[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3462":3,"related-3462":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":24,"tags":28,"search_phrases":34,"slug":37,"view_count":38,"doi":39,"paper":40,"created_at":53},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,16,15,13,6,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[29,30,31,32,33],"数字乡村","智慧农业","农业人工智能","可持续发展","遥感",[35,36],"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",0,"10.5281\u002Fzenodo.22933761",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":38,"oa_url":6,"card":45,"direction":51,"ingested_from":52},"W7214172367",[43],{"name":44,"orcid":9},"Saloni Ananda Patil",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"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。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:08.894307Z",{"total":21,"page":22,"page_size":21,"items":55},[56,91,134,170,213,236],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":10,"source_url":59,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":62,"score_detail":63,"sources":67,"tags":71,"search_phrases":73,"slug":76,"view_count":38,"doi":77,"paper":78,"created_at":90},2333,"Empowering India's Digital Future through Artificial Intelligence in Geography","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705830","The convergence of Geographic Information Systems (GIS), high-resolution Earth observation (EO) systems, and Artificial Intelligence (AI) has established a new paradigm: Geospatial Artificial Intelligence (GeoAI). In the context of India's rapid digital transformation, GeoAI serves as a core technology driving sustainable urbanization, precision agriculture, disaster risk reduction, and multi-modal logistics planning. Anchored by strategic national frameworks such as the National Geospatial Policy (NGP), PM Gati Shakti National Master Plan, and the SVAMITVA scheme, this paper examines how AI-integrated spatial analysis accelerates India's digital governance. Through technical methodologies, statistical hypothesis testing, applied case studies, and systemic evaluation, this study demonstrates the pivotal role of GeoAI in building resilient spatial data infrastructure and offers strategic policy recommendations for scalable national adoption.","地理信息系统（GIS）、高分辨率对地观测（EO）系统与人工智能（AI）的融合，已确立了一种新范式：地理空间人工智能（GeoAI）。在印度快速数字化转型的背景下，GeoAI成为推动可持续城市化、精准农业、灾害风险降低和多式联运物流规划的核心技术。本文以国家地理空间政策（NGP）、PM Gati Shakti国家总体规划以及SVAMITVA计划等战略性国家框架为依托，考察了AI集成的空间分析如何加速印度的数字治理。