[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1764":3,"related-1764":101},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":100},1764,"Human-Centric, AI-Enabled Concurrent Engineering: Towards a cross-domain framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jii.2026.101192","The convergence of Artificial Intelligence (AI) and Concurrent Engineering (CE) has begun to reshape how industrial systems are designed, validated, and operated. We argue that the next, and arguably more consequential, step is to embed this convergence inside a coherent Industrial Information Integration (III) substrate that is explicitly human-centric and that generalises beyond the classical manufacturing core. This paper develops such a framework. We propose the H-AICE architecture (Human-centric AI-enabled Concurrent Engineering), which couples (i) a five-dimensional taxonomy of AI interventions in CE, (ii) a time-dependent overall-risk model Roverall(t) that integrates internal and external risk along the design lifecycle, and (iii) an integration layer based on shared ontologies, knowledge graphs, and digital twins. The framework is then instantiated and critically discussed in six application scenarios that exhibit very different data regimes, regulatory contexts, and human stakes: precision agriculture (Agriculture 5.0), biotechnology and life sciences, climate-aware industrial sustainability, sustainable forestry (Forestry 5.0), medical device development, and livestock breeding. We show that the same III primitives—data quality, semantic interoperability, explainable models, and human-in-the-loop oversight—recur across all six scenarios, and that the principal failure modes are organisational rather than algorithmic. We conclude with a research agenda for human-centric, AI-enabled CE that emphasises explainability, governance, and sector-specific certification, and that positions III as a load-bearing rather than a cosmetic discipline.","人工智能（AI）与并行工程（CE）的融合已开始重塑工业系统的设计、验证与运行方式。我们认为，下一步——或许也是更具深远意义的一步——是将这种融合嵌入一个连贯的工业信息集成（III）基底之中，该基底明确以人为中心，并超越传统制造业核心进行泛化。本文构建了这样一个框架。我们提出H-AICE架构（以人为中心的AI赋能并行工程），该架构结合了（i）AI在CE中干预措施的五维分类法，（ii）一个随时间变化的总体风险模型Roverall(t)，该模型沿设计生命周期整合内部与外部风险，以及（iii）基于共享本体、知识图谱和数字孪生的集成层。随后，该框架在六个应用场景中得到实例化并加以批判性讨论，这些场景展现出截然不同的数据体制、监管环境及人类利害关系：精准农业（农业5.0）、生物技术与生命科学、气候感知型工业可持续性、可持续林业（林业5.0）、医疗器械开发以及畜牧育种。我们表明，相同的III原语——数据质量、语义互操作性、可解释模型及人在回路监督——在全部六个场景中反复出现，且主要失效模式属于组织层面而非算法层面。最后，我们提出一个以人为中心的AI赋能CE研究议程，该议程强调可解释性、治理机制及行业特定认证，并将III定位为一门承重学科而非装饰性学科。",null,"Journal of Industrial Information Integration","2026-09-03T00:00:00Z","论文",10,true,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,14,5,1,"提出跨领域人本AI并发工程框架，覆盖农业5.0等场景，具前瞻性与方法论价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","智慧农业","农业人工智能","知识图谱","农业5.0",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,54,62,67],{"code":47,"score":13,"supported":14,"title":48,"abstract":49,"tags":53},"I1",[],[50,51,52],"精准农业","数字孪生","precision agriculture",[28],{"code":55,"score":56,"supported":14,"title":57,"abstract":58,"tags":61},"C",6,[],[59,60],"畜牧","livestock",[],{"code":33,"score":21,"supported":14,"title":63,"abstract":64,"tags":66},[],[65],"artificial intelligence",[29,30],{"code":68,"score":22,"supported":41,"title":69,"abstract":70,"tags":71},"I6",[],[],[27],[73,74],"农业人工智能 数字乡村 智慧农业 知识图谱","农业人工智能 数字乡村","农业人工智能数字乡村智慧农业知识图谱-1764",4,"10.1016\u002Fj.jii.2026.101192",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":90,"oa_url":91,"card":92,"direction":98,"ingested_from":99},"W7167326837",[81,84,87],{"name":82,"orcid":83},"Andreas Holzinger","https:\u002F\u002Forcid.org\u002F0000-0002-6786-5194",{"name":85,"orcid":86},"Heimo Müller","https:\u002F\u002Forcid.org\u002F0000-0002-9691-4872",{"name":88,"orcid":89},"Josip Stjepandić","https:\u002F\u002Forcid.org\u002F0000-0003-0877-3799",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2452414X26001342\u002Fpdf",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出人本AI并发工程框架，跨领域应用于农业等六场景。","提出H-AICE架构，结合AI干预分类、风险模型、本体与数字孪生。","跨场景共性为数据质量、语义互操作、可解释模型和人机协同，主要失败模式是组织性的。","其他","农业5.0场景中，可研究人机协同决策与组织治理对AI采纳的影响，以及可解释模型在精准农业中的实际应用。","智慧农业 \u002F 