[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3022":3,"related-3022":99},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":94,"slug":97,"view_count":20,"doi":8,"paper":8,"created_at":98},3022,"农业农村部党组召开会议强调：大力推进\"人工智能+\"农业 拓展无人机、物联网等应用场景","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202609\u002Ft20260920_8872540.htm","农业农村部党组9月20日召开会议，传达学习习近平总书记关于山东青岛市北海造船厂一货轮火灾事故的重要指示精神，部署农业安全生产、农机装备产业发展等工作。会议强调，要大力推进\"人工智能+\"农业，拓展无人机、物联网等应用场景，让新质生产力更好赋能现代农业发展；要全力抓好\"三秋\"生产，分区域分作物指导抓细秋粮田管，强化农业防灾减灾救灾，精心组织开展秋收，压茬推进秋冬种，确保秋粮丰收到手、冬小麦冬油菜种足种好。",null,"[![Image 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\n即可将网页分享给您的微信好友或朋友圈。","中国农业农村信息网","2026-09-20T08:00:00Z","政策",10,true,87,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":13,"relevant":20,"comment":21},27,20,15,1,"农业农村部党组会议明确推进“人工智能+”农业并拓展无人机、物联网应用场景，属全国性政策信号，权威性与时效性俱佳，值得进入每日精选。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","农业物联网","农业无人机","农机装备",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":38,"locked":39,"version":40,"method":41,"evidence":42},"classified",false,"2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":14},[44,50,58,65,72,78,84,89],{"code":31,"score":45,"supported":14,"title":46,"abstract":48,"tags":49},12,[47],"物联网",[47],[27],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":57},"J4",6,[],[55,56],"秋收","秋冬种",[],{"code":59,"score":60,"supported":14,"title":61,"abstract":62,"tags":64},"F5",4,[],[63],"农机",[29],{"code":66,"score":67,"supported":14,"title":68,"abstract":69,"tags":71},"A2",3,[],[70],"小麦",[],{"code":73,"score":67,"supported":14,"title":74,"abstract":75,"tags":77},"A4",[],[76],"油菜",[],{"code":79,"score":67,"supported":14,"title":80,"abstract":81,"tags":83},"H7",[],[82],"防灾减灾",[],{"code":85,"score":20,"supported":39,"title":86,"abstract":87,"tags":88},"I1",[],[],[25],{"code":90,"score":20,"supported":39,"title":91,"abstract":92,"tags":93},"I2",[],[],[26],[95,96],"农业农村部 人工智能","农业无人机 物联网 应用场景","农业农村部人工智能-3022","2026-09-21T00:04:31.528736Z",{"total":52,"page":20,"page_size":52,"items":100},[101,180,231,313,381,474],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":8,"source_name":107,"source_url":104,"published_at":108,"category":109,"cover_url":8,"hotness":13,"is_selected":39,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":121,"classification":122,"search_phrases":159,"slug":162,"view_count":163,"doi":8,"paper":164,"created_at":179},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","论文",78,{"impact":112,"substance":113,"depth":112,"authority":45,"freshness":114,"relevant":20,"comment":115},18,22,8,"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。",[117],{"name":107,"url":104},[25,26,27,28,119],"精准农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":123},{"candidates":124,"agricultural_evidence":14},[125,133,138,145,152],{"code":31,"score":13,"supported":14,"title":126,"abstract":127,"tags":132},[],[47,128,129,130,131],"边缘计算","internet of things","IoT","edge computing",[27],{"code":85,"score":114,"supported":14,"title":134,"abstract":135,"tags":137},[],[119,136],"precision agriculture",[25,119],{"code":90,"score":139,"supported":14,"title":140,"abstract":141,"tags":144},7,[],[142,143],"计算机视觉","computer vision",[26],{"code":146,"score":52,"supported":14,"title":147,"abstract":148,"tags":151},"H2",[],[149,150],"土壤","soil",[],{"code":153,"score":52,"supported":14,"title":154,"abstract":155,"tags":158},"H3",[],[156,157],"灌溉","irrigation",[],[160,161],"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",2,{"doi":8,"openalex_id":165,"authors":166,"venue":107,"cited_by_count":171,"oa_url":104,"card":172,"direction":176,"ingested_from":178},"W7218223913",[167,169],{"name":168,"orcid":8},"Володимир Панченко",{"name":170,"orcid":8},"Георгій Кучук",0,{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-03T23:30:16.357874Z",{"id":181,"title":182,"url":183,"summary":105,"summary_zh":184,"content":8,"source_name":185,"source_url":183,"published_at":108,"category":109,"cover_url":8,"hotness":13,"is_selected":39,"score":110,"score_detail":186,"sources":188,"tags":190,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":216,"slug":217,"view_count":20,"doi":218,"paper":219,"created_at":230},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":112,"substance":113,"depth":112,"authority":45,"freshness":114,"relevant":20,"comment":187},"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。",[189],{"name":185,"url":183},[25,26,27,28,119],