通过技术方法、统计假设检验、应用案例研究和系统性评估，本研究表明GeoAI在构建韧性空间数据基础设施中发挥关键作用，并为全国范围的可扩展应用提供了战略性政策建议。",72,{"impact":17,"substance":64,"depth":17,"authority":20,"freshness":65,"relevant":22,"comment":66},20,3,"论文系统阐述GeoAI在印度精准农业、数字治理中的应用与政策建议，方法扎实但发布日期在未来，时效性存疑。",[68,69],{"name":10,"url":59},{"name":10,"url":70},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705829",[29,30,31,33,72],"地理信息",[74,75],"农业人工智能 地理信息 数字乡村 智慧农业","农业人工智能 地理信息","农业人工智能地理信息数字乡村智慧农业-2333","10.5281\u002Fzenodo.22705830",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":38,"oa_url":59,"card":83,"direction":89,"ingested_from":52},"W7212270621",[81],{"name":82,"orcid":9},"Lalitkumar G. Thakur",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"本文探讨地理空间人工智能（GeoAI）如何推动印度数字治理与可持续发展。","结合GIS、高分辨率对地观测与AI，采用案例研究、统计检验和政策评估。","GeoAI在精准农业、灾害减灾等领域关键，需国家政策框架支持规模化应用。","农业遥感与作物表型","可研究GeoAI在印度小农精准农业中的适配性、数据鸿沟与政策落地机制。","数字乡村与农业信息化","2026-09-13T23:30:29.245252Z",{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":99,"is_selected":14,"score":100,"score_detail":101,"sources":106,"tags":108,"search_phrases":111,"slug":114,"view_count":38,"doi":115,"paper":116,"created_at":133},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management","2026-09-24T00:00:00Z",10,62,{"impact":102,"substance":103,"depth":19,"authority":20,"freshness":104,"relevant":22,"comment":105},12,14,8,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[107],{"name":97,"url":94},[30,31,109,33,110],"精准施肥","土壤制图",[112,113],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":115,"openalex_id":117,"authors":118,"venue":97,"cited_by_count":38,"oa_url":9,"card":127,"direction":131,"ingested_from":52},"W7214144821",[119,122,124],{"name":120,"orcid":121},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":120,"orcid":123},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":125,"orcid":126},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":128,"method":129,"finding":130,"direction":131,"opportunity":132},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","农业人工智能与决策模型","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":9,"source_name":140,"source_url":137,"published_at":98,"category":12,"cover_url":9,"hotness":99,"is_selected":14,"score":141,"score_detail":142,"sources":146,"tags":148,"search_phrases":151,"slug":154,"view_count":38,"doi":155,"paper":156,"created_at":169},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。","Asian Research Journal of Agriculture",80,{"impact":17,"substance":143,"depth":17,"authority":20,"freshness":144,"relevant":22,"comment":145},22,9,"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。",[147],{"name":140,"url":137},[29,30,31,149,150],"农业技术推广","小农户",[152,153],"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","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":155,"openalex_id":157,"authors":158,"venue":140,"cited_by_count":38,"oa_url":137,"card":164,"direction":89,"ingested_from":52},"W7214205238",[159,161],{"name":160,"orcid":9},"Pravangkar