[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3691":3,"related-3691":37},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":8,"created_at":36},3691,"江苏省数字乡村建设工作现场推进会在海安召开","https:\u002F\u002Fwww.haian.gov.cn\u002Fhasrmzf\u002Fhayw\u002Fcontent\u002Ff148bf33-fa41-4473-9a92-2afc386ead36.html","9月17日江苏省委网信办、省农业农村厅在海安召开数字乡村建设工作现场推进会，公布第三批省级数字乡村试点地区名单。强调以人工智能+农业141行动为抓手，聚焦数智兴业、数智惠民、数智善治，推动数字技术与农业生产、经营、服务深度融合。",null,"9月17日，省委网信办、省农业农村厅在海安召开江苏省数字乡村建设工作现场推进会，总结交流数字乡村建设进展成效，研究安排下一阶段重点工作。省委宣传部副部长、省委网信办主任杨力群，省委农办主任、省农业农村厅厅长、党组书记季辉出席会议并讲话，省农业农村厅副厅长曹丽虹公布第三批省级数字乡村试点地区名单，省委网信办副主任赵明主持会议，南通市副市长张一峰致辞，海安市委常委、宣传部部长朱元星作交流发言。\n\n会议指出，数字乡村发展是乡村振兴的战略方向，是数字时代推进农业农村现代化进程的必由之路。“十五五”时期是基本实现社会主义现代化夯实基础、全面发力的关键时期，也是全省数字乡村发展从夯基垒台向整体跃升转变的关键阶段。要进一步增强做好数字乡村建设工作的责任感使命感，加快推进网络强省、数字江苏和农业强省建设，以数字乡村高质量发展推动农业增效益、农民增收入、农村增活力。\n\n会议强调，要以强农、惠农、富农为目标，高质量推进数字乡村建设，为推进乡村全面振兴、加快农业农村现代化提供坚实信息化支撑。要聚焦数智兴业，推动乡村产业提质增效，以人工智能+农业“141”行动为抓手，加快技术创新集成，分类拓展产业应用，畅通农产品“数智流通”渠道，培育乡村“数字新业态”，推动数字技术与农业生产、经营、服务深度融合。要聚焦数智惠民，推动乡村建设提质增效，强化数字基础设施支撑，加强信息惠农便民，打造智慧绿色乡村，以数智化赋能宜居宜业和美乡村建设。要聚焦数智善治，推动乡村治理提质增效，提升数字治理服务效能，加强涉农数据资源整合共享，深化“指尖上的形式主义”整治，构建共建共治共享的乡村数字治理新格局。\n\n近年来，海安深入学习贯彻习近平总书记关于网络强国的重要思想，认真落实党中央决策部署和省委、省政府工作要求，围绕“产业数字化、治理智能化、服务便捷化”核心目标，坚持“强体系、布场景、优平台、共发展”工作思路，全域推进数字乡村建设，各项工作取得一定成效。“海略协作”入选国家数字乡村机制共建型试点，创成国家现代农业产业园，入选国家农业现代化示范区创建名单，累计培育市级以上农业龙头企业106家（国家级5家），粮食生产全程机械化率达98.6%，华艺集团“AI智能机器人赋能非遗扎染”项目入选长三角数字乡村建设典型案例。\n\n2024年，海安与略阳共同入选第二批国家数字乡村试点，一个是江海之滨的“吨粮县”，一个是秦岭腹地的“宝藏县”，两地跨越千里、携手同行，肩负起东西部协作机制共建的国家使命。入选试点以来，海安始终坚持“略阳所需、海安所能”，以数字赋能为纽带，探索出了一条山海协作、数智赋能的乡村振兴新路子。把机制建设作为“先手棋”，搭建组织架构、强化资金保障、深化治理互鉴，推动两地信息互通、资源共享。把海安智慧农业经验转化为可输出、可复制的协作资源，推动智慧种养落地、促进数字平台共享、畅通产销对接渠道，推动略阳农业生产方式由“会”变“慧”。坚持线上线下联动，医疗帮扶“云端守护”、基础教育“数字赋能”、技能培育“山海联动”，把数字红利跨越山海送到略阳百姓身边。下一步，海安将以此次现场推进会为契机，锚定国家数字乡村试点验收目标，学习借鉴先进经验，持续深化海略全方位协作，以硬核数字化成果激活乡村振兴新动能，奋力书写东西部协作和数字乡村建设优异答卷。\n\n无锡宜兴市、常州市天宁区、苏州市相城区、南通市通州区、连云港市赣榆区、盐城市建湖县、宿迁市宿豫区等7个县（市、区）入选第三批江苏省数字乡村试点。\n\n会上，有关专家作人工智能赋能数字乡村建设专题辅导，南京市高淳区、海安市、常州市金坛区儒林镇、省农垦集团公司等作交流发言。","海安市人民政府","2026-09-17T12:00:00Z","政策",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},22,18,15,5,1,"省级数字乡村推进会，含第三批试点名单与人工智能+农业行动部署，政策信息量足但时效稍旧，可入精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","农业人工智能","乡村振兴","东西部协作","试点建设",[32,33],"江苏 数字乡村 推进会","海安 略阳 数字乡村试点","江苏数字乡村推进会-3691",0,"2026-09-29T00:08:18.529642Z",{"total":38,"page":21,"page_size":38,"items":39},6,[40,66,90,114,150,186],{"id":41,"title":42,"url":43,"summary":44,"summary_zh":8,"content":45,"source_name":46,"source_url":8,"published_at":47,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":49,"score_detail":50,"sources":56,"tags":58,"search_phrases":61,"slug":64,"view_count":35,"doi":8,"paper":8,"created_at":65},2552,"数智助农成服贸会金融专题展亮点 农业银行展示AI数字人农小耘","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685207598819279360\u002F","9月14日服贸会报道。