[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3630":3,"related-3630":38},{"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":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":8,"paper":8,"created_at":37},3630,"中国移动以数智之力绘就秋日好'丰'景——河南开封兰考打造全国首个'万兆+全链条智慧农业'样板，亩均增产约220斤","https:\u002F\u002Fwww.rmzxw.com.cn\u002Fc\u002F2026-09-23\u002F3980493.shtml?n2m=1","9月23日人民政协网报道，中国移动以5G、物联网、AI与自主区块链技术助力秋收：河南开封兰考县打造全国首个'万兆+全链条智慧农业'样板，依托万兆光网与'万象耕耘'农业大模型，兰考高标准农田实现亩均粮食增产约220斤，节水、节肥、节药率均超过10%，农民亩均增收节支超过600元；广东梅州金柚园区5G物联网果园监测+智慧喷淋灌溉+水肥精准调控全链条上线；黑龙江佳木斯汤原县实施智慧农业示范工程，构建大米全链路可信溯源平台、农业数据中台与多模态AI大模型。",null,"在传统印象里，养殖靠的是日积月累的经验和日复一日的盯守。如今，物联网与AI为畜禽“精准把脉”，让养殖从“凭感觉”走向“凭数据”，牧歌也唱出了新韵脚。\n\n在广东省梅州市，中国移动打造5G智慧肉鸽养殖场景。鸽舍内，AI智能管控系统为鸽子营造舒适的生长环境。在大数据可视化平台上，每一栏肉鸽的出壳时间、生长状态、出栏节点一目了然。科学养殖让肉鸽出栏更稳定、品质更可期，曾经靠人工巡棚观察的繁重劳作，如今一台屏幕便尽收眼底。岭南好货借着数智之力走出山区、走向更广阔的市场，畜牧丰收有了高质量发展的新注脚。","人民政协网 2026-09-23","2026-09-23T00:00:00Z","报道",10,false,54,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,12,8,11,5,1,"标题主打兰考万兆智慧农业样板，但正文实际写的是梅州肉鸽养殖，文题不符、内容偏企业宣传，信息增量有限，不建议进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","智慧农业","AI农业","万兆光网","5G养殖",[33,34],"兰考 万兆 智慧农业","中国移动 5G 智慧肉鸽","兰考万兆智慧农业-3630",0,"2026-09-28T00:02:57.971460Z",{"total":39,"page":22,"page_size":39,"items":40},6,[41,84,123,155,182,204],{"id":42,"title":43,"url":44,"summary":45,"summary_zh":46,"content":8,"source_name":47,"source_url":44,"published_at":48,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":50,"score_detail":51,"sources":56,"tags":58,"search_phrases":62,"slug":65,"view_count":36,"doi":66,"paper":67,"created_at":83},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","2026-09-24T00:00:00Z","论文",80,{"impact":17,"substance":52,"depth":17,"authority":53,"freshness":54,"relevant":22,"comment":55},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。",[57],{"name":47,"url":44},[27,28,59,60,61],"农业人工智能","农业技术推广","小农户",[63,64],"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":66,"openalex_id":68,"authors":69,"venue":47,"cited_by_count":36,"oa_url":44,"card":75,"direction":81,"ingested_from":82},"W7214205238",[70,72],{"name":71,"orcid":8},"Pravangkar Boruah",{"name":73,"orcid":74},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"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",{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":49,"cover_url":8,"hotness":92,"is_selected":14,"score":93,"score_detail":94,"sources":98,"tags":102,"search_phrases":105,"slug":108,"view_count":36,"doi":109,"paper":110,"created_at":122},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":17,"substance":95,"depth":96,"authority":53,"freshness":39,"relevant":22,"comment":97},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。",[99,100],{"name":90,"url":87},{"name":90,"url":101},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[27,28,59,103,104],"可持续发展","遥感",[106,107],"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":109,"openalex_id":111,"authors":112,"venue":90,"cited_by_count":36,"oa_url":87,"card":115,"direction":121,"ingested_from":82},"W7214172367",[113],{"name":114,"orcid":8},"Saloni