农业物联网","openalex","2026-09-06T23:30:12.843367Z",{"total":56,"page":22,"page_size":56,"items":102},[103,159,256,329,376,456],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":9,"content":108,"source_name":109,"source_url":9,"published_at":110,"category":111,"cover_url":9,"hotness":13,"is_selected":41,"score":112,"score_detail":113,"sources":117,"tags":119,"primary_topic":122,"secondary_topics":127,"classification":129,"search_phrases":154,"slug":157,"view_count":21,"doi":9,"paper":9,"created_at":158},4464,"山东省农科院发布舜耕粮安农业遥感大模型 精度95%效率提升超100%","http:\u002F\u002Fwww.md2we.com\u002Fitems\u002F4263","山东省农科院发布舜耕粮安农业遥感大模型，基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产全链条智能监测体系，精度高达95%，效率提升超100%；山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。","每日热点 大众新闻 2026-10-04·10月4日 · 周日 报道\n\n阅读 0\n\nAI 评分\n\n精选\n\n影响力 24\n\n信息增量 22\n\n专业深度 18\n\n信源权威 10\n\n时效性 8\n\n省级“人工智能+农业”行动部署叠加舜耕·粮安遥感大模型95%精度等实质数据，信息增量与专业深度俱佳，值得进入每日精选。\n\n摘要\n\n山东省农科院发布舜耕粮安农业遥感大模型，基于20年2000万份人工样本，覆盖山东全域全地貌全物候周期，构建耕地智能提取-肥力墒情预测-作物精准识别-长势动态监测-病虫草害预警-作物智能估产全链条智能监测体系，精度95%、效率提升超100%。山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。\n\n文 全文\n\n### 山东实施“人工智能+农业”行动：数据“种”进泥土，云端守护丰收\n\n2026-10-06 02:39:49\n\n![Image 1: Image 1](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a29336dd947_750.JPEG)\n\n麦田上空，无人机低空掠过，哪片麦苗缺氮、哪处有蚜虫，屏幕上即时标红；大棚内，人工智能（AI）算法值守，缺水、缺肥时，系统立刻报警；分拣车间里，AI视觉系统每秒“端详”上百颗苹果，按色泽、果径、瑕疵精准分级，误判率不到百分之一……\n\n从“靠天吃饭”到“知天而作”，当前人工智能正成为助力农业稳产提质、带动农民增收致富的“好帮手”，为农业农村现代化提供全新范式。\n\n2026年中央一号文件明确提出“因地制宜发展农业新质生产力，促进人工智能与农业发展相结合”，将AI技术从单点应用升级为贯穿农业全产业链的战略引擎。多家行业机构预测，2026年中国智慧农业市场规模将突破1340亿元，年复合增长率保持在15%以上。\n\n刚刚召开的省委十二届十一次全会，着眼把农业建成现代化大产业，推动农业数智化转型升级，明确提出实施“人工智能+农业”行动。何谓“人工智能+农业”？简言之，就是以人工智能技术为核心，驱动传统农业向智能化、高效化转型的新型生产体系。与会同志认为，从更深层次看，“人工智能+农业”不是简单的技术叠加，而是农业生产方式、组织形式和价值链条的系统性重塑。\n\n**AI种地，田野阡陌释放出更多可能**\n\n技术的生命力在于解决实际问题。放眼全国，农业面临着劳动力老龄化、气候变化风险加剧等压力，也存在特色产业附加值低、品牌力不强等问题。人工智能的介入推动农业从“靠经验”转向“靠数据”，为端牢“中国饭碗”、塑强农业产业注入确定性。在不少人看来，人工智能的应用推广，恰逢其时。\n\n“山东是农业大省，也是农业产业化发源地，产业规模大、业态全，生产者队伍素质高、接受新事物能力强。与此同时，山东工业基础雄厚，人工智能技术发展迅速。新技术在农业方面的转化推广具有显著的成本和效率优势。”省委党校（山东行政学院）教授、山东乡村振兴研究院院长刘岳认为，山东有着发展“人工智能+农业”的坚实基础，在完整的产业体系、可观市场规模、丰富应用场景等优势加持下，田野阡陌将释放出更多可能。\n\n夏日的鲁北平原，玉米拔节生长，一片生机盎然。在齐河县金穗粮食种植专业合作社的示范田里，一套黑色管网发挥着大作用。“这是我们铺设的水肥一体化设施。每30厘米有一个出水孔，能精准滴灌到玉米根底部位置。相比大水漫灌，它的灌溉速度快3倍，可省水30%，一人一天能管理200亩地。”合作社理事长袁本刚说。依托大数据平台，合作社还可在“云端”实施全程监管，动动手指就能依据检测数据制订分地块水肥管理方案，为各村玉米田定制个性化基肥配方，同步开展测土配方、作物叶绿素检测等服务。\n\n今年山东夏粮逆势丰收，连续4年实现面积、单产、总产“三增”，这离不开水肥一体化技术在大田的推广。数据显示，2025年，全省应用水肥一体化技术的粮油作物面积已达到630万亩。“水肥一体化与减垄增地、‘大小行’密植等结合，加速了良种、良法、良机、良田深度融合，有效提升了农田规模化、标准化种植水平。”袁本刚体会深切，水肥一体化的落地，让丰收有了更充实的底气。\n\n![Image 2: Image 2](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a30f6bab0_750.JPEG)\n\n种子是农业的“芯片”，也是保障粮食安全的关键，智能育种已成为育种科研人员聚焦的热点领域。\n\n今年4月，由中国农业科学院作物科学研究所与山东极智生物科技有限公司联合研发的“iWheat小麦智能设计育种平台”发布。对青岛农业大学小麦遗传育种团队来说，iWheat的应用正在帮助其攻克小麦育种“周期长、效率低、成本高”等行业难题。\n\n这一平台整合3万余份小麦种质资源的基因型、442个功能基因、6000余个功能位点及40余万条表型数据，为每一份种质建立起“数字身份证”，实现资源的精准检索与高效利用。“结合快速繁育体系，能将传统8年的育种周期压缩2至3年。”团队负责人张玉梅说，加速新品种的培育进程、降低育种成本，意味着更能提升整个种业行业的创新能力和竞争力，进而赋能小麦产业链上下游实现高质量发展。\n\n**让“技术盆景”真正落地为“产业风景”**\n\n农业大模型能够整合气象、土壤、作物生长等多维度数据，显著提升农业生产的精准性和预见性。我省明确，要加强农业大模型与人工智能体落地应用。\n\n在“中国蔬菜之乡”寿光，蔬菜大棚迎来了一批特殊“员工”。一群脚步灵活的机器狗，沿着田垄灵活穿梭，搭载的高清摄像头与传感器如同“火眼金睛”，精准识别病虫害迹象。巡检结束，数据实时传回后台，人工智能模型迅速给出种植决策。如今，寿光新建大棚已迭代升级至第七代智能物联网“云棚”，物联网应用率超过85%，劳动生产效率较以往翻倍。\n\n![Image 3: Image 3](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a2155c1ff_750.JPEG)\n\n就在一个多月前，省农科院发布“舜耕·粮安”农业遥感大模型。省农科院党委书记梁金光介绍，该模型基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建起“耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产”全链条智能监测体系，精度高达95%，效率提升超100%。\n\n在中游加工环节，大模型也能有效提升产品附加值与标准化水平。在烟台莱山区，由浪潮云洲旗下数农云（山东）信息科技有限公司联合中国农业大学计算中心、烟台葡乡农业开发有限公司共同打造的“莱山葡萄种植和酿造大模型”，一方面在种植端自动生成气候适应性调控方案，实现水肥精准配施、病虫害智能预警与防治，葡萄产量与品质同步提升；另一方面在酿造端用AI模拟发酵、陈酿全过程，动态优化关键参数，完成原料分级、发酵控制和酒体风味数字化建模。以此，让每一批葡萄酒的口感更稳定、品质更可控。\n\n当然，让人工智能与广袤乡村实现更深层次的双向赋能，还有很长的路要走——\n\n一套AI系统，传感终端、边缘计算、决策模型全算上，初始投入比较大。普通农户能不能用得起、用得好？\n\n农业生产周期长、环境复杂、区域差异大，怎样才能收集利用一批高质量农业数据，推动AI模型迭代？\n\n下沉到村、懂技术懂农业的青年人才较为短缺，农业人工智能技术人才怎样引育？\n\n让“技术盆景”真正落地为“产业风景”，刘岳认为，关键是鼓励龙头企业、高校院所与新型农业经营主体协同攻关，围绕粮食、果蔬、畜牧、水产等优势产业，打造一批“听得懂农事、算得清账本、管得了全程”的行业大模型。\n\n此外，刘岳还建议，要更加重视构建与农村新质生产力相适应的农村新型生产关系，盘活整合农村各类资产资源，为充分发挥数字化、智能化技术进步作用提供保障。“推动农村数字化从‘局域网’向‘互联网’转化，要破除长期存在的‘信息孤岛’，构建乡村振兴的数据底座。”\n\n（大众新闻记者 于新悦 刘兵）\n\n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。  \n 本文来源：大众新闻·大众日报\n\n查看更多\n\n相关检索","大众新闻 2026-10-06","2026-10-06T00:00:00Z","报道",76,{"impact":18,"substance":114,"depth":115,"authority":13,"freshness":56,"relevant":22,"comment":116},21,17,"省级农科院发布农业遥感大模型，含95%精度、2000万份样本等实质数据，信息增量与专业深度俱佳，值得进入每日精选。",[118],{"name":109,"url":106},[27,28,29,120,121],"农业大模型","遥感大模型",{"code":123,"slug":124,"name":125,"path":126,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[128],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":130},{"candidates":131,"agricultural_evidence":14},[132,138,144,150],{"code":123,"score":20,"supported":14,"title":133,"abstract":135,"tags":137},[134],"遥感",[134,136],"估产",[],{"code":33,"score":139,"supported":14,"title":140,"abstract":142,"tags":143},13,[141],"大模型",[141],[29,120],{"code":47,"score":145,"supported":14,"title":146,"abstract":147,"tags":149},7,[],[28,148],"数字农业",[28],{"code":68,"score":22,"supported":41,"title":151,"abstract":152,"tags":153},[],[],[27],[155,156],"山东省农科院 舜耕粮安 遥感大模型","舜耕粮安 农业遥感 精度95%","山东省农科院舜耕粮安遥感大模型-4464","2026-10-09T00:04:56.436776Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":166,"score_detail":167,"sources":171,"tags":173,"primary_topic":176,"secondary_topics":181,"classification":183,"search_phrases":231,"slug":234,"view_count":21,"doi":235,"paper":236,"created_at":255},4422,"Artificial intelligence driven framework for enhancing sustainability and resilience in communal cattle insurance in Lupane District Zimbabwe","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44279-026-00786-y","Communal cattle farming in Sub-Saharan Africa continues to face destabilization from non-linear climate variability, yet insurance penetration remains negligible. In Lupane District, Zimbabwe, uptake is exceptionally low at approximately 0.01%, despite recurrent droughts that threaten household livelihoods. This study aimed to develop an AI-driven framework to enhance sustainability and resilience in communal cattle insurance within the district. A positivist research philosophy guided the study, and a quantitative approach was adopted. The Kothari sample size determination formula was applied to derive a statistically representative sample of 219 households from a population of 49,841 communal cattle farmers distributed across four villages in 23 wards. Data were collected through KoboCollect, transmitted to secure servers, and analyzed using JAMOVI version 2.6.44. Descriptive statistics indicated that the majority of farmers were male (62%), with an average age of 47 years and herd sizes ranging from 3 to 25 cattle. Education levels were generally low, with 54% reporting primary schooling and only 12% having secondary or higher qualifications. Income sources were largely subsistence-based, and 71% of respondents relied on cattle as their primary asset. Binary logistic regression revealed that affordability (β = 1.25, p \u003C 0.01) and awareness (β = 0.99, p \u003C 0.01) were the strongest determinants of adoption, while institutional trust (β = 0.74, p \u003C 0.05) emerged as a significant direct predictor of adoption willingness. The study was theoretically anchored in the Technology Acceptance Model (TAM), which explains how perceived usefulness and perceived ease of use influence uptake. Informed by findings from Objectives 1 and 2, the study proposes the Tri-Modular AI Resilience Engine (TMAIE). TMAIE integrates Convolutional Neural Networks (CNN) for biometric cattle identification, Recurrent Neural Networks (RNN) for satellite-derived NDVI index triggers, and Transformer-based Natural Language Processing (NLP) localized in IsiNdebele and ChiShona for inclusive voice-based access. The study findings suggest that adoption is not a linear process but an adaptive evolution requiring both technological and social mechanisms. The proposed framework reflects these relationships through the integration of technological precision and community-based mediation. Overall, the findings provide empirical evidence on the determinants of cattle insurance adoption and inform the development of a conceptual AI-driven framework that may support future climate-resilient livestock insurance innovations, subject to further validation and testing.","撒哈拉以南非洲的社区养牛业持续面临非线性气候变率的冲击，然而保险渗透率仍然微不足道。在津巴布韦卢潘内区，尽管反复发生的干旱威胁着家庭生计，但参保率仍极低，约为0.01%。本研究旨在开发一个人工智能驱动的框架，以提升该区社区养牛保险的可持续性和韧性。本研究以实证主义研究哲学为指导，采用定量研究方法。应用Kothari样本量确定公式，从分布于23个选区4个村庄的49841名社区养牛户总体中，推导出具有统计代表性的219户样本。