中国农业银行以数智助农乡村振兴为主题展示智能化金融服务体系，AI能力嵌入信贷评估、风险核查等环节；农户在村里线上提交申请后系统可辅助完成信息核验与初步授信。北京农商银行展示非接触AI健康检测机器人；北京银行展示的科创企业技术已在智慧农业、农产品溯源等场景落地。","## 数智助农成服贸会金融专题展亮点\n\n2026-09-14 10:12·[中国网三农](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAbAvyMQxxaA9sEwA2aUWsAOJjrH-pK95hBvQUB6GaSEg\u002F?source=tuwen_detail)\n\n近日，北京首钢园迎来2026年中国国际服务贸易交易会（以下简称“服贸会”），金融服务专题展区以“智焕新生 金融聚力”为主题。记者在展区现场看到，各家展台前人头攒动，数智金融如何下沉乡村，成为不少观众关注的焦点。\n\n中国农业银行展台以绿色为主调，“数智助农”“乡村振兴”字样醒目。一位观众朝屏幕招手，AI数字人“农小耘”随即回应。工作人员介绍，农行此次展示了智能化金融服务体系，AI能力可嵌入信贷评估、风险核查等环节，农户在村里线上提交申请后，系统可辅助完成信息核验与初步授信，不必再往返县城网点。服务半径由此延伸到村口，数字技术让金融支农更直达。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fc75fa582de054ded9aaf0da332891c36~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121915&x-signature=%2FPNFaboCnXA%2F%2F0wpGhoOm4cWxLI%3D)\n\n中国农业银行展区，观众正在智能点餐机前扫码点单\n\n另一侧，北京农商银行展区排起小队。观众刘女士坐到一台智能检测机前，无需接触硬件，数秒后健康筛查报告呈现在显示屏上。“这是我们研发的非接触AI健康检测机器人，数秒完成多维度生理指标筛查。”耀眼科技副经理王俊才介绍，该设备已在北京亦庄荣华街道智慧康养机器人养老驿站试点，千余名体验者中农民占三分之一。由于部分农村地区居民健康筛查不便，银行网点正好可以成为服务下沉的入口。北京农商银行与耀眼科技合作，计划将AI健康检测机器人嵌入网点，依托养老助残卡、第三代社保卡，构建“金融网点+健康服务”便民模式。\n\n北京银行展台同样热闹。环抱式“金融会客厅”里，中轴线手绘长卷与科技产品相映成趣。环廊陈列的25件前沿科技产品，多来自该行长期支持的科创企业。记者了解到，这些企业的技术已在智慧农业、农产品溯源等场景落地，部分科创企业还获得该行专项信贷支持，将AI、物联网技术带入设施农业园区。从支持科创企业到技术反哺农业，金融正成为连接科技与乡村的桥梁。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002F21c30bfe934940439449f459252e41d8~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790121915&x-signature=9CqAJQPWYzu3ZBgbNjWmOZ%2FwyxA%3D)\n\n北京银行展区，观众驻足观看机器人演奏\n\n穿行展区，记者感到，本次服贸会金融服务专题展示的不只是技术，更是服务民生、助力“三农”的路径探索。资金、科技与乡村加速融合，数智技术正让服务更贴近乡土，为乡村全面振兴提供持久支撑。\n\n来源：农民日报","中国网三农 2026-09-14","2026-09-14T02:00:00Z","报道",55,{"impact":51,"substance":52,"depth":51,"authority":53,"freshness":54,"relevant":21,"comment":55},12,13,11,7,"服贸会金融展上的数智助农展示，属企业宣传性综合报道，有AI数字人、非接触健康检测等具体案例，但无政策条款或新数据，适合作为主题页聚合素材而非每日精选头条。",[57],{"name":46,"url":43},[26,59,27,28,60],"智慧农业","数字金融",[62,63],"农业人工智能 乡村振兴 数字乡村 数字金融","农业人工智能 乡村振兴","农业人工智能乡村振兴数字乡村数字金融-2552","2026-09-16T00:03:45.025672Z",{"id":67,"title":68,"url":69,"summary":70,"summary_zh":8,"content":8,"source_name":71,"source_url":8,"published_at":72,"category":48,"cover_url":8,"hotness":73,"is_selected":14,"score":74,"score_detail":75,"sources":79,"tags":84,"search_phrases":86,"slug":88,"view_count":21,"doi":8,"paper":8,"created_at":89},2186,"人民日报理论版:推动人工智能更好服务乡村全面振兴","https:\u002F\u002Ftheory.people.com.cn\u002Fn1\u002F2026\u002F0908\u002Fc40531-40794561.html","人民日报理论版9月8日刊发国家发改委产业经济与技术经济研究所涂圣伟研究员等文章,系统阐述人工智能驱动乡村振兴的内在机理。