Ananda Patil",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"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":124,"title":125,"url":126,"summary":127,"summary_zh":8,"content":8,"source_name":128,"source_url":8,"published_at":129,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":136,"tags":138,"search_phrases":142,"slug":145,"view_count":36,"doi":8,"paper":146,"created_at":154},3450,"《数智技术赋能农业新质生产力：内在机理、驱动要素与实现进路》","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fjianghuailuntan\u002Fmulu\u002F678750.html","孙壮珍从数智技术赋能农业新质生产力内在机理入手，分析数智技术赋能农业新质生产力驱动要素，并从主体-组织-制度全域视角提出通过构建利益导向机制、培育新型载体平台、推进制度调适的实现进路。文章认为农业新质生产力有高度的渗透性与广泛的链接性，能够进一步拓展农业的生产空间与效率边界，变革农业生产的工艺、技术与流程，加速我国农业强国建设的进程。","《江淮论坛》\u002F西南科技大学","2026-09-20T00:00:00Z",75,{"impact":17,"substance":132,"depth":133,"authority":134,"freshness":39,"relevant":22,"comment":135},20,17,14,"核心期刊论文，从机理、要素到实现进路系统论述数智技术赋能农业新质生产力，理论增量明确，但属学术探讨而非政策落地，时效性一般。",[137],{"name":128,"url":126},[27,28,139,140,141],"农业新质生产力","农业强国","数智技术",[143,144],"数智技术 农业新质生产力","江淮论坛 农业新质生产力","数智技术农业新质生产力-3450",{"doi":8,"openalex_id":8,"authors":147,"venue":8,"cited_by_count":36,"oa_url":8,"card":148,"direction":81,"ingested_from":153},[],{"tldr":149,"method":150,"finding":151,"direction":81,"opportunity":152},"分析数智技术赋能农业新质生产力的内在机理、驱动要素与实现进路。","理论分析，从主体-组织-制度全域视角提出机制、平台与制度调适路径。","农业新质生产力具高渗透性与广链接性，可拓展生产空间与效率边界，加速农业强国建设。","可实证检验数智技术赋能农业新质生产力的机制与制度调适效果，弥补纯理论分析不足。","agent","2026-09-25T00:09:34.845621Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":8,"content":8,"source_name":160,"source_url":8,"published_at":129,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":50,"score_detail":161,"sources":164,"tags":166,"search_phrases":170,"slug":173,"view_count":36,"doi":8,"paper":174,"created_at":181},3448,"《新质生产力赋能农业数智化转型论析》","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fhznydxxbshkxb\u002Fmulu\u002F559178.html","论文阐述新质生产力为农业数智化出场提供马克思主义生产力理论基础；指明新一轮科技革命和产业变革的历史交汇；阐明农业数智化的跃迁升级源于新质生产力的持续赋能。指出新质生产力赋能农业数智化转型面临'人-物-链'系统性、闭环型困境：农业数智化人才匮乏、基础设施滞后、产业链供应链集成化水平较低、产业政策支持力度不足。建议以推动农业颠覆性科技创新运用为核心，以构建农业数智化生态链和加速农业全要素提质增效为两翼。","《华中农业大学学报（社会科学版）》",{"impact":52,"substance":162,"depth":17,"authority":134,"freshness":21,"relevant":22,"comment":163},21,"核心期刊论文，从马克思主义生产力理论切入系统剖析农业数智化转型的“人-物-链”困境与对策，理论深度与政策参考价值兼具，但属学术论析而非新政策或数据发布，时效性一般。",[165],{"name":160,"url":158},[27,28,167,168,169],"新质生产力","农业科技创新","农业数智化",[171,172],"华中农业大学学报 农业数智化","新质生产力 