数据通过KoboCollect采集，传输至安全服务器，并使用JAMOVI 2.6.44版本进行分析。描述性统计表明，大多数农户为男性（62%），平均年龄47岁，牛群规模为3至25头。教育水平普遍较低，54%报告仅接受过小学教育，仅12%拥有中学或更高学历。收入来源大多以自给自足为基础，71%的受访者将牛作为其主要资产。二元逻辑回归显示，可负担性（β = 1.25，p \u003C 0.01）和认知度（β = 0.99，p \u003C 0.01）是参保的最强决定因素，而制度信任（β = 0.74，p \u003C 0.05）则是参保意愿的显著直接预测因素。本研究在理论上以技术接受模型（TAM）为锚点，该模型解释了感知有用性和感知易用性如何影响采纳行为。基于目标1和目标2的研究结果，本研究提出了三模块人工智能韧性引擎（TMAIE）。TMAIE整合了用于牛只生物特征识别的卷积神经网络（CNN）、用于卫星衍生NDVI指数触发的循环神经网络（RNN），以及基于Transformer的、以恩德贝莱语和绍纳语本地化的自然语言处理（NLP），以实现包容性的语音访问。研究结果表明，采纳并非线性过程，而是一种需要技术和社会机制共同作用的自适应演化。所提出的框架通过整合技术精确性与社区中介来反映这些关系。总体而言，研究结果提供了关于养牛保险采纳决定因素的实证证据，并为","Discover Agriculture",70,{"impact":168,"substance":114,"depth":115,"authority":168,"freshness":169,"relevant":22,"comment":170},12,8,"以津巴布韦Lupane地区社区养牛保险为对象，提出融合CNN牛只识别、NDVI遥感触发与本地语言NLP的AI框架，方法具体、数据扎实，但属区域性案例研究，对国内农业信息化有借鉴意义而非直接影响。",[172],{"name":165,"url":162},[27,28,29,174,175],"农业保险","遥感监测",{"code":177,"slug":178,"name":179,"path":180,"parent_code":55},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants",[182],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":184},{"candidates":185,"agricultural_evidence":14},[186,192,197,202,208,214,219,223,227],{"code":177,"score":187,"supported":14,"title":188,"abstract":190,"tags":191},11,[189],"cattle",[189],[],{"code":33,"score":193,"supported":14,"title":194,"abstract":195,"tags":196},9,[65],[],[29],{"code":55,"score":198,"supported":14,"title":199,"abstract":200,"tags":201},3,[],[60],[],{"code":203,"score":198,"supported":14,"title":204,"abstract":205,"tags":207},"H7",[],[206],"干旱",[],{"code":209,"score":198,"supported":14,"title":210,"abstract":211,"tags":213},"I7",[],[212],"研究方法",[],{"code":215,"score":22,"supported":41,"title":216,"abstract":217,"tags":218},"G3",[],[],[174],{"code":47,"score":22,"supported":41,"title":220,"abstract":221,"tags":222},[],[],[28],{"code":123,"score":22,"supported":41,"title":224,"abstract":225,"tags":226},[],[],[175],{"code":68,"score":22,"supported":41,"title":228,"abstract":229,"tags":230},[],[],[27],[232,233],"津巴布韦 Lupane 牛保险","AI 牲畜保险 框架","津巴布韦Lupane牛保险-4422","10.1007\u002Fs44279-026-00786-y",{"doi":235,"openalex_id":237,"authors":238,"venue":165,"cited_by_count":90,"oa_url":248,"card":249,"direction":98,"ingested_from":99},"W7220655164",[239,242,245],{"name":240,"orcid":241},"Nyasha Nyakuchena","https:\u002F\u002Forcid.org\u002F0009-0005-3219-2275",{"name":243,"orcid":244},"Joseph P. Musara","https:\u002F\u002Forcid.org\u002F0000-0002-2226-3616",{"name":246,"orcid":247},"Emmanuel Zivenge","https:\u002F\u002Forcid.org\u002F0000-0003-4740-8225","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44279-026-00786-y.pdf",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"研究提出AI驱动的三模块框架，以提升津巴布韦Lupane地区社区牛保险的可持续性和韧性。","对219户农户进行问卷调查，用二元逻辑回归分析，并设计CNN、RNN和Trans","可负担性和意识是保险采纳的最强决定因素，机构信任也显著影响采纳意愿。","数字乡村与农业信息化","可验证TMAIE框架在类似地区的适用性，并探索AI语音交互对低教育水平农户保险采纳的长期影响。","2026-10-08T23:30:11.018464Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":9,"content":261,"source_name":262,"source_url":9,"published_at":263,"category":111,"cover_url":9,"hotness":13,"is_selected":41,"score":264,"score_detail":265,"sources":268,"tags":270,"primary_topic":273,"secondary_topics":279,"classification":281,"search_phrases":324,"slug":327,"view_count":90,"doi":9,"paper":9,"created_at":328},4275,"四川眉山科技慧种田 水稻单产品质双提升 AI大模型试点亩产863.6公斤","https:\u002F\u002Fwww.sctv.com\u002Fnews\u002F2105963737230815234","四川眉山洪雅县止戈镇水稻百亩超高产攻关片三台联合收割机同步作业，水稻实测亩产824.6公斤。东坡区太和镇永丰村300亩首次应用水稻专属AI大模型管理，超高产籼粳杂交稻昇两优222亩产达863.6公斤。彭山区公义镇数智水稻示范基地通过物联网监测智能灌溉实现水稻耕种管收全流程精准管控。","金秋时节，稻谷飘香。眼下，眉山市水稻收获接近尾声，良种良法配套、人工智能赋能、数智化管理等农业新技术落地田间，助力今年水稻单产提升、品质向好。\n\n在洪雅县止戈镇五龙社区，水稻百亩超高产攻关片的试验田里三台联合收割机同步完成割稻、脱粒、秸秆还田作业，现场水稻实测亩产达824.6公斤。\n\n眉山市洪雅县止戈镇五龙社区种粮大户 张雪勤：今天测产的品种是去年我们通过评比试验筛选出来的“荃优879”，全程按照标准化、科学化的方式进行管理，长势和产量效果都非常好。\n\n良种是粮食增产增收的关键，当地针对盆周山区多雨高湿、病虫害多发的气候特点，建成100亩水稻中试熟化基地，精准筛选适配本地水土、抗逆性强的水稻品种。\n\n眉山市洪雅县农业农村局农技站站长 白蓉钢：常态化开展粮油新品种引进、试验、筛选与示范推广，同时持续推广良种良法配套技术，持续提升粮食综合生产能力，全力稳住全县粮食生产基本盘。\n\n在东坡区太和镇永丰村，千亩高标准农田刚刚完成了收割。其中，300亩首次应用水稻专属AI大模型管理的试点田格外引人注目。\n\n眉山市东坡区太和镇永丰村种粮大户 赵友勇：现在的话我们更多会用这个无人机来巡田，也会及时地给我们推送水稻里面的一个病虫害的情况，根据这个推送的情况我们做针对性地处理。\n\n经过现场实测，超高产籼粳杂交稻“昇两优222”亩产达863.6公斤。稳产丰产的底气，源自科技赋能。通过无人机航拍采集田间数据，AI大模型可实现水稻全生长周期智慧管控，预测最佳收割时间、预判粮食产量，种植管理更精细科学。\n\n四川农业大学教授 马均：我们从田间的长势长相的情况来看，表现还是挺不错的，智慧化的精准条播技术，还包括我们AI技术的运用，初步显示出来一些就是好的效果。