basis for automated monitoring, decision support, and adaptive control across agricultural processes. However, the value of such data depends not only on sensing technologies and connectivity, but also on how data are generated, transferred, processed, and interpreted throughout their lifecycle. This paper is organized in two parts. The first part reports a narrative review of the literature, based on a documented search protocol, and provides an overview of sensor data and sensor data transmission networks in smart agriculture. It examines the main challenges, potentials, and limitations associated with IoT-based field deployments in 4IR environments, and consolidates them into ten open problems. These problems are ordered from the device layer to the socio-economic layer and span physical robustness, device security, rural connectivity, aerial sensing, interoperability, labelled-data scarcity, explainability, federated access control, data ownership, and cost. The second part responds to these open problems with the conceptualization of a layered architecture and a set of design proposals, covering hierarchical edge security, offline-first synchronization, semantic interoperability, explainable decision support, and farmer-centric data governance, that are grounded in established techniques and standards but have not been implemented or empirically validated in this work; they are intended as a design blueprint and a starting point for future prototyping and evaluation rather than as measured results. The paper is accordingly positioned as a conceptual, perspective-oriented contribution: it states the procedure by which the open problems were derived from the reviewed works, it states what the proposed integration is claimed to add over existing smart-agriculture architectures, and it sets out an evaluation plan that fixes the metrics and the baselines against which each proposed component would have to be tested. Throughout both parts, particular attention is given to the role of data integrity and provenance as foundational elements for trustworthy agricultural data, enabling reliable analysis, auditability, and long-term reuse. By synthesizing current approaches, open issues, and conceptual solution patterns, this paper aims to support the design of resilient, transparent, and future-ready smart agriculture systems aligned with the principles of the 4IR.","智慧农业是第四次工业革命（4IR）的关键应用领域，信息物理系统、物联网（IoT）和数据驱动分析在此汇聚，以提升生产力、可持续性和资源效率。在此背景下，田间采集的大量传感器数据构成了农业过程自动化监测、决策支持和自适应控制的基础。然而，此类数据的价值不仅取决于传感技术和连接能力，还取决于数据在其生命周期中如何生成、传输、处理和解释。本文分为两部分。第一部分基于有据可查的检索方案，对文献进行叙述性综述，概述智慧农业中的传感器数据及传感器数据传输网络。它考察了4IR环境下基于物联网的田间部署所面临的主要挑战、潜力和局限，并将其归纳为十个开放性问题。这些问题从设备层到社会经济层依次排列，涵盖物理鲁棒性、设备安全、农村连接、空中传感、互操作性、标注数据稀缺、可解释性、联邦访问控制、数据所有权和成本。第二部分针对这些开放性问题，提出了分层架构的概念化和一组设计建议，涵盖分层边缘安全、离线优先同步、语义互操作性、可解释决策支持以及以农户为中心的数据治理。这些建议以已有技术和标准为基础，但尚未在本文中实现或经过实证验证；它们旨在作为设计蓝图和未来原型开发与评估的起点，而非实测结果。因此，本文定位为概念性、视角导向的贡献：它说明了开放性问题如何从所综述的工作中推导出来的过程，阐明了所提出的集成方案相对于现有智慧农业架构声称增加的贡献，并提出了一个评估计划，确定了每个所提组件必须接受测试的指标和基线。在两部分中，特别关注数据完整性和来源作为可信农业数据基础要素的作用，从而实现可靠分析、可审计性和长期复用。","Signals","2026-10-10T00:00:00Z",71,{"impact":90,"substance":18,"depth":91,"authority":92,"freshness":20,"relevant":21,"comment":93},15,17,13,"系统梳理智慧农业传感器数据传输的十大开放问题并提出分层架构蓝图，属概念性综述，有参考价值但未经实证验证。",[95],{"name":86,"url":83},[26,27,97,28,98],"农业物联网","传感器网络",{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":107,"locked":13,"version":35,"method":36,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":59},[110,115,122,127],{"code":45,"score":90,"supported":59,"title":111,"abstract":113,"tags":114},[112],"smart agriculture",[26,112],[26],{"code":100,"score":12,"supported":59,"title":116,"abstract":117,"tags":121},[],[118,119,120],"物联网","internet