[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3358":3,"related-3358":111},{"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":29,"primary_topic":35,"secondary_topics":41,"classification":47,"search_phrases":75,"slug":78,"view_count":20,"doi":79,"paper":80,"created_at":110},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。",null,"Preprints.org","2026-09-23T00:00:00Z","论文",25,false,50,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},6,16,15,4,9,1,"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"Computers","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15100663",[30,31,32,33,34],"智慧农业","农业人工智能","农业物联网","精准农业","农业教育",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[42],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":48,"locked":14,"version":49,"method":50,"evidence":51},"classified","2026-10-22.2","rules",{"candidates":52,"agricultural_evidence":55},[53,62,68],{"code":36,"score":54,"supported":55,"title":56,"abstract":58,"tags":61},18,true,[57],"IoT",[59,60,57],"物联网","internet of things",[32],{"code":43,"score":63,"supported":55,"title":64,"abstract":66,"tags":67},12,[65],"artificial intelligence",[65],[31],{"code":69,"score":70,"supported":55,"title":71,"abstract":72,"tags":74},"I1",8,[],[33,73],"precision agriculture",[30,33],[76,77],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":108,"ingested_from":109},"W7214071608",[83,86,89,92,95,98],{"name":84,"orcid":85},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":87,"orcid":88},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":90,"orcid":91},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":93,"orcid":94},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":96,"orcid":97},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":99,"orcid":100},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","智慧农业 \u002F 农业物联网","openalex","2026-09-24T23:30:13.353443Z",{"total":17,"page":22,"page_size":17,"items":112},[113,176,267,366,430,487],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":126,"tags":130,"primary_topic":132,"secondary_topics":133,"classification":135,"search_phrases":155,"slug":158,"view_count":22,"doi":9,"paper":159,"created_at":175},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",47,{"impact":70,"substance":123,"depth":124,"authority":17,"freshness":17,"relevant":22,"comment":125},14,13,"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[127,128],{"name":119,"url":116},{"name":119,"url":129},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[30,31,32,33,131],"低功耗通信",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[134],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":136},{"candidates":137,"agricultural_evidence":55},[138,144,151],{"code":36,"score":54,"supported":55,"title":139,"abstract":140,"tags":143},[57],[59,141,60,57,142],"边缘计算","edge computing",[32],{"code":43,"score":145,"supported":55,"title":146,"abstract":147,"tags":150},10,[],[148,149,65],"机器学习","machine learning",[31],{"code":69,"score":70,"supported":55,"title":152,"abstract":153,"tags":154},[],[33,73],[30,33],[156,157],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":9,"openalex_id":160,"authors":161,"venue":119,"cited_by_count":101,"oa_url":116,"card":170,"direction":108,"ingested_from":109},"W7221414266",[162,164,166,168],{"name":163,"orcid":9},"Shital Chavan",{"name":165,"orcid":9},"Anam Bedrekar",{"name":167,"orcid":9},"Sanika Babar",{"name":169,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":171,"method":172,"finding":173,"direction":108,"opportunity":174},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":189,"tags":194,"primary_topic":197,"secondary_topics":198,"classification":206,"search_phrases":251,"slug":254,"view_count":22,"doi":255,"paper":256,"created_at":266},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",81,{"impact":186,"substance":187,"depth":54,"authority":124,"freshness":70,"relevant":22,"comment":188},22,20,"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[190,191],{"name":182,"url":179},{"name":192,"url":193},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[30,31,195,32,33,196],"农业机器人","自动驾驶农机",{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},[199,205],{"code":200,"slug":201,"name":202,"path":203,"parent_code":204},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":207},{"candidates":208,"agricultural_evidence":55},[209,215,223,227,234,239,246],{"code":43,"score":54,"supported":55,"title":210,"abstract":211,"tags":214},[65],[148,212,149,65,213],"计算机视觉","computer