\n\n走进彭山区公义镇的数智水稻示范基地，刚刚收割完成的稻谷直接送进现代化烘干车间。通过物联网监测、智能灌溉等智慧设备，实现水稻耕种管收全流程精准管控。\n\n眉山市彭山区公义镇欣荣村种粮大户 王程淋：我们现在种田不但要靠经验、更要靠数据。土壤湿度、田间温度、病虫害情况，手机上实时就能看到。智能设备自动管控水肥，产量和品质都比往年更好。","四川新闻联播 2026-10-04","2026-10-04T11:00:00Z",71,{"impact":19,"substance":17,"depth":266,"authority":13,"freshness":169,"relevant":22,"comment":267},15,"四川眉山AI大模型水稻试点实测亩产863.6公斤，数据具体、多方信源，具智慧农业示范价值。",[269],{"name":262,"url":259},[27,28,29,271,272],"良种良法","水稻单产",{"code":274,"slug":275,"name":276,"path":277,"parent_code":278},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[280],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":282},{"candidates":283,"agricultural_evidence":14},[284,289,293,299,305,311,316,320],{"code":274,"score":20,"supported":14,"title":285,"abstract":286,"tags":288},[276],[276,287],"杂交稻",[],{"code":33,"score":168,"supported":14,"title":290,"abstract":291,"tags":292},[141],[141],[29],{"code":294,"score":198,"supported":14,"title":295,"abstract":296,"tags":298},"F5",[],[297],"收割机",[],{"code":300,"score":198,"supported":14,"title":301,"abstract":302,"tags":304},"H3",[],[303],"灌溉",[],{"code":306,"score":198,"supported":14,"title":307,"abstract":308,"tags":310},"I5",[],[309],"物联网",[],{"code":312,"score":22,"supported":41,"title":313,"abstract":314,"tags":315},"F1",[],[],[271],{"code":47,"score":22,"supported":41,"title":317,"abstract":318,"tags":319},[],[],[28],{"code":68,"score":22,"supported":41,"title":321,"abstract":322,"tags":323},[],[],[27],[325,326],"眉山 永丰村 AI大模型 水稻","昇两优222 亩产863.6公斤","眉山永丰村AI大模型水稻-4275","2026-10-06T00:05:02.998709Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":9,"content":334,"source_name":335,"source_url":9,"published_at":336,"category":111,"cover_url":9,"hotness":13,"is_selected":41,"score":337,"score_detail":338,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":348,"search_phrases":371,"slug":374,"view_count":193,"doi":9,"paper":9,"created_at":375},4263,"山东实施人工智能+农业行动 省农科院舜耕粮安遥感大模型精度95%","http:\u002F\u002Fgzylhyzx.com\u002Fdetail.html?newsId=52418975&cpid=T17","山东省农科院发布舜耕粮安农业遥感大模型，基于20年2000万份人工样本，覆盖山东全域全地貌全物候周期，构建耕地智能提取-肥力墒情预测-作物精准识别-长势动态监测-病虫草害预警-作物智能估产全链条智能监测体系，精度95%、效率提升超100%。山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。","### 山东实施“人工智能+农业”行动：数据“种”进泥土，云端守护丰收\n\n2026-10-06 02:39:49\n\n![Image 1](https:\u002F\u002Fappimg.dzwww.com\u002Fshare\u002F2026\u002F08\u002F02\u002Ff9c9a29336dd947_750.JPEG)\n\n麦田上空，无人机低空掠过，哪片麦苗缺氮、哪处有蚜虫，屏幕上即时标红；大棚内，人工智能（AI）算法值守，缺水、缺肥时，系统立刻报警；分拣车间里，AI视觉系统每秒“端详”上百颗苹果，按色泽、果径、瑕疵精准分级，误判率不到百分之一……\n\n从“靠天吃饭”到“知天而作”，当前人工智能正成为助力农业稳产提质、带动农民增收致富的“好帮手”，为农业农村现代化提供全新范式。\n\n2026年中央一号文件明确提出“因地制宜发展农业新质生产力，促进人工智能与农业发展相结合”，将AI技术从单点应用升级为贯穿农业全产业链的战略引擎。多家行业机构预测，2026年中国智慧农业市场规模将突破1340亿元，年复合增长率保持在15%以上。\n\n刚刚召开的省委十二届十一次全会，着眼把农业建成现代化大产业，推动农业数智化转型升级，明确提出实施“人工智能+农业”行动。何谓“人工智能+农业”？简言之，就是以人工智能技术为核心，驱动传统农业向智能化、高效化转型的新型生产体系。与会同志认为，从更深层次看，“人工智能+农业”不是简单的技术叠加，而是农业生产方式、组织形式和价值链条的系统性重塑。\n\n**AI种地，田野阡陌释放出更多可能**\n\n技术的生命力在于解决实际问题。放眼全国，农业面临着劳动力老龄化、气候变化风险加剧等压力，也存在特色产业附加值低、品牌力不强等问题。人工智能的介入推动农业从“靠经验”转向“靠数据”，为端牢“中国饭碗”、塑强农业产业注入确定性。在不少人看来，人工智能的应用推广，恰逢其时。\n\n“山东是农业大省，也是农业产业化发源地，产业规模大、业态全，生产者队伍素质高、接受新事物能力强。与此同时，山东工业基础雄厚，人工智能技术发展迅速。新技术在农业方面的转化推广具有显著的成本和效率优势。”省委党校（山东行政学院）教授、山东乡村振兴研究院院长刘岳认为，山东有着发展“人工智能+农业”的坚实基础，在完整的产业体系、可观市场规模、丰富应用场景等优势加持下，田野阡陌将释放出更多可能。\n\n夏日的鲁北平原，玉米拔节生长，一片生机盎然。在齐河县金穗粮食种植专业合作社的示范田里，一套黑色管网发挥着大作用。“这是我们铺设的水肥一体化设施。每30厘米有一个出水孔，能精准滴灌到玉米根底部位置。相比大水漫灌，它的灌溉速度快3倍，可省水30%，一人一天能管理200亩地。”合作社理事长袁本刚说。依托大数据平台，合作社还可在“云端”实施全程监管，动动手指就能依据检测数据制订分地块水肥管理方案，为各村玉米田定制个性化基肥配方，同步开展测土配方、作物叶绿素检测等服务。\n\n今年山东夏粮逆势丰收，连续4年实现面积、单产、总产“三增”，这离不开水肥一体化技术在大田的推广。数据显示，2025年，全省应用水肥一体化技术的粮油作物面积已达到630万亩。“水肥一体化与减垄增地、‘大小行’密植等结合，加速了良种、良法、良机、良田深度融合，有效提升了农田规模化、标准化种植水平。”袁本刚体会深切，水肥一体化的落地，让丰收有了更充实的底气。\n\n![Image 2](https:\u002F\u002Fappimg.dzwww.com\u002Fshare\u002F2026\u002F08\u002F02\u002Ff9c9a2a30f6bab0_750.JPEG)\n\n种子是农业的“芯片”，也是保障粮食安全的关键，智能育种已成为育种科研人员聚焦的热点领域。\n\n今年4月，由中国农业科学院作物科学研究所与山东极智生物科技有限公司联合研发的“iWheat小麦智能设计育种平台”发布。对青岛农业大学小麦遗传育种团队来说，iWheat的应用正在帮助其攻克小麦育种“周期长、效率低、成本高”等行业难题。\n\n这一平台整合3万余份小麦种质资源的基因型、442个功能基因、6000余个功能位点及40余万条表型数据，为每一份种质建立起“数字身份证”，实现资源的精准检索与高效利用。“结合快速繁育体系，能将传统8年的育种周期压缩2至3年。”团队负责人张玉梅说，加速新品种的培育进程、降低育种成本，意味着更能提升整个种业行业的创新能力和竞争力，进而赋能小麦产业链上下游实现高质量发展。\n\n**让“技术盆景”真正落地为“产业风景”**\n\n农业大模型能够整合气象、土壤、作物生长等多维度数据，显著提升农业生产的精准性和预见性。我省明确，要加强农业大模型与人工智能体落地应用。\n\n在“中国蔬菜之乡”寿光，蔬菜大棚迎来了一批特殊“员工”。一群脚步灵活的机器狗，沿着田垄灵活穿梭，搭载的高清摄像头与传感器如同“火眼金睛”，精准识别病虫害迹象。