[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2866":3,"related-2866":110},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":91,"slug":94,"view_count":79,"doi":95,"paper":96,"created_at":109},2866,"Upcoming Technologies for Agriculture: Innovations Shaping the Future of Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816586","Agriculture has always been a cornerstone of human civilization, providing food, raw materials, and employment. However, with the global population projected to reach nearly 10 billion by 2050, the demand for food production is expected to increase substantially (Godfray et al., 2010). Traditional farming methods are increasingly challenged by climate change, resource limitations, and environmental concerns. To address these challenges, upcoming technologies in agriculture promise to revolutionize farming practices by enhancing productivity, sustainability, and resilience. This article reviews key emerging technologies, including precision agriculture, artificial intelligence (AI), gene editing, drone and robotic systems, Internet of Things (IoT) sensors, and sustainable farming innovations. The integration of these technologies is expected to transform agriculture into a more efficient, data-driven, and environmentally friendly sector.","农业一直是人类文明的基石，为人类提供食物、原材料和就业机会。然而，随着全球人口预计到2050年将接近100亿，粮食生产需求预计将大幅增加（Godfray等，2010）。传统耕作方式日益受到气候变化、资源限制和环境问题的挑战。为应对这些挑战，农业领域的新兴技术有望通过提高生产力、可持续性和韧性来彻底变革耕作方式。本文综述了关键新兴技术，包括精准农业、人工智能（AI）、基因编辑、无人机与机器人系统、物联网（IoT）传感器以及可持续农业创新。这些技术的融合有望将农业转变为一个更高效、数据驱动且环境友好的产业。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z","论文",25,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,14,15,13,8,1,"综述性论文系统梳理精准农业、AI、基因编辑等前沿技术，时效性尚可，但缺乏新数据与独家结论，适合作为主题聚合素材而非每日精选头条。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[29,30,31,32,33],"智慧农业","农业人工智能","农业物联网","精准农业","基因编辑",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,56,62,69,77,84],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":55},10,true,[],[52,53,54],"物联网","internet of things","IoT",[31],{"code":57,"score":21,"supported":49,"title":58,"abstract":59,"tags":61},"I1",[],[32,60],"precision agriculture",[29,32],{"code":63,"score":64,"supported":49,"title":65,"abstract":66,"tags":68},"F2",7,[],[33,67],"gene editing",[33],{"code":70,"score":71,"supported":49,"title":72,"abstract":73,"tags":76},"H7",6,[],[74,75],"气候变化","climate change",[],{"code":78,"score":79,"supported":49,"title":80,"abstract":81,"tags":83},"I2",4,[],[82],"artificial intelligence",[30],{"code":85,"score":86,"supported":49,"title":87,"abstract":88,"tags":90},"H8",3,[],[89],"可持续农业",[],[92,93],"精准农业 人工智能 无人机","农业物联网 传感器 机器人","精准农业人工智能无人机-2866","10.5281\u002Fzenodo.22816586",{"doi":95,"openalex_id":97,"authors":98,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7213515489",[99],{"name":100,"orcid":9},"Zorawar Singh",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"综述精准农业、AI、基因编辑、无人机、物联网等新兴技术如何重塑未来农业。","文献综述，整合精准农业、AI、基因编辑、无人机、IoT等关键技术。","技术融合将推动农业向高效、数据驱动和环境友好方向转型。","智慧农业 \u002F 农业物联网","可聚焦多技术集成落地中的成本、数据标准与农户采纳障碍等实证研究空白。","openalex","2026-09-18T23:30:14.975692Z",{"total":71,"page":22,"page_size":71,"items":111},[112,175,264,317,380,431],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":123,"tags":127,"primary_topic":129,"secondary_topics":130,"classification":135,"search_phrases":154,"slug":157,"view_count":22,"doi":9,"paper":158,"created_at":174},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":21,"substance":18,"depth":20,"authority":71,"freshness":71,"relevant":22,"comment":122},"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。",[124,125],{"name":118,"url":115},{"name":118,"url":126},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[29,30,31,32,128],"低功耗通信",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[131],{"code":78,"slug":132,"name":133,"path":134,"parent_code":39},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":42,"locked":14,"version":43,"method":44,"evidence":136},{"candidates":137,"agricultural_evidence":49},[138,144,150],{"code":35,"score":17,"supported":49,"title":139,"abstract":140,"tags":143},[54],[52,141,53,54,142],"边缘计算","edge