[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3823":3,"related-3823":106},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":75,"slug":78,"view_count":79,"doi":80,"paper":81,"created_at":105},3823,"Space–Air–Ground Integrated Networks for Smart Agriculture and Smart Breeding: A Review of SDN\u002FNFV-Enabled Intelligent Resource Management","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26196181","Smart agriculture and smart breeding are evolving from isolated sensing toward geographically distributed, long-term, data-driven closed-loop management. Dispersed farms and breeding sites, UAV and remote-sensing phenotyping, real-time field control, edge inference, and digital-twin synchronization impose heterogeneous demands on coverage, latency, bandwidth, reliability, computing, and energy. Space–air–ground integrated networks (SAGINs) combine satellites, unmanned aerial vehicles\u002Fhigh-altitude platforms, and terrestrial networks for wide-area coverage and elastic access, but introduce dynamic topologies, heterogeneous multi-domain resources, and complex cross-layer orchestration. Focusing on intelligent SAGIN resource management enabled by software-defined networking (SDN) and network function virtualization (NFV), this review examines controller placement, NFV\u002Fservice function chain orchestration, SDN\u002FNFV cooperation, learning-driven optimization, and security mechanisms. Representative studies are compared by objectives, decision variables, mechanisms, applicability boundaries, and engineering costs. These mechanisms are connected to smart agriculture and smart breeding through ubiquitous connectivity, edge computing, task offloading, phenotyping, and digital-twin closed loops, while distinguishing experimentally supported agricultural evidence from SAGIN-oriented architectural inference. Future research should move beyond single-metric optimization toward joint evaluation of state freshness, decision latency, reconfiguration cost, learning and security overhead, and agronomic outcomes to improve deployability, robustness, and verifiability of cross-domain resource management under real deployment conditions across heterogeneous agricultural environments.","智慧农业与智慧育种正从孤立感知向地理分布式、长期、数据驱动的闭环管理演进。分散的农场与养殖场、无人机与遥感表型分析、实时田间控制、边缘推理以及数字孪生同步，对覆盖范围、时延、带宽、可靠性、计算和能量提出了异构需求。天地空一体化网络（SAGIN）融合卫星、无人机\u002F高空平台与地面网络，实现广域覆盖和弹性接入，但引入了动态拓扑、异构多域资源和复杂的跨层编排。本综述聚焦于软件定义网络（SDN）与网络功能虚拟化（NFV）赋能的智能SAGIN资源管理，考察控制器部署、NFV\u002F服务功能链编排、SDN\u002FNFV协同、学习驱动的优化以及安全机制。代表性研究从目标、决策变量、机制、适用边界和工程成本等方面进行比较。这些机制通过泛在连接、边缘计算、任务卸载、表型分析和数字孪生闭环与智慧农业和智慧育种相联系，同时区分有实验支持的农业证据与面向SAGIN的架构推断。未来研究应超越单一指标优化，转向对状态新鲜度、决策时延、重构成本、学习与安全开销以及农艺结果的联合评估，以提升异构农业环境下真实部署条件中跨域资源管理的可部署性、鲁棒性和可验证性。",null,"Sensors","2026-09-29T00:00:00Z","论文",10,true,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"发表于核心期刊的系统性综述，梳理SDN\u002FNFV赋能的空天地一体化网络资源管理及其在智慧农业与智能育种中的应用，学术价值高但属架构性推演，产业落地影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","边缘计算","智能育种","空天地一体化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":40,"version":41,"method":42,"evidence":43},"classified",false,"2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":14},[45,52,58,65,70],{"code":46,"score":17,"supported":14,"title":47,"abstract":49,"tags":51},"I1",[48],"smart agriculture",[26,50,48],"数字孪生",[26],{"code":32,"score":53,"supported":14,"title":54,"abstract":55,"tags":57},7,[],[28,56],"edge computing",[28],{"code":59,"score":60,"supported":14,"title":61,"abstract":62,"tags":64},"I3",3,[],[63],"遥感",[],{"code":66,"score":21,"supported":40,"title":67,"abstract":68,"tags":69},"F2",[],[],[29],{"code":71,"score":21,"supported":40,"title":72,"abstract":73,"tags":74},"I2",[],[],[27],[76,77],"SAGIN 