[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2925":3,"related-2925":61},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":60},2925,"From tradition to smart agriculture: how value and technology readiness shape farmers’ adoption decisions","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fjstpm-03-2025-0084","Purpose This study aims to investigate the slow adoption of smart farming technologies (SFTs) in developing regions, where farmers continue relying on traditional methods despite modernization policies. It examines how farmers’ value perceptions and technology readiness (TR) shape adoption intentions by integrating the value-based adoption model (VAM) with the theory of consumption values (TCV). Specifically, this study develops a value-centric framework in which TR moderates the link between overall perceived value (OPV) and adoption intention (INT). Design\u002Fmethodology\u002Fapproach Data were collected from 351 smallholder farmers through self-administered surveys and analyzed using partial least squares-structural equation modeling. Findings OPV emerged as the strongest predictor of adoption, driven by perceived efficiency, facilitating conditions and financial incentive policy. Among OPV dimensions, conditional value outweighed functional and monetary values, underscoring the importance of enabling conditions such as financial aid, training and infrastructure. Contrary to expectations, perceived cost increased OPV, while perceived complexity and uncertainty were insignificant, suggesting that familiarity or outsourcing may reduce traditional barriers. TR moderated the link between OPV and INT, where motivated farmers move toward adoption regardless, but reluctant farmers require stronger OPV to ease concerns. Originality\u002Fvalue To the best of the authors’ knowledge, this study is among the first to integrate VAM and TCV to explain the adoption of SFTs, establishing the value-centric perspective as a powerful lens for understanding technology acceptance in agriculture. It also demonstrates that TR differentially shapes how farmers rely on OPV to drive adoption.","目的 本研究旨在探究发展中地区智能农业技术（SFTs）采用缓慢的问题，尽管有现代化政策推动，农民仍继续依赖传统方法。研究通过将价值基础采用模型（VAM）与消费价值理论（TCV）相结合，考察农民的价值感知和技术准备度（TR）如何塑造采用意愿。具体而言，本研究构建了一个以价值为核心的框架，其中TR调节整体感知价值（OPV）与采用意愿（INT）之间的关系。设计\u002F方法\u002F路径 通过自填式问卷从351名小农户处收集数据，并采用偏最小二乘结构方程模型进行分析。研究发现 OPV是采用行为最强的预测因子，受感知效率、便利条件和财政激励政策驱动。在OPV各维度中，条件价值超过了功能价值和货币价值，凸显了财政援助、培训和基础设施等使能条件的重要性。与预期相反，感知成本增加了OPV，而感知复杂性和不确定性则不显著，表明熟悉度或外包可能降低了传统障碍。TR调节了OPV与INT之间的关系，有积极性的农民无论如何都会趋向采用，而犹豫不决的农民则需要更强的OPV来缓解顾虑。原创性\u002F价值 据作者所知，本研究是首批整合VAM和TCV来解释SFTs采用的研究之一，确立了以价值为核心的视角作为理解农业技术接受的有力透镜。研究还表明，TR以差异化方式塑造了农民依赖OPV来推动采用的过程。",null,"Journal of Science and Technology Policy Management","2026-09-18T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"该研究整合VAM与TCV模型，基于351户小农户调查揭示价值感知与技术准备度对智慧农业技术采纳的影响，方法新颖、结论对发展区域推广政策有参考价值，但属学术论文、影响范围偏细分领域。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","小农户","农业技术采纳","农业政策激励","技术准备度",[33,34],"智慧农业技术 小农户 采纳","价值感知 技术准备度 农户","智慧农业技术小农户采纳-2925",0,"10.1108\u002Fjstpm-03-2025-0084",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":9,"card":52,"direction":58,"ingested_from":59},"W7213541758",[41,44,47,50],{"name":42,"orcid":43},"Hazem Yusuf Osrof","https:\u002F\u002Forcid.org\u002F0000-0001-9622-5025",{"name":45,"orcid":46},"Cheng Ling Tan","https:\u002F\u002Forcid.org\u002F0000-0002-3759-6763",{"name":48,"orcid":49},"Sook Fern Yeo","https:\u002F\u002Forcid.org\u002F0000-0002-8060-5872",{"name":51,"orcid":9},"Kim