of things","IoT",[97],{"code":50,"score":65,"supported":59,"title":123,"abstract":124,"tags":126},[],[125],"decision support",[27],{"code":55,"score":65,"supported":59,"title":128,"abstract":129,"tags":131},[],[130],"agricultural data",[28],[133,134],"智慧农业 传感器数据传输","农业物联网 数据完整性","智慧农业传感器数据传输-4663","10.3390\u002Fsignals7050098",{"doi":136,"openalex_id":138,"authors":139,"venue":86,"cited_by_count":68,"oa_url":83,"card":153,"direction":157,"ingested_from":159},"W7221365687",[140,142,145,148,150],{"name":141,"orcid":8},"Alvertos Ioannis Mourikis",{"name":143,"orcid":144},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":146,"orcid":147},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":149,"orcid":8},"Romanos Kalamatianos",{"name":151,"orcid":152},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":154,"method":155,"finding":156,"direction":157,"opportunity":158},"综述智慧农业传感器数据传输挑战，归纳十大开放问题并提出分层架构设计蓝图。","文献综述与概念架构设计，基于搜索协议梳理IoT部署问题。","从设备到社会经济层归纳十大开放问题，提出边缘安全、语义互操作等设计提案。","智慧农业 \u002F 农业物联网","所提分层架构尚未实现与实证，可开展原型验证并量化数据完整性、溯源与离线同步效果。","openalex","2026-10-11T23:30:08.094185Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":8,"source_name":167,"source_url":164,"published_at":168,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":169,"score_detail":170,"sources":173,"tags":175,"primary_topic":178,"secondary_topics":182,"classification":183,"search_phrases":219,"slug":222,"view_count":65,"doi":223,"paper":224,"created_at":246},3621,"Beyond Precision Agriculture: AI and Data-driven Information Systems as Catalysts for Sustainable Agribusiness Transformation","https:\u002F\u002Fdoi.org\u002F10.56557\u002Fjgembr\u002F2026\u002Fv18i311156","The dominant narrative in digital agriculture centres on discrete technologies, including sensors, unmanned aerial vehicles, global positioning systems, remote sensing, and machine learning, evaluated primarily for their capacity to increase yields and improve input-use efficiency. This narrows the analytical lens to the farm gate and understates a more consequential shift: individual technologies generate value for agribusiness only when information systems capture, integrate, and route the data they produce into real operational and strategic decisions. This review critically examines the AI- and data-driven agribusiness transformation literature through a single organising pathway: data, analytics, artificial intelligence, information systems, decisions, sustainable agribusiness outcomes; it asks whether the field's technical achievements have matched comparable progress in systems integration and governance. A transparent narrative search and appraisal protocol synthesised evidence across ten thematic domains: agricultural big-data integration, predictive and prescriptive analytics, farm management information systems (FMIS), AI-enabled supply-chain analytics, climate-risk adaptation, sustainability and ESG analytics, data governance and cybersecurity, digital infrastructure and adoption, circular agriculture, and decision-support systems. The evidence shows that sensing and algorithmic capabilities have matured faster than the organisational and governance capacity required to convert those capabilities into adopted agribusiness decisions. Data-governance barriers, rather than technological ones, now dominate constraints on information-system integration in the literature published since 2023. Research, investment, and policy attention should therefore shift further toward the interoperability, governance, and organisational design layers of digital agriculture, rather than continuing to concentrate primarily on sensor accuracy or model performance.","数字农业的主流叙事聚焦于各类离散技术，包括传感器、无人机、全球定位系统、遥感和机器学习，并主要依据其提高产量和改善投入品利用效率的能力加以评价。