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":194},{"candidates":195,"agricultural_evidence":14},[196,200,204,208,212],{"code":31,"score":112,"supported":14,"title":197,"abstract":198,"tags":199},[129],[47,128,129,130,131],[27],{"code":85,"score":114,"supported":14,"title":201,"abstract":202,"tags":203},[],[119,136],[25,119],{"code":90,"score":139,"supported":14,"title":205,"abstract":206,"tags":207},[],[142,143],[26],{"code":146,"score":52,"supported":14,"title":209,"abstract":210,"tags":211},[],[149,150],[],{"code":153,"score":52,"supported":14,"title":213,"abstract":214,"tags":215},[],[156,157],[],[160,161],"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":218,"openalex_id":220,"authors":221,"venue":185,"cited_by_count":171,"oa_url":183,"card":225,"direction":176,"ingested_from":178},"W7216591198",[222,224],{"name":223,"orcid":8},"Volodymyr Panchenko",{"name":170,"orcid":8},{"tldr":226,"method":227,"finding":228,"direction":176,"opportunity":229},"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":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":8,"source_name":237,"source_url":234,"published_at":238,"category":109,"cover_url":8,"hotness":239,"is_selected":39,"score":240,"score_detail":241,"sources":246,"tags":250,"primary_topic":253,"secondary_topics":257,"classification":259,"search_phrases":290,"slug":293,"view_count":171,"doi":294,"paper":295,"created_at":312},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":113,"substance":242,"depth":243,"authority":244,"freshness":171,"relevant":20,"comment":245},21,17,13,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[247,248],{"name":237,"url":234},{"name":237,"url":249},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[25,26,27,251,252],"区块链溯源","精准灌溉",{"code":153,"slug":157,"name":254,"path":255,"parent_code":256},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[258],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":39,"version":40,"method":41,"evidence":260},{"candidates":261,"agricultural_evidence":14},[262,266,270,275,281,286],{"code":153,"score":19,"supported":14,"title":263,"abstract":264,"tags":265},[157],[156,157],[252],{"code":31,"score":13,"supported":14,"title":267,"abstract":268,"tags":269},[],[47,129,130],[27],{"code":90,"score":60,"supported":14,"title":271,"abstract":272,"tags":274},[],[273],"artificial intelligence",[26],{"code":276,"score":67,"supported":14,"title":277,"abstract":278,"tags":280},"G2",[],[279],"小农户",[],{"code":282,"score":20,"supported":39,"title":283,"abstract":284,"tags":285},"E4",[],[],[251],{"code":85,"score":20,"supported":39,"title":287,"abstract":288,"tags":289},[],[],[25],[291,292],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":294,"openalex_id":296,"authors":297,"venue":237,"cited_by_count":171,"oa_url":234,"card":306,"direction":176,"ingested_from":178},"W7223034914",[298,301,303],{"name":299,"orcid":300},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":302,"orcid":8},"Dr.T.Baskar",{"name":304,"orcid":305},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":307,"method":308,"finding":309,"direction":310,"opportunity":311},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":320,"category":109,"cover_url":8,"hotness":13,"is_selected":39,"score":321,"score_detail":322,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":337,"search_phrases":355,"slug":358,"view_count":171,"doi":359,"paper":360,"created_at":380},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":323,"substance":242,"depth":112,"authority":45,"freshness":324,"relevant":20,"comment":325},16,9,"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[327],{"name":319,"url":316},[25,26,27,329,330],"大语言模型","传感器标准化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[333],{"code":90,"slug":334,"name":335,"path":336,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":39,"version":40,"method":41,"evidence":338},{"candidates":339,"agricultural_evidence":14},[340,344,350],{"code":31,"score":112,"supported":14,"title":341,"abstract":342,"tags":343},[130],[47,129,130],[27],{"code":90,"score":323,"supported":14,"title":345,"abstract":347,"tags":349},[346],"decision support",[348,346],"large language model",[26,329],{"code":85,"score":139,"supported":14,"title":351,"abstract":352,"tags":354},[],[25,353],"smart agriculture",[25],[356,357],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":359,"openalex_id":361,"authors":362,"venue":319,"cited_by_count":171,"oa_url":316,"card":375,"direction":176,"ingested_from":178},"W7222920424",[363,365,367,369,371,373],{"name":364,"orcid":8},"Leqi