[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4785":3,"related-4785":84},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":8,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":13,"score_detail":14,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":68,"slug":71,"view_count":20,"doi":72,"paper":73,"created_at":83},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。",null,"Frontiers in Robotics and AI 2026","论文",10,false,72,{"impact":15,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,17,14,3,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","农业机器人","农业物联网","精准农业",[],{"status":32,"locked":12,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":67},[37,44,50,56,62],{"code":38,"score":39,"supported":40,"title":41,"abstract":42,"tags":43},"I1",5,true,[],[29],[25,29],{"code":45,"score":46,"supported":40,"title":47,"abstract":48,"tags":49},"F6",4,[],[27],[27],{"code":51,"score":46,"supported":40,"title":52,"abstract":53,"tags":55},"I5",[],[54],"IoT",[28],{"code":57,"score":19,"supported":40,"title":58,"abstract":59,"tags":61},"H2",[],[60],"土壤",[],{"code":63,"score":20,"supported":12,"title":64,"abstract":65,"tags":66},"I2",[],[],[26],"多个产业证据相当，需要确认主归属",[69,70],"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":72,"openalex_id":8,"authors":74,"venue":8,"cited_by_count":75,"oa_url":8,"card":76,"direction":80,"ingested_from":82},[],0,{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"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。","agent","2026-10-13T00:08:38.520864Z",{"total":85,"page":20,"page_size":85,"items":86},6,[87,189,265,328,386,455],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":8,"source_name":93,"source_url":90,"published_at":94,"category":10,"cover_url":8,"hotness":95,"is_selected":12,"score":96,"score_detail":97,"sources":102,"tags":107,"primary_topic":109,"secondary_topics":114,"classification":125,"search_phrases":172,"slug":175,"view_count":20,"doi":176,"paper":177,"created_at":188},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",25,81,{"impact":98,"substance":16,"depth":15,"authority":99,"freshness":100,"relevant":20,"comment":101},22,13,8,"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。",[103,104],{"name":93,"url":90},{"name":105,"url":106},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[25,26,27,28,29,108],"自动驾驶农机",{"code":63,"slug":110,"name":111,"path":112,"parent_code":113},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[115,121],{"code":116,"slug":117,"name":118,"path":119,"parent_code":120},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":51,"slug":122,"name":123,"path":124,"parent_code":113},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":126,"locked":12,"version":33,"method":34,"evidence":127},"classified",{"candidates":128,"agricultural_evidence":40},[129,138,145,151,158,162,168],{"code":63,"score":15,"supported":40,"title":130,"abstract":132,"tags":137},[131],"artificial intelligence",[133,134,135,131,136],"机器学习","计算机视觉","machine learning","computer vision",[26],{"code":116,"score":17,"supported":40,"title":139,"abstract":141,"tags":144},[140],"agricultural machinery",[142,143,140],"农业机械","收割机",[],{"code":51,"score":11,"supported":40,"title":146,"abstract":147,"tags":150},[],[148,149,54],"物联网","internet of things",[28],{"code":152,"score":85,"supported":40,"title":153,"abstract":154,"tags":157},"I3",[],[155,156],"遥感","remote