巡检结束，数据实时传回后台，人工智能模型迅速给出种植决策。如今，寿光新建大棚已迭代升级至第七代智能物联网“云棚”，物联网应用率超过85%，劳动生产效率较以往翻倍。\n\n![Image 3](https:\u002F\u002Fappimg.dzwww.com\u002Fshare\u002F2026\u002F08\u002F02\u002Ff9c9a2a2155c1ff_750.JPEG)\n\n就在一个多月前，省农科院发布“舜耕·粮安”农业遥感大模型。省农科院党委书记梁金光介绍，该模型基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建起“耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产”全链条智能监测体系，精度高达95%，效率提升超100%。\n\n在中游加工环节，大模型也能有效提升产品附加值与标准化水平。在烟台莱山区，由浪潮云洲旗下数农云（山东）信息科技有限公司联合中国农业大学计算中心、烟台葡乡农业开发有限公司共同打造的“莱山葡萄种植和酿造大模型”，一方面在种植端自动生成气候适应性调控方案，实现水肥精准配施、病虫害智能预警与防治，葡萄产量与品质同步提升；另一方面在酿造端用AI模拟发酵、陈酿全过程，动态优化关键参数，完成原料分级、发酵控制和酒体风味数字化建模。以此，让每一批葡萄酒的口感更稳定、品质更可控。\n\n当然，让人工智能与广袤乡村实现更深层次的双向赋能，还有很长的路要走——\n\n一套AI系统，传感终端、边缘计算、决策模型全算上，初始投入比较大。普通农户能不能用得起、用得好？\n\n农业生产周期长、环境复杂、区域差异大，怎样才能收集利用一批高质量农业数据，推动AI模型迭代？\n\n下沉到村、懂技术懂农业的青年人才较为短缺，农业人工智能技术人才怎样引育？\n\n让“技术盆景”真正落地为“产业风景”，刘岳认为，关键是鼓励龙头企业、高校院所与新型农业经营主体协同攻关，围绕粮食、果蔬、畜牧、水产等优势产业，打造一批“听得懂农事、算得清账本、管得了全程”的行业大模型。\n\n此外，刘岳还建议，要更加重视构建与农村新质生产力相适应的农村新型生产关系，盘活整合农村各类资产资源，为充分发挥数字化、智能化技术进步作用提供保障。“推动农村数字化从‘局域网’向‘互联网’转化，要破除长期存在的‘信息孤岛’，构建乡村振兴的数据底座。”\n\n（大众新闻记者 于新悦 刘兵）\n\n  \n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。\n本文来源：大众新闻·大众日报\n\n查看更多","每日热点 大众新闻 2026-10-04","2026-10-04T00:00:00Z",82,{"impact":339,"substance":18,"depth":19,"authority":13,"freshness":169,"relevant":22,"comment":340},24,"省级“人工智能+农业”行动部署叠加舜耕·粮安遥感大模型95%精度等实质数据，信息增量与专业深度俱佳，值得进入每日精选。",[342],{"name":335,"url":332},[27,28,29,344,121],"种业振兴",{"code":123,"slug":124,"name":125,"path":126,"parent_code":37},[347],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":349},{"candidates":350,"agricultural_evidence":14},[351,355,359,363,367],{"code":123,"score":20,"supported":14,"title":352,"abstract":353,"tags":354},[134],[134,136],[],{"code":33,"score":168,"supported":14,"title":356,"abstract":357,"tags":358},[141],[141],[29],{"code":47,"score":145,"supported":14,"title":360,"abstract":361,"tags":362},[],[28,148],[28],{"code":312,"score":22,"supported":41,"title":364,"abstract":365,"tags":366},[],[],[344],{"code":68,"score":22,"supported":41,"title":368,"abstract":369,"tags":370},[],[],[27],[372,373],"舜耕粮安 遥感大模型","山东 人工智能 行动","舜耕粮安遥感大模型-4263","2026-10-06T00:05:02.084808Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":383,"score_detail":384,"sources":386,"tags":388,"primary_topic":390,"secondary_topics":391,"classification":392,"search_phrases":428,"slug":431,"view_count":432,"doi":433,"paper":434,"created_at":455},4211,"An integrated scientometric and BERTopic-based analysis on examining the role of artificial intelligence in achieving Sustainable Development Goals (SDGs)","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04841-y","This study investigates the role of Artificial Intelligence (AI) in achieving the United Nations Sustainable Development Goals (SDGs) through a comprehensive scientometric analysis. It explores the evolution of scholarly research, identifies leading contributors, maps thematic trends, examines how AI technologies contribute to advancing sustainability across multiple domains, and identifies latent semantic themes. A total of 3262 unique scholarly records published between 1989 and 2025, after deduplication and screening, were finalized from the Scopus and Web of Science databases. The dataset was analysed using Biblioshiny (R-based Bibliometrix) and VOSviewer to examine publication trends, citation impact, collaborative networks, thematic evolution, and conceptual structures. Indicators such as Relative Growth Rate (RGR), Doubling Time (DT), h-index, g-index, and Lotka’s Law were applied to evaluate research productivity and scholarly influence. In addition, BERTopic-based abstract-level topic modelling was applied to identify latent semantic themes, SDG-linked thematic domains, temporal evolution and potential research gaps. Results reveal a significant growth in AI-SDG research, particularly after 2015, with an accelerated increase post-2020. India, USA, and China emerged as the most productive contributors, while countries like USA, India, United Kingdom and Australia lead in research impact and citation quality. Thematic evolution analysis reveals a transition from foundational themes such as AI, machine learning, climate change, and decision support systems toward more integrated and