Hua Tan",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"整合VAM与TCV模型，研究农户价值感知与技术准备度如何影响智慧农业技术采纳意愿。","对351户小农户问卷调查，采用PLS-SEM结构方程建模分析。","总体感知价值是采纳最强预测因子，条件价值作用最大，技术准备度起调节作用。","数字乡村与农业信息化","可探索不同区域与文化背景下价值维度差异，及技术准备度干预对采纳的长期效应。","智慧农业 \u002F 农业物联网","openalex","2026-09-19T23:30:10.915432Z",{"total":62,"page":22,"page_size":62,"items":63},6,[64,99,133,179,215,256],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":69,"content":9,"source_name":70,"source_url":67,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":76,"tags":78,"search_phrases":82,"slug":85,"view_count":36,"doi":86,"paper":87,"created_at":98},2948,"Machine learning and remote sensing for smallholder precision agriculture in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04538-2","While machine learning (ML) and remote sensing (RS) are frequently heralded as the definitive solutions for agricultural resilience in Sub-Saharan Africa, a profound ‘implementation gap’ persists between laboratory-validated computational maturity and field-level utility for smallholder farmers. This systematic review, conducted under preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, critically analyzes why sophisticated models optimized for large-scale monocultures fail within the fragmented, intercropped landscapes of Ethiopia. By synthesizing empirical evidence across four domains—in-season crop yield forecasting, digital soil mapping, real-time biotic stress detection, and agro-meteorological modeling—the review uncover a fundamental scale mismatch between coarse-resolution satellite observations and sub-hectare micro-plots. The critique identifies localized data scarcity, hardware constraints, and the ‘last-mile’ connectivity divide as the primary friction points obstructing the transition from macro-level pixels to actionable, site-specific agricultural intelligence. Moving beyond simple summary, the study propose a strategic roadmap centered on decentralized edge computing, tinyML optimizations, and a restructuring of extension services to integrate digital intelligence into daily smallholder decision-making. These structural shifts are essential to bridge the digital divide and secure Ethiopia’s national food security against escalating climate variability. This review foregrounds the significance of digital agriculture within the context of the sustainable development goals (SDGs), specifically addressing SDG 2 (zero hunger) and SDG 13 (climate action) by enhancing crop productivity and building resilience in smallholder systems.","尽管机器学习（ML）与遥感（RS）常被标榜为撒哈拉以南非洲农业韧性的终极解决方案，但实验室验证的计算成熟度与小农户田间实用性之间仍存在深刻的“实施鸿沟”。本系统综述依据系统综述和荟萃分析首选报告条目（PRISMA）2020指南开展，批判性地分析了为何针对大规模单一种植优化的复杂模型在埃塞俄比亚碎片化、间作化的景观中失效。通过综合四个领域的实证证据——季内作物产量预测、数字土壤制图、实时生物胁迫检测和农业气象建模——本综述揭示了粗分辨率卫星观测与亚公顷微地块之间的根本性尺度错配。该批判性分析将局部数据稀缺、硬件约束和“最后一公里”连接鸿沟确定为阻碍从宏观像元向可操作、因地制宜的农业智能转化的主要摩擦点。本研究超越简单的总结，提出了一条以去中心化边缘计算、tinyML优化和推广服务体系重构为核心的战略路线图，旨在将数字智能融入小农户的日常决策。这些结构性转变对于弥合数字鸿沟、保障埃塞俄比亚在日益加剧的气候变率下的国家粮食安全至关重要。本综述凸显了数字农业在可持续发展目标（SDGs）背景下的重要意义，特别是通过提升作物生产力和增强小农系统韧性来回应SDG 2（零饥饿）和SDG 13（气候行动）。","Discover Sustainability",79,{"impact":73,"substance":18,"depth":73,"authority":20,"freshness":74,"relevant":22,"comment":75},18,9,"系统综述揭示机器学习与遥感在小农场景的落地鸿沟，并提出边缘计算与tinyML路线图，对数字农业与SDG研究有参考价值。",[77],{"name":70,"url":67},[27,79,28,80,81],"农业人工智能","数字鸿沟","遥感",[83,84],"埃塞俄比亚 小农户 精准农业","机器学习 遥感 小农","埃塞俄比亚小农户精准农业-2948","10.1007\u002Fs43621-026-04538-2",{"doi":86,"openalex_id":88,"authors":89,"venue":70,"cited_by_count":36,"oa_url":67,"card":92,"direction":96,"ingested_from":59},"W7213562005",[90],{"name":91,"orcid":9},"Abrha