这种视角将分析视野收窄至农场大门，低估了一个更具深远意义的转变：只有当信息系统能够捕获、整合单个技术所产生的数据，并将其导入实际运营和战略决策时，这些技术才能为农业企业创造价值。本综述通过一条单一的组织路径——数据、分析、人工智能、信息系统、决策、可持续农业企业成果——批判性地审视人工智能和数据驱动的农业企业转型文献，并追问该领域的技术成就是否与系统集成和治理方面的相应进展相匹配。一项透明的叙述性检索与评价方案综合了十个主题领域的证据：农业大数据集成、预测性与规范性分析、农场管理信息系统(FMIS)、人工智能赋能的供应链分析、气候风险适应、可持续性与ESG分析、数据治理与网络安全、数字基础设施与采用、循环农业以及决策支持系统。证据表明，感知和算法能力的成熟速度快于将这些能力转化为已被采用的农业企业决策所需的组织和治理能力。在2023年以来发表的文献中，数据治理障碍而非技术障碍，已成为信息系统集成的主要制约因素。因此，研究、投资和政策关注应进一步转向数字农业的互操作性、治理和组织设计层面，而非继续主要集中于传感器精度或模型性能。","Journal of Global Economics Management and Business Research","2026-09-26T00:00:00Z",80,{"impact":18,"substance":17,"depth":18,"authority":92,"freshness":171,"relevant":21,"comment":172},9,"该综述指出数据治理与系统集成而非传感技术本身才是数字农业落地的关键瓶颈，观点有实质增量，但属学术综述、产业影响偏间接。",[174],{"name":167,"url":164},[26,27,176,28,177],"农业大数据","农业信息体系",{"code":50,"slug":179,"name":180,"path":181,"parent_code":104},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":107,"locked":13,"version":35,"method":36,"evidence":184},{"candidates":185,"agricultural_evidence":59},[186,193,200,207,212],{"code":45,"score":90,"supported":59,"title":187,"abstract":189,"tags":192},[188],"precision agriculture",[190,191],"数字农业","digital agriculture",[26],{"code":50,"score":12,"supported":59,"title":194,"abstract":195,"tags":199},[],[196,197,198],"机器学习","machine learning","artificial intelligence",[27],{"code":201,"score":78,"supported":59,"title":202,"abstract":203,"tags":206},"I3",[],[204,205],"遥感","remote sensing",[],{"code":55,"score":208,"supported":59,"title":209,"abstract":210,"tags":211},5,[],[176],[176,28],{"code":213,"score":214,"supported":59,"title":215,"abstract":216,"tags":218},"H8",3,[],[217],"可持续农业",[],[220,221],"农业信息体系 数据治理","农场管理信息系统 FMIS","农业信息体系数据治理-3621","10.56557\u002Fjgembr\u002F2026\u002Fv18i311156",{"doi":223,"openalex_id":225,"authors":226,"venue":167,"cited_by_count":68,"oa_url":164,"card":239,"direction":245,"ingested_from":159},"W7214471201",[227,229,231,233,235,237],{"name":228,"orcid":8},"Timilehin Fawibe",{"name":230,"orcid":8},"Gbenga Samuel Ernest",{"name":232,"orcid":8},"Joshua Emmanuel",{"name":234,"orcid":8},"Simon Jacob",{"name":236,"orcid":8},"Olawale Akinleye",{"name":238,"orcid":8},"Tosin Olorunwo",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"综述指出农业数字化瓶颈已从技术转向数据治理与系统集成，呼吁研究重心转向互操作与组织设计。","采用透明叙事检索与评价协议，综合十大主题领域的AI与数据驱动农业文献。","2023年后数据治理障碍而非技术障碍，成为农业信息系统集成的主要制约。","数字乡村与农业信息化","可研究农业数据互操作标准、治理机制与组织采纳路径如何影响可持续农业商业决策。","农业遥感与作物表型","2026-09-27T23:30:46.092194Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":8,"source_name":253,"source_url":250,"published_at":254,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":255,"sources":259,"tags":261,"primary_topic":265,"secondary_topics":266,"classification":267,"search_phrases":310,"slug":313,"view_count":214,"doi":314,"paper":315,"created_at":340},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",{"impact":17,"substance":256,"depth":257,"authority":19,"freshness":20,"relevant":21,"comment":258},23,19,"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。",[260],{"name":253,"url":250},[26,27,262,263,264,29],"农业遥感","可持续发展","区块链溯源",{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},[],{"status":107,"locked":13,"version":35,"method":36,"evidence":268},{"candidates":269,"agricultural_evidence":59},[270,274,279,286,293,297,301,306],{"code":100,"score":171,"supported":59,"title":271,"abstract":272,"tags":273},[],[118,119,120],[],{"code":201,"score":275,"supported":59,"title":276,"abstract":277,"tags":278},7,[],[204,205],[262],{"code":280,"score":78,"supported":59,"title":281,"abstract":282,"tags":285},"H6",[],[283,284],"生物多样性","biodiversity",[],{"code":287,"score":78,"supported":59,"title":288,"abstract":289,"tags":292},"H7",[],[290,291],"气候变化","climate