sustainability-oriented research themes, including sustainable development and SDGs. Thematic linkages further demonstrate the persistent foundational role of AI and machine learning, alongside the growing centrality of climate change and sustainable development, reflecting the increasing convergence of AI research with global sustainability priorities. BERTopic analysis further revealed eleven interpretable abstract-level thematic domains after excluding one heterogeneous outlier category. The dominant themes included digital transformation and green innovation, AI-enabled education, digital health, smart agriculture, renewable energy systems, clean water and sanitation, remote sensing, NLP-based decision support, sustainable construction, tourism, and sustainable chemistry\u002Fpharmaceutical applications. This study maps the AI–SDG research landscape by integrating bibliometric indicators with thematic evolution, conceptual analysis, and abstract-level semantic topic modelling. The findings provide insights into the development and thematic orientation of AI-related research in the context of sustainable development. The use of BERTopic complements conventional bibliometric approaches by examining semantic patterns within publication abstracts alongside keyword and citation-based analyses.","本研究通过一项全面的科学计量分析，探讨了人工智能（Artificial Intelligence, AI）在实现联合国可持续发展目标（Sustainable Development Goals, SDGs）中的作用。研究考察了学术研究的演进历程，识别了主要贡献者，绘制了主题趋势图谱，分析了AI技术如何在多个领域推动可持续发展，并识别了潜在语义主题。经去重和筛选后，最终从Scopus和Web of Science数据库中确定了1989年至2025年间发表的3262条独特学术记录。使用Biblioshiny（基于R的Bibliometrix）和VOSviewer对数据集进行分析，以考察出版趋势、引用影响、合作网络、主题演进和概念结构。采用相对增长率（Relative Growth Rate, RGR）、倍增时间（Doubling Time, DT）、h指数、g指数和洛特卡定律（Lotka's Law）等指标评估研究生产力和学术影响力。此外，应用基于BERTopic的摘要级主题建模，以识别潜在语义主题、与SDG相关的主题领域、时间演变和潜在研究空白。结果显示，AI-SDG研究显著增长，尤其在2015年之后，2020年后增长加速。印度、美国和中国是最多产的贡献者，而美国、印度、英国和澳大利亚等国家在研究影响力和引用质量方面领先。主题演进分析揭示了从AI、机器学习、气候变化和决策支持系统等基础主题向更综合、面向可持续发展的研究主题（包括可持续发展和SDGs）的转变。主题关联进一步表明AI和机器学习的持续基础性作用，同时气候变化和可持续发展的中心性日益增强，反映了AI研究与全球可持续发展优先事项日益融合。BERTopic分析在排除一个异质性离群类别后，进一步揭示了十一个可解释的摘要级主题领域。主导主题包括数字化转型与绿色创新、AI赋能教育、数字健康、智慧农业、可再生能源系统、清洁水和卫生设施、遥感、基于自然语言处理的决策支持、可持续建筑、旅游业以及可持续化学\u002F制药应用。本研究通过整合文献计量","Discover Sustainability",80,{"impact":19,"substance":18,"depth":19,"authority":139,"freshness":193,"relevant":22,"comment":385},"该研究以3262篇文献的计量与BERTopic主题建模系统梳理AI支撑SDGs的研究格局，其中智慧农业、遥感、数字健康等主题与农业信息化高度相关，方法新颖、数据规模大，值得进入每日精选。",[387],{"name":382,"url":379},[27,28,29,389,134],"可持续发展",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":393},{"candidates":394,"agricultural_evidence":14},[395,402,407,412,418,424],{"code":33,"score":19,"supported":14,"title":396,"abstract":397,"tags":401},[65],[398,399,65,400],"机器学习","machine learning","decision support",[29],{"code":47,"score":145,"supported":14,"title":403,"abstract":404,"tags":406},[],[28,405],"smart agriculture",[28],{"code":123,"score":145,"supported":14,"title":408,"abstract":409,"tags":411},[],[134,410],"remote sensing",[134],{"code":203,"score":56,"supported":14,"title":413,"abstract":414,"tags":417},[],[415,416],"气候变化","climate change",[],{"code":209,"score":56,"supported":14,"title":419,"abstract":420,"tags":423},[],[421,422],"文献计量","bibliometric",[],{"code":68,"score":22,"supported":41,"title":425,"abstract":426,"tags":427},[],[],[27],[429,430],"AI SDG 文献计量","BERTopic 可持续农业","AISDG文献计量-4211",2,"10.1007\u002Fs43621-026-04841-y",{"doi":433,"openalex_id":435,"authors":436,"venue":382,"cited_by_count":90,"oa_url":379,"card":449,"direction":98,"ingested_from":99},"W7219683711",[437,440,443,446],{"name":438,"orcid":439},"Reetu Verma","https:\u002F\u002Forcid.org\u002F0000-0002-0567-7219",{"name":441,"orcid":442},"Ashok Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-3279-2895",{"name":444,"orcid":445},"Kavita Pabreja","https:\u002F\u002Forcid.org\u002F0000-0001-9856-0900",{"name":447,"orcid":448},"Saurabh Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-5924-6425",{"tldr":450,"method":451,"finding":452,"direction":453,"opportunity":454},"用科学计量与BERTopic分析3262篇文献，揭示AI助力可持续发展目标的研究格局与主题演化。","Scopus\u002FWoS文献，Biblioshiny、VOSviewer计量分析加B","AI-SDG研究2015年后激增，主题从基础AI转向可持续发展，识别出智慧农业等11个主题域。","农业人工智能与决策模型","可聚焦智慧农业主题，探究AI在农业SDG中的具体贡献路径与区域差异，填补细粒度实证空白。","2026-10-05T23:30:09.006483Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":9,"source_name":462,"source_url":459,"published_at":463,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":464,"score_detail":465,"sources":467,"tags":469,"primary_topic":472,"secondary_topics":473,"classification":474,"search_phrases":489,"slug":492,"view_count":432,"doi":493,"paper":494,"created_at":504},3857,"AI-Enabled