Boruah",{"name":162,"orcid":163},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":165,"method":166,"finding":167,"direction":131,"opportunity":168},"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。","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。","2026-09-25T23:30:39.745514Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":98,"category":12,"cover_url":9,"hotness":99,"is_selected":14,"score":177,"score_detail":178,"sources":180,"tags":182,"search_phrases":185,"slug":188,"view_count":38,"doi":189,"paper":190,"created_at":212},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International",66,{"impact":102,"substance":17,"depth":18,"authority":102,"freshness":104,"relevant":22,"comment":179},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[181],{"name":176,"url":173},[30,31,33,183,184],"GIS","干旱监测",[186,187],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":189,"openalex_id":191,"authors":192,"venue":176,"cited_by_count":38,"oa_url":173,"card":207,"direction":87,"ingested_from":52},"W7214156857",[193,195,197,199,201,203,205],{"name":194,"orcid":9},"V. Dhanalakshmi",{"name":196,"orcid":9},"N. Manikandan",{"name":198,"orcid":9},"V. S. Jinsy",{"name":200,"orcid":9},"K. V. Sumesh",{"name":202,"orcid":9},"P. Nideesh",{"name":204,"orcid":9},"P. S. Manju",{"name":206,"orcid":9},"N. Gopika",{"tldr":208,"method":209,"finding":210,"direction":87,"opportunity":211},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":9,"content":9,"source_name":218,"source_url":9,"published_at":219,"category":220,"cover_url":9,"hotness":99,"is_selected":14,"score":221,"score_detail":222,"sources":226,"tags":228,"search_phrases":231,"slug":234,"view_count":38,"doi":9,"paper":9,"created_at":235},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",85,{"impact":223,"substance":224,"depth":17,"authority":102,"freshness":104,"relevant":22,"comment":225},24,23,"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。",[227],{"name":218,"url":216},[29,30,31,229,230],"种业振兴","数字农业发展县",[232,233],"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",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":9,"content":241,"source_name":242,"source_url":9,"published_at":219,"category":243,"cover_url":9,"hotness":99,"is_selected":244,"score":245,"score_detail":246,"sources":249,"tags":251,"search_phrases":255,"slug":258,"view_count":38,"doi":9,"paper":9,"created_at":259},3373,"让数智技术更好服务强农惠农富农——三部门解读《行动计划》中的智慧农业与农村电商布局","https:\u002F\u002Fwww.digitalchina.gov.cn\u002F2026\u002Fxwzx\u002Fszkx\u002F202609\u002Ft20260923_5375046.htm","截至2025年底，我国累计推广应用各类农机北斗终端超350万台套。《行动计划》提出加快良田良种良机良法与数字化有机融合，集成推广主要作物大面积单产提升数智化解决方案。中国信通院赵佳佳表示智慧农业是推动产量产能、生产生态、增产增收协同发展的重要支撑；北京大学王悦研究员指出2025年全国农村网络零售额首破3万亿元，'十五五'时期农村电商将更加突出提质增效和体系化发展；文件明确'在确保安全的前提下，按需有序推广低空物流'。","让数智技术更好服务强农惠农富农\n\n*   [首页](https:\u002F\u002Fwww.digitalchina.gov.cn\u002F \"首页\")\n*   [新闻中心](https:\u002F\u002Fwww.digitalchina.gov.cn\u002F2026\u002Fxwzx\u002F \"新闻中心\")\n*   [数字快讯](https:\u002F\u002Fwww.digitalchina.gov.cn\u002F2026\u002Fxwzx\u002Fszkx\u002F \"数字快讯\")\n\n发布时间：2026-09-23 10:00 文章来源：人民邮电报\n\n近日，中央网信办、农业农村部、工业和信息化部联合印发《数字乡村高质量发展行动计划（2026—2030年）》（以下简称《行动计划》），部署5个方面24项重点任务，提出推进乡村产业数字化、乡村建设信息化、乡村治理智慧化一体发展。