[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3567":3,"related-3567":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"search_phrases":24,"slug":27,"view_count":15,"doi":28,"paper":29,"created_at":44},3567,"Dira App: AI-Driven Decision Support for Career Path and Employment Opportunity Recommendations: A Case Study of Tanzania","https:\u002F\u002Fdoi.org\u002F10.37284\u002Feajit.9.2.5847","Career and employment decisions among Tanzanian secondary school students and graduates are still largely shaped by informal advice from parents, relatives, and peers rather than by objective, data-driven guidance. Graduate unemployment in Tanzania remains high, and structured support for self-employment remains limited. This paper presents the Dira App, Dira being the Swahili word for \"compass\", reflecting the system's role in helping users navigate career and employment decisions. It is a web and mobile Decision Support System (DSS) that combines a machine learning Career Predictor, an AI assistant (Dira AI) that personalises guidance using each user's academic history and predictions, and a booking and video counselling subsystem for continuous, personalised support. The system was developed using Agile methodology on a Vue.js, Flask, and PostgreSQL stack. A questionnaire-based needs assessment of 114 respondents (20 secondary students and 94 graduates) confirmed the problem: 60% of graduate respondents were unemployed, only 7% were formally employed, and a combined 86% expressed willingness to pursue self-employment, while career decisions among student respondents were shown to be predominantly influenced by parents and peers. These findings directly informed the system's functional requirements, architecture, and machine learning feature set. The resulting system integrates five core subsystems: role-based User Management, ML Career Prediction, Dira AI, Booking & Video Counselling, and Reporting & Analytics behind a secure, role-based interface. The developed system successfully centralised user profiling and career\u002Femployment data, automated personalised recommendation generation through a Career Predictor trained with a Random Forest Classifier (test accuracy 94.76%, train accuracy 95.66%), and supported continuous AI-driven and human counsellor guidance for informed career decision-making. The evaluation reported here is limited to a single-institution needs assessment and functional\u002Ftechnical testing rather than field deployment with end users, and the predictive model was trained on data reflecting the Tanzanian context, which may limit generalisability elsewhere. By combining locally grounded predictive modelling with continuous AI-driven and human-counsellor support, the Dira App directly addresses both the local data gap and the continuity gap identified in existing career guidance literature, offering a validated, replicable decision support model for other resource-constrained East African institutions.","坦桑尼亚中学生和毕业生的职业与就业决策在很大程度上仍由父母、亲戚和同伴的非正式建议所塑造，而非基于客观的数据驱动指导。