[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2933":3,"related-2933":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":22,"primary_topic":27,"secondary_topics":33,"classification":34,"search_phrases":72,"slug":75,"view_count":68,"doi":76,"paper":77,"created_at":91},2933,"**The Hidden Physical Cost of Artificial Intelligence: A Materials Science and Engineering Optimization Perspective on AI Infrastructure, Resource Constraints, and Sustainable Innovation in India**","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22827899","## Alternative Titles 1. **AI Is Not in the Cloud: The Physical Infrastructure, Resource Paradox, and Sustainable Future of Artificial Intelligence**2. **From Cloud to Ground: Engineering the Sustainable Physical Foundations of Artificial Intelligence**3. **The Invisible Infrastructure of AI: Energy, Water, and Material Challenges in the Age of Intelligent Machines**4. **Artificial Intelligence and the Planet: A Materials Science Perspective on Data Centers, Resource Consumption, and Green Engineering**5. **Beyond the Algorithm: The Physical Realities of AI Infrastructure and the Path to Sustainable Innovation**6. **The Cloud Beneath Our Feet: How AI's Hidden Physical Costs Demand a New Era of Sustainable Engineering**7. **Intelligence vs. Resources: Engineering a Sustainable Future for Artificial Intelligence in India and Beyond** --- ## Subtitles 1. **The Cloud Is on the Ground — Powered by Electricity, Cooled by Water, and Sustained by Engineering**2. **Why AI's Future Depends Not on Smarter Algorithms Alone, but on Sustainable Materials and Infrastructure**3. **A Materials Science and Metallurgy Perspective on the Physical Revolution Disguised as a Digital One**4. **Balancing Technological Advancement with Ecological Responsibility in the 21st Century**5. **The Technology-Environment Paradox of AI: Resource Challenges, Policy Imperatives, and Engineering Solutions for India**6. **From Data Centers to Critical Minerals: The Hidden Resource Footprint of Artificial Intelligence**7. **Rethinking AI Sustainability: Energy Efficiency, Water Security, and Transparent Environmental Governance** --- ## Detailed Description ### Overview This research paper critically examines the often-overlooked physical and material foundations of Artificial Intelligence (AI), challenging the pervasive misconception that AI operates in a weightless, ethereal \"cloud.\" While users interact with AI through seamless digital interfaces—receiving instant responses from systems like ChatGPT, Gemini, and Claude—the reality is that every AI query triggers a cascade of physical events: electricity racing through billions of transistors, processors performing trillions of calculations, and cooling systems working relentlessly to dissipate enormous heat. The paper argues that AI is not merely a software revolution but a profound **infrastructure revolution** that operates firmly within the laws of thermodynamics, energy conservation, and resource constraints. ### The Problem: The Invisible Physical Footprint of AI Drawing on authoritative data from the **International Energy Agency (IEA)** and the **United Nations University Institute for Water, Environment and Health (UNU-INWEH)** , the paper reveals the staggering scale of AI's resource consumption: - **Energy:** Global data centers consumed approximately **485 TWh** of electricity in 2025—equivalent to the annual consumption of a medium-sized country. By 2030, this demand is projected to reach **950 TWh**, representing roughly **3% of global electricity consumption**.- **Water:** Freshwater is increasingly extracted for cooling purposes, placing stress on local water systems, particularly in water-scarce regions.- **Critical Minerals:** Advanced semiconductor chips require rare earth elements and critical minerals, often mined in environmentally sensitive areas.