[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2081":3,"related-2081":62},{"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,"primary_topic":8,"secondary_topics":32,"classification":33,"search_phrases":56,"slug":59,"view_count":60,"doi":8,"paper":8,"created_at":61},2081,"山东临沭罗庄黄山镇\"低成本轻量化\"基层数字治理经验获山东省大数据局推介","https:\u002F\u002Fdsjj.linyi.gov.cn\u002Finfo\u002F1312\u002F6520.htm","山东临沂罗庄区黄山镇依托低成本物联改造、轻量化数字履职、全维度巡防感知,因地制宜探索基层数字治理模式,获评山东省大数据局2025年度公共视频智能应用示范案例。该镇布设55组智能接收终端完成应急广播数字化改造,为344户重点农户安装一氧化碳中毒智能报警器,搭建\"电力大数据\"监管模型、空天地一体感知网络,实现\"人力蹲守\"向\"智能预警\"转变。",null,"[无障碍浏览](javascript:void(0);)\n\n*   [首页](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex.htm \"首页\")\n*   [机构职能](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fzfxxgk\u002Fjgzn.htm \"机构职能\")\n*   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37130202372003号](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=37130202372003)[网站地图](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex\u002Fwzdt.htm)","临沂市大数据局","2026-09-07T00:00:00Z","报道",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,15,9,6,1,"山东省级示范案例，低成本轻量化数字治理路径具体可复制，数据与场景细节扎实，对数字乡村建设有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","物联网","基层治理","电力大数据","智能巡防",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":54,"reason":55},[39,44,49],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"G5",[],[],[29],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"I5",[],[],[28],{"code":50,"score":22,"supported":14,"title":51,"abstract":52,"tags":53},"I6",[],[],[27],true,"缺少足够的主题证据",[57,58],"电力大数据 基层治理 数字乡村 智能巡防","电力大数据 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数字乡村试点","美人腿 数字乡村驾驶舱","相城区数字乡村试点-3705","2026-09-29T00:08:19.875863Z",{"id":117,"title":118,"url":119,"summary":120,"summary_zh":8,"content":121,"source_name":122,"source_url":8,"published_at":71,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":126,"tags":128,"primary_topic":8,"secondary_topics":131,"classification":132,"search_phrases":146,"slug":149,"view_count":60,"doi":8,"paper":8,"created_at":150},3694,"智治平台托起群众身边小事","https:\u002F\u002Fwww.zyshgzb.gov.cn\u002Fn1\u002F2026\u002F0924\u002Fc462652-40805045.html","浙江缙云迭代升级141基层治理体系，融合矛盾纠纷调处、社会治安防控、应急管理指挥、社会工作服务四大板块。游客反映景区商户强买强卖问题当日完成核查处置反馈。贵州新蒲新区六角井社区智慧管理主控平台联动居民使用端口，AI智慧眼系统一分钟内完成消防通道工单派发。","数字技术是推进基层治理现代化的重要支撑。当前，各地党委社会工作部门立足基层治理实际，把数字化建设作为破题抓手，搭建智治平台、打通数据壁垒、重塑处置流程，推动基层治理从被动处置向主动服务、分散管理向系统集成、线下跑腿向云端智办转变，不断释放治理效能，让群众切实感受到可感可及的民生温度。\n\n平台集成是数字赋能基层治理的基础，通过整合分散资源，破解条块分割、多头管理等难题。浙江省丽水市缙云县以迭代升级“141”基层治理体系为总牵引，重构基层治理平台，迭代矛盾纠纷调处、社会治安防控、应急管理指挥、社会工作服务等四大板块功能，融合乡镇岗位管理改革，健全闭环运行体系，理顺“以块为主、条块结合”运行模式，实现跨部门快速联动处置。\n\n“反映的问题在一天内就完成联合处置，效率太高了。”一位来到缙云县的游客由衷赞叹。针对游客反映的某景区商户强买强卖问题，缙云县委社会工作部管理社会治理中心依托“141”基层治理体系联动优势，快速统筹市场监管、旅游主管部门及属地乡镇，组建联合执法队伍，当日便完成核查、处置、反馈全流程，切实维护游客合法权益，彰显了基层治理的速度与力度。\n\n此外，缙云县打造“云上社区”平台，归集民政、人社、医保等20余个部门150余项便民服务，吸纳百余家社会组织入驻，群众可以一键上报隐患、查询政策、反馈诉求，构建“指尖办事、线上共治”的基层治理新模式，不断提升群众的获得感、幸福感、安全感。\n\n贵州省遵义市新蒲新区六角井社区创新引入智慧社区建设试点项目，通过搭建智慧管理主控平台（电脑端）、工作人员使用端口（APP）、居民使用端口（小程序）三个主要端口，联动激活社区、网格员和居民群众手中的“三块屏幕”，以高效率数智赋能推动打造高质量智慧社区。新蒲新区礼仪街道新民社区依托人工智能“智慧眼”系统，能够实现人脸识别、火情预警等智能分析，有效降低社区安全风险。社区一栋单元楼的消防通道被外来车辆占用后，人工智能系统迅速捕捉异常并触发预警，一分钟内完成工单派发，社区工作者快速抵达现场劝导，消防通道迅速恢复畅通。\n\n数字平台不仅是技术载体，更是治理资源的整合枢纽，通过平台重构，打破了部门壁垒，实现治理力量的集约调度。\n\n畅通需求渠道、健全闭环处置机制，让群众需求事事有回音，是数字治理的目标。