坦桑尼亚的毕业生失业率仍然居高不下，而对自主创业的结构性支持依然有限。本文介绍了Dira应用，Dira在斯瓦希里语中意为“指南针”，体现了该系统帮助用户导航职业与就业决策的作用。它是一个基于网页和移动端的决策支持系统（Decision Support System, DSS），集成了机器学习职业预测器、利用每位用户的学业历史和预测结果提供个性化指导的AI助手（Dira AI），以及用于持续个性化支持的预约与视频咨询子系统。该系统采用敏捷方法论，基于Vue.js、Flask和PostgreSQL技术栈开发。一项基于问卷的需求评估调查了114名受访者（20名中学生和94名毕业生），证实了上述问题：60%的毕业生受访者处于失业状态，仅7%有正式工作，合计86%表示愿意从事自主创业，而学生受访者的职业决策被证明主要受父母和同伴影响。这些发现直接为系统的功能需求、架构和机器学习特征集提供了依据。最终形成的系统集成了五个核心子系统：基于角色的用户管理、机器学习职业预测、Dira AI、预约与视频咨询，以及报告与分析，均置于安全的基于角色的界面之后。所开发的系统成功实现了用户画像和职业\u002F就业数据的集中管理，通过使用随机森林分类器（测试准确率94.76%，训练准确率95.66%）训练的职业预测器自动生成个性化推荐，并支持持续的AI驱动和人工咨询师指导，以促进明智的职业决策。本文所报告的评价仅限于单一机构的需求评估和功能\u002F技术测试，而非面向最终用户的实地部署，且预测模型是在反映坦桑尼亚背景的数据上训练的，这可能限制其在其他地区的普适性。通过将扎根当地的预测建模与持续的AI驱动及人工咨询师支持相结合，Dira应用直接弥补了现有职业指导文献中发现的本地数据缺口和连续性缺口，提供了一种经过验证、可复制的决策支持",null,"East African Journal of Information Technology","2026-09-24T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦坦桑尼亚学生职业与就业决策支持系统，属教育信息化与就业服务领域，与三农、农业信息化、智慧农业、数字乡村等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"数字乡村","农业人工智能","就业决策支持",[25,26],"Dira App 坦桑尼亚","AI 职业推荐 决策支持系统","DiraApp坦桑尼亚-3567","10.37284\u002Feajit.9.2.5847",{"doi":28,"openalex_id":30,"authors":31,"venue":10,"cited_by_count":15,"oa_url":36,"card":37,"direction":41,"ingested_from":43},"W7214211658",[32,34],{"name":33,"orcid":9},"Ester Philipo Lulale",{"name":35,"orcid":9},"Alfred Kajirunga","https:\u002F\u002Fjournals.eanso.org\u002Findex.php\u002Feajit\u002Farticle\u002Fdownload\u002F5847\u002F6175",{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"开发Dira App，用机器学习与AI助手为坦桑尼亚学生和毕业生提供职业与就业决策支持。","敏捷开发Vue.js\u002FFlask\u002FPostgreSQL，随机森林分类器，114人","60%毕业生失业，86%愿自雇；职业预测模型测试准确率94.76%。","农业人工智能与决策模型","可将该职业决策支持框架迁移至农业领域，为农户或农技人员提供就业与创业智能推荐。","openalex","2026-09-26T23:30:50.413537Z",{"total":46,"page":47,"page_size":46,"items":48},6,1,[49,89,128,153,177,210],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":54,"content":9,"source_name":55,"source_url":52,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":56,"score_detail":57,"sources":63,"tags":65,"search_phrases":69,"slug":72,"view_count":15,"doi":73,"paper":74,"created_at":88},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":58,"substance":59,"depth":58,"authority":60,"freshness":61,"relevant":47,"comment":62},18,22,13,9,"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。",[64],{"name":55,"url":52},[21,66,22,67,68],"智慧农业","农业技术推广","小农户",[70,71],"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":73,"openalex_id":75,"authors":76,"venue":55,"cited_by_count":15,"oa_url":52,"card":82,"direction":87,"ingested_from":43},"W7214205238",[77,79],{"name":78,"orcid":9},"Pravangkar