Lai",{"name":366,"orcid":8},"Langtao Duan",{"name":368,"orcid":8},"Yuhao Chen",{"name":370,"orcid":8},"Yuhang Zheng",{"name":372,"orcid":8},"Jinyu Yang",{"name":374,"orcid":8},"Mingyao Wen",{"tldr":376,"method":377,"finding":378,"direction":176,"opportunity":379},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":8,"source_name":387,"source_url":384,"published_at":388,"category":109,"cover_url":8,"hotness":13,"is_selected":39,"score":389,"score_detail":390,"sources":392,"tags":394,"primary_topic":397,"secondary_topics":403,"classification":410,"search_phrases":451,"slug":454,"view_count":171,"doi":8,"paper":455,"created_at":473},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":45,"substance":242,"depth":243,"authority":324,"freshness":114,"relevant":20,"comment":391},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[393],{"name":387,"url":384},[25,26,27,395,396],"温室环境","小气候预测",{"code":398,"slug":399,"name":400,"path":401,"parent_code":402},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[404,405],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":406,"slug":407,"name":408,"path":409,"parent_code":402},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":38,"locked":39,"version":40,"method":41,"evidence":411},{"candidates":412,"agricultural_evidence":14},[413,417,424,433,437,443,447],{"code":31,"score":19,"supported":14,"title":414,"abstract":415,"tags":416},[130],[47,130],[27],{"code":398,"score":418,"supported":14,"title":419,"abstract":421,"tags":423},14,[420],"greenhouse",[422,420],"温室",[],{"code":406,"score":324,"supported":14,"title":425,"abstract":426,"tags":432},[],[427,428,429,430,431],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":85,"score":139,"supported":14,"title":434,"abstract":435,"tags":436},[],[119,136],[25],{"code":90,"score":139,"supported":14,"title":438,"abstract":439,"tags":442},[],[440,441],"深度学习","deep learning",[26],{"code":146,"score":52,"supported":14,"title":444,"abstract":445,"tags":446},[],[149,150],[],{"code":153,"score":52,"supported":14,"title":448,"abstract":449,"tags":450},[],[156,157],[],[452,453],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":8,"openalex_id":456,"authors":457,"venue":387,"cited_by_count":171,"oa_url":8,"card":468,"direction":176,"ingested_from":178},"W7222909837",[458,460,462,464,466],{"name":459,"orcid":8},"Siman Emmanuel*",{"name":461,"orcid":8},"Edward Onyebueke Agu",{"name":463,"orcid":8},"Musa Gani",{"name":465,"orcid":8},"Oladunjoye John Abiodun",{"name":467,"orcid":8},"Anagu Emmanuel John",{"tldr":469,"method":470,"finding":471,"direction":176,"opportunity":472},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":8,"source_name":480,"source_url":477,"published_at":481,"category":109,"cover_url":8,"hotness":13,"is_selected":39,"score":482,"score_detail":483,"sources":485,"tags":487,"primary_topic":490,"secondary_topics":491,"classification":493,"search_phrases":515,"slug":518,"view_count":67,"doi":519,"paper":520,"created_at":532},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":45,"substance":112,"depth":323,"authority":244,"freshness":324,"relevant":20,"comment":484},"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[486],{"name":480,"url":477},[25,26,27,488,489],"遥感监测","作物估产",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[492],{"code":90,"slug":334,"name":335,"path":336,"parent_code":35},{"status":38,"locked":39,"version":40,"method":41,"evidence":494},{"candidates":495,"agricultural_evidence":14},[496,500,504,510],{"code":31,"score":112,"supported":14,"title":497,"abstract":498,"tags":499},[130],[47,128,129,130,131],[27],{"code":85,"score":19,"supported":14,"title":501,"abstract":502,"tags":503},[353],[119,136],[25],{"code":90,"score":13,"supported":14,"title":505,"abstract":506,"tags":509},[],[440,507,508,441,273],"机器学习","machine learning",[26],{"code":511,"score":163,"supported":39,"title":512,"abstract":513,"tags":514},"I3",[],[],[489,488],[516,517],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":519,"openalex_id":521,"authors":522,"venue":480,"cited_by_count":171,"oa_url":477,"card":527,"direction":176,"ingested_from":178},"W7221560555",[523,525],{"name":524,"orcid":8},"Keerthana T",{"name":526,"orcid":8},"Janarthanan R",{"tldr":528,"method":529,"finding":530,"direction":176,"opportunity":531},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z"]