[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4487":3,"related-4487":101},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":81,"slug":84,"view_count":22,"doi":85,"paper":86,"created_at":100},4487,"DIDACTIC MODEL FOR ORGANIZING PRACTICAL CLASSES IN SPECIALIZED SUBJECTS BASED ON \"SMART AGRICULTURE\"","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23243749","The thesis proposes a didactic model for organizing practical classes in specialized subjects based on Smart Agriculture technologies at higher agricultural education institutions. The model is based on the sequence “Question — Data — Analysis — Decision — Presentation — Reflection.” Students work with real or simulated agrotechnological data using IoT sensors, GIS, digital monitoring, remote sensing, and artificial intelligence tools. This approach is aimed at connecting theoretical knowledge with practical tasks, developing data literacy, and fostering skills for making informed professional decisions.","本论文提出了一种基于智慧农业（Smart Agriculture）技术的教学模式，用于组织高等农业院校专业课程的实践教学。该模型遵循“问题—数据—分析—决策—展示—反思”的顺序。学生利用物联网传感器、地理信息系统、数字监测、遥感及人工智能工具，处理真实或模拟的农业技术数据。该方法旨在将理论知识与实践任务相结合，培养数据素养，并促进做出有依据的专业决策的能力。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z","论文",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,16,15,12,9,1,"提出面向智慧农业的实践教学模型，方法框架清晰但属教育应用研究，产业影响有限，时效性佳。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业物联网","农业教育","数据素养",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,67,74],{"code":46,"score":19,"supported":47,"title":48,"abstract":50,"tags":51},"I1",true,[49],"smart agriculture",[27,49],[27],{"code":33,"score":53,"supported":47,"title":54,"abstract":55,"tags":58},7,[],[56,57],"物联网","IoT",[29],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":66},"I3",6,[],[64,65],"遥感","remote sensing",[],{"code":68,"score":61,"supported":47,"title":69,"abstract":70,"tags":73},"I4",[],[71,72],"地理信息","GIS",[],{"code":75,"score":76,"supported":47,"title":77,"abstract":78,"tags":80},"I2",4,[],[79],"artificial intelligence",[28],[82,83],"Smart Agriculture 实践教学 模型","农业院校 智慧农业 课程","SmartAgriculture实践教学模型-4487","10.5281\u002Fzenodo.23243749",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":98,"ingested_from":99},"W7220891703",[89],{"name":90,"orcid":9},"Shamsiyeva Ogiloy",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出基于智慧农业的实践课教学模式，用真实或模拟农业数据训练学生决策能力。","采用“问题—数据—分析—决策—展示—反思”六步模型，结合IoT、GIS、遥感与A","该模式能连接理论与实操，提升学生数据素养和基于证据的专业决策能力。","数字乡村与农业信息化","可实证检验该教学模式对数据素养与决策能力的提升效果，并开发配套课程与评价体系。","智慧农业 \u002F 农业物联网","openalex","2026-10-09T23:30:07.667690Z",{"total":61,"page":22,"page_size":61,"items":102},[103,179,262,324,420,481],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":111,"is_selected":14,"score":112,"score_detail":113,"sources":115,"tags":120,"primary_topic":122,"secondary_topics":123,"classification":128,"search_phrases":146,"slug":149,"view_count":76,"doi":150,"paper":151,"created_at":178},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。","Preprints.org","2026-09-23T00:00:00Z",25,50,{"impact":61,"substance":18,"depth":19,"authority":76,"freshness":21,"relevant":22,"comment":114},"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[116,117],{"name":109,"url":106},{"name":118,"url":119},"Computers","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15100663",[27,28,29,121,30],"精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[124],{"code":75,"slug":125,"name":126,"path":127,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,137,141],{"code":33,"score":132,"supported":47,"title":133,"abstract":134,"tags":136},18,[57],[56,135,57],"internet of things",[29],{"code":75,"score":20,"supported":47,"title":138,"abstract":139,"tags":140},[79],[79],[28],{"code":46,"score":17,"supported":47,"title":142,"abstract":143,"tags":145},[],[121,144],"precision agriculture",[27,121],[147,148],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":150,"openalex_id":152,"authors":153,"venue":109,"cited_by_count":91,"oa_url":106,"card":172,"direction":98,"ingested_from":99},"W7214071608",[154,157,160,163,166,169],{"name":155,"orcid":156},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":158,"orcid":159},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":161,"orcid":162},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":164,"orcid":165},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":167,"orcid":168},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":170,"orcid":171},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","2026-09-24T23:30:13.353443Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":10,"source_url":182,"published_at":185,"category":12