Boruah",{"name":80,"orcid":81},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":83,"method":84,"finding":85,"direction":41,"opportunity":86},"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。","数字乡村与农业信息化","2026-09-25T23:30:39.745514Z",{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":97,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":107,"search_phrases":110,"slug":113,"view_count":15,"doi":114,"paper":115,"created_at":127},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,68,{"impact":58,"substance":100,"depth":101,"authority":60,"freshness":46,"relevant":47,"comment":102},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。",[104,105],{"name":95,"url":92},{"name":95,"url":106},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[21,66,22,108,109],"可持续发展","遥感",[111,112],"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":114,"openalex_id":116,"authors":117,"venue":95,"cited_by_count":15,"oa_url":92,"card":120,"direction":126,"ingested_from":43},"W7214172367",[118],{"name":119,"orcid":9},"Saloni Ananda Patil",{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"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","2026-09-25T23:30:08.894307Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":9,"content":9,"source_name":133,"source_url":9,"published_at":134,"category":135,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":143,"tags":145,"search_phrases":148,"slug":151,"view_count":15,"doi":9,"paper":9,"created_at":152},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":138,"substance":139,"depth":58,"authority":140,"freshness":141,"relevant":47,"comment":142},24,23,12,8,"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。",[144],{"name":133,"url":131},[21,66,22,146,147],"种业振兴","数字农业发展县",[149,150],"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":154,"title":155,"url":156,"summary":157,"summary_zh":9,"content":158,"source_name":159,"source_url":9,"published_at":134,"category":160,"cover_url":9,"hotness":13,"is_selected":161,"score":162,"score_detail":163,"sources":166,"tags":168,"search_phrases":172,"slug":175,"view_count":15,"doi":9,"paper":9,"created_at":176},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":164,"substance":138,"depth":58,"authority":101,"freshness":141,"relevant":47,"comment":165},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。",[167],{"name":159,"url":156},[169,21,66,22,170,171],"农村电商","农机北斗","低空物流",[173,174],"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",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":95,"source_url":180,"published_at":96,"category":12,"cover_url":9,"hotness":97,"is_selected":14,"score":183,"score_detail":184,"sources":186,"tags":190,"search_phrases":194,"slug":197,"view_count":15,"doi":198,"paper":199,"created_at":209},