智慧农业 资源管理","SDN NFV 智能育种","SAGIN智慧农业资源管理-3823",2,"10.3390\u002Fs26196181",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":97,"oa_url":6,"card":98,"direction":102,"ingested_from":104},"W7214724892",[84,86,89,92,95],{"name":85,"orcid":9},"Yixiang Zhao",{"name":87,"orcid":88},"Bo Li","https:\u002F\u002Forcid.org\u002F0000-0003-4326-4519",{"name":90,"orcid":91},"Chenglin Xu","https:\u002F\u002Forcid.org\u002F0000-0001-9133-613X",{"name":93,"orcid":94},"Jiao Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-9456-3992",{"name":96,"orcid":9},"L. Wang",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"综述SDN\u002FNFV赋能的空天地一体化网络智能资源管理，并关联智慧农业与智慧育种。","文献综述，比较控制器部署、NFV编排、学习优化与安全机制。","现有研究多单指标优化，需联合评估状态新鲜度、时延、重构成本与农艺效果。","智慧农业 \u002F 农业物联网","可研究面向农业异构环境的跨域资源管理联合优化与真实部署验证，兼顾农艺产出。","openalex","2026-09-30T23:30:09.636727Z",{"total":107,"page":21,"page_size":107,"items":108},6,[109,198,288,355,406,468],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":117,"is_selected":40,"score":118,"score_detail":119,"sources":124,"tags":128,"primary_topic":131,"secondary_topics":132,"classification":139,"search_phrases":176,"slug":179,"view_count":21,"doi":9,"paper":180,"created_at":197},4671,"A COMPREHENSIVE REVIEW ON IOT AND EDGE AI-BASED PRECISION AGRICULTURE SYSTEM FOR WATER OPTIMIZATION","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F316","Water scarcity and inefficient irrigation practices remain major challenges in modern agriculture, leading to reduced crop yield, resource wastage, and increased production costs. The proposed project, “IoT and Edge AI-Based Precision Agriculture System for Water Optimisation,” aims to develop an intelligent, real-time irrigation management system that optimises water usage through data-driven decision-making at the edge. The system integrates Internet of Things (IoT) sensors such as soil moisture, temperature, humidity, and light intensity sensors to continuously monitor field conditions. The collected data is processed locally using an Edge AI module, enabling real-time analysis and predictive irrigation control without relying entirely on cloud connectivity. By deploying machine learning algorithms at the edge, the system can determine optimal watering schedules based on soil conditions, crop requirements, and environmental factors, thereby minimising water wastage and ensuring efficient irrigation. A cloud platform is incorporated for long-term data storage, visualisation, and remote monitoring via a web or mobile dashboard. This hybrid architecture enhances scalability, reduces latency, and improves reliability in rural areas with limited internet connectivity. Automated irrigation control using smart valves or pumps ensures precise water delivery tailored to specific crop zones.","水资源短缺和灌溉效率低下仍是现代农业面临的主要挑战，导致作物减产、资源浪费和生产成本增加。本项目“基于物联网与边缘人工智能的水资源优化精准农业系统”旨在开发一种智能实时灌溉管理系统，通过边缘端的数据驱动决策来优化水资源利用。该系统集成了物联网（IoT）传感器，如土壤湿度、温度、湿度和光照强度传感器，以持续监测田间状况。采集的数据通过边缘人工智能模块在本地进行处理，实现实时分析和预测性灌溉控制，而无需完全依赖云连接。通过在边缘端部署机器学习算法，系统能够根据土壤条件、作物需求和环境因素确定最佳灌溉计划，从而最大限度地减少水资源浪费并确保高效灌溉。系统还包含一个云平台，用于长期数据存储、可视化以及通过网页或移动端仪表盘进行远程监控。这种混合架构增强了可扩展性，降低了延迟，并提高了互联网连接有限的农村地区的可靠性。利用智能阀门或水泵的自动灌溉控制可确保针对特定作物区域进行精准供水。