山西省吕梁市中阳县创新探索“书记接单马上就办”云端便民服务，在直播间搭建政民互动直通车，通过弹幕留言、在线连麦、后台私信3种方式，建立线上反映渠道，同时健全“群众点单—平台接单—分类派单—限时办理—群众回访—办结销号”全链条闭环机制，明确办理责任与时限，组建督查小组动态督办，办结事项公开公示，接受群众评价监督。同步开展线下入户走访，兼顾不同群体需求，推动便民服务从群众上门求助向干部主动响应转变。\n\n从“事后处置”转向“未诉先办”，数字技术正推动治理逻辑发生根本性转变。四川省成都市武侯区坚持党建引领，立足实用管用、惠民便民导向，打造一系列本土化智慧治理场景，推动服务端口前移，实现民情早知晓、难题早化解、服务快落地，让智慧治理惠及千家万户。该区打造“社区主题数据库”，整合相关数据，依托智能算法自动沉淀、动态更新、实时校准，兼具自定义查询、数据导出、工作预判等功能，让社区治理实现底数清、靶向准、服务实。武侯区晋阳街道吉福社区立足老年居民占比高的实际情况，建设智慧应用场景，安装智能防摔倒感应装置，一旦监测到异常情况，第一时间向物业服务企业、微网格长推送预警信息，实现风险早发现、隐患快处置，全力守护老年人居家安全。\n\n数字赋能基层治理，技术是手段，为民是根本。数字化改革绝非简单把线下工作搬到线上，而是机制重塑、资源整合、服务升级的系统性工程。各地党委社会工作部门坚持党建引领，以群众获得感作为检验成效标尺，持续优化数字平台功能，完善网格与数字平台联动机制，引导社会组织、群众广泛参与，让数字技术真正扎根基层土壤，不断绘就共建共治共享的基层治理画卷。","中共中央社会工作部",0,{"impact":123,"substance":123,"depth":123,"authority":123,"freshness":123,"relevant":123,"comment":125},"内容为基层数字治理与智慧社区实践，属社会治理范畴，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。",[127],{"name":122,"url":119},[27,129,29,130],"数字治理","智慧社区",[],{"status":133,"locked":14,"version":35,"method":36,"evidence":134},"needs_review",{"candidates":135,"agricultural_evidence":14,"reason":145},[136,141],{"code":40,"score":137,"supported":54,"title":138,"abstract":139,"tags":140},5,[],[29],[29,129],{"code":50,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[27],"农业相关性需要复核",[147,148],"缙云县 基层治理体系","新蒲新区 智慧社区 试点","缙云县基层治理体系-3694","2026-09-29T00:08:18.894955Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":8,"content":8,"source_name":156,"source_url":8,"published_at":157,"category":158,"cover_url":8,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":8,"secondary_topics":168,"classification":169,"search_phrases":185,"slug":188,"view_count":189,"doi":8,"paper":190,"created_at":199},2613,"新质生产力赋能农村基层数字治理的困境与路径","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1074544479_121123708","《当代农村财经》2026年第9期刊发。探讨新质生产力赋能农村基层数字治理的困境与路径。","当代农村财经 2026年第9期","2026-09-10T01:00:00Z","论文",70,{"impact":17,"substance":18,"depth":161,"authority":74,"freshness":21,"relevant":22,"comment":162},17,"核心期刊论文，聚焦新质生产力赋能农村基层数字治理的困境与路径，属数字乡村治理议题，有专业深度但偏理论探讨，时效性一般。",[164],{"name":156,"url":154},[27,166,29,167],"新质生产力","农村数字治理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":170},{"candidates":171,"agricultural_evidence":54,"reason":55},[172,176,180],{"code":40,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[29],{"code":50,"score":22,"supported":14,"title":177,"abstract":178,"tags":179},[],[],[27],{"code":181,"score":22,"supported":14,"title":182,"abstract":183,"tags":184},"J2",[],[],[166],[186,187],"农村数字治理 新质生产力 基层治理 数字乡村","农村数字治理 新质生产力","农村数字治理新质生产力基层治理数字乡村-2613",3,{"doi":8,"openalex_id":8,"authors":191,"venue":8,"cited_by_count":123,"oa_url":8,"card":192,"direction":196,"ingested_from":198},[],{"tldr":193,"method":194,"finding":195,"direction":196,"opportunity":197},"探讨新质生产力赋能农村基层数字治理的困境与路径。","政策分析与理论探讨，无实证数据。","新质生产力可赋能农村基层数字治理，但面临现实困境需路径优化。","数字乡村与农业信息化","可结合案例或实证数据，检验新质生产力对农村数字治理绩效的影响机制。","agent","2026-09-16T00:03:52.570396Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":8,"source_name":206,"source_url":203,"published_at":207,"category":158,"cover_url":8,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":212,"tags":214,"primary_topic":219,"secondary_topics":220,"classification":221,"search_phrases":248,"slug":251,"view_count":123,"doi":252,"paper":253,"created_at":270},5581,"How