Asefa",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"系统综述埃塞俄比亚小农精准农业中机器学习和遥感的应用鸿沟与出路。","PRISMA 2020 系统综述，综合四领域实证证据。","粗分辨率卫星与亚公顷微地块尺度不匹配，数据稀缺和连接鸿沟阻碍落地。","农业遥感与作物表型","面向碎片化间作小农的 tinyML 边缘计算与本地化数据采集，是填补落地鸿沟的关键方向。","2026-09-19T23:30:33.334404Z",{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":111,"tags":113,"search_phrases":116,"slug":119,"view_count":36,"doi":120,"paper":121,"created_at":132},2641,"Smart Agriculture and Sustainable Development in Agro-Ecosystems: Innovative Computational Modeling for Digital Twins","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232015.2026.22.80","By boosting yields, improving efficiency, and reducing costs (while managing resources), digital technologies have driven innovation in agro-ecosystems in recent years. As a means to overcome ever-limited resources, Smart Agriculture – sometimes also referred to as Agriculture 4.0 – has increasingly leveraged digital solutions to achieve efficiency and sustainability. Digital twins in rural systems perform as computational (in silico) replicas of food production lines, which can drive innovation by enabling real-time optimization as well as predictive decision-making. Considering that both non-human and human actors are directly involved, this paper discusses (from a strategic alignment viewpoint) a prospective symbiotic ethos that farmers and ranchers may pursue when it comes to integrated, participatory and efficient transfer and usage of digital technology in rural activities. Bearing particularly in mind smallholders, challenges and opportunities for Smart Agriculture include: (i) implementation of hybrid in-house simulators of agro-ecosystems by suitably combining mechanistic modeling with data-driven simulation; (ii) use of dimensionless mathematical modeling to expedite scale-up, optimization, and translation of innovative digital technologies; and (iii) validating as well as transferring novel digital solutions in consideration of strategic issues identified by rural end-users.","近年来，数字技术通过提高产量、提升效率、降低成本（同时管理资源），推动了农业生态系统的创新。作为克服日益有限的资源的一种手段，智慧农业（有时也被称为农业4.0）越来越多地利用数字解决方案来实现效率和可持续性。农村系统中的数字孪生作为食品生产线的计算（计算机模拟）副本，通过实现实时优化和预测性决策来推动创新。考虑到非人类和人类参与者都直接参与其中，本文（从战略协同的视角）探讨了农民和牧场主在将数字技术整合、参与式和高效地转移及应用于农村活动时可能追求的一种前瞻性共生理念。特别考虑到小农户，智慧农业面临的挑战和机遇包括：（i）通过将机理建模与数据驱动模拟适当结合，实施农业生态系统的混合内部模拟器；（ii）使用无量纲数学建模来加速创新数字技术的规模化、优化和转化；（iii）在考虑农村终端用户所识别的战略问题的基础上，验证和转移新型数字解决方案。","WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","2026-09-15T00:00:00Z",74,{"impact":17,"substance":109,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":110},20,"核心期刊论文，提出农业生态数字孪生的混合建模与无量纲化方法，对小农户数字化转型有参考价值，但偏理论、时效略滞后。",[112],{"name":105,"url":102},[27,79,114,28,115],"数字孪生","农业建模",[117,118],"农业人工智能 农业建模 数字孪生 智慧农业","农业人工智能 农业建模","农业人工智能农业建模数字孪生智慧农业-2641","10.37394\u002F232015.2026.22.80",{"doi":120,"openalex_id":122,"authors":123,"venue":105,"cited_by_count":36,"oa_url":126,"card":127,"direction":58,"ingested_from":59},"W7213267978",[124],{"name":125,"orcid":9},"Jose Rabi","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb625115-032(2026).pdf",{"tldr":128,"method":129,"finding":130,"direction":58,"opportunity":131},"探讨数字孪生与计算建模在智慧农业可持续发展中的应用，聚焦小农户的挑战与机遇。","混合机理与数据驱动模拟、无量纲数学建模、数字孪生计算副本。","提出农民与牧场主共生的数字技术转移理念，强调小农户的参与式整合。","可研究小农户场景下混合模拟器的轻量化与低成本部署，以及数字孪生技术的参与式验证方法。","2026-09-16T23:30:10.078180Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":138,"content":9,"source_name":139,"source_url":136,"published_at":140,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":141,"score_detail":142,"sources":146,"tags":148,"search_phrases":152,"slug":155,"view_count":36,"doi":156,"paper":157,"created_at":178},2516,"Scaling