computing",[31],{"code":78,"score":48,"supported":49,"title":145,"abstract":146,"tags":149},[],[147,148,82],"机器学习","machine learning",[30],{"code":57,"score":21,"supported":49,"title":151,"abstract":152,"tags":153},[],[32,60],[29,32],[155,156],"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":159,"authors":160,"venue":118,"cited_by_count":101,"oa_url":115,"card":169,"direction":106,"ingested_from":108},"W7221414266",[161,163,165,167],{"name":162,"orcid":9},"Shital Chavan",{"name":164,"orcid":9},"Anam Bedrekar",{"name":166,"orcid":9},"Sanika Babar",{"name":168,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":170,"method":171,"finding":172,"direction":106,"opportunity":173},"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":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":10,"source_url":178,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":204,"search_phrases":248,"slug":251,"view_count":22,"doi":252,"paper":253,"created_at":263},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。","2026-10-09T00:00:00Z",81,{"impact":184,"substance":185,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":186},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。",[188,189],{"name":10,"url":178},{"name":190,"url":191},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[29,30,193,31,32,194],"农业机器人","自动驾驶农机",{"code":78,"slug":132,"name":133,"path":134,"parent_code":39},[197,203],{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":205},{"candidates":206,"agricultural_evidence":49},[207,213,221,225,232,237,243],{"code":78,"score":17,"supported":49,"title":208,"abstract":209,"tags":212},[82],[147,210,148,82,211],"计算机视觉","computer vision",[30],{"code":198,"score":214,"supported":49,"title":215,"abstract":217,"tags":220},17,[216],"agricultural machinery",[218,219,216],"农业机械","收割机",[],{"code":35,"score":48,"supported":49,"title":222,"abstract":223,"tags":224},[],[52,53,54],[31],{"code":226,"score":71,"supported":49,"title":227,"abstract":228,"tags":231},"I3",[],[229,230],"遥感","remote sensing",[],{"code":57,"score":233,"supported":49,"title":234,"abstract":235,"tags":236},5,[],[32],[29,32],{"code":238,"score":86,"supported":49,"title":239,"abstract":240,"tags":242},"F4",[],[241],"除草",[],{"code":244,"score":22,"supported":14,"title":245,"abstract":246,"tags":247},"F6",[],[],[193],[249,250],"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":252,"openalex_id":254,"authors":255,"venue":10,"cited_by_count":101,"oa_url":178,"card":258,"direction":106,"ingested_from":108},"W7221138004",[256],{"name":257,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":259,"method":260,"finding":261,"direction":106,"opportunity":262},"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":265,"title":266,"url":267,"summary":268,"summary_zh":9,"content":9,"source_name":269,"source_url":267,"published_at":270,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":271,"score_detail":272,"sources":277,"tags":279,"primary_topic":281,"secondary_topics":282,"classification":283,"search_phrases":299,"slug":302,"view_count":22,"doi":303,"paper":304,"created_at":316},4339,"GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11233315\u002Fv1","GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture。Research Square","Research Square","2026-10-06T00:00:00Z",63,{"impact":273,"substance":17,"depth":274,"authority":21,"freshness":275,"relevant":22,"comment":276},12,16,9,"提出可解释AI与物联网结合的离线精准农业框架，方法有新意但属预印本，权威性与产业影响有限。",[278],{"name":269,"url":267},[29,30,31,280,32],"可解释AI",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":284},{"candidates":285,"agricultural_evidence":49},[286,290,294],{"code":57,"score":20,"supported":49,"title":287,"abstract":288,"tags":289},[60],[60],[29,32],{"code":35,"score":273,"supported":49,"title":291,"abstract":292,"tags":293},[54],[54],[31],{"code":78,"score":295,"supported":14,"title":296,"abstract":297,"tags":298},2,[],[],[30,280],[300,301],"GreenAI 精准农业 离线框架","可解释AI 农业物联网","GreenAI精准农业离线框架-4339","10.21203\u002Frs.3.rs-11233315\u002Fv1",{"doi":303,"openalex_id":305,"authors":306,"venue":269,"cited_by_count":101,"oa_url":315,"card":9,"direction":106,"ingested_from":108},"W7220656746",[307,310,313],{"name":308,"orcid":309},"Qasim Ali Hashmi","https:\u002F\u002Forcid.org\u002F0009-0009-2894-3688",{"name":311,"orcid":312},"Zain ul Sajjad","https:\u002F\u002Forcid.org\u002F0009-0003-8354-8134",{"name":314,"orcid":9},"Saqib Ali³","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11233315\u002Flatest.pdf","2026-10-07T23:30:08.629402Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":332,"secondary_topics":333,"classification":334,"search_phrases":363,"slug":366,"view_count":295,"doi":9,"paper":367,"created_at":379},4072,"Безпілотні літальні апарати та Інтернет речей у сільському господарстві: систематичний огляд технологічних напрямів і перспектив розвитку","https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fview\u002F372716","Relevance. Modern agriculture is undergoing a profound cyber-physical transformation driven by the need to shift from extensive to precision, high-accuracy, and environmentally balanced production methods. The technological foundation of this transition is the integration of unmanned aerial vehicles (UAVs) and wireless Internet of Things (IoT) sensor networks, which together form the concept of Agriculture 5.0. Despite the rapid growth of publications on this topic, the accumulated body of research remains fragmented across directions (computer vision, network technologies, robotics, adoption economics), which complicates the formation of a holistic picture of the field and of priority directions for further research. The subject of this study are methods and technologies of applying UAVs and IoT networks to precision-agriculture tasks. The purpose of the article to systematize and classify current scientific research on the use of UAVs and IoT in agriculture, and to determine the leading technological directions, their quantitative proportions, and promising development vectors. The following results were obtained. A total of 198 scientific publications from 2024-2026 were processed and classified into seven thematic classes: crop health and biological threat monitoring, hardware engineering, edge computing and spraying, navigation, swarm intelligence and data collection, phenotyping, biomass and yield forecasting, soil science, water balance and precision irrigation, economics, ecology and socio-cultural perception, and specialized agrotechnical operations. For each class, consolidated tables of applied methods and results, as well as diagrams revealing the internal structure of the direction, were constructed. The dominance of navigation\u002Fswarm-control tasks and biological-threat monitoring was identified, along with key technical barriers (limited battery energy density, beyond-visual-line-of-sight flight regulations, and fragmentation of data-exchange standards). Conclusion. The obtained