[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4784":3,"related-4784":98},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":83,"slug":86,"view_count":87,"doi":8,"paper":88,"created_at":97},4784,"Smart Farming and Modern Agricultural Technologies","https:\u002F\u002Fscientiafoundation.in\u002Fpopular-science--article\u002Fsmart-farming-and-modern-agricultural","Reddy等学者在Scientia Review 2026年第2卷第10期发表综述,系统梳理智慧农业从传统实践到数字农业的演进路径。文章解析精准农业、物联网、遥感与无人机、人工智能、机器人、智能灌溉、保护地栽培、畜牧监测、数字市场与溯源等关键技术的工作原理与效益,讨论成本、数字素养、融资、培训等阻碍因素,特别关注小农户和边缘农户的采纳瓶颈。",null,"Scientia Review 2026-10-07","2026-10-07T00:00:00Z","论文",10,false,73,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,17,13,5,1,"系统梳理智慧农业关键技术与小农户采纳瓶颈的综述，信息增量足但属学术综述、时效一般，可作主题聚合素材。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业物联网","精准农业","小农户",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":82},[38,46,52,58,65,71,77],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"I1",11,true,[],[26,29,44],"数字农业",[26,29],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":51},"G2",4,[],[30],[30],{"code":53,"score":48,"supported":41,"title":54,"abstract":55,"tags":57},"I5",[],[56],"物联网",[28],{"code":59,"score":60,"supported":41,"title":61,"abstract":62,"tags":64},"C",3,[],[63],"畜牧",[],{"code":66,"score":60,"supported":41,"title":67,"abstract":68,"tags":70},"H3",[],[69],"灌溉",[],{"code":72,"score":60,"supported":41,"title":73,"abstract":74,"tags":76},"I3",[],[75],"遥感",[],{"code":78,"score":21,"supported":13,"title":79,"abstract":80,"tags":81},"I2",[],[],[27],"多个产业证据相当，需要确认主归属",[84,85],"Scientia Review 智慧农业","Reddy 智慧农业 数字农业","ScientiaReview智慧农业-4784",0,{"doi":8,"openalex_id":8,"authors":89,"venue":8,"cited_by_count":87,"oa_url":8,"card":90,"direction":94,"ingested_from":96},[],{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"综述智慧农业从传统到数字化的演进，梳理关键技术、效益与采纳障碍。","文献综述，覆盖精准农业、物联网、遥感、AI、机器人等关键技术。","技术效益显著，但成本、数字素养与融资障碍制约小农户采纳。","数字乡村与农业信息化","可针对小农户低成本、低门槛的智慧农业适配方案与采纳机制开展实证研究。","agent","2026-10-13T00:08:38.433355Z",{"total":99,"page":21,"page_size":99,"items":100},6,[101,157,231,314,367,455],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":8,"content":8,"source_name":106,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":107,"score_detail":108,"sources":111,"tags":113,"primary_topic":8,"secondary_topics":115,"classification":116,"search_phrases":143,"slug":146,"view_count":21,"doi":147,"paper":148,"created_at":156},4785,"Food's future: sustainability and agricultural robotics","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffrobt.2025.1696483\u002Ffull","Nleya等学者发表于Frontiers in Robotics and AI,围绕全球粮食系统面临的可持续挑战,系统梳理农业机器人如何助力农业可持续转型。文章提出五维分析框架:危机视角、技术分类、与精准农业IoT与AI与大数据的融合、环境经济社会可持续性评估,以及澳大利亚、美国、巴西、印度、中国等全球案例对比。文章指出机器人在降低化学品投入、节水、土壤改良、劳动力替代方面有显著效益。","Frontiers in Robotics and AI 2026",72,{"impact":16,"substance":17,"depth":18,"authority":109,"freshness":60,"relevant":21,"comment":110},14,"核心期刊综述，提出五维分析框架并含多国案例，对智慧农业与农业机器人方向有参考价值，但时效性偏弱。",[112],{"name":106,"url":104},[26,27,114,28,29],"农业机器人",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":117},{"candidates":118,"agricultural_evidence":41,"reason":82},[119,123,128,133,139],{"code":39,"score":20,"supported":41,"title":120,"abstract":121,"tags":122},[],[29],[26,29],{"code":124,"score":48,"supported":41,"title":125,"abstract":126,"tags":127},"F6",[],[114],[114],{"code":53,"score":48,"supported":41,"title":129,"abstract":130,"tags":132},[],[131],"IoT",[28],{"code":134,"score":60,"supported":41,"title":135,"abstract":136,"tags":138},"H2",[],[137],"土壤",[],{"code":78,"score":21,"supported":13,"title":140,"abstract":141,"tags":142},[],[],[27],[144,145],"Frontiers in Robotics and AI 农业机器人","农业机器人 粮食系统 可持续","FrontiersinRoboticsandAI农业机器人-4785","10.3389\u002Ffrobt.2025.1696483\u002Ffull",{"doi":147,"openalex_id":8,"authors":149,"venue":8,"cited_by_count":87,"oa_url":8,"card":150,"direction":154,"ingested_from":96},[],{"tldr":151,"method":152,"finding":153,"direction":154,"opportunity":155},"系统梳理农业机器人助力粮食系统可持续转型的路径与全球案例。","文献综述与五维分析框架，结合多国案例对比。","农业机器人在减药、节水、改土和替代劳动力方面效益显著。","智慧农业 \u002F 农业物联网","可量化评估机器人对减药节水贡献，并研究小农户采纳与成本效益。","2026-10-13T00:08:38.520864Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":8,"source_name":163,"source_url":160,"published_at":164,"category":11,"cover_url":8,"hotness":165,"is_selected":13,"score":166,"score_detail":167,"sources":170,"tags":174,"primary_topic":176,"secondary_topics":181,"classification":186,"search_phrases":209,"slug":212,"view_count":21,"doi":8,"paper":213,"created_at":230},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",25,47,{"impact":168,"substance":109,"depth":19,"authority":99,"freshness":99,"relevant":21,"comment":169},8,"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。",[171,172],{"name":163,"url":160},{"name":163,"url":173},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[26,27,28,29,175],"低功耗通信",{"code":53,"slug":177,"name":178,"path":179,"parent_code":180},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[182],{"code":78,"slug":183,"name":184,"path":185,"parent_code":180},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":187,"locked":13,"version":34,"method":35,"evidence":188},"classified",{"candidates":189,"agricultural_evidence":41},[190,197,204],{"code":53,"score":16,"supported":41,"title":191,"abstract":192,"tags":196},[131],[56,193,194,131,195],"边缘计算","internet of things","edge computing",[28],{"code":78,"score":12,"supported":41,"title":198,"abstract":199,"tags":203},[],[200,201,202],"机器学习","machine learning","artificial intelligence",[27],{"code":39,"score":168,"supported":41,"title":205,"abstract":206,"tags":208},[],[29,207],"precision agriculture",[26,29],[210,211],"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":8,"openalex_id":214,"authors":215,"venue":163,"cited_by_count":87,"oa_url":160,"card":224,"direction":154,"ingested_from":229},"W7221414266",[216,218,220,222],{"name":217,"orcid":8},"Shital Chavan",{"name":219,"orcid":8},"Anam Bedrekar",{"name":221,"orcid":8},"Sanika Babar",{"name":223,"orcid":8},"Prof. M. V. Naiknavare",{"tldr":225,"method":226,"finding":227,"direction":154,"opportunity":228},"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。","openalex","2026-10-11T23:30:16.397155Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":8,"source_name":237,"source_url":234,"published_at":238,"category":11,"cover_url":8,"hotness":165,"is_selected":13,"score":239,"score_detail":240,"sources":243,"tags":248,"primary_topic":250,"secondary_topics":251,"classification":259,"search_phrases":298,"slug":301,"view_count":21,"doi":302,"paper":303,"created_at":313},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":241,"substance":17,"depth":16,"authority":19,"freshness":168,"relevant":21,"comment":242},22,"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。",[244,245],{"name":237,"url":234},{"name":246,"url":247},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[26,27,114,28,29,249],"自动驾驶农机",{"code":78,"slug":183,"name":184,"path":185,"parent_code":180},[252,258],{"code":253,"slug":254,"name":255,"path":256,"parent_code":257},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":53,"slug":177,"name":178,"path":179,"parent_code":180},{"status":187,"locked":13,"version":34,"method":35,"evidence":260},{"candidates":261,"agricultural_evidence":41},[262,268,275,279,284,288,294],{"code":78,"score":16,"supported":41,"title":263,"abstract":264,"tags":267},[202],[200,265,201,202,266],"计算机视觉","computer vision",[27],{"code":253,"score":18,"supported":41,"title":269,"abstract":271,"tags":274},[270],"agricultural machinery",[272,273,270],"农业机械","收割机",[],{"code":53,"score":12,"supported":41,"title":276,"abstract":277,"tags":278},[],[56,194,131],[28],{"code":72,"score":99,"supported":41,"title":280,"abstract":281,"tags":283},[],[75,282],"remote