climate-smart agriculture through multi-channel advisory services: economic evaluation of weather information systems in sub-Saharan Africa","https:\u002F\u002Fdoi.org\u002F10.1080\u002F14735903.2026.2731746","Smallholder agriculture in sub-Saharan Africa is 95% rain-fed and exposed to climate variability. This post-implementation evaluation applies activity-based costing to weather advisory services delivered by PlantVillage in Kenya and Burkina Faso from 2022 to 2024 across television, radio, short message service (SMS), and community-based channels. A combined investment of USD 551,966 reached 9.41 million farmers at confirmed exposure and an estimated 14.5 million under a moderate-reach scenario (USD 0.038 per farmer). Because extended reach relies on literature-derived multipliers rather than direct measurement, we report a sensitivity range of USD 0.034 to 0.058; even at the conservative end, the cost advantage over Farmer Field Schools (USD 36 to 70 per participant) remains a factor of 620 to 1,200. Mass media achieved the lowest unit cost (USD 0.027 to 0.031), with evidence limited to confirmed exposure, whereas SMS supported intensive engagement at USD 0.60. In a convenience sample of 4,272 registered iShamba users in Kenya, 83.7% (n = 3,577) reported that advisories influenced their decisions, predominantly planting timing (51%) and farm-activity planning (23%). No comparable data were collected in Burkina Faso, and channel contributions appear complementary rather than demonstrably synergistic. Yields and income were not measured; causal impact requires experimental and longitudinal designs.","撒哈拉以南非洲的小农农业95%依赖雨养，易受气候变率影响。本项实施后评估采用作业成本法，对PlantVillage于2022至2024年间在肯尼亚和布基纳法索通过电视、广播、短信服务（SMS）及社区渠道提供的天气咨询服务进行成本核算。合计投资551,966美元，在确认触达口径下覆盖941万农民，在中度触达情景下估计覆盖1,450万农民（每农民0.038美元）。由于扩展触达依赖文献推导的乘数而非直接测量，我们报告0.034至0.058美元的敏感性区间；即使在保守端，相较于农民田间学校（每位参与者36至70美元）的成本优势仍达620至1,200倍。大众媒体的单位成本最低（0.027至0.031美元），但证据仅限于确认触达；短信则以每农民0.60美元支持深度参与。在肯尼亚4,272名注册iShamba用户的便利样本中，83.7%（n = 3,577）报告咨询信息影响了其决策，主要是种植时间（51%）和农事活动规划（23%）。布基纳法索未收集可比数据，各渠道的贡献似乎互补，而非可证明的协同。产量和收入未予测量；因果影响需要实验和纵向设计。","International Journal of Agricultural Sustainability","2026-09-13T00:00:00Z",78,{"impact":73,"substance":143,"depth":73,"authority":20,"freshness":144,"relevant":22,"comment":145},22,7,"基于成本核算的天气信息服务大规模评估，单位成本数据与渠道对比结论扎实，对农业气象信息化推广有参考价值。",[147],{"name":139,"url":136},[149,27,150,28,151],"数字乡村","撒哈拉以南非洲","农业气象服务",[153,154],"撒哈拉以南非洲 农业气象服务 数字乡村 智慧农业","撒哈拉以南非洲 农业气象服务","撒哈拉以南非洲农业气象服务数字乡村智慧农业-2516","10.1080\u002F14735903.2026.2731746",{"doi":156,"openalex_id":158,"authors":159,"venue":139,"cited_by_count":36,"oa_url":172,"card":173,"direction":58,"ingested_from":59},"W7212902218",[160,163,166,168,170],{"name":161,"orcid":162},"Rimnoma S. Ouedraogo","https:\u002F\u002Forcid.org\u002F0000-0003-3339-300X",{"name":164,"orcid":165},"Bipana Paudel Timilsena","https:\u002F\u002Forcid.org\u002F0000-0003-3584-0563",{"name":167,"orcid":9},"Nebnoma R. Tiendrebeogo",{"name":169,"orcid":9},"Derek Morr",{"name":171,"orcid":9},"D M