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,53,{"impact":120,"substance":19,"depth":121,"authority":107,"freshness":122,"relevant":21,"comment":123},12,13,8,"综述性论文，系统梳理IoT与边缘AI在水肥优化中的应用，方法框架清晰但缺乏原创数据与实证，属细分领域参考性内容。",[125,126],{"name":115,"url":112},{"name":115,"url":127},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[26,27,129,28,130],"农业物联网","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[133],{"code":134,"slug":135,"name":136,"path":137,"parent_code":138},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":39,"locked":40,"version":41,"method":42,"evidence":140},{"candidates":141,"agricultural_evidence":14},[142,150,157,163,169],{"code":32,"score":143,"supported":14,"title":144,"abstract":146,"tags":149},19,[145],"IoT",[147,148,145],"物联网","internet of things",[129,28],{"code":46,"score":151,"supported":14,"title":152,"abstract":154,"tags":156},15,[153],"precision agriculture",[155,153],"精准农业",[26],{"code":134,"score":13,"supported":14,"title":158,"abstract":159,"tags":162},[],[160,161,135],"灌溉","水资源",[130],{"code":71,"score":53,"supported":14,"title":164,"abstract":165,"tags":168},[],[166,167],"机器学习","machine learning",[27],{"code":170,"score":107,"supported":14,"title":171,"abstract":172,"tags":175},"H2",[],[173,174],"土壤","soil",[],[177,178],"IoT 边缘AI 精准灌溉","智慧灌溉 土壤墒情 传感器","IoT边缘AI精准灌溉-4671",{"doi":9,"openalex_id":181,"authors":182,"venue":115,"cited_by_count":97,"oa_url":112,"card":192,"direction":102,"ingested_from":104},"W7221411136",[183,185,187,189],{"name":184,"orcid":9},"Om Gaikwad",{"name":186,"orcid":9},"Shriraj Chavan",{"name":188,"orcid":9},"Krushnadev Gaikwad",{"name":190,"orcid":191},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":193,"method":194,"finding":195,"direction":102,"opportunity":196},"综述基于物联网与边缘AI的精准农业系统，实现实时灌溉优化与节水。","集成土壤湿度等IoT传感器与边缘AI，本地处理数据并云端存储可视化。","边缘AI可实时预测灌溉需求，减少水浪费并适应网络受限农村环境。","可探索边缘AI模型轻量化与多源数据融合，提升复杂农田环境下的灌溉决策精度。","2026-10-11T23:30:16.298334Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":205,"score_detail":206,"sources":208,"tags":210,"primary_topic":212,"secondary_topics":213,"classification":218,"search_phrases":250,"slug":253,"view_count":97,"doi":254,"paper":255,"created_at":287},4489,"Artificial Intelligence and Internet of Things in Smart Agriculture: A Bibliometric and Thematic Review of Data Infrastructures and Sustainability Implications","https:\u002F\u002Fdoi.org\u002F10.54175\u002Fhsustain5040036","Artificial intelligence (AI) is reshaping smart agriculture through prediction, automation, resource optimization, and data-driven decision-making. However, its effective and sustainable deployment depends on reliable Internet of Things (IoT) data infrastructures supporting sensing, connectivity, data acquisition, transmission, and distributed processing. This review maps research at the intersection of AI, IoT, and smart agriculture and examines its data-infrastructure requirements and sustainability implications. Drawing on 441 Scopus-indexed, peer-reviewed journal articles published between 2017 and 2025, the study combines bibliometric performance analysis, science mapping, and content-based thematic interpretation to identify publication trends, intellectual foundations, influential research fronts, emerging themes, and deployment challenges. The bibliometric analyses characterize AI–IoT research in smart agriculture, while IoT data infrastructure provides the principal analytical lens, and sustainability and responsible deployment are examined through interpretive synthesis. The findings reveal rapid publication growth and prominent themes involving connected sensing, predictive analytics, edge intelligence, remote sensing, smart irrigation, cybersecurity, blockchain, and edge–cloud processing. Persistent barriers include limited interoperability, inconsistent data quality, energy requirements, scalability constraints, cybersecurity and governance risks, affordability, and unequal adoption in resource-constrained contexts. The review concludes that sustainable AI-enabled agriculture requires reliable, secure, interoperable, energy-efficient, inclusive, and context-appropriate digital infrastructures.","人工智能（AI）正通过预测、自动化、资源优化和数据驱动决策重塑智慧农业。