change",[],{"code":50,"score":65,"supported":59,"title":294,"abstract":295,"tags":296},[],[198],[27],{"code":40,"score":21,"supported":13,"title":298,"abstract":299,"tags":300},[],[],[29],{"code":302,"score":21,"supported":13,"title":303,"abstract":304,"tags":305},"E4",[],[],[264],{"code":45,"score":21,"supported":13,"title":307,"abstract":308,"tags":309},[],[],[26],[311,312],"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":314,"openalex_id":316,"authors":317,"venue":253,"cited_by_count":68,"oa_url":333,"card":334,"direction":157,"ingested_from":159},"W7213239181",[318,320,323,325,327,330],{"name":319,"orcid":8},"S. U. Parvathy",{"name":321,"orcid":322},"Vysakh Kani Kolil","https:\u002F\u002Forcid.org\u002F0000-0003-2035-3439",{"name":324,"orcid":8},"Raghu Raman",{"name":326,"orcid":8},"Nripendra P. Rana",{"name":328,"orcid":329},"Sasangan Ramanathan","https:\u002F\u002Forcid.org\u002F0000-0002-7459-0934",{"name":331,"orcid":332},"Krishnashree Achuthan","https:\u002F\u002Forcid.org\u002F0000-0003-2618-0882","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63273-w_reference.pdf",{"tldr":335,"method":336,"finding":337,"direction":338,"opportunity":339},"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":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":348,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":349,"score_detail":350,"sources":352,"tags":354,"primary_topic":358,"secondary_topics":359,"classification":360,"search_phrases":385,"slug":388,"view_count":214,"doi":389,"paper":390,"created_at":409},2292,"A governance-first framework integrates artificial intelligence and blockchain to advance sustainable and inclusive horticultural value chains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1894057","Horticultural value chains face persistent agronomic challenges including production inefficiencies, postharvest losses, poor resource management, and limited supply-chain transparency, particularly in smallholder-dominated systems across the Global South. These constraints undermine agricultural productivity, market access, and the long-term sustainability of horticultural farming. Despite growing research interest in digital agriculture, artificial intelligence (AI) and blockchain technology are increasingly explored together, including in emerging integrated AI-blockchain-IoT architectures for agricultural traceability. However, these existing integrations remain predominantly technology-centric, prioritizing traceability, automation, and data verification, without an explicit governance, equity, or data-sovereignty architecture designed specifically for smallholder-dominated horticultural production and distribution systems. This study proposes a comprehensive conceptual framework that integrates AI’s predictive and decision-support capabilities with blockchain’s data integrity, traceability, and automation features to enhance sustainability, inclusivity, and governance in horticultural value chains. Drawing on an integrative review of peer-reviewed sources identified through Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar, and grounded in the Technology-Organisation-Environment (TOE) framework, Sociotechnical Systems Theory, and Sustainable Digital Transformation Theory, the study addresses the agronomic, governance, and equity dimensions of digital transformation in horticulture. The framework is structured around five interlinked operational layers, data