- **Electronic Waste:** The rapid obsolescence of AI hardware contributes to growing volumes of e-waste, often exported to developing nations for disposal. The paper emphasizes that these environmental burdens are **not evenly distributed**. While the benefits of AI are enjoyed globally and instantly, the costs—energy depletion, water scarcity, mineral extraction, and waste accumulation—are concentrated in communities hosting data centers, regions supplying freshwater, and countries managing e-waste. ### The Paradox: AI as Both Consumer and Savior A central paradox explored in this paper is that AI is simultaneously a **major consumer of resources** and a **potential tool for resource optimization**. Intelligent algorithms are already being deployed to:- Optimize electricity grids and improve renewable energy forecasting- Reduce traffic congestion and minimize industrial waste- Enhance precision agriculture and increase energy efficiency in buildings and factories The paper argues that properly deployed, AI may ultimately **save more resources than it consumes**—but only if its own infrastructure is built on sustainable foundations. ### The Indian Context: A Unique Crossroads Using **India** as a case study, the paper highlights the nation's unique position as both a **beneficiary of AI technology** and a **bearer of resource burdens**. India's ambitious digital initiatives—**Digital India, Aadhaar, UPI, DigiLocker, ONDC, and the IndiaAI Mission**—have created one of the world's largest AI-enabled digital ecosystems. However, this digital transformation requires an equally massive **physical transformation**, raising urgent questions about:- Water availability for data center cooling (e.g., Google's proposed project near Visakhapatnam)- Renewable energy capacity to power AI infrastructure- Sustainable sourcing of critical minerals for semiconductor manufacturing- India's ability to balance its **net-zero commitment by 2070** with its AI ambitions The paper argues that debates surrounding large data center projects should not be viewed as obstacles to progress but as essential reflections of a growing recognition that **AI must be built on environmentally responsible foundations**. ### Proposed Solutions: Engineering a Sustainable AI Future From a **Materials Science and Metallurgy** perspective, the paper proposes several engineering and policy strategies: 1. **Renewable-Powered Data Centers:** New AI facilities should increasingly rely on solar, wind, and other renewable energy sources.2. **Water-Efficient Cooling Technologies:** Adoption of advanced cooling systems, including liquid immersion cooling and treated wastewater recycling.3. **Energy-Efficient AI Chips:** Development of specialized hardware that delivers higher computational performance per watt.4. **Transparency in Environmental Reporting:** Mandatory disclosure of electricity, water, and carbon footprints by large AI facilities.5. **Greener Algorithms:** Research focus on optimizing AI models for energy efficiency, not just accuracy.6. **Circular Economy for Critical Minerals:** Recycling and responsible sourcing of rare earth elements used in AI hardware.7. **Policy Integration:** Embedding environmental sustainability into national AI strategies and engineering design curricula. ### Conclusion: The Cloud Is Not in the Sky The paper concludes with a powerful assertion: **The cloud is not in the sky. It is on the ground—powered by electricity, cooled by water, and sustained by engineering.** Every AI prompt begins with electricity, travels through complex engineering systems, and returns as intelligence. Whether that journey leaves behind a carbon footprint or a blueprint for sustainable innovation depends on the choices made today by researchers, engineers, policymakers, and technology companies. The future of AI will not be decided only in research laboratories or corporate boardrooms. It will be shaped in **power plants, renewable energy farms, water management systems, engineering design centers, and public policy institutions**. The paper calls for a dual commitment: embracing AI's transformative potential while rigorously engineering the physical infrastructure that makes it possible—ensuring that the intelligence of the 21st century does not come at the expense of the planet's ecological limits. ### Target Audience This paper is intended for:- Researchers and academics in **materials science, engineering, and environmental studies**- Policymakers and government officials involved in **AI governance, energy policy, and sustainable development**- Technology industry professionals working on **AI infrastructure, data center design, and green computing**- Environmental scientists and sustainability advocates- Students and educators in **engineering, computer science, and environmental science** programs ### Keywords Artificial Intelligence; Data Centers; Energy Consumption; Water Usage; Critical Minerals; Electronic Waste; Sustainable Engineering; Materials Science; Green Algorithms; IndiaAI Mission; Net-Zero Emissions; Resource Constraints; Environmental Policy --- ## Suggested Citation Sudhakar, G. (2026). The Hidden Physical Cost of Artificial Intelligence: A Materials Science and Engineering Optimization Perspective on AI Infrastructure, Resource Constraints, and Sustainable Innovation in India. *Department of Material Science and Metallurgy, RGUKT IIIT Basar, Telangana, India.