然而，其有效且可持续的部署依赖于可靠的物联网（IoT）数据基础设施，以支持感知、连接、数据采集、传输和分布式处理。本综述梳理了人工智能、物联网与智慧农业交叉领域的研究，并考察其数据基础设施需求及可持续性影响。基于2017年至2025年间发表的441篇Scopus索引同行评审期刊论文，本研究结合文献计量绩效分析、科学知识图谱和基于内容的主题解读，以识别发文趋势、知识基础、有影响力的研究前沿、新兴主题及部署挑战。文献计量分析刻画了智慧农业中人工智能—物联网研究的特征，物联网数据基础设施则作为主要分析视角，可持续性与负责任部署则通过解释性综合加以考察。研究发现发文量快速增长，突出主题涉及互联感知、预测分析、边缘智能、遥感、智能灌溉、网络安全、区块链及边云处理。持续存在的障碍包括互操作性有限、数据质量不一致、能源需求、可扩展性约束、网络安全与治理风险、可负担性以及在资源受限情境中的不均衡采用。本综述得出结论：可持续的人工智能赋能农业需要可靠、安全、可互操作、节能、包容且情境适宜的数字基础设施。","Highlights of Sustainability",79,{"impact":17,"substance":18,"depth":17,"authority":121,"freshness":122,"relevant":21,"comment":207},"基于441篇文献的AI-IoT智慧农业文献计量综述，系统梳理数据基础设施与可持续部署瓶颈，学术参考价值较高。",[209],{"name":204,"url":201},[26,27,129,28,211],"数据基础设施",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[214],{"code":71,"slug":215,"name":216,"path":217,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":40,"version":41,"method":42,"evidence":219},{"candidates":220,"agricultural_evidence":14},[221,225,229,236,241,245],{"code":32,"score":143,"supported":14,"title":222,"abstract":223,"tags":224},[148],[147,148,145],[129,28],{"code":46,"score":151,"supported":14,"title":226,"abstract":227,"tags":228},[48],[26,48],[26],{"code":230,"score":19,"supported":14,"title":231,"abstract":233,"tags":235},"I7",[232],"bibliometric",[234,232],"文献计量",[],{"code":71,"score":120,"supported":14,"title":237,"abstract":239,"tags":240},[238],"artificial intelligence",[238],[27],{"code":134,"score":107,"supported":14,"title":242,"abstract":243,"tags":244},[],[160,135],[],{"code":59,"score":107,"supported":14,"title":246,"abstract":247,"tags":249},[],[63,248],"remote sensing",[],[251,252],"AI IoT 智慧农业 文献计量","农业物联网 数据基础设施","AIIoT智慧农业文献计量-4489","10.54175\u002Fhsustain5040036",{"doi":254,"openalex_id":256,"authors":257,"venue":204,"cited_by_count":97,"oa_url":281,"card":282,"direction":102,"ingested_from":104},"W7220924310",[258,261,264,266,269,272,275,278],{"name":259,"orcid":260},"Altyeb Ali Abaker Omer","https:\u002F\u002Forcid.org\u002F0000-0001-9420-9910",{"name":262,"orcid":263},"Yajie Dong","https:\u002F\u002Forcid.org\u002F0009-0001-2843-6135",{"name":265,"orcid":9},"Ibrahim Issa Mohamed Issa",{"name":267,"orcid":268},"Yousif Yahia Ahmed Abuker","https:\u002F\u002Forcid.org\u002F0009-0002-8111-9283",{"name":270,"orcid":271},"Mohamed Ibrahim Abdallh Babeker","https:\u002F\u002Forcid.org\u002F0009-0008-0904-9501",{"name":273,"orcid":274},"Alfadil Aboud Alfadil Hassan","https:\u002F\u002Forcid.org\u002F0009-0005-0656-1903",{"name":276,"orcid":277},"Zhiguo Shan","https:\u002F\u002Forcid.org\u002F0000-0002-6165-8258",{"name":279,"orcid":280},"Yinxue Shi","https:\u002F\u002Forcid.org\u002F0009-0002-1465-8666","https:\u002F\u002Fwww.hos.pub\u002Farticles\u002Fhsustain5040036\u002Fpdf",{"tldr":283,"method":284,"finding":285,"direction":102,"opportunity":286},"综述2017-2025年441篇文献，梳理AI与IoT在智慧农业中的数据基础设施与可持续性。","Scopus文献计量、科学映射与主题内容分析，以IoT数据基础设施为分析视角。","研究快速增长，但互操作性、数据质量、能耗、安全与公平采用仍是主要障碍。","可针对资源受限小农户场景，研究低能耗、可互操作且包容的农业IoT数据基础设施与治理机制。","2026-10-09T23:30:07.811940Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":294,"source_url":291,"published_at":295,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":296,"score_detail":297,"sources":299,"tags":301,"primary_topic":302,"secondary_topics":303,"classification":305,"search_phrases":334,"slug":337,"view_count":21,"doi":338,"paper":339,"created_at":354},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",55,{"impact":122,"substance":17,"depth":151,"authority":107,"freshness":122,"relevant":21,"comment":298},"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[300],{"name":294,"url":291},[26,27,28,147,130],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[304],{"code":71,"slug":215,"name":216,"path":217,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":306},{"candidates":307,"agricultural_evidence":14},[308,313,322,326,330],{"code":32,"score":309,"supported":14,"title":310,"abstract":311,"tags":312},11,[],[147,148,145],[147,28],{"code":71,"score":13,"supported":14,"title":314,"abstract":315,"tags":321},[],[316,317,318,319,320],"深度学习","计算机视觉","deep