collection, AI-driven analytics and decision-making, blockchain validation and traceability, smart contracts and decision support, and user interface and stakeholder feedback, all operating within a governance architecture and ethical design layer that functions as a structural precondition for the framework rather than a downstream feature. This governing layer embeds algorithmic transparency, data sovereignty through community-governed data cooperatives, and inclusive digital access for smallholder farmers as non-negotiable design principles. The framework explicitly aligns with Sustainable Development Goals 2, 9, 10, 12, 13, and 16, and includes a three-phase empirical validation pathway comprising expert Delphi elicitation, simulation modeling, and pilot implementation in horticultural cooperatives. The integration of AI and blockchain within an institutionally governed, equity-oriented architecture represents a paradigm shift from technology-first to governance-first digital transformation in horticulture, providing a theoretically robust and practically actionable foundation for future research, systems design, and policy development, with direct application to resource-constrained smallholder systems in sub-Saharan Africa and comparable Global South contexts.","园艺价值链面临持续存在的农艺挑战，包括生产效率低下、采后损失、资源管理不善以及供应链透明度有限，在全球南方以小农为主的体系中尤为突出。这些制约因素削弱了农业生产力、市场准入以及园艺种植的长期可持续性。尽管数字农业的研究兴趣日益增长，人工智能（AI）与区块链技术正越来越多地被共同探索，包括用于农业溯源的新兴AI-区块链-物联网集成架构。然而，现有这些集成仍以技术为中心，优先考虑溯源、自动化和数据验证，缺乏专门针对以小农为主的园艺生产和分销体系而设计的明确的治理、公平或数据主权架构。本研究提出一个综合性概念框架，将AI的预测与决策支持能力与区块链的数据完整性、溯源性和自动化特征相结合，以增强园艺价值链的可持续性、包容性和治理水平。基于对通过Scopus、Web of Science、IEEE Xplore、ScienceDirect和Google Scholar识别的同行评审文献的整合性综述，并以技术-组织-环境（TOE）框架、社会技术系统理论和可持续数字化转型理论为基础，本研究探讨了园艺数字转型中的农艺、治理和公平维度。该框架围绕五个相互关联的操作层构建：数据采集、AI驱动的分析与决策、区块链验证与溯源、智能合约与决策支持，以及用户界面与利益相关者反馈，所有这些都在治理架构与伦理设计层内运作，该层作为框架的结构性前提而非下游功能发挥作用。这一治理层将算法透明度、通过社区治理的数据合作社实现的数据主权，以及小农的包容性数字接入作为不可协商的设计原则。该框架明确与可持续发展目标2、9、10、12、13和16相一致，并包含一个三阶段实证验证路径，包括专家德尔菲征询、仿真建模以及在","Frontiers in Sustainable Food Systems","2026-09-11T00:00:00Z",78,{"impact":18,"substance":17,"depth":18,"authority":92,"freshness":275,"relevant":21,"comment":351},"该研究提出治理优先的AI-区块链融合框架，聚焦小农园艺价值链的可持续与包容性，方法新颖、理论扎实，对数字农业政策设计有参考价值，但属概念框架尚未实证，时效性一般。",[353],{"name":347,"url":344},[26,27,28,355,356,357],"小农户","农业区块链","园艺价值链",{"code":50,"slug":179,"name":180,"path":181,"parent_code":104},[],{"status":107,"locked":13,"version":35,"method":36,"evidence":361},{"candidates":362,"agricultural_evidence":59},[363,367,371,375,381],{"code":50,"score":90,"supported":59,"title":364,"abstract":365,"tags":366},[198],[198,125],[27],{"code":45,"score":275,"supported":59,"title":368,"abstract":369,"tags":370},[],[190,191],[26],{"code":100,"score":78,"supported":59,"title":372,"abstract":373,"tags":374},[],[118,120],[],{"code":376,"score":65,"supported":59,"title":377,"abstract":378,"tags":380},"G2",[],[379],"smallholder",[355],{"code":55,"score":21,"supported":13,"title":382,"abstract":383,"tags":384},[],[],[28],[386,387],"农业人工智能 农业区块链 园艺价值链 数据治理","农业人工智能 农业区块链","农业人工智能农业区块链园艺价值链数据治理-2292","10.3389\u002Ffsufs.2026.1894057",{"doi":389,"openalex_id":391,"authors":392,"venue":347,"cited_by_count":68,"oa_url":344,"card":404,"direction":157,"ingested_from":159},"W7212272522",[393,395,398,401],{"name":394,"orcid":8},"Olaoluwa Olarewaju",{"name":396,"orcid":397},"Tobi Fadiji","https:\u002F\u002Forcid.org\u002F0000-0001-5525-4495",{"name":399,"orcid":400},"Olaniyi Amos Fawole","https:\u002F\u002Forcid.org\u002F0000-0001-5591-4633",{"name":402,"orcid":403},"Lembe Samukelo