[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3512":3,"related-3512":54},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":53},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。",null,"Asian Research Journal of Agriculture","2026-09-24T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农业人工智能","农业技术推广","小农户",[32,33],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512",0,"10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":45,"direction":51,"ingested_from":52},"W7214205238",[40,42],{"name":41,"orcid":9},"Pravangkar Boruah",{"name":43,"orcid":44},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"综述AI农业咨询与诊断系统，聚焦印度东北小农，提出人监督多模态部署架构。","批判性叙述综述，整合2010-2026年数字推广、生成式AI与图像诊断证据。","AI输出技术可行但本地化、安全与田间效果证据不足，需人监督与检索增强。","农业人工智能与决策模型","可开展跨区跨季前瞻评估，连接模型质量与农户决策、产量、公平及成本效益。","数字乡村与农业信息化","openalex","2026-09-25T23:30:39.745514Z",{"total":55,"page":21,"page_size":55,"items":56},6,[57,94,128,174,218,254],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":64,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":66,"score_detail":67,"sources":70,"tags":74,"search_phrases":77,"slug":80,"view_count":21,"doi":81,"paper":82,"created_at":93},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":18,"substance":68,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":69},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。",[71,72],{"name":63,"url":60},{"name":63,"url":73},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[26,27,28,75,30,76],"农业物联网","精准灌溉",[78,79],"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":81,"openalex_id":83,"authors":84,"venue":63,"cited_by_count":35,"oa_url":60,"card":87,"direction":91,"ingested_from":52},"W7212357737",[85],{"name":86,"orcid":9},"Pranit Kamble",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"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。","智慧农业 \u002F 农业物联网","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":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":63,"source_url":97,"published_at":11,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":100,"score_detail":101,"sources":105,"tags":109,"search_phrases":112,"slug":115,"view_count":35,"doi":116,"paper":117,"created_at":127},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。",77,{"impact":17,"substance":102,"depth":103,"authority":19,"freshness":20,"relevant":21,"comment":104},20,17,"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[106,107],{"name":63,"url":97},{"name":63,"url":108},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[110,27,28,30,111],"数字农业","技术采纳",[113,114],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":116,"openalex_id":118,"authors":119,"venue":63,"cited_by_count":35,"oa_url":97,"card":122,"direction":91,"ingested_from":52},"W7214187008",[120],{"name":121,"orcid":9},"Chilufya Banda",{"tldr":123,"method":124,"finding":125,"direction":51,"opportunity":126},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","2026-09-25T23:30:18.463733Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":11,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":135,"score_detail":136,"sources":140,"tags":145,"search_phrases":148,"slug":151,"view_count":35,"doi":152,"paper":153,"created_at":173},3470,"AI-generated advice as a reinforcement layer in climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.landusepol.2026.108336","Climate-smart agricultural (CSA) practices are central to food-system decarbonisation, yet adoption often falls short of