vision",[31],{"code":200,"score":216,"supported":55,"title":217,"abstract":219,"tags":222},17,[218],"agricultural machinery",[220,221,218],"农业机械","收割机",[],{"code":36,"score":145,"supported":55,"title":224,"abstract":225,"tags":226},[],[59,60,57],[32],{"code":228,"score":17,"supported":55,"title":229,"abstract":230,"tags":233},"I3",[],[231,232],"遥感","remote sensing",[],{"code":69,"score":235,"supported":55,"title":236,"abstract":237,"tags":238},5,[],[33],[30,33],{"code":240,"score":241,"supported":55,"title":242,"abstract":243,"tags":245},"F4",3,[],[244],"除草",[],{"code":247,"score":22,"supported":14,"title":248,"abstract":249,"tags":250},"F6",[],[],[195],[252,253],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":255,"openalex_id":257,"authors":258,"venue":182,"cited_by_count":101,"oa_url":179,"card":261,"direction":108,"ingested_from":109},"W7221138004",[259],{"name":260,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":262,"method":263,"finding":264,"direction":108,"opportunity":265},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":145,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":282,"secondary_topics":283,"classification":291,"search_phrases":341,"slug":344,"view_count":101,"doi":345,"paper":346,"created_at":365},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)","2026-10-21T00:00:00Z",80,{"impact":54,"substance":186,"depth":54,"authority":124,"freshness":21,"relevant":22,"comment":277},"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。",[279],{"name":273,"url":270},[30,31,59,33,281],"气候韧性",{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},[284,285],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":286,"slug":287,"name":288,"path":289,"parent_code":290},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":48,"locked":14,"version":49,"method":50,"evidence":292},{"candidates":293,"agricultural_evidence":55},[294,300,304,308,314,322,329,335],{"code":43,"score":54,"supported":55,"title":295,"abstract":296,"tags":299},[65],[297,148,212,149,298,213],"深度学习","deep learning",[31],{"code":69,"score":18,"supported":55,"title":301,"abstract":302,"tags":303},[73],[33,73],[30,33],{"code":36,"score":145,"supported":55,"title":305,"abstract":306,"tags":307},[],[59,141,57,142],[59],{"code":286,"score":21,"supported":55,"title":309,"abstract":310,"tags":313},[],[311,312,287],"灌溉","水资源",[],{"code":315,"score":316,"supported":55,"title":317,"abstract":318,"tags":321},"H7",7,[],[319,320],"气候变化","climate change",[281],{"code":323,"score":17,"supported":55,"title":324,"abstract":325,"tags":328},"H2",[],[326,327],"土壤","soil",[],{"code":330,"score":241,"supported":55,"title":331,"abstract":332,"tags":334},"A7",[],[333],"间作",[],{"code":336,"score":241,"supported":55,"title":337,"abstract":338,"tags":340},"F3",[],[339],"养分管理",[],[342,343],"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":345,"openalex_id":347,"authors":348,"venue":273,"cited_by_count":101,"oa_url":270,"card":359,"direction":108,"ingested_from":109},"W7223121456",[349,352,354,356],{"name":350,"orcid":351},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":353,"orcid":9},"Oluwadare OYEBODE",{"name":355,"orcid":9},"Iretiayo Adelaiye",{"name":357,"orcid":358},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":360,"method":361,"finding":362,"direction":363,"opportunity":364},"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",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":9,"source_name":372,"source_url":369,"published_at":373,"category":12,"cover_url":9,"hotness":145,"is_selected":14,"score":374,"score_detail":375,"sources":378,"tags":380,"primary_topic":383,"secondary_topics":384,"classification":386,"search_phrases":404,"slug":407,"view_count":101,"doi":408,"paper":409,"created_at":429},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":18,"substance":376,"depth":54,"authority":63,"freshness":21,"relevant":22,"comment":377},21,"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[379],{"name":372,"url":369},[30,31,32,381,382],"大语言模型","传感器标准化",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[385],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":387},{"candidates":388,"agricultural_evidence":55},[389,393,399],{"code":36,"score":54,"supported":55,"title":390,"abstract":391,"tags":392},[57],[59,60,57],[32],{"code":43,"score":18,"supported":55,"title":394,"abstract":396,"tags":398},[395],"decision