Hughes","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F14735903.2026.2731746?needAccess=true",{"tldr":174,"method":175,"finding":176,"direction":56,"opportunity":177},"评估撒哈拉以南非洲多渠天气信息服务成本与农户决策影响。","采用活动成本法，分析2022-2024年肯尼亚和布基纳法索四类渠道数据。","人均成本仅0.038美元，远低于农民田间学校，83.7%用户称信息影响决策。","需实验与纵向设计验证天气信息对产量收入的因果影响及渠道协同效应。","2026-09-15T23:30:10.644934Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":187,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":196,"search_phrases":199,"slug":202,"view_count":36,"doi":203,"paper":204,"created_at":214},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-12T00:00:00Z",25,85,{"impact":143,"substance":190,"depth":73,"authority":20,"freshness":74,"relevant":22,"comment":191},23,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[193,194],{"name":185,"url":182},{"name":185,"url":195},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[149,27,79,197,28,198],"农业物联网","精准灌溉",[200,201],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":203,"openalex_id":205,"authors":206,"venue":185,"cited_by_count":36,"oa_url":182,"card":209,"direction":58,"ingested_from":59},"W7212357737",[207],{"name":208,"orcid":9},"Pranit Kamble",{"tldr":210,"method":211,"finding":212,"direction":58,"opportunity":213},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":141,"score_detail":223,"sources":225,"tags":227,"search_phrases":231,"slug":234,"view_count":36,"doi":235,"paper":236,"created_at":255},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",{"impact":73,"substance":143,"depth":73,"authority":20,"freshness":144,"relevant":22,"comment":224},"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。",[226],{"name":221,"url":218},[27,79,228,28,229,230],"数据治理","农业区块链","园艺价值链",[232,233],"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":235,"openalex_id":237,"authors":238,"venue":221,"cited_by_count":36,"oa_url":218,"card":250,"direction":58,"ingested_from":59},"W7212272522",[239,241,244,247],{"name":240,"orcid":9},"Olaoluwa Olarewaju",{"name":242,"orcid":243},"Tobi Fadiji","https:\u002F\u002Forcid.org\u002F0000-0001-5525-4495",{"name":245,"orcid":246},"Olaniyi Amos Fawole","https:\u002F\u002Forcid.org\u002F0000-0001-5591-4633",{"name":248,"orcid":249},"Lembe Samukelo Magwaza","https:\u002F\u002Forcid.org\u002F0000-0001-9809-2254",{"tldr":251,"method":252,"finding":253,"direction":56,"opportunity":254},"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":257,"title":258,"url":259,"summary":260,"summary_zh":9,"content":9,"source_name":261,"source_url":259,"published_at":262,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":268,"tags":270,"search_phrases":273,"slug":276,"view_count":36,"doi":277,"paper":278,"created_at":292},2041,"Smart Farms, Unequal Fields: The Social and Technical Issues of Climate-Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41055-026-00234-z","Smart Farms, Unequal Fields: The Social and Technical Issues of Climate-Smart Agriculture。Food Ethics","Food Ethics","2026-09-09T00:00:00Z",64,{"impact":265,"substance":17,"depth":266,"authority":20,"freshness":21,"relevant":22,"comment":267},12,15,"核心期刊论文，从社会与技术双重视角审视气候智慧农业的不平等问题，视角新颖但属学术探讨，公共影响有限。",[269],{"name":261,"url":259},[27,28,80,271,272],"气候智慧农业","农业伦理",[274,275],"气候智慧农业 农业伦理 数字鸿沟 智慧农业","气候智慧农业 农业伦理","气候智慧农业农业伦理数字鸿沟智慧农业-2041","10.1007\u002Fs41055-026-00234-z",{"doi":277,"openalex_id":279,"authors":280,"venue":261,"cited_by_count":36,"oa_url":9,"card":9,"direction":58,"ingested_from":59},"W7212029239",[281,283,285,288,290],{"name":282,"orcid":9},"Shaine  Justin Lagunda",{"name":284,"orcid":9},"Francis  Ann Sy",{"name":286,"orcid":287},"Cyrene Napoles","https:\u002F\u002Forcid.org\u002F0009-0008-3091-8636",{"name":289,"orcid":9},"Devora Dian Palero",{"name":291,"orcid":9},"Faye Matunog","2026-09-10T23:30:09.411958Z"]