learning","computer vision","decision support",[27],{"code":134,"score":53,"supported":14,"title":323,"abstract":324,"tags":325},[],[160,135],[130],{"code":46,"score":53,"supported":14,"title":327,"abstract":328,"tags":329},[],[155,153],[26],{"code":170,"score":107,"supported":14,"title":331,"abstract":332,"tags":333},[],[173,174],[],[335,336],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":338,"openalex_id":340,"authors":341,"venue":294,"cited_by_count":97,"oa_url":291,"card":349,"direction":102,"ingested_from":104},"W7220855887",[342,345,347],{"name":343,"orcid":344},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":346,"orcid":9},"Shetty Chirag",{"name":348,"orcid":9},"Sunay Kanade",{"tldr":350,"method":351,"finding":352,"direction":102,"opportunity":353},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":9,"source_name":361,"source_url":358,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":363,"score_detail":364,"sources":368,"tags":370,"primary_topic":372,"secondary_topics":373,"classification":374,"search_phrases":390,"slug":393,"view_count":60,"doi":394,"paper":395,"created_at":405},4352,"Hybrid Butterworth Filter and Autoencoder for Time-Series Anomaly Detection in Agricultural IoT Sensors","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23186427","Internet of Things (IoT) sensors deployed in agricultural environments frequently suffer from noise interference caused by environmental fluctuations, which degrades the performance of deep-learning-based anomaly detection. This work presents a hybrid pipeline combining a Butterworth low-pass filter and an autoencoder to detect anomalies from sensor time-series data. The Butterworth filter suppresses high-frequency environmental noise before data enters the autoencoder, improving the quality of input features. The autoencoder reconstructs normal sensor signals and identifies anomalous samples by measuring reconstruction error. We validate the proposed method on the NAB real-world sensor dataset and conduct ablation experiments to analyse the contribution of the filtering module. Experimental results show that the pre-filtering step effectively reduces false positives and improves detection accuracy compared with using a raw-data autoencoder baseline. This hybrid approach is lightweight and suitable for edge agricultural IoT devices with limited computational resources.","部署于农业环境中的物联网（IoT）传感器常受环境波动引起的噪声干扰，导致基于深度学习的异常检测性能下降。本文提出了一种将巴特沃斯低通滤波器与自编码器相结合的混合流程，用于从传感器时间序列数据中检测异常。巴特沃斯滤波器在数据进入自编码器之前抑制高频环境噪声，从而提高输入特征的质量。自编码器重建正常传感器信号，并通过测量重建误差来识别异常样本。我们在NAB真实传感器数据集上验证了所提方法，并进行消融实验以分析滤波模块的贡献。实验结果表明，与使用原始数据的自编码器基线相比，预滤波步骤有效减少了假阳性并提高了检测精度。该混合方法轻量，适用于计算资源有限的边缘农业物联网设备。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-06T00:00:00Z",72,{"impact":120,"substance":365,"depth":366,"authority":121,"freshness":20,"relevant":21,"comment":367},21,17,"面向农业IoT传感器时序数据的滤波+自编码器轻量异常检测方法，方法新颖、有消融实验与NAB数据集验证，适合边缘设备，但属细分技术进展，公共影响有限。",[369],{"name":361,"url":358},[26,27,129,28,371],"异常检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":375},{"candidates":376,"agricultural_evidence":14},[377,381,386],{"code":32,"score":143,"supported":14,"title":378,"abstract":379,"tags":380},[145],[147,148,145],[129,28],{"code":71,"score":382,"supported":14,"title":383,"abstract":384,"tags":385},4,[],[316],[27],{"code":46,"score":21,"supported":40,"title":387,"abstract":388,"tags":389},[],[],[26],[391,392],"农业物联网 