Digital Connectivity and Agricultural Productivity in Remote Rural Saudi Arabia Under Vision 2030: Evidence from ARDL and NARDL","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2473.v1","This study examines the relationship between AI-enabled digital connectivity and agricultural productivity in Saudi Arabia, with particular relevance to remote rural settings. Using annual time-series data covering 2000–2025, the study employs the Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) approaches to investigate long-run relationships, short-run dynamics, and asymmetries in the productivity response to changes in digital connectivity. Because direct and consistent national-level measures of agricultural AI adoption are unavailable, mobile cellular subscriptions are used as a proxy for AI-enabling digital connectivity and the underlying digital infrastructure that facilitates technology-enabled agricultural applications. The ARDL bounds test confirms a stable long-run relationship between digital connectivity and agricultural productivity, with the long-run estimate indicating a positive and statistically significant, but moderate, association between connectivity and cereal yield. The short-run results reveal dynamic and delayed responses, consistent with adjustment processes associated with technological adoption and integration. The NARDL estimates further demonstrate statistically significant asymmetry, with the Wald tests rejecting symmetry in both the long run and short run. In particular, negative changes in digital connectivity generate a delayed adverse productivity response, highlighting potential vulnerability to disruptions in digital access. Overall, the findings indicate that digital connectivity functions as a complementary long-run productivity factor and that its benefits depend not only on expanding access but also on reliable infrastructure, technological capabilities, and supportive institutional conditions. The results underscore the importance of resilient and inclusive digitalization strategies for agricultural development in resource-constrained and remote rural environments.","本研究考察了沙特阿拉伯人工智能赋能的数字连接与农业生产率之间的关系，尤其关注偏远农村地区。研究使用涵盖2000—2025年的年度时间序列数据，采用自回归分布滞后（ARDL）和非线性自回归分布滞后（NARDL）方法，考察长期关系、短期动态以及生产率对数字连接变化的非对称响应。由于无法获得直接且一致的全国层面农业人工智能采用指标，本研究以移动蜂窝订阅量作为人工智能赋能数字连接以及支撑技术赋能农业应用的底层数字基础设施的代理变量。ARDL边界检验证实，数字连接与农业生产率之间存在稳定的长期关系，长期估计表明连接性与谷物产量之间存在正向且统计显著但程度中等的关联。短期结果揭示了动态且滞后的响应，这与技术采用和整合相关的调整过程相一致。NARDL估计进一步表明存在统计显著的非对称性，Wald检验在长期和短期均拒绝了对称性。特别是，数字连接的负向变化会产生滞后的不利生产率响应，凸显了数字接入中断可能带来的脆弱性。总体而言，研究结果表明，数字连接作为一种互补性的长期生产率要素发挥作用，其效益不仅取决于扩大接入，还取决于可靠的基础设施、技术能力以及支持性的制度条件。研究结果强调了在资源受限和偏远农村环境中，具有韧性和包容性的数字化战略对农业发展的重要性。","Preprints.org","2026-09-29T00:00:00Z",60,{"impact":169,"substance":17,"depth":115,"authority":56,"freshness":193,"relevant":22,"comment":466},"预印本研究用ARDL\u002FNARDL量化沙特偏远农村数字连接与谷物单产的非对称关系，方法规范但以移动订阅作AI代理、结论中等且属预印本，参考价值有限。",[468],{"name":462,"url":459},[27,28,29,470,471],"粮食产量","数字连接",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":475},{"candidates":476,"agricultural_evidence":14},[477,481,485],{"code":33,"score":76,"supported":14,"title":478,"abstract":479,"tags":480},[],[29],[29],{"code":47,"score":22,"supported":41,"title":482,"abstract":483,"tags":484},[],[],[28],{"code":68,"score":22,"supported":41,"title":486,"abstract":487,"tags":488},[],[],[27],[490,491],"沙特阿拉伯 数字连接 农业生产力","农业人工智能 数字乡村 数字连接 智慧农业","沙特阿拉伯数字连接农业生产力-3857","10.20944\u002Fpreprints202609.2473.v1",{"doi":493,"openalex_id":495,"authors":496,"venue":462,"cited_by_count":90,"oa_url":459,"card":499,"direction":253,"ingested_from":99},"W7214805442",[497],{"name":498,"orcid":9},"Elamin Altahir Ahmed",{"tldr":500,"method":501,"finding":502,"direction":253,"opportunity":503},"用ARDL和NARDL分析沙特2000-2025年数字连接与农业生产力关系，发现长期正向但存在不对称","ARDL与NARDL时间序列模型，以移动蜂窝订阅量作为数字连接代理变量。","数字连接与谷物产量长期正相关但效应中等，负向冲击会带来延迟的负面生产力响应。","可探索村级或农户层面AI采用数据，替代宏观代理变量，并检验基础设施中断的恢复机制。","2026-09-30T23:30:40.602253Z"]