,"cover_url":9,"hotness":111,"is_selected":14,"score":186,"score_detail":187,"sources":193,"tags":197,"primary_topic":200,"secondary_topics":206,"classification":208,"search_phrases":240,"slug":243,"view_count":91,"doi":244,"paper":245,"created_at":261},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。","2026-11-25T00:00:00Z",73,{"impact":188,"substance":189,"depth":190,"authority":191,"freshness":91,"relevant":22,"comment":192},22,21,17,13,"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。",[194,195],{"name":10,"url":182},{"name":10,"url":196},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,28,29,198,199],"区块链溯源","精准灌溉",{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[207],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":209},{"candidates":210,"agricultural_evidence":47},[211,216,220,224,231,236],{"code":201,"score":19,"supported":47,"title":212,"abstract":213,"tags":215},[202],[214,202],"灌溉",[199],{"code":33,"score":13,"supported":47,"title":217,"abstract":218,"tags":219},[],[56,135,57],[29],{"code":75,"score":76,"supported":47,"title":221,"abstract":222,"tags":223},[],[79],[28],{"code":225,"score":226,"supported":47,"title":227,"abstract":228,"tags":230},"G2",3,[],[229],"小农户",[],{"code":232,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},"E4",[],[],[198],{"code":46,"score":22,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[27],[241,242],"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":244,"openalex_id":246,"authors":247,"venue":10,"cited_by_count":91,"oa_url":182,"card":256,"direction":98,"ingested_from":99},"W7223034914",[248,251,253],{"name":249,"orcid":250},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":252,"orcid":9},"Dr.T.Baskar",{"name":254,"orcid":255},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":257,"method":258,"finding":259,"direction":96,"opportunity":260},"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。","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":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":273,"tags":275,"primary_topic":278,"secondary_topics":279,"classification":281,"search_phrases":298,"slug":301,"view_count":91,"doi":302,"paper":303,"created_at":323},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":18,"substance":189,"depth":132,"authority":20,"freshness":21,"relevant":22,"comment":272},"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。",[274],{"name":268,"url":265},[27,28,29,276,277],"大语言模型","传感器标准化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[280],{"code":75,"slug":125,"name":126,"path":127,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":282},{"candidates":283,"agricultural_evidence":47},[284,288,294],{"code":33,"score":132,"supported":47,"title":285,"abstract":286,"tags":287},[57],[56,135,57],[29],{"code":75,"score":18,"supported":47,"title":289,"abstract":291,"tags":293},[290],"decision support",[292,290],"large language model",[28,276],{"code":46,"score":53,"supported":47,"title":295,"abstract":296,"tags":297},[],[27,49],[27],[299,300],"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":302,"openalex_id":304,"authors":305,"venue":268,"cited_by_count":91,"oa_url":265,"card":318,"direction":98,"ingested_from":99},"W7222920424",[306,308,310,312,314,316],{"name":307,"orcid":9},"Leqi Lai",{"name":309,"orcid":9},"Langtao Duan",{"name":311,"orcid":9},"Yuhao Chen",{"name":313,"orcid":9},"Yuhang Zheng",{"name":315,"orcid":9},"Jinyu Yang",{"name":317,"orcid":9},"Mingyao Wen",{"tldr":319,"method":320,"finding":321,"direction":98,"opportunity":322},"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":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":332,"score_detail":333,"sources":335,"tags":337,"primary_topic":340,"secondary_topics":346,"classification":353,"search_phrases":397,"slug":400,"view_count":91,"doi":9,"paper":401,"created_at":419},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":20,"substance":189,"depth":190,"authority":21,"freshness":17,"relevant":22,"comment":334},"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。",[336],{"name":330,"url":327},[27,28,29,338,339],"温室环境","小气候预测",{"code":341,"slug":342,"name":343,"path":344,"parent_code":345},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[347,348],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":349,"slug":350,"name":351,"path":352,"parent_code":345},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":40,"locked":14,"version":41,"method":42,"evidence":354},{"candidates":355,"agricultural_evidence":47},[356,360,367,376,380,386,393],{"code":33,"score":19,"supported":47,"title":357,"abstract":358,"tags":359},[57],[56,57],[29],{"code":341,"score":361,"supported":47,"title":362,"abstract":364,"tags":366},14,[363],"greenhouse",[365,363],"温室",[],{"code":349,"score":21,"supported":47,"title":368,"abstract":369,"tags":375},[],[370,371,372,373,374],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":46,"score":53,"supported":47,"title":377,"abstract":378,"tags":379},[],[121,144],[27],{"code":75,"score":53,"supported":47,"title":381,"abstract":382,"tags":385},[],[383,384],"深度学习","deep