传感器 异常检测","Butterworth 自编码器 时序数据","农业物联网传感器异常检测-4352","10.5281\u002Fzenodo.23186427",{"doi":394,"openalex_id":396,"authors":397,"venue":361,"cited_by_count":97,"oa_url":358,"card":400,"direction":102,"ingested_from":104},"W7220674657",[398],{"name":399,"orcid":9},"Sicheng Su",{"tldr":401,"method":402,"finding":403,"direction":102,"opportunity":404},"提出巴特沃斯低通滤波与自编码器混合方法，检测农业物联网传感器时序异常。","巴特沃斯滤波预处理加自编码器重构误差，在NAB真实数据集上做消融实验。","预滤波有效降低误报并提升检测精度，方法轻量适合边缘农业物联网设备。","可探索自适应滤波参数与轻量自编码器在边缘端多传感器联合异常检测中的泛化能力。","2026-10-07T23:30:14.174878Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":413,"score_detail":414,"sources":417,"tags":419,"primary_topic":422,"secondary_topics":423,"classification":429,"search_phrases":448,"slug":451,"view_count":60,"doi":452,"paper":453,"created_at":467},3839,"A remote sensing image processing method based on edge computing and deep learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00508-2","The rapid advancement of Artificial Intelligence (AI) and edge computing has increased the demand for accurate and low-latency remote sensing image classification; however, conventional cloud-centric approaches face challenges related to processing latency, communication overhead, and efficient analysis of diverse land-cover scenes. This research proposes a remote sensing image processing method based on edge computing and deep learning (DL), integrating an Adaptive Chameleon Swarm-tuned Efficient Three-Dimensional Convolutional Neural Network (ACS-E3DCNN). The Remote Sensing Image Dataset obtained from Kaggle contains 5,631 RGB images categorized into five land-cover classes, namely agriculture, barren land, urban, vegetation, and water. During preprocessing, image normalization, adaptive histogram equalization, and non-local means filtering are applied to reduce noise, enhance contrast, and improve image quality. Subsequently, Principal Component Analysis (PCA) is employed for dimensionality refinement. The Efficient Three-Dimensional Convolutional Neural Network captures discriminative image representations with reduced parameter complexity, while the Adaptive Chameleon Swarm algorithm optimizes critical hyperparameters, including learning rate, kernel depth, and filter dimensions, to improve convergence and classification performance. Experimental results implemented using Python 3.10.1 demonstrate that the proposed model outperforms existing approaches, achieving an accuracy of 98.10%, F1-score of 96.85%, PSNR of 38.05, SSIM of 0.97, and latency of 85 ms under the 80:20 data split, with a statistically significant paired t-test result ( p \u003C 0.05). The proposed model provides an intelligent, reliable, and efficient approach for remote sensing image classification, supporting accurate land-cover mapping, environmental monitoring, and low-latency edge-enabled image processing.","人工智能（AI）与边缘计算的快速发展，提升了对高精度、低延迟遥感图像分类的需求；然而，传统的以云为中心的方法在处理延迟、通信开销以及高效分析多样化土地覆盖场景方面面临挑战。本研究提出了一种基于边缘计算与深度学习（DL）的遥感图像处理方法，集成了自适应变色龙群优化高效三维卷积神经网络（ACS-E3DCNN）。从Kaggle获取的遥感图像数据集包含5，631幅RGB图像，分为农业、裸地、城市、植被和水体五个土地覆盖类别。在预处理阶段，采用图像归一化、自适应直方图均衡化和非局部均值滤波来降低噪声、增强对比度并提升图像质量。随后，采用主成分分析（PCA）进行降维优化。高效三维卷积神经网络以较低的参数复杂度捕获具有判别性的图像表征，而自适应变色龙群算法则优化关键超参数，包括学习率、卷积核深度和滤波器尺寸，以改善收敛性和分类性能。使用Python 3.10.1实现的实验结果表明，所提模型优于现有方法，在80：20数据划分下达到了98.10%的准确率、96.85%的F1分数、38.05的PSNR、0.97的SSIM以及85 ms的延迟，配对t检验结果具有统计学显著性（p \u003C 0.05）。