sensing",[],{"code":38,"score":39,"supported":40,"title":159,"abstract":160,"tags":161},[],[29],[25,29],{"code":163,"score":19,"supported":40,"title":164,"abstract":165,"tags":167},"F4",[],[166],"除草",[],{"code":45,"score":20,"supported":12,"title":169,"abstract":170,"tags":171},[],[],[27],[173,174],"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":176,"openalex_id":178,"authors":179,"venue":93,"cited_by_count":75,"oa_url":90,"card":182,"direction":80,"ingested_from":187},"W7221138004",[180],{"name":181,"orcid":8},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":183,"method":184,"finding":185,"direction":80,"opportunity":186},"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。","openalex","2026-10-10T23:30:13.826410Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":8,"source_name":195,"source_url":192,"published_at":196,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":197,"score_detail":198,"sources":201,"tags":203,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":241,"slug":244,"view_count":19,"doi":245,"paper":246,"created_at":264},4877,"IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85108","The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.","物联网（IoT）、自主机器人、人工智能（AI）、嵌入式系统、无线通信和精准农业的快速发展，为提升农业生产率和资源利用率创造了新的机遇。传统农业监测在很大程度上依赖人工田间巡检，这种方式耗时、费力，且往往无法持续提供作物和土壤状况的信息。自主农业巡田机器人通过实现移动传感、实时环境监测、作物观测、导航和精准农业作业，提供了一种替代方案。本文对面向智慧农业的物联网赋能自主农业巡田机器人进行了全面的文献综述，特别关注IEEE文献中报道的最新进展。现有研究按照巡田机器人架构、传感技术、导航技术、通信技术、物联网平台、农业应用、人工智能集成和自主性水平进行了分析。近期研究已证明基于ESP32的物联网巡田机器人、基于ROS的农业监测机器人、自主导航系统、精准灌溉平台、土壤监测机器人和人工智能赋能农业巡田机器人的可行性。然而，仍存在若干局限，包括非结构化环境中的自主导航受限、多参数传感能力有限、农村地区通信可靠性不足、人工智能的高计算需求、能量限制，以及监测与自主决策的集成不充分。基于所综述的文献，本文识别出一个研究空白，即缺乏一种低成本、模块化、物联网赋能的自主农业监测巡田机器人，能够同时执行土壤与环境传感、作物监测、避障、实时无线通信、位置感知作业和智能决策支持。","International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z",52,{"impact":100,"substance":18,"depth":199,"authority":85,"freshness":100,"relevant":20,"comment":200},16,"综述性论文，系统梳理IoT自主农业巡检机器人架构与瓶颈，但来源期刊影响力有限，属细分领域参考价值。",[202],{"name":195,"url":192},[25,26,27,148,29],{"code":51,"slug":122,"name":123,"path":124,"parent_code":113},[],{"status":126,"locked":12,"version":33,"method":34,"evidence":207},{"candidates":208,"agricultural_evidence":40},[209,213,219,225,230,237],{"code":51,"score":15,"supported":40,"title":210,"abstract":211,"tags":212},[54],[148,149,54],[148],{"code":38,"score":214,"supported":40,"title":215,"abstract":216,"tags":218},11,[],[25,29,217],"precision agriculture",[25,29],{"code":63,"score":220,"supported":40,"title":221,"abstract":222,"tags":224},7,[],[131,223],"decision support",[26],{"code":57,"score":85,"supported":40,"title":226,"abstract":227,"tags":229},[],[60,228],"soil",[],{"code":231,"score":85,"supported":40,"title":232,"abstract":233,"tags":236},"H3",[],[234,235],"灌溉","irrigation",[],{"code":45,"score":20,"supported":12,"title":238,"abstract":239,"tags":240},[],[],[27],[242,243],"ESP32 ROS 农业机器人","农业人工智能 农业机器人 智慧农业 精准农业","ESP32ROS农业机器人-4877","10.22214\u002Fijraset.2026.85108",{"doi":245,"openalex_id":247,"authors":248,"venue":195,"cited_by_count":75,"oa_url":192,"card":259,"direction":80,"ingested_from":187},"W7221682750",[249,251,253,255,257],{"name":250,"orcid":8},"Chaitra A S",{"name":252,"orcid":8},"Venugopal Gowda V",{"name":254,"orcid":8},"Vinay H",{"name":256,"orcid":8},"Jeevan T",{"name":258,"orcid":8},"Puneeth M",{"tldr":260,"method":261,"finding":262,"direction":80,"opportunity":263},"综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白","文献综述，聚焦IEEE近期研究，按架构、传感、导航、通信、AI与自主等级分类分析","现有系统在非结构化导航、多参数传感、农村通信、AI算力与能耗上存在局限，缺乏监测与决策一体化。","可研发低成本模块化IoT自主巡田机器人，融合多传感、避障、实时通信与智能决策，填补集成空白。","2026-10-14T23:30:08.717386Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":8,"content":8,"source_name":270,"source_url":8,"published_at":271,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":272,"score_detail":273,"sources":275,"tags":277,"primary_topic":8,"secondary_topics":279,"classification":280,"search_phrases":315,"slug":318,"view_count":75,"doi":8,"paper":319,"created_at":327},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。","Scientia