sensing",[],{"code":39,"score":20,"supported":41,"title":285,"abstract":286,"tags":287},[],[29],[26,29],{"code":289,"score":60,"supported":41,"title":290,"abstract":291,"tags":293},"F4",[],[292],"除草",[],{"code":124,"score":21,"supported":13,"title":295,"abstract":296,"tags":297},[],[],[114],[299,300],"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":302,"openalex_id":304,"authors":305,"venue":237,"cited_by_count":87,"oa_url":234,"card":308,"direction":154,"ingested_from":229},"W7221138004",[306],{"name":307,"orcid":8},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":309,"method":310,"finding":311,"direction":154,"opportunity":312},"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":315,"title":316,"url":317,"summary":318,"summary_zh":8,"content":8,"source_name":319,"source_url":317,"published_at":320,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":321,"score_detail":322,"sources":327,"tags":329,"primary_topic":331,"secondary_topics":332,"classification":333,"search_phrases":349,"slug":352,"view_count":21,"doi":353,"paper":354,"created_at":366},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":323,"substance":16,"depth":324,"authority":168,"freshness":325,"relevant":21,"comment":326},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。",[328],{"name":319,"url":317},[26,27,28,330,29],"可解释AI",{"code":53,"slug":177,"name":178,"path":179,"parent_code":180},[],{"status":187,"locked":13,"version":34,"method":35,"evidence":334},{"candidates":335,"agricultural_evidence":41},[336,340,344],{"code":39,"score":19,"supported":41,"title":337,"abstract":338,"tags":339},[207],[207],[26,29],{"code":53,"score":323,"supported":41,"title":341,"abstract":342,"tags":343},[131],[131],[28],{"code":78,"score":345,"supported":13,"title":346,"abstract":347,"tags":348},2,[],[],[27,330],[350,351],"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":353,"openalex_id":355,"authors":356,"venue":319,"cited_by_count":87,"oa_url":365,"card":8,"direction":154,"ingested_from":229},"W7220656746",[357,360,363],{"name":358,"orcid":359},"Qasim Ali Hashmi","https:\u002F\u002Forcid.org\u002F0009-0009-2894-3688",{"name":361,"orcid":362},"Zain ul Sajjad","https:\u002F\u002Forcid.org\u002F0009-0003-8354-8134",{"name":364,"orcid":8},"Saqib Ali³","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11233315\u002Flatest.pdf","2026-10-07T23:30:08.629402Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":8,"source_name":373,"source_url":370,"published_at":374,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":375,"score_detail":376,"sources":378,"tags":380,"primary_topic":382,"secondary_topics":386,"classification":387,"search_phrases":431,"slug":434,"view_count":345,"doi":435,"paper":436,"created_at":454},4297,"IoT -Based Smart Agriculture Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0711","Agriculture plays a crucial role in economic development and food security. Traditional farming methods often face challenges such as inefficient water usage, unpredictable environmental conditions, and limited real-time monitoring. This paper proposes an IoT-Based Smart Agriculture system integrated with Machine Learning to enhance farm productivity and resource management. The system employs IoT sensors to collect data related to soil moisture, temperature, humidity, and light intensity. These data are transmitted to a cloud platform where machine learning algorithms analyze environmental conditions, predict crop requirements, and generate intelligent recommendations for irrigation and crop management. The proposed solution enables continuous monitoring, reduces water wastage, improves crop health assessment, and supports data-driven decision-making. By combining IoT and Machine Learning, the system offers a cost-effective and scalable approach to precision farming, helping farmers increase yield, reduce operational costs, and promote sustainable agricultural practices.","农业在经济发展和粮食安全中发挥着至关重要的作用。传统耕作方式往往面临水资源利用效率低、环境条件不可预测以及实时监测能力有限等挑战。本文提出了一种基于物联网（IoT）并与机器学习相结合的智慧农业系统，以提升农业生产率和资源管理水平。该系统采用物联网传感器采集与土壤湿度、温度、湿度和光照强度相关的数据。