Does Digital Literacy Promote Inclusive Rural Growth? Micro-Level Evidence from Income Inequality Among Farm Households","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010689","Against the backdrop of the coordinated advancement of digital village construction and the common prosperity strategy, the widespread improvement in rural households’ digital literacy is emerging as a key force in reshaping rural income distribution and enabling sustainable rural development. Whether digital dividends can equitably benefit farm households with heterogeneous endowments has a direct bearing on the inclusive growth outcomes of digital village construction and the sustainable advancement of the common prosperity goal. Using three waves of panel data from the China Family Panel Studies (CFPS) covering 2018 to 2022, this paper empirically examines the impact of digital literacy on income inequality among rural households. The findings indicate that improvements in digital literacy significantly reduce rural household income inequality, and this conclusion remains robust across a series of robustness checks. Mechanism analysis reveals that digital literacy mainly curbs the widening of rural household income gaps through two core channels: expanding social capital and alleviating credit constraints. Heterogeneity analysis shows that the inequality-reducing effect of digital literacy is more pronounced among households that depend on non-agricultural income, have lower levels of human capital, or fall into relatively lower income brackets. Further analysis demonstrates that digital literacy significantly reduces inequalities in wage income, business income, and property income across rural households, whereas its mitigating effect on transfer income inequality is not statistically significant. Meanwhile, digital literacy significantly raises the probability of upward income mobility among rural households, thereby alleviating income inequality from a dynamic perspective. On the basis of these findings, this paper proposes policy implications in several areas: building a hierarchical and categorized rural digital literacy cultivation system, fostering a virtuous cycle of digital empowerment, equal opportunity, and income convergence, and optimizing income distribution structures and dynamic mobility mechanisms, with the aim of accelerating progress toward common prosperity.","在数字乡村建设与共同富裕战略协同推进的背景下，农户数字素养的普遍提升正成为重塑乡村收入分配格局、实现乡村可持续发展的重要力量。数字红利能否公平惠及禀赋异质性农户，直接关系到数字乡村建设的包容性增长成效与共同富裕目标的可持续推进。本文利用中国家庭追踪调查（CFPS）2018—2022年三期面板数据，实证检验数字素养对农户收入不平等的影响。研究发现，数字素养的提升显著降低了农户收入不平等，这一结论在一系列稳健性检验后依然成立。机制分析表明，数字素养主要通过拓展社会资本与缓解信贷约束两条核心渠道抑制农户收入差距的扩大。异质性分析显示，数字素养的减不平等效应在依赖非农收入、人力资本水平较低或处于相对较低收入区间的农户中更为明显。进一步分析发现，数字素养显著缩小了农户工资性收入、经营性收入和财产性收入的不平等，而对转移性收入不平等的缓解作用在统计上不显著。同时，数字素养显著提高了农户收入向上流动的概率，从而从动态视角缓解了收入不平等。