Review 2026-10-07","2026-10-07T00:00:00Z",73,{"impact":15,"substance":16,"depth":17,"authority":99,"freshness":39,"relevant":20,"comment":274},"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。",[276],{"name":270,"url":268},[25,26,28,29,278],"小农户",[],{"status":32,"locked":12,"version":33,"method":34,"evidence":281},{"candidates":282,"agricultural_evidence":40,"reason":67},[283,288,293,297,303,307,311],{"code":38,"score":214,"supported":40,"title":284,"abstract":285,"tags":287},[],[25,29,286],"数字农业",[25,29],{"code":289,"score":46,"supported":40,"title":290,"abstract":291,"tags":292},"G2",[],[278],[278],{"code":51,"score":46,"supported":40,"title":294,"abstract":295,"tags":296},[],[148],[28],{"code":298,"score":19,"supported":40,"title":299,"abstract":300,"tags":302},"C",[],[301],"畜牧",[],{"code":231,"score":19,"supported":40,"title":304,"abstract":305,"tags":306},[],[234],[],{"code":152,"score":19,"supported":40,"title":308,"abstract":309,"tags":310},[],[155],[],{"code":63,"score":20,"supported":12,"title":312,"abstract":313,"tags":314},[],[],[26],[316,317],"Scientia Review 智慧农业","Reddy 智慧农业 数字农业","ScientiaReview智慧农业-4784",{"doi":8,"openalex_id":8,"authors":320,"venue":8,"cited_by_count":75,"oa_url":8,"card":321,"direction":325,"ingested_from":82},[],{"tldr":322,"method":323,"finding":324,"direction":325,"opportunity":326},"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。","2026-10-13T00:08:38.433355Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":8,"source_name":334,"source_url":331,"published_at":335,"category":10,"cover_url":8,"hotness":95,"is_selected":12,"score":336,"score_detail":337,"sources":339,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":348,"search_phrases":365,"slug":368,"view_count":20,"doi":8,"paper":369,"created_at":385},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":100,"substance":18,"depth":99,"authority":85,"freshness":85,"relevant":20,"comment":338},"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。",[340,341],{"name":334,"url":331},{"name":334,"url":342},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[25,26,28,29,344],"低功耗通信",{"code":51,"slug":122,"name":123,"path":124,"parent_code":113},[347],{"code":63,"slug":110,"name":111,"path":112,"parent_code":113},{"status":126,"locked":12,"version":33,"method":34,"evidence":349},{"candidates":350,"agricultural_evidence":40},[351,357,361],{"code":51,"score":15,"supported":40,"title":352,"abstract":353,"tags":356},[54],[148,354,149,54,355],"边缘计算","edge computing",[28],{"code":63,"score":11,"supported":40,"title":358,"abstract":359,"tags":360},[],[133,135,131],[26],{"code":38,"score":100,"supported":40,"title":362,"abstract":363,"tags":364},[],[29,217],[25,29],[366,367],"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":370,"authors":371,"venue":334,"cited_by_count":75,"oa_url":331,"card":380,"direction":80,"ingested_from":187},"W7221414266",[372,374,376,378],{"name":373,"orcid":8},"Shital Chavan",{"name":375,"orcid":8},"Anam Bedrekar",{"name":377,"orcid":8},"Sanika Babar",{"name":379,"orcid":8},"Prof. M. V. Naiknavare",{"tldr":381,"method":382,"finding":383,"direction":80,"opportunity":384},"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":387,"title":388,"url":389,"summary":390,"summary_zh":8,"content":8,"source_name":391,"source_url":8,"published_at":8,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":392,"score_detail":393,"sources":396,"tags":398,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":443,"slug":446,"view_count":20,"doi":8,"paper":447,"created_at":454},4476,"智慧农业演进支撑欧洲绿色协议：物联网、人工智能与机器人可持续精准农业综述","https:\u002F\u002Fakjournals.com\u002Fview\u002Fjournals\u002F446\u002Faop\u002Farticle-10.1556-446.2026.00313\u002Farticle-10.1556-446.2026.00313.xml","Nyéki