文章认为人工智能推动农业生产由经验驱动转向数据驱动,在四川崇州天府粮仓核心区,人工智能温室控制使樱桃番茄年产量达每平方米18-20公斤,超过国内成熟产区平均水平;在广西河池宜州区,村务AI助理平台已注册用户超6万人,累计服务村民超2.1万次,推动乡村治理从粗放式向精细化转型。","人民日报理论 2026年9月8日","2026-09-08T00:00:00Z",25,86,{"impact":76,"substance":17,"depth":77,"authority":19,"freshness":38,"relevant":21,"comment":78},26,17,"人民日报理论版刊发国家发改委研究员文章，系统阐述AI驱动乡村振兴机理并附崇州、河池实测数据，政策层级与信息增量俱佳，虽已过4天仍具精选价值。",[80,81],{"name":71,"url":69},{"name":82,"url":83},"国际在线","https:\u002F\u002Fgr.cri.cn\u002F20260928\u002Ff2ee14b0-db6e-414b-83da-11327c9f357d.html",[26,85,59,27,28],"乡村治理",[87,63],"农业人工智能 乡村振兴 乡村治理 数字乡村","农业人工智能乡村振兴乡村治理数字乡村-2186","2026-09-12T00:06:35.444700Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":8,"content":95,"source_name":96,"source_url":8,"published_at":97,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":104,"tags":106,"search_phrases":109,"slug":112,"view_count":35,"doi":8,"paper":8,"created_at":113},3693,"数智活水润乡村：人工智能赋能乡村治理产业发展民生提质","http:\u002F\u002Fdicn.china.com.cn\u002F2026-09\u002F23\u002Fcontent_43495559.shtml","中央网信办等部门联合印发《数字乡村高质量发展行动计划(2026—2030年)》。AI化身政策宣讲员、智能机器人深度融入村级政务，AI数字人主播打破传统直播间时空限制，2025年农村网络零售额突破3万亿元，乡村电商新业态不断壮大。","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)》,明确了“十五五”时期推进数字乡村高质量发展的思路目标、重点任务和政策举措。如今,从高效便捷的田间“新农具”,到助农增收的直播间“新农活”,数字技术全方位赋能乡村治理、产业发展与民生提质,为乡村全面振兴注入强劲数智动能。\n\n推进数字乡村建设向纵深发展,不止于乡村网络基础设施的全面升级,更在于智能数字工具深度扎根乡村基层治理各场景,让乡村治理更高效、更精细、更便民。各地乡村积极活用人工智能优势,让其化身专属政策宣讲员,依托智能语音、方言适配等功能,常态化解读惠农补贴、医保社保、乡村建设等各类惠民政策,打破时空限制,将政策条文转化为接地气、听得懂的家常话语,让惠民政策直达民心。\n\n同时,智能机器人搭载智能导办、材料预审、信息推送等功能,深度融入村级政务服务体系,助力村民高频事项“全程网办、便捷快办”,大幅提升乡村政务服务效率。在乡村应急预警、网格管理、人居环境整治等工作中,智能监测体系精准助力乡村精细化治理,持续优化乡村治理模式,以科技力量筑牢乡村善治根基,让数字化治理红利惠及千家万户。\n\n在很多地方,数字技术深入乡村产业一线,成为助力农户增收、激活乡村业态的新力量。依托数字化技术优势,AI数字人主播打破传统直播间的时间、人力限制,可全天候值守电商直播间,生动推介乡村特色农产品、展示非遗手工技艺、传播乡土文旅资源,让藏在深山田野的优质物产、特色文化走出乡村、走向全国。\n\n在数智化工具的赋能加持下,乡村数字经济持续蓬勃发展,2025年农村网络零售额已突破3万亿元,乡村电商新业态不断壮大,乡村主播、农货选品师等新职业持续涌现。数字技术有效补齐乡村电商人才缺口、降低乡村创业门槛,搭建起高效畅通的农产品上行通道,让线上流量转化为产业增量、农户收益,以数字新业态激活乡村产业新活力,为产业振兴赋能增效。\n\n随着数字乡村建设持续深化,乡村数字素养培育工作稳步推进,到2030年我国农村成年人具备初级及以上数字素养与技能的占比要提升至55%,越来越多村民将熟练运用数字工具、拥抱数字红利。\n\n数字兴则乡村兴。各地要持续用好数字技术这把“金钥匙”,深耕乡村应用场景、拓宽赋能边界,让智能新农具扎根田野、数字新农活落地生根,持续释放数字乡村发展红利,全方位助力农业提质、农民增收,为乡村全面振兴注入源源不断的新动能。(唐代远)","中国网·数智中国","2026-09-23T00:00:00Z",81,{"impact":100,"substance":101,"depth":19,"authority":102,"freshness":20,"relevant":21,"comment":103},27,20,14,"紧扣三部委数字乡村新行动计划，政策层级高、数据与场景扎实，但属综述性报道，时效略滞后。",[105],{"name":96,"url":93},[107,26,85,27,108],"农村电商","数字素养",[110,111],"农村网络零售额 3万亿","农业人工智能 乡村治理 农村电商 数字乡村","农村网络零售额3万亿-3693","2026-09-29T00:08:18.803559Z",{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":8,"source_name":120,"source_url":117,"published_at":121,"category":122,"cover_url":8,"hotness":13,"is_selected":14,"score":35,"score_detail":123,"sources":125,"tags":127,"search_phrases":129,"slug":132,"view_count":35,"doi":133,"paper":134,"created_at":149},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","East African Journal of Information