results provide a holistic assessment of the state and structure of the research field on UAV and IoT applications in agriculture and can serve as a basis for shaping further research programs and educational courses in precision agriculture. Future research directions concern improving the energy autonomy of platforms, standardizing data-exchange protocols, and developing edge artificial-intelligence models resilient to complex field conditions.","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自于从粗放型向精准、高精度和环境均衡型生产方式转变的需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采用经济学），这使得形成该领域的整体图景及未来优先研究方向变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化和分类当前关于无人机和物联网在农业中应用的科学研 究，并确定主导技术方向、其定量比例及有前景的发展路径。获得了以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测、硬件工程、边缘计算与喷洒、导航、群体智能与数据采集、表型分析、生物量与产量预测、土壤科学、水分平衡与精准灌溉、经济学、生态学与社会文化感知，以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务和生物威胁监测的主导地位，以及关键技术障碍（电池能量密度有限、超视距飞行法规、数据交换标准的碎片化）。结论。所获得的结果对无人机和物联网在农业中应用的研究领域现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教学课程的基础。未来研究方向涉及提高平台的能量自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","2026-10-02T00:00:00Z",78,{"impact":17,"substance":184,"depth":17,"authority":273,"freshness":21,"relevant":22,"comment":327},"系统梳理2024-2026年198篇无人机与物联网农业应用文献，分类清晰、结论可靠，对智慧农业研究方向有实质参考价值。",[329],{"name":323,"url":320},[29,30,31,331,32],"农业无人机",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":335},{"candidates":336,"agricultural_evidence":49},[337,341,345,349,356],{"code":35,"score":48,"supported":49,"title":338,"abstract":339,"tags":340},[],[52,141,53,54,142],[31],{"code":57,"score":21,"supported":49,"title":342,"abstract":343,"tags":344},[],[32,60],[29,32],{"code":78,"score":64,"supported":49,"title":346,"abstract":347,"tags":348},[],[210,211],[30],{"code":350,"score":71,"supported":49,"title":351,"abstract":352,"tags":355},"H2",[],[353,354],"土壤","soil",[],{"code":357,"score":71,"supported":49,"title":358,"abstract":359,"tags":362},"H3",[],[360,361],"灌溉","irrigation",[],[364,365],"农业人工智能 农业无人机 农业物联网 智慧农业","农业人工智能 农业无人机","农业人工智能农业无人机农业物联网智慧农业-4072",{"doi":9,"openalex_id":368,"authors":369,"venue":323,"cited_by_count":101,"oa_url":320,"card":374,"direction":106,"ingested_from":108},"W7218223913",[370,372],{"name":371,"orcid":9},"Володимир Панченко",{"name":373,"orcid":9},"Георгій Кучук",{"tldr":375,"method":376,"finding":377,"direction":106,"opportunity":378},"系统综述2024-2026年198篇无人机与物联网农业应用文献，划分七类技术方向并指出发展瓶颈。","文献计量与主题分类，分析198篇论文的方法、结果与内部结构。","导航\u002F集群控制与生物威胁监测占主导，瓶颈为电池能量密度、超视距法规与数据标准碎片化。","可聚焦边缘AI模型在复杂田间条件下的鲁棒性、平台能量自治与跨厂商数据交换协议标准化研究。","2026-10-03T23:30:16.357874Z",{"id":381,"title":382,"url":383,"summary":321,"summary_zh":384,"content":9,"source_name":385,"source_url":383,"published_at":324,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":325,"score_detail":386,"sources":388,"tags":390,"primary_topic":391,"secondary_topics":392,"classification":393,"search_phrases":416,"slug":417,"view_count":22,"doi":418,"paper":419,"created_at":430},4071,"UNMANNED AERIAL VEHICLES AND THE INTERNET OF THINGS IN AGRICULTURE: A SYSTEMATIC REVIEW OF TECHNOLOGICAL DIRECTIONS AND DEVELOPMENT PROSPECTS","https:\u002F\u002Fdoi.org\u002F10.20998\u002F2522-9052.2026.4.06","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自从粗放型生产向精准、高精度和环境均衡型生产方式的转变需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采纳经济学），这使得形成对该领域整体图景及未来优先研究方向的把握变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化梳理和分类当前关于无人机与物联网在农业中应用的科学研 究，并确定主导技术方向、其数量比例及有前景的发展路径。获得以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测；硬件工程；边缘计算与喷洒；导航、群体智能与数据采集；表型分析、生物量与产量预测；土壤科学、水分平衡与精准灌溉；经济、生态与社会文化感知；以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务与生物威胁监测的主导地位，以及关键技术瓶颈（电池能量密度有限、超视距飞行法规、数据交换标准碎片化）。