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。",69,{"impact":59,"substance":58,"depth":100,"authority":60,"freshness":15,"relevant":47,"comment":185},"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[187,188],{"name":95,"url":180},{"name":95,"url":189},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[21,66,22,191,192,193],"农业物联网","气候韧性","遥感监测",[195,196],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":198,"openalex_id":200,"authors":201,"venue":95,"cited_by_count":15,"oa_url":180,"card":204,"direction":126,"ingested_from":43},"W7214083098",[202],{"name":203,"orcid":9},"Twinkal Prakash Sawant",{"tldr":205,"method":206,"finding":207,"direction":126,"opportunity":208},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":9,"content":215,"source_name":216,"source_url":9,"published_at":134,"category":160,"cover_url":9,"hotness":13,"is_selected":161,"score":162,"score_detail":217,"sources":220,"tags":222,"search_phrases":226,"slug":229,"view_count":47,"doi":9,"paper":9,"created_at":230},3289,"专家解读：乘数而上向智而行——大力发展智慧农业 加快建设数字乡村 智慧农业与小农户有机衔接","https:\u002F\u002Fwww.thepaper.cn\u002FnewsDetail_forward_34116699","专家解读文章指出：近年来农机北斗终端实现快速规模化推广，截至2025年底累计推广超350万台套。各类农业社会化服务组织加速布点，通过集采智能装备、统一调度作业、提供菜单式服务，将智能农机、无人机植保、精准施肥等先进技术和装备转化为小农户点单即享的标准化服务。十五五时期是基本实现农业农村现代化的关键时期，《加快农业农村现代化十五五规划》明确要推进人工智能运用和智慧农业发展；农业大模型、智能装备加速在生物育种、农情监测、生产管理、动植物疫病识别与防控、产量预测等场景落地。","习近平总书记高度重视数字乡村建设和智慧农业发展，作出重要指示强调，“瞄准农业现代化主攻方向，提高农业生产智能化、经营网络化水平，帮助广大农民增加收入”“要用好现代信息技术，创新乡村治理方式，提高乡村善治水平”。2019年，中共中央办公厅、国务院办公厅印发了《数字乡村发展战略纲要》。此后，中央一号文件连续八年对推进数字乡村和智慧农业作出重要部署。各地区各有关部门持续推进数字技术与农业生产、乡村生活日益融合，数字乡村建设和智慧农业发展取得重要阶段性成效。近日，国家互联网信息办公室、农业农村部联合发布《中国数字乡村发展报告（2019—2025年）》（以下简称《报告》），系统总结了七年来我国数字乡村发展的成就和经验。《报告》立足新形势新要求，展示了以信息基础设施为底座、数据资源体系为核心、智慧农业与乡村数字经济为重点、数字文化与数字治理为支撑、信息服务与智慧美丽乡村为拓展、政策机制与人才队伍为保障的体系化发展路径。该《报告》不仅为全面了解发展成效、科学谋划“十五五”数字乡村发展蓝图提供了重要参考，也积极回应各方关切，向国际社会展示了我国借助数字技术推动农业与乡村治理数智化转型的经验。回顾七年历程，智慧农业作为数字乡村建设的重要内容，已由试点探索转向快速起步、由点状突破迈向系统推进，正乘“数”而上，向“智”而行，为推进农业农村现代化提供有力支撑。\n\n一、智慧农业正从“盆景”走向“风景”\n\n数字乡村涵盖乡村经济、治理、文化、服务等多个维度，内涵丰富。《报告》提出，智慧农业是“农业新质生产力的重要内容，是乡村产业数字化的关键着力点”。智慧农业为数字乡村高质量发展提供了坚实的产业支撑，成为推动数字乡村发展的关键动能。《报告》显示，七年的探索推进和快速发展，推动智慧农业实现了“四个跨越”。\n\n第一，智慧农业基础设施实现从“基础覆盖”到“深化赋能”的跨越。完善的网络基础设施为智慧农业在田间地头、池塘圈舍的落地拓展提供了基础支撑。截至2025年底，农村地区互联网普及率达69.5%，较2018年底提升31.1个百分点。传统基础设施数字化为智慧农业提供了更加坚实的硬件底座和场景支撑，农村水利、农田、电网、公路及寄递物流等持续升级完善。农业数据资源日益丰富，为智慧农业落地应用提供了基础资源和创新引擎，“天空地一体化”监测网络等新型基础设施加快建设，全国农产品批发市场价格信息等涉农数据开发利用不断深入。\n\n第二，关键技术装备实现从“基础”到“核心”的跨越。智能农机装备研发应用取得重要进展，新一代信息技术与农业装备深度融合，正推动农业生产方式从“靠天吃饭”向“知天而作”加速转变。产学研用相衔接的智慧农业创新体系加快形成，支撑取得一批关键智慧农业技术装备创新成果。《报告》显示，截至2025年底，累计建设智慧农业创新中心、分中心34个，智慧农业创新应用项目116个，104项关键智慧农业技术和62项整机智能装备研发取得突破。智慧农业技术装备质量管控更加严格、应用推广不断拓展，布局建设国家农机装备产业计量测试中心，强化农机装备产业计算测试技术研究与应用。\n\n第三，主要产业数字化实现从“单点试验”到“面上推广”的跨越。大田种植领域，天空地一体化农情感知与数据驱动模式初步构建，实现水稻、小麦、玉米苗情长势动态监测。截至2025年底，累计推广应用各类农机北斗终端超350万台套，农用无人机保有量超过30万架、年作业面积突破4.6亿亩。智能农机共享租赁加速普及。