Technology","2026-09-24T00:00:00Z","论文",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":124},"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。",[126],{"name":120,"url":117},[26,27,128],"就业决策支持",[130,131],"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":133,"openalex_id":135,"authors":136,"venue":120,"cited_by_count":35,"oa_url":141,"card":142,"direction":146,"ingested_from":148},"W7214211658",[137,139],{"name":138,"orcid":8},"Ester Philipo Lulale",{"name":140,"orcid":8},"Alfred Kajirunga","https:\u002F\u002Fjournals.eanso.org\u002Findex.php\u002Feajit\u002Farticle\u002Fdownload\u002F5847\u002F6175",{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"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",{"id":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":8,"source_name":156,"source_url":153,"published_at":121,"category":122,"cover_url":8,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":161,"tags":163,"search_phrases":166,"slug":169,"view_count":35,"doi":170,"paper":171,"created_at":185},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":18,"substance":17,"depth":18,"authority":52,"freshness":159,"relevant":21,"comment":160},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。",[162],{"name":156,"url":153},[26,59,27,164,165],"农业技术推广","小农户",[167,168],"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":170,"openalex_id":172,"authors":173,"venue":156,"cited_by_count":35,"oa_url":153,"card":179,"direction":184,"ingested_from":148},"W7214205238",[174,176],{"name":175,"orcid":8},"Pravangkar Boruah",{"name":177,"orcid":178},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":180,"method":181,"finding":182,"direction":146,"opportunity":183},"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":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":192,"source_url":189,"published_at":193,"category":122,"cover_url":8,"hotness":73,"is_selected":14,"score":194,"score_detail":195,"sources":198,"tags":202,"search_phrases":205,"slug":208,"view_count":35,"doi":209,"paper":210,"created_at":222},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",68,{"impact":18,"substance":196,"depth":19,"authority":52,"freshness":38,"relevant":21,"comment":197},16,"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。",[199,200],{"name":192,"url":189},{"name":192,"url":201},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[26,59,27,203,204],"可持续发展","遥感",[206,207],"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":209,"openalex_id":211,"authors":212,"venue":192,"cited_by_count":35,"oa_url":189,"card":215,"direction":221,"ingested_from":148},"W7214172367",[213],{"name":214,"orcid":8},"Saloni Ananda Patil",{"tldr":216,"method":217,"finding":218,"direction":219,"opportunity":220},"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"]