基于上述发现，本文从构建分层分类的农户数字素养培育体系、形成数字赋能—机会均等—收入收敛的良性循环、优化收入分配结构与动态流动机制等方面提出政策启示，以期为加快共同富裕进程提供参考。","Sustainability","2026-10-21T00:00:00Z",80,{"impact":18,"substance":210,"depth":18,"authority":74,"freshness":20,"relevant":22,"comment":211},22,"基于CFPS三期面板数据的微观实证，方法规范、结论有新意，对数字乡村与共同富裕政策有参考价值，值得进入每日精选。",[213],{"name":206,"url":203},[27,215,216,217,218],"数字素养","共同富裕","农民收入","收入不平等",{"code":50,"slug":84,"name":85,"path":86,"parent_code":87},[],{"status":90,"locked":14,"version":35,"method":36,"evidence":222},{"candidates":223,"agricultural_evidence":54},[224,230,237,243],{"code":50,"score":225,"supported":54,"title":226,"abstract":227,"tags":229},8,[],[27,228],"digital village",[27,215],{"code":231,"score":21,"supported":54,"title":232,"abstract":233,"tags":236},"G6",[],[234,235],"农户收入","rural income",[],{"code":238,"score":189,"supported":54,"title":239,"abstract":240,"tags":242},"G",[],[241],"rural development",[],{"code":244,"score":22,"supported":14,"title":245,"abstract":246,"tags":247},"G4",[],[],[216],[249,250],"CFPS 数字素养 农户收入","数字素养 农村收入差距","CFPS数字素养农户收入-5581","10.3390\u002Fsu182010689",{"doi":252,"openalex_id":254,"authors":255,"venue":206,"cited_by_count":123,"oa_url":203,"card":264,"direction":196,"ingested_from":269},"W7223105459",[256,259,262],{"name":257,"orcid":258},"Yong Feng","https:\u002F\u002Forcid.org\u002F0000-0002-8151-8594",{"name":260,"orcid":261},"Shuokai  kai Wang","https:\u002F\u002Forcid.org\u002F0009-0002-7439-1764",{"name":263,"orcid":8},"Fangping Cao",{"tldr":265,"method":266,"finding":267,"direction":196,"opportunity":268},"基于CFPS三期面板数据，实证检验数字素养对农户收入不平等的影响。","使用CFPS 2018-2022三期面板数据，进行机制与异质性分析。","数字素养通过扩展社会资本和缓解信贷约束显著缩小农户收入差距。","可延伸研究数字素养对不同收入来源不平等的差异化机制及动态收入流动路径。","openalex","2026-10-22T11:20:47.675827Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":8,"source_name":277,"source_url":274,"published_at":207,"category":158,"cover_url":8,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"primary_topic":287,"secondary_topics":291,"classification":292,"search_phrases":359,"slug":362,"view_count":123,"doi":363,"paper":364,"created_at":373},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。","International Journal of Drug Delivery Technology",75,{"impact":18,"substance":210,"depth":161,"authority":225,"freshness":13,"relevant":22,"comment":280},"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[282],{"name":277,"url":274},[81,284,28,285,286],"农业人工智能","杂草识别","病虫害监测",{"code":87,"slug":288,"name":289,"path":290,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":293,"locked":14,"version":35,"method":36,"evidence":294},"partial",{"candidates":295,"agricultural_evidence":54,"reason":358},[296,307,314,323,330,337,344,351],{"code":297,"score":18,"supported":54,"title":298,"abstract":300,"tags":306},"I2",[299],"deep learning",[301,302,303,299,304,305],"深度学习","计算机视觉","病虫害识别","artificial intelligence","computer vision",[284],{"code":45,"score":18,"supported":54,"title":308,"abstract":310,"tags":313},[309],"IoT",[28,311,309,312],"internet of things","edge computing",[28],{"code":315,"score":13,"supported":54,"title":316,"abstract":317,"tags":322},"F4",[],[318,319,320,321],"农药","除草","pesticide","crop protection",[285],{"code":107,"score":324,"supported":54,"title":325,"abstract":326,"tags":329},7,[],[327,328],"精准农业","precision