Magwaza","https:\u002F\u002Forcid.org\u002F0000-0001-9809-2254",{"tldr":405,"method":406,"finding":407,"direction":243,"opportunity":408},"提出治理优先框架，整合AI与区块链提升园艺价值链可持续性与包容性。","整合性文献综述，基于TOE、社会技术系统与可持续数字化转型理论。","治理架构应作为结构性前提，嵌入算法透明、数据主权与包容性数字接入。","可实证检验社区数据合作社与治理层对小型农户采纳和公平收益的影响。","2026-09-13T23:30:07.102172Z",{"id":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":8,"source_name":416,"source_url":413,"published_at":417,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":418,"score_detail":419,"sources":423,"tags":425,"primary_topic":428,"secondary_topics":433,"classification":435,"search_phrases":451,"slug":454,"view_count":68,"doi":455,"paper":456,"created_at":470},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":20,"substance":420,"depth":18,"authority":421,"freshness":12,"relevant":21,"comment":422},21,12,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[424],{"name":416,"url":413},[26,27,355,426,427],"植物病害识别","可解释性",{"code":376,"slug":429,"name":430,"path":431,"parent_code":432},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[434],{"code":50,"slug":179,"name":180,"path":181,"parent_code":104},{"status":107,"locked":13,"version":35,"method":36,"evidence":436},{"candidates":437,"agricultural_evidence":59},[438,442,447],{"code":376,"score":421,"supported":59,"title":439,"abstract":440,"tags":441},[379],[379],[355],{"code":50,"score":12,"supported":59,"title":443,"abstract":445,"tags":446},[444],"computer vision",[],[27,426],{"code":45,"score":21,"supported":13,"title":448,"abstract":449,"tags":450},[],[],[26],[452,453],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":455,"openalex_id":457,"authors":458,"venue":416,"cited_by_count":68,"oa_url":413,"card":465,"direction":73,"ingested_from":159},"W7223137323",[459,461,463],{"name":460,"orcid":8},"Akshay Ramgoolam",{"name":462,"orcid":8},"Priya Bundhoo Appadoo",{"name":464,"orcid":8},"Jean-Noël LEspérance",{"tldr":466,"method":467,"finding":468,"direction":73,"opportunity":469},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":8,"source_name":416,"source_url":474,"published_at":417,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":418,"score_detail":477,"sources":479,"tags":481,"primary_topic":485,"secondary_topics":491,"classification":493,"search_phrases":516,"slug":519,"view_count":68,"doi":520,"paper":521,"created_at":533},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":20,"substance":17,"depth":18,"authority":421,"freshness":171,"relevant":21,"comment":478},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[480],{"name":416,"url":474},[26,27,482,483,484],"灌溉决策","安全保证","试验设计",{"code":486,"slug":487,"name":488,"path":489,"parent_code":490},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[492],{"code":50,"slug":179,"name":180,"path":181,"parent_code":104},{"status":107,"locked":13,"version":35,"method":36,"evidence":494},{"candidates":495,"agricultural_evidence":59},[496,501,505,512],{"code":486,"score":90,"supported":59,"title":497,"abstract":498,"tags":500},[487],[499,487],"灌溉",[482],{"code":50,"score":421,"supported":59,"title":502,"abstract":503,"tags":504},[198],[125],[27],{"code":506,"score":78,"supported":59,"title":507,"abstract":508,"tags":511},"H2",[],[509,510],"土壤","soil",[],{"code":45,"score":21,"supported":13,"title":513,"abstract":514,"tags":515},[],[],[26],[517,518],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":520,"openalex_id":522,"authors":523,"venue":416,"cited_by_count":68,"oa_url":474,"card":528,"direction":73,"ingested_from":159},"W7223131205",[524,526],{"name":525,"orcid":8},"Nassima Benkhelifa",{"name":527,"orcid":8},"Yacine Aït Ahmed",{"tldr":529,"method":530,"finding":531,"direction":73,"opportunity":532},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z"]