support",[397,395],"large language model",[31,381],{"code":69,"score":316,"supported":55,"title":400,"abstract":401,"tags":403},[],[30,402],"smart agriculture",[30],[405,406],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":408,"openalex_id":410,"authors":411,"venue":372,"cited_by_count":101,"oa_url":369,"card":424,"direction":108,"ingested_from":109},"W7222920424",[412,414,416,418,420,422],{"name":413,"orcid":9},"Leqi Lai",{"name":415,"orcid":9},"Langtao Duan",{"name":417,"orcid":9},"Yuhao Chen",{"name":419,"orcid":9},"Yuhang Zheng",{"name":421,"orcid":9},"Jinyu Yang",{"name":423,"orcid":9},"Mingyao Wen",{"tldr":425,"method":426,"finding":427,"direction":108,"opportunity":428},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":145,"is_selected":14,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":446,"secondary_topics":447,"classification":449,"search_phrases":469,"slug":472,"view_count":241,"doi":473,"paper":474,"created_at":486},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":63,"substance":54,"depth":18,"authority":124,"freshness":21,"relevant":22,"comment":440},"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[442],{"name":436,"url":433},[30,31,32,444,445],"遥感监测","作物估产",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[448],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":450},{"candidates":451,"agricultural_evidence":55},[452,456,460,464],{"code":36,"score":54,"supported":55,"title":453,"abstract":454,"tags":455},[57],[59,141,60,57,142],[32],{"code":69,"score":19,"supported":55,"title":457,"abstract":458,"tags":459},[402],[33,73],[30],{"code":43,"score":145,"supported":55,"title":461,"abstract":462,"tags":463},[],[297,148,149,298,65],[31],{"code":228,"score":465,"supported":14,"title":466,"abstract":467,"tags":468},2,[],[],[445,444],[470,471],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":473,"openalex_id":475,"authors":476,"venue":436,"cited_by_count":101,"oa_url":433,"card":481,"direction":108,"ingested_from":109},"W7221560555",[477,479],{"name":478,"orcid":9},"Keerthana T",{"name":480,"orcid":9},"Janarthanan R",{"tldr":482,"method":483,"finding":484,"direction":108,"opportunity":485},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":488,"title":489,"url":490,"summary":491,"summary_zh":492,"content":9,"source_name":119,"source_url":490,"published_at":120,"category":12,"cover_url":9,"hotness":145,"is_selected":14,"score":493,"score_detail":494,"sources":496,"tags":498,"primary_topic":501,"secondary_topics":505,"classification":508,"search_phrases":533,"slug":536,"view_count":101,"doi":9,"paper":537,"created_at":553},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。",61,{"impact":63,"substance":18,"depth":19,"authority":63,"freshness":17,"relevant":22,"comment":495},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[497],{"name":119,"url":490},[30,31,499,32,500],"精准施肥","智能农机",{"code":336,"slug":502,"name":503,"path":504,"parent_code":204},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",[506,507],{"code":43,"slug":44,"name":45,"path":46,"parent_code":40},{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":48,"locked":14,"version":49,"method":50,"evidence":509},{"candidates":510,"agricultural_evidence":55},[511,517,521,525,529],{"code":336,"score":19,"supported":55,"title":512,"abstract":514,"tags":516},[513],"fertilizer",[515,513],"肥料",[499],{"code":43,"score":145,"supported":55,"title":518,"abstract":519,"tags":520},[],[148,149,65],[31],{"code":36,"score":145,"supported":55,"title":522,"abstract":523,"tags":524},[],[59,60,57],[32],{"code":69,"score":316,"supported":55,"title":526,"abstract":527,"tags":528},[],[33,73],[30],{"code":200,"score":22,"supported":14,"title":530,"abstract":531,"tags":532},[],[],[500],[534,535],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":9,"openalex_id":538,"authors":539,"venue":119,"cited_by_count":101,"oa_url":490,"card":548,"direction":108,"ingested_from":109},"W7221394563",[540,542,544,546],{"name":541,"orcid":9},"Anjali Manoj Kumbhar",{"name":543,"orcid":9},"Shruti Prashant Kulkarni",{"name":545,"orcid":9},"Snehal Santosh Khune",{"name":547,"orcid":9},"B. B. Godbole",{"tldr":549,"method":550,"finding":551,"direction":108,"opportunity":552},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z"]