农业转型","华中农业大学学报农业数智化-3448",{"doi":8,"openalex_id":8,"authors":175,"venue":8,"cited_by_count":36,"oa_url":8,"card":176,"direction":81,"ingested_from":153},[],{"tldr":177,"method":178,"finding":179,"direction":81,"opportunity":180},"从马克思主义生产力理论出发，分析新质生产力赋能农业数智化转型的困境与路径。","理论分析与政策论述，围绕'人-物-链'框架展开。","农业数智化面临人才匮乏、设施滞后、产业链集成低、政策支持不足等系统性困境。","可针对'人-物-链'困境开展实证测度与区域差异研究，检验政策干预效果。","2026-09-25T00:09:34.724043Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":8,"content":187,"source_name":188,"source_url":8,"published_at":189,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":193,"tags":195,"search_phrases":199,"slug":202,"view_count":36,"doi":8,"paper":8,"created_at":203},3417,"卫星技术赋能黑土良田——佳木斯打造'卫星+农业'特色样板，沿江镇'中国智慧农业第一镇'项目启动","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7687111511378117146\u002F","第四届佳木斯卫星产业发展大会暨商业航天产业技术创新联盟会议将于9月21—23日召开。作为全国首批北斗规模应用试点城市，佳木斯大力发展'卫星+农业'应用场景。今年5月由佳木斯市工信局、郊区人民政府、黑龙江省农科院佳木斯分院等单位联合发起的中国智慧农业第一镇暨'卫星+农业'项目正式启动，以郊区沿江镇为先行示范区，按照'单品示范破题—农户规模参与—镇域全链产业化'三步走路径梯次推进。","## 卫星技术赋能黑土良田佳木斯打造“卫星＋农业”特色样板\n\n2026-09-19 13:19·[金台资讯](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAL8m7IqT5eR3-VgZjzlMhg66-z57FyRmydYIImMLAsQ4\u002F?source=tuwen_detail)\n\n人民网哈尔滨9月19日电 (记者尚城)第四届佳木斯卫星产业发展大会暨商业航天产业技术创新联盟会议将于9月21日—23日正式拉开帷幕。这是“华夏东极”连续举办的第四届卫星产业大会，也是佳木斯再一次向全国公众展示佳木斯商业航天发展成果的舞台。\n\n作为全国首批北斗规模应用试点城市，佳木斯充分发挥黑土、水系、农林等地理优势及全国重要商品粮基地的产业优势，大力发展“卫星+农业”应用场景。今年5月，由佳木斯市工信局、郊区人民政府、黑龙江省农科院佳木斯分院等单位联合发起的中国智慧农业第一镇暨“卫星+农业”项目正式启动，并以郊区沿江镇为先行示范区，依托其独特区位禀赋、优质黑土农业资源、完备基础设施及卫星产业配套优势，着力打造国家级智慧农业技术集成高地和现代农业示范观摩窗口，积极探索可复制、可推广的智慧农业发展佳木斯样板。\n\n据了解，该项目按照“单品示范破题—农户规模参与—镇域全链产业化”三步走路径梯次推进。搭建集遥感苗情监测、AI病虫害诊断、墒情灾情预警、线上农技服务、农产品溯源等于一体的智慧农业管理平台，构建全链条数字化闭环管理体系，有效减少了化肥、农药施用量，提升主要粮油作物平均单产。通过卫星数据与农业生产的深度融合，把卫星数据转化为田间生产力，探索可复制、可推广的“卫星+农业”佳木斯模式，为保障国家粮食安全注入航天科技力量。\n\n依托本届卫星产业发展大会，佳木斯将持续做强卫星测控优势，拓展空天信息应用场景，以卫星技术赋能黑土粮仓，全力打造国内知名的卫星测控与空天应用产业高地。","金台资讯\u002F人民网哈尔滨 2026-09-19","2026-09-19T00:00:00Z",72,{"impact":52,"substance":17,"depth":96,"authority":18,"freshness":21,"relevant":22,"comment":192},"央媒报道的卫星遥感赋能黑土农业示范项目，路径清晰、应用场景具体，具备行业参考价值。",[194],{"name":188,"url":185},[27,28,196,197,198],"黑土地保护","卫星遥感","北斗应用",[200,201],"佳木斯 卫星 沿江镇","北斗 智慧农业 黑土","佳木斯卫星沿江镇-3417","2026-09-25T00:09:31.179492Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":8,"content":8,"source_name":209,"source_url":8,"published_at":210,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":211,"score_detail":212,"sources":214,"tags":216,"search_phrases":220,"slug":223,"view_count":36,"doi":8,"paper":8,"created_at":224},3403,"指尖办农事、数字助粮安——齐鲁现代农事综合服务中心管理平台上线——省农科院水肥智慧大模型支撑","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260921A06Z6K00","山东省委一号文件要求布局建设现代农事综合服务中心，全省新增水肥一体化推广面积300万亩。齐鲁现代农事综合服务中心管理平台在德州首发，把政府政策、农户需求、金融资源、社会服务有效贯通，推动农业社会化服务从'碎片化供给'向'集成化服务'跃升。山东省农科院作物所副所长李娜娜发布'水肥智慧大模型'，汇聚土壤、墒情、气象、苗情等海量数据，针对不同地块、不同作物智能输出个性化的灌溉施肥方案。","腾讯新闻\u002F大众日报 2026-09-21","2026-09-21T00:00:00Z",74,{"impact":52,"substance":132,"depth":95,"authority":13,"freshness":39,"relevant":22,"comment":213},"省级平台首发叠加水肥智慧大模型落地，政策与数据细节扎实，对农业社会化服务数字化有示范价值。",[215],{"name":209,"url":207},[27,28,217,218,219],"农业大模型","水肥一体化","农业社会化服务",[221,222],"山东 现代农事综合服务中心","水肥智慧大模型 山东","山东现代农事综合服务中心-3403","2026-09-25T00:09:30.111193Z"]