farmers’ stated intentions, weakening the impact of incentives and extension under capacity constraints. We test whether spatially targeted, AI-generated advice can narrow this intention-action gap in a randomised field experiment with 1529 row crop farmers in Iowa, Illinois and Indiana during the cover crop decision window. Farmers assigned to receive four AI-generated emails were 4.45 %age points more likely to plant cover crops than controls (z = 2.81, p = 0.005), despite high baseline intentions in both groups. Effects operated on the extensive margin: there was no detectable change in the share of land planted among adopters. Survey responses indicate high engagement and a shift from untested optimism to more calibrated trust after exposure. Supervised AI advice can provide a low-cost, scalable complement to existing extension, improving follow-through and modestly expanding uptake without displacing human expertise.","气候智慧型农业（CSA）实践是食品系统脱碳的核心，然而在能力受限的情况下，农户的实际采用往往低于其声称的意愿，削弱了激励措施和推广服务的效果。我们在爱荷华州、伊利诺伊州和印第安纳州开展了一项随机田间试验，覆盖1529名大田作物种植户，在覆盖作物决策窗口期测试了空间靶向的AI生成建议能否缩小这一意愿—行动差距。被分配接收四封AI生成电子邮件的农户种植覆盖作物的概率比对照组高4.45个百分点（z = 2.81，p = 0.005），尽管两组基线意愿均较高。效应体现在广延边际上：采用者中种植土地比例未检测到显著变化。调查回复表明参与度较高，且在接触建议后，农户从未经检验的乐观转向更为校准的信任。有监督的AI建议可作为现有推广服务的低成本、可扩展补充，改善后续落实并适度扩大采用，而不会取代人类专业知识。","Land Use Policy",87,{"impact":18,"substance":68,"depth":137,"authority":138,"freshness":20,"relevant":21,"comment":139},19,14,"随机对照试验证实AI生成建议可低成本缩小农户意愿与行动差距，对智慧农业推广具参考价值。",[141,142],{"name":134,"url":131},{"name":143,"url":144},"Apollo","https:\u002F\u002Fdoi.org\u002F10.17863\u002Fcam.134741",[27,28,29,146,147],"覆盖作物","气候智慧农业",[149,150],"AI生成建议 覆盖作物","爱荷华 伊利诺伊 印第安纳 覆盖作物","AI生成建议覆盖作物-3470","10.1016\u002Fj.landusepol.2026.108336",{"doi":152,"openalex_id":154,"authors":155,"venue":134,"cited_by_count":35,"oa_url":131,"card":168,"direction":91,"ingested_from":52},"W7214223143",[156,159,161,163,166],{"name":157,"orcid":158},"Callum Alexander","https:\u002F\u002Forcid.org\u002F0009-0007-5275-4583",{"name":160,"orcid":9},"Aiora Zabala",{"name":162,"orcid":9},"Andreas Kontoleon",{"name":164,"orcid":165},"Shalamar Armstrong","https:\u002F\u002Forcid.org\u002F0000-0002-1326-9936",{"name":167,"orcid":9},"Anuoluwa Sangotayo",{"tldr":169,"method":170,"finding":171,"direction":49,"opportunity":172},"随机试验检验AI生成建议能否缩小农户覆盖作物种植的意图-行动差距。","1529户美国中西部农户随机对照试验，四次AI生成邮件干预。","AI建议使覆盖作物种植率提高4.45个百分点，效果体现在是否采纳而非种植面积。","可探索AI建议与人工推广协同、长期持续效果及不同作物区域的异质性影响。","2026-09-25T23:30:09.887867Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":181,"sources":183,"tags":185,"search_phrases":188,"slug":191,"view_count":35,"doi":192,"paper":193,"created_at":217},3469,"ARTIFICIAL INTELLIGENCE FOR AGRICULTURE: A SYSTEMATIC REVIEW OF FARMERS' PERCEPTIONS, ACCEPTANCE, ADOPTION AND BARRIERS","https:\u002F\u002Fdoi.org\u002F10.64013\u002Fbbasrjlifess.v2026i1.70","Artificial intelligence (AI) is rapidly reshaping agriculture, enabling the application of data for efficient decision-making, precision farming, crop monitoring, pest management, disease detection, smart irrigation, yield prediction, and various aspects of farm automation. Yet, to successfully gain a foothold in farms, factors like farmers' perceptions, their acceptance, desire, and readiness to adopt, as well as the capability to surmount socioeconomic, technological, and institutional challenges, must also make a difference. This article systematically collates the available information on farmers' perspectives on, acceptance