数据显示，2018年至2025年，我国农村地区互联网普及率由38.4％提升至69.5％。网络加快普及、应用持续拓展，如何让数智技术更好服务农业生产、农民增收、乡村公共服务和基层治理，成为新课题。\n\n**智慧农业聚焦增产增效**\n\n截至2025年底，我国累计推广应用各类农机北斗终端超350万台套。装备应用有了基础，怎样更好适应不同地区、不同作物的生产需要？《行动计划》提出，加快良田良种良机良法与数字化有机融合，聚焦重点区域重点品种，推广一批主要作物大面积单产提升数智化解决方案。\n\n中国信息通信研究院政策与经济研究所副研究员赵佳佳表示，面向“十五五”，智慧农业是推动产量产能、生产生态、增产增收协同发展的重要支撑。应推广人工智能与农业融合应用场景，结合智能育种、无人农机作业等方向的研发与试验，让技术更好适应农业生产需求。\n\n技术落地还需要服务跟进。赵佳佳认为，农业社会化服务是数智技术落地的重要依托，应让数字化、智能化手段更好服务农户的生产需要。《行动计划》明确提出，发挥现代农事综合服务中心等作用，提升农业社会化服务数字化、智能化水平。\n\n**农村电商提质要靠流通和服务**\n\n北京大学现代农学院中国农业政策研究中心研究员王悦表示，“十四五”期间，我国农村电商规模持续扩大，“十五五”时期将更加突出提质增效和体系化发展。\n\n数据显示，2025年，全国农村网络零售额首次突破3万亿元。市场规模扩大，对配套服务和物流协同提出更高要求。《行动计划》提出，构建多层次农村电商综合服务体系，培育多元化新型农村电商主体，推进县级物流配送中心、乡镇快递网点数字化、智能化改造，并面向中西部地区开展“数商兴农”进地方活动。\n\n其中，低空物流受到关注。文件明确，“在确保安全的前提下，按需有序推广低空物流”。王悦认为，无人机等新型运输方式有望提升山区、偏远地区农产品出村和消费品进村的末端物流效率，未来仍需立足实际需求，完善管理制度并加强专业人才支撑。\n\n常规寄递网络也要继续完善。截至2025年底，约80％的建制村建有村级寄递物流综合服务站。《行动计划》将2030年目标值设为90％。站点覆盖与配送能力，仍是农村电商需要持续做实的基础。\n\n**数智技术拓展就业增收空间**\n\n数智技术还能为乡村带来哪些发展机会？《行动计划》部署开展乡村旅游数字提升行动、乡村“小而美”文旅业态提升行动，积极探索共享农场、共享农机、共享集市等网络经营模式，并鼓励有条件的地区发展呼叫服务、数据标注等业态，拓宽农民就业增收渠道。\n\n中央民族大学数字经济研究院副院长、研究员李成明认为，乡村数字劳务、乡村数字文旅以及数字普惠金融创新，是值得重点关注的方向。\n\n新业态在乡村落地，需要适用的技术，也需要本土人才和经营主体的参与。李成明表示，应避免简单移植城市的平台、技术和商业模式，要研发低成本、低门槛、适配农业农村场景的专用技术与终端。同时，重点培育本土数字人才和新型经营主体，提升农民数字素养。\n\n**乡村信息服务突出需求导向**\n\n数智技术带来的变化，也体现在乡村居民看病、上学等日常生活中。优质资源向农村延伸后，怎样让服务更好用、可持续？\n\n国务院发展研究中心农村经济研究部副研究员殷浩栋认为，“十四五”时期，乡村数字公共服务主要着眼于资源下沉和服务覆盖。“十五五”时期，应更加关注服务质量和可持续性，顺应乡村人口结构变化，把服务送到有需要的人和家庭身边。面对专业人力短缺、人口外流等现实情况，人工智能等新技术有助于让乡村获得更多专业服务能力，维持基本教育、医疗和兜底服务质量。此外，主动发现最需要服务的个体，也是技术改善公共服务的重要着力点。\n\n服务上线之后能否用起来，还取决于建设和运营如何衔接。殷浩栋建议，以农民群众的获得感、幸福感、安全感为衡量标尺，围绕看病、上学、办事、领补贴、社保医保等高频需求做深做好具体应用，把运维、更新和资源对接纳入长期安排，避免重上线、轻运营。\n\n**人工智能融入基层治理**\n\n办事材料怎样准备，政策适用条件如何理解？围绕农村政务服务高频、共性需求，《行动计划》提出稳妥推进人工智能大模型部署应用，协助开展智能导办、材料预审、政策解读、信息推送等服务。\n\n中国信息通信研究院政策与经济研究所主任工程师李昭表示，农村“三务”信息化建设持续深化，让农民办事更便捷、村务运行更透明，推动乡村治理方式从经验判断转向数据支撑、从被动响应转向主动预防。\n\n在应急管理领域，数智技术应用也将进一步深化。《行动计划》提出推动智慧应急与基层治理有机融合，加快“久安”大模型、应急指挥“一张图”等融合应用。李昭认为，新一代人工智能等技术将与农村“三务”管理、政务服务、应急防控更好融合，推动乡村居民参与乡村治理全过程。\n\n数字乡村建设需要持续运营才能发挥实效。《行动计划》将“数字乡村可持续发展机制更加健全”列为2030年目标之一，并在分级分类推进数字乡村发展的部署中，按不同类型村庄明确建设重点。把建设内容与当地需求相匹配，让农民能够参与、受益，是数智技术服务乡村全面振兴的落脚点。\n\n（作者 佳文）\n\n扫一扫在手机上查看当前页面","数字中国建设峰会官网 2026-09-23","报道",true,93,{"impact":247,"substance":223,"depth":17,"authority":19,"freshness":104,"relevant":22,"comment":248},28,"三部门联合印发五年期数字乡村行动计划，含多项新目标数据与专家解读，政策层级高、信息增量足，值得进入每日精选。",[250],{"name":242,"url":239},[252,29,30,31,253,254],"农村电商","农机北斗","低空物流",[256,257],"农村网络零售额 3万亿","农业人工智能 低空物流 农机北斗 农村电商","农村网络零售额3万亿-3373","2026-09-25T00:09:26.464671Z"]