Anikó（生物工程与精准技术系）发表智慧农业演进综述，基于2000—2025年Scopus、Web of Science和Google Scholar数据库的同行评审研究；提出政策驱动的数字农业生态管理（PDAM）框架，建立基于欧盟目标（农药、养分、土壤、生物多样性和气候）过去趋势的可适应指标；最有效的工具支持GNSS机器控制、变量率应用和遥感，而AI决策支持工具、自主除草和数字孪生田间验证仍较弱；本研究为IoT、AI和机器人在欧洲可持续精准农业的整合应用提供政策与技术参考。","Progress in Agricultural Engineering Sciences 2026",71,{"impact":15,"substance":16,"depth":15,"authority":99,"freshness":394,"relevant":20,"comment":395},2,"基于2000—2025年文献的智慧农业综述，提出PDAM政策框架并指出AI决策与数字孪生验证短板，具政策与技术参考价值，但非国内动态且时效性弱。",[397],{"name":391,"url":389},[25,26,27,29,399],"欧洲绿色协议",{"code":51,"slug":122,"name":123,"path":124,"parent_code":113},[],{"status":126,"locked":12,"version":33,"method":34,"evidence":403},{"candidates":404,"agricultural_evidence":40},[405,411,415,420,427,431,435,439],{"code":38,"score":406,"supported":40,"title":407,"abstract":408,"tags":410},27,[25,29],[25,29,286,409],"数字孪生",[25,29],{"code":51,"score":214,"supported":40,"title":412,"abstract":413,"tags":414},[148],[54],[],{"code":163,"score":85,"supported":40,"title":416,"abstract":417,"tags":419},[],[418,166],"农药",[],{"code":421,"score":85,"supported":40,"title":422,"abstract":423,"tags":426},"H6",[],[424,425],"生物多样性","农业生态",[],{"code":57,"score":19,"supported":40,"title":428,"abstract":429,"tags":430},[],[60],[],{"code":152,"score":19,"supported":40,"title":432,"abstract":433,"tags":434},[],[155],[],{"code":45,"score":20,"supported":12,"title":436,"abstract":437,"tags":438},[],[],[27],{"code":63,"score":20,"supported":12,"title":440,"abstract":441,"tags":442},[],[],[26],[444,445],"Nyéki Anikó 精准农业","欧洲绿色协议 数字农业生态管理","NyékiAnikó精准农业-4476",{"doi":8,"openalex_id":8,"authors":448,"venue":8,"cited_by_count":75,"oa_url":8,"card":449,"direction":80,"ingested_from":82},[],{"tldr":450,"method":451,"finding":452,"direction":80,"opportunity":453},"综述2000—2025年智慧农业演进，提出政策驱动的数字农业生态管理框架以支撑欧洲绿色协议。","基于Scopus、WoS、Google Scholar文献综述，构建PDAM框架","GNSS机器控制、变量率施用与遥感最有效；AI决策、自主除草与数字孪生田间验证仍薄弱。","AI决策支持、自主除草与数字孪生的田间验证不足，是精准农业落地研究的重点空白。","2026-10-09T00:05:02.372715Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":8,"content":8,"source_name":460,"source_url":458,"published_at":461,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":462,"score_detail":463,"sources":467,"tags":469,"primary_topic":471,"secondary_topics":472,"classification":473,"search_phrases":488,"slug":491,"view_count":75,"doi":492,"paper":493,"created_at":505},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":464,"substance":15,"depth":199,"authority":100,"freshness":465,"relevant":20,"comment":466},12,9,"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。",[468],{"name":460,"url":458},[25,26,28,470,29],"可解释AI",{"code":51,"slug":122,"name":123,"path":124,"parent_code":113},[],{"status":126,"locked":12,"version":33,"method":34,"evidence":474},{"candidates":475,"agricultural_evidence":40},[476,480,484],{"code":38,"score":99,"supported":40,"title":477,"abstract":478,"tags":479},[217],[217],[25,29],{"code":51,"score":464,"supported":40,"title":481,"abstract":482,"tags":483},[54],[54],[28],{"code":63,"score":394,"supported":12,"title":485,"abstract":486,"tags":487},[],[],[26,470],[489,490],"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":492,"openalex_id":494,"authors":495,"venue":460,"cited_by_count":75,"oa_url":504,"card":8,"direction":80,"ingested_from":187},"W7220656746",[496,499,502],{"name":497,"orcid":498},"Qasim Ali Hashmi","https:\u002F\u002Forcid.org\u002F0009-0009-2894-3688",{"name":500,"orcid":501},"Zain ul Sajjad","https:\u002F\u002Forcid.org\u002F0009-0003-8354-8134",{"name":503,"orcid":8},"Saqib Ali³","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11233315\u002Flatest.pdf","2026-10-07T23:30:08.629402Z"]