of, readiness to adopt, and the obstacles related to AI in the agricultural context. The PRISMA 2020 guideline has been followed to select studies that are related, evaluated for inclusion, and finally, per the criteria, combined into a systematic synthesis. The review mainly discusses the aspects that influence the use of AI, like perceived usefulness, ease of use, trust, digital literacy, affordability, farm size, socioeconomic characteristics, availability of digital infrastructure, and access to agricultural technical advisory services. Main challenges identified involve the high cost of implementing AI, poor connectivity, weak rural infrastructure, low levels of technological knowledge, unavailability of support services, concerns about data privacy, distrust of algorithmic bias, language barriers, and inequalities impacting smallholder farmers. Besides barriers, the review indicates the potential of leveraging AI through extension services, agricultural mobile apps, precision farming, climate-smart agriculture, early-warning systems, and tailored farm advisories. By drawing out technical, behavioral, socioeconomic, and institutional perspectives, the review pinpoints important research questions and presents a farmholder-oriented setup to explain the process of taking up AI in farming. The outcomes can be used by scientific experts, policymakers, extension personnel, and software developers to create low-cost, reliable, and accessible AI systems aimed at promoting sustainable agricultural development.","人工智能（AI）正迅速重塑农业，使数据得以应用于高效决策、精准农业、作物监测、病虫害管理、病害检测、智能灌溉、产量预测以及农场自动化的各个方面。然而，要在农场中成功立足，农民的认知、接受度、采用意愿和准备程度，以及克服社会经济、技术和制度挑战的能力，同样至关重要。本文系统梳理了现有关于农民对农业领域人工智能的看法、接受度、采用准备程度及相关障碍的信息。研究遵循PRISMA 2020指南，筛选相关研究，评估其纳入资格，并最终依据标准进行系统性综合。综述主要探讨了影响人工智能使用的因素，包括感知有用性、易用性、信任、数字素养、可负担性、农场规模、社会经济特征、数字基础设施的可用性以及农业技术咨询服务的获取。识别出的主要挑战包括人工智能实施成本高昂、网络连接不佳、农村基础设施薄弱、技术水平低下、支持服务缺乏、数据隐私担忧、对算法偏见的疑虑、语言障碍以及影响小农户的不平等问题。除障碍外，综述还指出了通过推广服务、农业移动应用、精准农业、气候智慧型农业、预警系统和定制化农场咨询来利用人工智能的潜力。通过梳理技术、行为、社会经济和制度层面的视角，本综述指出了重要的研究问题，并提出了一个以农场主为导向的框架，以解释在农业中采用人工智能的过程。研究结果可供科学专家、政策制定者、推广人员和软件开发者用于创建低成本、可靠且可及的人工智能系统，以促进可持续农业发展。","Journal of Life and Social Sciences",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":182},"基于PRISMA的系统综述，系统梳理农户对AI的认知、接受度与采纳障碍，对智慧农业推广与政策设计有实质参考价值。",[184],{"name":180,"url":177},[27,28,186,187,30],"数字素养","农户采纳",[189,190],"农民 AI 采纳 障碍","农业人工智能 系统综述","农民AI采纳障碍-3469","10.64013\u002Fbbasrjlifess.v2026i1.70",{"doi":192,"openalex_id":194,"authors":195,"venue":180,"cited_by_count":35,"oa_url":177,"card":212,"direction":91,"ingested_from":52},"W7214189410",[196,198,200,202,204,206,208,210],{"name":197,"orcid":9},"MM JAMEEL",{"name":199,"orcid":9},"M SAEED",{"name":201,"orcid":9},"SA SHER",{"name":203,"orcid":9},"Z ALI",{"name":205,"orcid":9},"Q HAYYAT",{"name":207,"orcid":9},"S KIRBAG",{"name":209,"orcid":9},"S KHAN",{"name":211,"orcid":9},"HN AHMAD",{"tldr":213,"method":214,"finding":215,"direction":51,"opportunity":216},"系统综述农民对农业AI的感知、接受度、采纳意愿及障碍。","遵循PRISMA 2020指南，系统筛选并综合相关文献。","成本、基础设施、数字素养与信任是主要障碍，小农户受影响最大。","可研究低成本、本地化AI采纳模型及小农户数字包容机制。","2026-09-25T23:30:09.670568Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":9,"source_name":63,"source_url":221,"published_at":224,"category":12,"cover_url":9,"hotness":65,"is_selected":14,"score":225,"score_detail":226,"sources":230,"tags":234,"search_phrases":237,"slug":240,"view_count":35,"doi":241,"paper":242,"created_at":253},3462,"AI For Sustainable Development Opportunities & Innovation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933761","Abstract Artificial Intelligence is increasingly being explored as a tool for accelerating progress toward sustainable development. AI can analyse large datasets, identify patterns, forecast events, optimise systems and support decision-making across agriculture, energy, water, healthcare, education, cities, industry and environmental management. Recent research shows that AI-for-SDG research is expanding rapidly, while also revealing gaps in social inclusion, governance and assessment of AI’s own environmental footprint. This project examines the major opportunities for AI-enabled sustainable development, including smart agriculture, renewable-energy optimisation, climate and disaster forecasting, intelligent waste management, sustainable cities, healthcare and education. It also discusses innovation pathways such as machine learning, computer vision, remote sensing, generative AI, digital twins and edge AI. The study emphasises that technological capability alone is insufficient: responsible AI requires reliable data, transparency, privacy, human oversight, equitable access, energy-efficient computing and lifecycle environmental assessment.","摘要 人工智能正日益被视为加速可持续发展进程的工具。人工智能可以分析大型数据集、识别模式、预测事件、优化系统，并在农业、能源、水资源、医疗、教育、城市、工业与环境管理等领域支持决策。近期研究表明，人工智能促进可持续发展目标（SDG）的研究正在迅速扩展，同时也揭示了在社会包容、治理以及人工智能自身环境足迹评估方面的不足。本项目考察了人工智能赋能可持续发展的主要机遇，包括智慧农业、可再生能源优化、气候与灾害预测、智能废物管理、可持续城市、医疗和教育。项目还讨论了机器学习、计算机视觉、遥感、生成式人工智能、数字孪生和边缘人工智能等创新路径。研究强调，仅靠技术能力是不够的：负责任的人工智能需要可靠的数据、透明度、隐私保护、人类监督、公平获取、节能计算以及生命周期环境评估。","2026-09-30T00:00:00Z",68,{"impact":17,"substance":227,"depth":228,"authority":19,"freshness":55,"relevant":21,"comment":229},16,15,"系统梳理AI赋能农业等可持续发展领域的机遇与治理挑战，属综合性研究综述，对智慧农业方向有参考价值但非突破性成果。",[231,232],{"name":63,"url":221},{"name":63,"url":233},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[26,27,28,235,236],"可持续发展","遥感",[238,239],"AI 可持续发展 智慧农业","农业人工智能 可持续发展 数字乡村 智慧农业","AI可持续发展智慧农业-3462","10.5281\u002Fzenodo.22933761",{"doi":241,"openalex_id":243,"authors":244,"venue":63,"cited_by_count":35,"oa_url":221,"card":247,"direction":91,"ingested_from":52},"W7214172367",[245],{"name":246,"orcid":9},"Saloni Ananda Patil",{"tldr":248,"method":249,"finding":250,"direction":251,"opportunity":252},"综述AI在可持续发展各领域的机会与创新路径，并强调负责任AI的治理要求。","文献综述，覆盖机器学习、计算机视觉、遥感、数字孪生与边缘AI等。","AI-for-SDG研究快速扩张，但社会包容、治理与AI自身环境足迹评估仍存缺口。","农业绿色发展与碳","可量化AI自身能耗与碳足迹，并评估其在农业减排中的净环境效益。","2026-09-25T23:30:08.894307Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":9,"content":9,"source_name":259,"source_url":9,"published_at":260,"category":261,"cover_url":9,"hotness":13,"is_selected":14,"score":66,"score_detail":262,"sources":267,"tags":269,"search_phrases":272,"slug":275,"view_count":35,"doi":9,"paper":9,"created_at":276},3382,"山东出台《关于大力发展智慧农业的实施意见》——到2030年建设80个数字农业发展县","http:\u002F\u002Fnync.shandong.gov.cn\u002Fzwgk\u002Fzcwj\u002Fzcjd\u002F202609\u002Ft20260923_4998286.html","山东省政府办公厅印发《关于大力发展智慧农业的实施意见》：到2030年建设80个数字农业发展县、重点打造300个高水平智慧农业应用场景，全省农业生产信息化率达到60%以上；粮油作物水肥一体化应用面积达到2000万亩；建设省级种质资源数据库平台，推动人工智能、大数据与传统育种深度融合；建好用好'齐鲁农云'山东省数字农业农村综合管理服务平台，拓展'鲁农码'应用，实现涉农业务'一码通行'。","山东省农业农村厅 2026-09-23","2026-09-23T00:00:00Z","政策",{"impact":263,"substance":68,"depth":17,"authority":264,"freshness":265,"relevant":21,"comment":266},24,12,8,"省级智慧农业顶层政策，量化目标与平台抓手明确，信息增量足，值得进入每日精选。",[268],{"name":259,"url":257},[26,27,28,270,271],"种业振兴","数字农业发展县",[273,274],"山东 智慧农业实施意见","齐鲁农云 鲁农码","山东智慧农业实施意见-3382","2026-09-25T00:09:27.283777Z"]