结论。所获结果对无人机与物联网在农业中应用研究领域的现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教育课程的基础。未来研究方向涉及提升平台能源自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","Advanced Information Systems",{"impact":17,"substance":184,"depth":17,"authority":273,"freshness":21,"relevant":22,"comment":387},"系统综述198篇2024-2026年文献，梳理无人机与物联网在农业中的七大技术方向及瓶颈，方法学扎实、信息增量高，对智慧农业研究布局有参考价值。",[389],{"name":385,"url":383},[29,30,31,331,32],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":394},{"candidates":395,"agricultural_evidence":49},[396,400,404,408,412],{"code":35,"score":17,"supported":49,"title":397,"abstract":398,"tags":399},[53],[52,141,53,54,142],[31],{"code":57,"score":21,"supported":49,"title":401,"abstract":402,"tags":403},[],[32,60],[29,32],{"code":78,"score":64,"supported":49,"title":405,"abstract":406,"tags":407},[],[210,211],[30],{"code":350,"score":71,"supported":49,"title":409,"abstract":410,"tags":411},[],[353,354],[],{"code":357,"score":71,"supported":49,"title":413,"abstract":414,"tags":415},[],[360,361],[],[364,365],"农业人工智能农业无人机农业物联网智慧农业-4071","10.20998\u002F2522-9052.2026.4.06",{"doi":418,"openalex_id":420,"authors":421,"venue":385,"cited_by_count":101,"oa_url":383,"card":425,"direction":106,"ingested_from":108},"W7216591198",[422,424],{"name":423,"orcid":9},"Volodymyr Panchenko",{"name":373,"orcid":9},{"tldr":426,"method":427,"finding":428,"direction":106,"opportunity":429},"系统综述2024-2026年198篇无人机与物联网农业应用文献，划分七类技术方向并指出发展重点。","文献计量与主题分类，分析198篇论文的方法、成果及技术瓶颈。","导航\u002F蜂群控制与生物威胁监测占主导，瓶颈为电池能量、超视距法规与数据标准碎片化。","可聚焦边缘AI模型在复杂田间条件下的鲁棒性、平台能量自治与数据交换协议标准化研究。","2026-10-03T23:30:16.292508Z",{"id":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":9,"source_name":437,"source_url":434,"published_at":438,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":442,"tags":447,"primary_topic":449,"secondary_topics":450,"classification":452,"search_phrases":467,"slug":470,"view_count":79,"doi":471,"paper":472,"created_at":499},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。","Preprints.org","2026-09-23T00:00:00Z",50,{"impact":71,"substance":274,"depth":19,"authority":79,"freshness":275,"relevant":22,"comment":441},"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。",[443,444],{"name":437,"url":434},{"name":445,"url":446},"Computers","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15100663",[29,30,31,32,448],"农业教育",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[451],{"code":78,"slug":132,"name":133,"path":134,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":453},{"candidates":454,"agricultural_evidence":49},[455,459,463],{"code":35,"score":17,"supported":49,"title":456,"abstract":457,"tags":458},[54],[52,53,54],[31],{"code":78,"score":273,"supported":49,"title":460,"abstract":461,"tags":462},[82],[82],[30],{"code":57,"score":21,"supported":49,"title":464,"abstract":465,"tags":466},[],[32,60],[29,32],[468,469],"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":471,"openalex_id":473,"authors":474,"venue":437,"cited_by_count":101,"oa_url":434,"card":493,"direction":106,"ingested_from":108},"W7214071608",[475,478,481,484,487,490],{"name":476,"orcid":477},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":479,"orcid":480},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":482,"orcid":483},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":485,"orcid":486},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":488,"orcid":489},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":491,"orcid":492},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":494,"method":495,"finding":496,"direction":497,"opportunity":498},"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。","2026-09-24T23:30:13.353443Z"]