这些数据被传输至云平台，由机器学习算法分析环境条件、预测作物需求，并生成灌溉和作物管理的智能建议。所提出的解决方案能够实现持续监测、减少水资源浪费、改进作物健康评估，并支持数据驱动的决策。通过将物联网与机器学习相结合，该系统为精准农业提供了一种具有成本效益且可扩展的方法，帮助农民提高产量、降低运营成本并促进可持续农业实践。","International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-10-05T00:00:00Z",46,{"impact":168,"substance":323,"depth":40,"authority":99,"freshness":325,"relevant":21,"comment":377},"论文提出IoT与机器学习结合的智慧农业系统，思路常规、缺乏实测数据与规模验证，学术权威性一般，仅具参考价值。",[379],{"name":373,"url":370},[26,27,28,381,29],"智能灌溉",{"code":180,"slug":383,"name":384,"path":385,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":388,"locked":13,"version":34,"method":35,"evidence":389},"partial",{"candidates":390,"agricultural_evidence":41,"reason":430},[391,397,402,406,412,419,424],{"code":39,"score":392,"supported":41,"title":393,"abstract":395,"tags":396},19,[394],"smart agriculture",[26,29,394],[26,29],{"code":78,"score":398,"supported":41,"title":399,"abstract":400,"tags":401},15,[201],[200,201],[27],{"code":53,"score":398,"supported":41,"title":403,"abstract":404,"tags":405},[131],[56,131],[28],{"code":66,"score":12,"supported":41,"title":407,"abstract":408,"tags":411},[],[69,409,410],"水资源","irrigation",[381],{"code":413,"score":99,"supported":41,"title":414,"abstract":415,"tags":418},"A8",[],[416,417],"粮食安全","food security",[],{"code":134,"score":99,"supported":41,"title":420,"abstract":421,"tags":423},[],[137,422],"soil",[],{"code":425,"score":60,"supported":41,"title":426,"abstract":427,"tags":429},"H8",[],[428],"可持续农业",[],"一级领域明确，二级方向需要细分",[432,433],"IoT 智慧农业 机器学习","土壤墒情 智能灌溉 云平台","IoT智慧农业机器学习-4297","10.47392\u002Firjaeh.2026.0711",{"doi":435,"openalex_id":437,"authors":438,"venue":373,"cited_by_count":87,"oa_url":370,"card":449,"direction":154,"ingested_from":229},"W7220440757",[439,441,443,445,447],{"name":440,"orcid":8},"Priyanka Behera",{"name":442,"orcid":8},"Satyabrata Satapathy",{"name":444,"orcid":8},"Nakshytrajeet Kar",{"name":446,"orcid":8},"Sucharita NathSharma",{"name":448,"orcid":8},"Priyanka Shit",{"tldr":450,"method":451,"finding":452,"direction":154,"opportunity":453},"提出物联网结合机器学习的智慧农业系统，实现环境监测与智能灌溉决策。","部署土壤湿度、温湿度、光照等IoT传感器，云端机器学习分析预测作物需求。","系统可连续监测、减少水资源浪费、改善作物健康评估并支持数据驱动决策。","可探索边缘计算与轻量模型在田间实时决策中的应用，降低云端依赖与延迟。","2026-10-06T23:30:08.015155Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":8,"source_name":461,"source_url":458,"published_at":462,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":463,"score_detail":464,"sources":466,"tags":468,"primary_topic":470,"secondary_topics":471,"classification":472,"search_phrases":496,"slug":499,"view_count":345,"doi":8,"paper":500,"created_at":512},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":16,"substance":241,"depth":16,"authority":323,"freshness":168,"relevant":21,"comment":465},"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。",[467],{"name":461,"url":458},[26,27,28,469,29],"农业无人机",{"code":53,"slug":177,"name":178,"path":179,"parent_code":180},[],{"status":187,"locked":13,"version":34,"method":35,"evidence":473},{"candidates":474,"agricultural_evidence":41},[475,479,483,488,492],{"code":53,"score":12,"supported":41,"title":476,"abstract":477,"tags":478},[],[56,193,194,131,195],[28],{"code":39,"score":168,"supported":41,"title":480,"abstract":481,"tags":482},[],[29,207],[26,29],{"code":78,"score":484,"supported":41,"title":485,"abstract":486,"tags":487},7,[],[265,266],[27],{"code":134,"score":99,"supported":41,"title":489,"abstract":490,"tags":491},[],[137,422],[],{"code":66,"score":99,"supported":41,"title":493,"abstract":494,"tags":495},[],[69,410],[],[497,498],"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":8,"openalex_id":501,"authors":502,"venue":461,"cited_by_count":87,"oa_url":458,"card":507,"direction":154,"ingested_from":229},"W7218223913",[503,505],{"name":504,"orcid":8},"Володимир Панченко",{"name":506,"orcid":8},"Георгій Кучук",{"tldr":508,"method":509,"finding":510,"direction":154,"opportunity":511},"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"]