所提模型为遥感图像分类提供了一种智能、可靠且高效的方法，支持精确的土地覆盖制图、环境监测以及低延迟的边缘图像处理。","Discover Internet of Things",67,{"impact":120,"substance":17,"depth":415,"authority":120,"freshness":20,"relevant":21,"comment":416},16,"该论文提出边缘计算与深度学习结合的遥感图像分类方法，在农业土地覆盖识别上精度高、延迟低，对智慧农业遥感应用有参考价值，但属方法类研究，产业影响有限。",[418],{"name":412,"url":409},[26,27,420,28,421],"农业遥感","土地覆盖分类",{"code":71,"slug":215,"name":216,"path":217,"parent_code":36},[424,428],{"code":59,"slug":425,"name":426,"path":427,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":430},{"candidates":431,"agricultural_evidence":14},[432,436,440,444],{"code":71,"score":17,"supported":14,"title":433,"abstract":434,"tags":435},[318],[316,318,238],[27],{"code":59,"score":151,"supported":14,"title":437,"abstract":438,"tags":439},[248],[63,248],[420],{"code":32,"score":151,"supported":14,"title":441,"abstract":442,"tags":443},[56],[28,56],[28],{"code":46,"score":21,"supported":40,"title":445,"abstract":446,"tags":447},[],[],[26],[449,450],"遥感图像 土地覆盖 分类","边缘计算 深度学习 遥感","遥感图像土地覆盖分类-3839","10.1007\u002Fs43926-026-00508-2",{"doi":452,"openalex_id":454,"authors":455,"venue":412,"cited_by_count":97,"oa_url":409,"card":461,"direction":465,"ingested_from":104},"W7214769118",[456,458],{"name":457,"orcid":9},"Yuli Song",{"name":459,"orcid":460},"Hong Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3176-3994",{"tldr":462,"method":463,"finding":464,"direction":465,"opportunity":466},"提出边缘计算与深度学习结合的遥感图像分类方法，用改进3D CNN实现低延迟高精度土地覆盖识别。","ACS-E3DCNN模型，结合自适应变色龙群优化超参数，用Kaggle 5631","准确率98.10%、F1为96.85%、延迟85ms，优于现有方法，支持低延迟边缘处理。","农业遥感与作物表型","可探索边缘端轻量化模型在农田实时监测中的部署，结合多光谱与时序数据提升作物分类泛化性。","2026-09-30T23:30:27.868009Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":9,"content":9,"source_name":473,"source_url":471,"published_at":474,"category":12,"cover_url":9,"hotness":296,"is_selected":40,"score":475,"score_detail":476,"sources":478,"tags":487,"primary_topic":489,"secondary_topics":490,"classification":493,"search_phrases":523,"slug":526,"view_count":382,"doi":527,"paper":528,"created_at":543},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",74,{"impact":17,"substance":18,"depth":17,"authority":121,"freshness":60,"relevant":21,"comment":477},"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[479,480,482,484],{"name":473,"url":471},{"name":361,"url":481},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":361,"url":483},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":485,"url":486},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[26,27,28,147,488],"智能灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[491,492],{"code":134,"slug":135,"name":136,"path":137,"parent_code":138},{"code":71,"slug":215,"name":216,"path":217,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":494},{"candidates":495,"agricultural_evidence":14},[496,501,505,509,515,519],{"code":32,"score":497,"supported":14,"title":498,"abstract":499,"tags":500},27,[145,56],[148,145,56],[147,28],{"code":134,"score":120,"supported":14,"title":502,"abstract":503,"tags":504},[135],[135],[488],{"code":71,"score":120,"supported":14,"title":506,"abstract":507,"tags":508},[167],[167],[27],{"code":510,"score":60,"supported":14,"title":511,"abstract":512,"tags":514},"A8",[],[513],"food security",[],{"code":170,"score":60,"supported":14,"title":516,"abstract":517,"tags":518},[],[174],[],{"code":46,"score":21,"supported":40,"title":520,"abstract":521,"tags":522},[],[],[26],[524,525],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":527,"openalex_id":529,"authors":530,"venue":473,"cited_by_count":97,"oa_url":537,"card":538,"direction":102,"ingested_from":104},"W7204189894",[531,534],{"name":532,"orcid":533},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":535,"orcid":536},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":539,"method":540,"finding":541,"direction":102,"opportunity":542},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z"]