畜禽养殖领域，精准饲喂、环境控制、行为分析等智能技术广泛应用于生猪养殖和家禽立体高效养殖中。全国659个动物防疫通道纳入信息化管理，动物检疫监督更加智能化、便捷化和高效化。渔业领域，数字技术持续赋能多元化养殖模式，智能化网箱设备、投料机器人等智能装备加速迭代，海洋养殖智能化水平不断提升。\n\n第四，粮食安全保障实现从“人工管控”到“数智赋能”的跨越。粮食安全是“国之大者”，数智技术正在为其构筑起坚实保障。在耕地保护方面，“三区三线”等“一张图”相关基础数据库进一步完善，让“藏粮于地”有了更坚实的数据底座，助力守牢18亿亩耕地红线。在种业振兴方面，中国种业大数据平台建成运行，全国农作物种质资源信息平台已上线58.8万份国家级库圃种质资源信息，为育种创新提供了坚实的资源基础。在防灾减损领域，气象预警信息全面接入全国123万个应急广播终端并在16个省份386个市县试行开展“闪信”技术应用，以气象预警为先导的应急响应联动机制更加健全。在仓储方面，借助数字化仓储技术，粮库储粮周期内综合损耗率控制在1%以内，支撑节粮减损效果明显。从种到收、从田间到粮仓，数智技术正在全链条赋能国家粮食安全保障体系。\n\n二、智慧农业发展需要坚定走好符合国情农情的路子\n\n七年来，在信息革命加速农业深刻变革的进程中，智慧农业加快发展、数字乡村建设深入推进，推动农业成为更有奔头的产业、农村成为更加宜居宜业的家园，为网络强国、农业强国建设贡献了重要力量。回顾七年实践，我们进一步深化了对智慧农业发展的规律性认识。\n\n一是政府引导与市场机制协同发力。党中央、国务院印发的《加快建设农业强国规划（2024—2035年）》、农业农村部印发的《关于大力发展智慧农业的指导意见》《全国智慧农业行动计划（2024—2028年）》等文件构建了智慧农业“四梁八柱”。七年来，从智慧农业创新中心布局到创新应用项目建设实施，从主推技术遴选、典型案例推介到智慧农业创新大赛，政府的“有形之手”在搭建平台、降低门槛、推动产业化等方面发挥了重要作用。同时，平台经济等推动拓展创业空间，返乡青年、家庭农场、农民合作社和农村个体商户以平台化方式进入市场、链接消费和重构经营模式，农村电商、数字服务等新业态加速发展，市场的“无形之手”进一步增强了智慧农业发展的动力活力。\n\n二是技术创新与农情农艺深度结合。农业不同于工业、农村不同于城市，发展智慧农业、建设数字乡村必须坚持问题导向、应用导向，走适宜化、低成本、易操作的技术路线。近年来，农机北斗终端实现快速规模化推广，在于其有效契合了播种、收获等关键环节的实际生产需求，让农民“用得上、用得起、用得好”。\n\n三是智慧农业与小农户有机衔接。“大国小农”的基本国情农情决定了智慧农业要实现大规模落地应用，必须坚持让小农户共享数字红利的现实路径。各类农业社会化服务组织加速布点，通过集采智能装备、统一调度作业、提供“菜单式”服务，将智能农机、无人机植保、精准施肥等先进技术和装备转化为小农户“点单即享”的标准化服务，有效破解小农户“买不起、用不好”的难题。以社会化服务为纽带，智慧农业正成为促进小农户与现代农业发展有机衔接的重要手段。\n\n三、奋力推进“十五五”时期智慧农业建设\n\n“十五五”时期是基本实现农业农村现代化的关键时期。展望未来五年，数字乡村将加快迈向数智乡村，智慧农业建设将进入创新发展、落地见效的关键阶段。《加快农业农村现代化“十五五”规划》明确，要“推进人工智能运用和智慧农业发展”。这要求我们既要总结运用好实践中积累形成的宝贵经验，又要准确把握未来数智技术和农业发展新趋势。\n\n当前，人工智能等数智技术加速演进，深刻重塑农业发展的底层逻辑，为智慧农业发展带来了前所未有的新机遇。一是数据产业加快培育，释放要素价值潜能。数据从支撑农业农村发展的辅助性工具，逐步发展为具有独立价值、可市场化运营的新型生产要素，其基础资源和创新引擎作用日渐显现，数智技术加速内化成为农业农村领域的发展动能，要抓实数据这个根本，进一步加快“统筹部署农业农村数据基础设施”“发展农业农村领域数据产业”。二是应用场景全链拓展，场景驱动成为重要引擎。农业大模型、智能装备加速在生物育种、农情监测、生产管理、动植物疫病识别与防控、产量预测等场景落地，场景驱动技术迭代的效能日益凸显，要打造丰富多样的应用场景，“加快农业人工智能应用场景拓展”。三是新兴产业加快培育，拓展农业发展新空间。智能设计育种、新能源农机、农业低空经济等先导性产业规模化发展，开辟智慧农业高质量发展全新赛道，要从智能育种等产业急需领域做起，加快“培育发展乡村新产业新业态”。\n\n《报告》的发布既是阶段性总结，更是新征程的动员。面向“十五五”，我们要坚决贯彻党中央、国务院关于大力推进“人工智能+”农业的部署要求，在基础设施上强基固本，在关键技术装备上聚力攻坚，在产业数智化上扩面提效，在粮食安全保障上筑牢数字防线，加快推动智慧农业从“点上突破”迈向“面上成势”，从“量的积累”转向“质的跃升”。以智慧农业的创新发展，推动数字乡村高质量发展，为加快农业农村现代化、扎实推进乡村全面振兴注入更加澎湃的数智动能。\n\n作者：李韶民 农业农村部信息中心副主任\n\n原标题：《专家解读｜乘“数”而上 向“智”而行——大力发展智慧农业 加快建设数字乡村》\n\n[阅读原文](https:\u002F\u002Fmp.weixin.qq.com\u002Fs?__biz=Mzg2ODA4MDIwNg==&mid=2247812104&idx=2&sn=0d26f89082d00b4df0d545a746ae8920&chksm=cf86c6565a4df065b13c07ba7a865f15b705b1f5daea9595cb732f4e76ca7a07f6080524f4d7&scene=7)","澎湃新闻 2026年09月23日",{"impact":164,"substance":138,"depth":58,"authority":218,"freshness":61,"relevant":47,"comment":219},14,"农业农村部信息中心专家对《中国数字乡村发展报告（2019—2025年）》的系统解读，含大量权威数据与“十五五”方向判断，政策参考价值高。",[221],{"name":216,"url":213},[21,66,22,223,224,225],"十五五规划","粮食安全","农业社会化服务",[227,228],"中国数字乡村发展报告 2019-2025","智慧农业 小农户 社会化服务","中国数字乡村发展报告2019-2025-3289","2026-09-24T00:03:56.916759Z"]