learning",[28],{"code":387,"score":61,"supported":47,"title":388,"abstract":389,"tags":392},"H2",[],[390,391],"土壤","soil",[],{"code":201,"score":61,"supported":47,"title":394,"abstract":395,"tags":396},[],[214,202],[],[398,399],"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":9,"openalex_id":402,"authors":403,"venue":330,"cited_by_count":91,"oa_url":9,"card":414,"direction":98,"ingested_from":99},"W7222909837",[404,406,408,410,412],{"name":405,"orcid":9},"Siman Emmanuel*",{"name":407,"orcid":9},"Edward Onyebueke Agu",{"name":409,"orcid":9},"Musa Gani",{"name":411,"orcid":9},"Oladunjoye John Abiodun",{"name":413,"orcid":9},"Anagu Emmanuel John",{"tldr":415,"method":416,"finding":417,"direction":98,"opportunity":418},"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":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":428,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":437,"classification":439,"search_phrases":463,"slug":466,"view_count":226,"doi":467,"paper":468,"created_at":480},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":20,"substance":132,"depth":18,"authority":191,"freshness":21,"relevant":22,"comment":430},"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。",[432],{"name":426,"url":423},[27,28,29,434,435],"遥感监测","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[438],{"code":75,"slug":125,"name":126,"path":127,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":440},{"candidates":441,"agricultural_evidence":47},[442,448,452,458],{"code":33,"score":132,"supported":47,"title":443,"abstract":444,"tags":447},[57],[56,445,135,57,446],"边缘计算","edge computing",[29],{"code":46,"score":19,"supported":47,"title":449,"abstract":450,"tags":451},[49],[121,144],[27],{"code":75,"score":13,"supported":47,"title":453,"abstract":454,"tags":457},[],[383,455,456,384,79],"机器学习","machine learning",[28],{"code":60,"score":459,"supported":14,"title":460,"abstract":461,"tags":462},2,[],[],[435,434],[464,465],"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":467,"openalex_id":469,"authors":470,"venue":426,"cited_by_count":91,"oa_url":423,"card":475,"direction":98,"ingested_from":99},"W7221560555",[471,473],{"name":472,"orcid":9},"Keerthana T",{"name":474,"orcid":9},"Janarthanan R",{"tldr":476,"method":477,"finding":478,"direction":98,"opportunity":479},"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",{"id":482,"title":483,"url":484,"summary":485,"summary_zh":486,"content":9,"source_name":487,"source_url":484,"published_at":11,"category":12,"cover_url":9,"hotness":111,"is_selected":14,"score":488,"score_detail":489,"sources":491,"tags":495,"primary_topic":497,"secondary_topics":498,"classification":500,"search_phrases":515,"slug":518,"view_count":22,"doi":9,"paper":519,"created_at":535},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",47,{"impact":17,"substance":361,"depth":191,"authority":61,"freshness":61,"relevant":22,"comment":490},"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。",[492,493],{"name":487,"url":484},{"name":487,"url":494},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[27,28,29,121,496],"低功耗通信",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[499],{"code":75,"slug":125,"name":126,"path":127,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":501},{"candidates":502,"agricultural_evidence":47},[503,507,511],{"code":33,"score":132,"supported":47,"title":504,"abstract":505,"tags":506},[57],[56,445,135,57,446],[29],{"code":75,"score":13,"supported":47,"title":508,"abstract":509,"tags":510},[],[455,456,79],[28],{"code":46,"score":17,"supported":47,"title":512,"abstract":513,"tags":514},[],[121,144],[27,121],[516,517],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":9,"openalex_id":520,"authors":521,"venue":487,"cited_by_count":91,"oa_url":484,"card":530,"direction":98,"ingested_from":99},"W7221414266",[522,524,526,528],{"name":523,"orcid":9},"Shital Chavan",{"name":525,"orcid":9},"Anam Bedrekar",{"name":527,"orcid":9},"Sanika Babar",{"name":529,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":531,"method":532,"finding":533,"direction":98,"opportunity":534},"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"]