agriculture",[81],{"code":331,"score":21,"supported":54,"title":332,"abstract":333,"tags":336},"A2",[],[334,335],"小麦","wheat",[],{"code":338,"score":21,"supported":54,"title":339,"abstract":340,"tags":343},"A3",[],[341,342],"玉米","maize",[],{"code":345,"score":21,"supported":54,"title":346,"abstract":347,"tags":350},"A4",[],[348,349],"大豆","soybean",[],{"code":352,"score":21,"supported":54,"title":353,"abstract":354,"tags":357},"B1",[],[355,356],"蔬菜","vegetable",[],"一级领域明确，二级方向需要细分",[360,361],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556","10.25258\u002Fijddt.16.76s.32",{"doi":363,"openalex_id":365,"authors":366,"venue":277,"cited_by_count":123,"oa_url":274,"card":367,"direction":371,"ingested_from":269},"W7223145687",[],{"tldr":368,"method":369,"finding":370,"direction":371,"opportunity":372},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","智慧农业 \u002F 农业物联网","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","2026-10-22T11:20:18.540359Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":379,"content":8,"source_name":380,"source_url":377,"published_at":207,"category":158,"cover_url":8,"hotness":13,"is_selected":14,"score":208,"score_detail":381,"sources":383,"tags":385,"primary_topic":387,"secondary_topics":391,"classification":402,"search_phrases":452,"slug":455,"view_count":123,"doi":456,"paper":457,"created_at":476},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",{"impact":18,"substance":210,"depth":18,"authority":74,"freshness":20,"relevant":22,"comment":382},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[384],{"name":380,"url":377},[81,284,28,327,386],"气候韧性",{"code":297,"slug":388,"name":389,"path":390,"parent_code":87},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[392,396],{"code":45,"slug":393,"name":394,"path":395,"parent_code":87},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":397,"slug":398,"name":399,"path":400,"parent_code":401},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":90,"locked":14,"version":35,"method":36,"evidence":403},{"candidates":404,"agricultural_evidence":54},[405,411,415,420,426,433,440,446],{"code":297,"score":18,"supported":54,"title":406,"abstract":407,"tags":410},[304],[301,408,302,409,299,305],"机器学习","machine learning",[284],{"code":107,"score":17,"supported":54,"title":412,"abstract":413,"tags":414},[328],[327,328],[81,327],{"code":45,"score":13,"supported":54,"title":416,"abstract":417,"tags":419},[],[28,418,309,312],"边缘计算",[28],{"code":397,"score":20,"supported":54,"title":421,"abstract":422,"tags":425},[],[423,424,398],"灌溉","水资源",[],{"code":427,"score":324,"supported":54,"title":428,"abstract":429,"tags":432},"H7",[],[430,431],"气候变化","climate change",[386],{"code":434,"score":21,"supported":54,"title":435,"abstract":436,"tags":439},"H2",[],[437,438],"土壤","soil",[],{"code":441,"score":189,"supported":54,"title":442,"abstract":443,"tags":445},"A7",[],[444],"间作",[],{"code":447,"score":189,"supported":54,"title":448,"abstract":449,"tags":451},"F3",[],[450],"养分管理",[],[453,454],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":456,"openalex_id":458,"authors":459,"venue":380,"cited_by_count":123,"oa_url":377,"card":470,"direction":371,"ingested_from":269},"W7223121456",[460,463,465,467],{"name":461,"orcid":462},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":464,"orcid":8},"Oluwadare OYEBODE",{"name":466,"orcid":8},"Iretiayo Adelaiye",{"name":468,"orcid":469},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":471,"method":472,"finding":473,"direction":474,"opportunity":475},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z"]