* --- This combined title, alternative titles, subtitles, and detailed description provide a comprehensive framework for the research paper, suitable for academic publication, conference presentation, or policy briefing.","## 备选标题\n\n1. **人工智能不在云端：人工智能的物理基础设施、资源悖论与可持续未来**\n2. **从云端到地面：构建人工智能可持续的物理基础**\n3. **人工智能的隐形基础设施：智能机器时代的能源、水资源与材料挑战**\n4. **人工智能与地球：从材料科学视角看数据中心、资源消耗与绿色工程**\n5. **超越算法：人工智能基础设施的物理现实与可持续创新之路**\n6. **我们脚下的云：人工智能隐藏的物理成本如何呼唤可持续工程的新时代**\n7. **智能与资源：为印度及全球人工智能构建可持续未来的工程路径**\n\n---\n\n## 副标题\n\n1. **云在地面之上——由电力驱动，以水冷却，靠工程维系**\n2. **为何人工智能的未来不仅取决于更智能的算法，更取决于可持续的材料与基础设施**\n3. **从材料科学与冶金学视角看一场伪装成数字革命的物理革命**\n4. **在21世纪平衡技术进步与生态责任**\n5. **人工智能的技术-环境悖论：印度的资源挑战、政策紧迫性与工程解决方案**\n6. **从数据中心到关键矿产：人工智能隐藏的资源足迹**\n7. **重新思考人工智能可持续性：能源效率、水资源安全与环境治理透明度**\n\n---\n\n## 详细描述\n\n### 概述\n\n本研究论文批判性地审视了人工智能（AI）常被忽视的物理与材料基础，挑战了AI运行于无重量、虚无缥缈的“云”中这一普遍误解。尽管用户通过无缝的数字界面与AI交互——从ChatGPT、Gemini和Claude等系统获得即时响应——但现实是，每一次AI查询都会触发一连串物理事件：电力在数十亿晶体管中疾驰，处理器执行数万亿次计算，冷却系统不间断运转以消散巨大热量。论文认为，AI不仅仅是一场软件革命，更是一场深刻的**基础设施革命**，它严格运行在热力学定律、能量守恒与资源约束之内。\n\n### 问题：不可见",null,"Zenodo (CERN European Organization for Nuclear 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SDG 12（人工智能与中国农业可持续消费效率：来自可持续发展目标12的证据）","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002Faccepted","本研究从联合国2030年可持续发展目标12（SDG 12，确保可持续消费和生产模式）视角出发，识别AI技术对中国农业可持续消费效率的影响及其传导路径。文章构建包含人工智能基础设施部署、AI技术应用广度与应用深度三维一体的指标体系，运用双向固定效应模型、机制检验模型与门槛模型，对中国30个省份2015-2024年的面板数据展开系统实证。研究发现：AI应用显著提升了农业可持续消费效率，且在东中部、粮食主产区效应更突出；机制检验表明，AI通过促进农业生产要素重组、推动农产品加工链条低碳化、引导农村消费结构升级三条路径释放效率红利；门槛分析进一步显示，存在以农技推广覆盖率为单一门槛的\"低、高\"两种区制，AI边际效应在低覆盖率地区先升后降。研究对完善农业人工智能政策、推进农村消费革命具有重要意义，为\"十五五\"数字乡村建设背景下农业新质生产力发展提供经验依据。","Frontiers in Sustainable Food Systems 2026 年 10 月 06 日 Volume 10 Sec. Climate-Smart Food Systems (Accepted)","2026-10-05T16:00:00Z",85,{"impact":220,"substance":221,"depth":106,"authority":108,"freshness":171,"relevant":68,"comment":222},22,23,"基于30省十年面板数据的实证研究，方法扎实、结论具体，对数字乡村与农业AI政策有参考价值，值得进入每日精选。",[224],{"name":216,"url":214},[23,24,226,227,228],"农业新质生产力","农技推广","可持续消费",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[],{"status":35,"locked":14,"version":36,"method":37,"evidence":232},{"candidates":233,"agricultural_evidence":42},[234,238,243,247,253],{"code":28,"score":41,"supported":42,"title":235,"abstract":236,"tags":237},[44],[24],[24],{"code":239,"score":130,"supported":42,"title":240,"abstract":241,"tags":242},"G1",[],[227],[227],{"code":67,"score":130,"supported":42,"title":244,"abstract":245,"tags":246},[],[23],[23],{"code":248,"score":49,"supported":42,"title":249,"abstract":250,"tags":252},"E1",[],[251],"农产品加工",[],{"code":254,"score":68,"supported":14,"title":255,"abstract":256,"tags":257},"J2",[],[],[226],[259,260],"农业人工智能 可持续消费效率","SDG 12 中国农业 AI","农业人工智能可持续消费效率-4548","10.3389\u002Ffsufs.2026.xxxxx",{"doi":262,"openalex_id":9,"authors":264,"venue":9,"cited_by_count":15,"oa_url":9,"card":265,"direction":269,"ingested_from":271},[],{"tldr":266,"method":267,"finding":268,"direction":269,"opportunity":270},"实证检验AI对中国农业可持续消费效率的影响及传导路径。","构建AI三维指标体系，用双向固定效应、机制与门槛模型分析30省面板数据。","AI显著提升农业可持续消费效率，通过要素重组、加工低碳化、消费升级传导，存在农技推广门槛。","农业人工智能与决策模型","可探究AI应用深度与农技推广覆盖率的协同机制，及低覆盖率区AI效应先升后降的微观成因。","agent","2026-10-10T00:15:03.509764Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":9,"content":278,"source_name":279,"source_url":9,"published_at":280,"category":102,"cover_url":9,"hotness":103,"is_selected":14,"score":281,"score_detail":282,"sources":286,"tags":288,"primary_topic":292,"secondary_topics":297,"classification":299,"search_phrases":322,"slug":325,"view_count":326,"doi":9,"paper":9,"created_at":327},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",76,{"impact":220,"substance":283,"depth":284,"authority":103,"freshness":93,"relevant":68,"comment":285},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。",[287],{"name":279,"url":276},[23,289,24,290,291],"智慧农业","农业大模型","遥感大模型",{"code":293,"slug":294,"name":295,"path":296,"parent_code":32},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[298],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},{"status":35,"locked":14,"version":36,"method":37,"evidence":300},{"candidates":301,"agricultural_evidence":42},[302,309,313,318],{"code":293,"score":303,"supported":42,"title":304,"abstract":306,"tags":308},14,[305],"遥感",[305,307],"估产",[],{"code":28,"score":108,"supported":42,"title":310,"abstract":311,"tags":312},[125],[125],[24,290],{"code":61,"score":122,"supported":42,"title":314,"abstract":315,"tags":317},[],[289,316],"数字农业",[289],{"code":67,"score":68,"supported":14,"title":319,"abstract":320,"tags":321},[],[],[23],[323,324],"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",5,"2026-10-09T00:04:56.436776Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":280,"category":12,"cover_url":9,"hotness":103,"is_selected":14,"score":335,"score_detail":336,"sources":339,"tags":341,"primary_topic":344,"secondary_topics":350,"classification":352,"search_phrases":399,"slug":402,"view_count":326,"doi":403,"paper":404,"created_at":422},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":41,"substance":283,"depth":284,"authority":41,"freshness":337,"relevant":68,"comment":338},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。",[340],{"name":334,"url":331},[23,289,24,342,343],"农业保险","遥感监测",{"code":345,"slug":346,"name":347,"path":348,"parent_code":349},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[351],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},{"status":35,"locked":14,"version":36,"method":37,"evidence":353},{"candidates":354,"agricultural_evidence":42},[355,361,365,370,376,382,387,391,395],{"code":345,"score":356,"supported":42,"title":357,"abstract":359,"tags":360},11,[358],"cattle",[358],[],{"code":28,"score":171,"supported":42,"title":362,"abstract":363,"tags":364},[44],[],[24],{"code":349,"score":49,"supported":42,"title":366,"abstract":367,"tags":369},[],[368],"livestock",[],{"code":371,"score":49,"supported":42,"title":372,"abstract":373,"tags":375},"H7",[],[374],"干旱",[],{"code":377,"score":49,"supported":42,"title":378,"abstract":379,"tags":381},"I7",[],[380],"研究方法",[],{"code":383,"score":68,"supported":14,"title":384,"abstract":385,"tags":386},"G3",[],[],[342],{"code":61,"score":68,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[289],{"code":293,"score":68,"supported":14,"title":392,"abstract":393,"tags":394},[],[],[343],{"code":67,"score":68,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[23],[400,401],"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":403,"openalex_id":405,"authors":406,"venue":334,"cited_by_count":15,"oa_url":416,"card":417,"direction":89,"ingested_from":90},"W7220655164",[407,410,413],{"name":408,"orcid":409},"Nyasha Nyakuchena","https:\u002F\u002Forcid.org\u002F0009-0005-3219-2275",{"name":411,"orcid":412},"Joseph P. Musara","https:\u002F\u002Forcid.org\u002F0000-0002-2226-3616",{"name":414,"orcid":415},"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":418,"method":419,"finding":420,"direction":208,"opportunity":421},"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。","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":424,"title":425,"url":426,"summary":427,"summary_zh":9,"content":428,"source_name":429,"source_url":9,"published_at":430,"category":102,"cover_url":9,"hotness":103,"is_selected":14,"score":431,"score_detail":432,"sources":436,"tags":438,"primary_topic":441,"secondary_topics":447,"classification":449,"search_phrases":491,"slug":494,"view_count":15,"doi":9,"paper":9,"created_at":495},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":106,"substance":433,"depth":434,"authority":103,"freshness":337,"relevant":68,"comment":435},20,15,"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。",[437],{"name":429,"url":426},[23,289,24,439,440],"良种良法","水稻单产",{"code":442,"slug":443,"name":444,"path":445,"parent_code":446},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[448],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},{"status":35,"locked":14,"version":36,"method":37,"evidence":450},{"candidates":451,"agricultural_evidence":42},[452,457,461,467,472,478,483,487],{"code":442,"score":303,"supported":42,"title":453,"abstract":454,"tags":456},[444],[444,455],"杂交稻",[],{"code":28,"score":41,"supported":42,"title":458,"abstract":459,"tags":460},[125],[125],[24],{"code":462,"score":49,"supported":42,"title":463,"abstract":464,"tags":466},"F5",[],[465],"收割机",[],{"code":48,"score":49,"supported":42,"title":468,"abstract":469,"tags":471},[],[470],"灌溉",[],{"code":473,"score":49,"supported":42,"title":474,"abstract":475,"tags":477},"I5",[],[476],"物联网",[],{"code":479,"score":68,"supported":14,"title":480,"abstract":481,"tags":482},"F1",[],[],[439],{"code":61,"score":68,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[289],{"code":67,"score":68,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[23],[492,493],"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"]