[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3614":3,"related-3614":59},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":58},3614,"Integration of digital twin and IoT for virtual greenhouse management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sciaf.2026.e03665","The convergence of Digital Twin (DT) and Internet of Things (IoT) technologies represents a key enabler of Agriculture 5.0, providing real-time synchronization between physical and virtual environments. When coupled with Virtual Reality (VR), these technologies enable immersive and interactive systems that enhance intelligent agriculture monitoring, control, and decision-making. This study proposes an advanced and fully integrated VR–DT–IoT framework for virtual greenhouse management, designed not only to simulate and visualize but also to synchronize and control the physical system in real time. The proposed framework achieves seamless cyber-physical fusion by establishing bidirectional communication among IoT sensors, DT models, and VR interfaces. This enables users to trigger actuator commands and observe instant physical feedback directly within the immersive environment. A real-world experimental greenhouse was developed as a testbed to validate the system’s architecture and functionality. IoT sensors continuously monitored environmental parameters with data streams dynamically synchronized to the DT model reproducing greenhouse dynamics. The VR interface provided context-aware interaction and spatial visualization, allowing users to explore live sensor data, analyze trends, and simulate adaptive control strategies in a realistic virtual environment. The experimental validation, conducted with 23 participants across seven training sessions, revealed a marked reduction in task completion time and error rates, along with high usability scores (SUS = 3.84\u002F5), confirming the system’s effectiveness in improving operational performance. The findings demonstrate that the proposed system significantly advances digital agriculture by merging modeling, real-time feedback, and immersive control into a unified human-in-the-loop platform. This research highlights the technological novelty and practical value of integrating DT, IoT, and VR into a single coherent framework, bridging physical and virtual domains.","数字孪生（Digital Twin, DT）与物联网（Internet of Things, IoT）技术的融合是农业5.0的关键使能技术，可实现物理环境与虚拟环境之间的实时同步。当与虚拟现实（Virtual Reality, VR）相结合时，这些技术能够构建沉浸式交互系统，从而增强智能农业的监测、控制与决策能力。本研究提出了一种先进且完全集成的VR–DT–IoT框架，用于虚拟温室管理，该框架不仅能够仿真和可视化，还能够实时同步和控制物理系统。所提出的框架通过建立IoT传感器、DT模型与VR界面之间的双向通信，实现了无缝的信息物理融合。用户可在沉浸式环境中直接触发执行器命令并观察即时物理反馈。本研究搭建了一个真实实验温室作为测试平台，以验证系统架构与功能。IoT传感器持续监测环境参数，数据流动态同步至DT模型，以复现温室动态变化。VR界面提供了情境感知交互与空间可视化功能，使用户能够在逼真的虚拟环境中探索实时传感器数据、分析趋势并仿真自适应控制策略。实验验证共有23名参与者参与，跨越七次培训课程，结果显示任务完成时间和错误率显著降低，同时可用性评分较高（SUS = 3.84\u002F5），证实了该系统在提升操作性能方面的有效性。研究结果表明，所提出的系统通过将建模、实时反馈与沉浸式控制融合为统一的人在回路平台，显著推进了数字农业发展。本研究凸显了将DT、IoT与VR集成到单一连贯框架中的技术创新性与实用价值，弥合了物理域与虚拟域之间的鸿沟。",null,"Scientific African","2026-09-25T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,8,1,"提出VR-DT-IoT全集成虚拟温室框架并经真实温室与23人实验验证，方法新颖、数据可靠，对智慧农业人机交互控制有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","数字孪生","虚拟现实","温室管理",[33,34],"数字孪生 物联网 虚拟温室","VR DT IoT 智慧农业","数字孪生物联网虚拟温室-3614",0,"10.1016\u002Fj.sciaf.2026.e03665",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":50,"card":51,"direction":55,"ingested_from":57},"W7214397681",[41,44,47],{"name":42,"orcid":43},"Hicham Slimani","https:\u002F\u002Forcid.org\u002F0000-0002-7946-4064",{"name":45,"orcid":46},"Abdelilah Jilbab","https:\u002F\u002Forcid.org\u002F0000-0002-1577-9040",{"name":48,"orcid":49},"Jamal El Mhamdi","https:\u002F\u002Forcid.org\u002F0000-0001-8219-3560","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2468227626004904\u002Fpdf",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"提出VR-DT-IoT框架，实现虚拟温室实时同步与沉浸式控制。","构建真实温室试验台，集成IoT传感器、数字孪生模型与VR界面，23人测试。","任务完成时间和错误率显著降低，可用性高（SUS=3.84\u002F5）。","智慧农业 \u002F 农业物联网","可探索多用户协同VR控制、AI预测性维护及跨温室数字孪生标准化。","openalex","2026-09-27T23:30:19.611108Z",{"total":60,"page":22,"page_size":60,"items":61},6,[62,86,129,180,216,250],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":9,"content":9,"source_name":67,"source_url":9,"published_at":68,"category":69,"cover_url":9,"hotness":13,"is_selected":14,"score":70,"score_detail":71,"sources":76,"tags":78,"search_phrases":81,"slug":84,"view_count":36,"doi":9,"paper":9,"created_at":85},2261,"北大荒集团智慧农业:4800万亩耕地打造\"地块链\"式数字孪生管理","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260907\u002F20260907_8\u002Fnmrb_20260907_13398_8_2096696093043691617.html","北大荒信息有限公司智慧农业大数据中心将4800万亩耕地切分成27万个地块,每块都有编号和二维码。遥感平台接入48颗卫星每5天做一次体检,5000台田间采集设备、600个气象站、500台虫情测报灯昼夜值守,3.3万台物联网设备统一接入\"地块链\"管理。5年累计为农户线上放贷400亿元。","农民日报","2026-09-06T16:00:00Z","报道",85,{"impact":72,"substance":73,"depth":19,"authority":74,"freshness":60,"relevant":22,"comment":75},26,23,12,"北大荒4800万亩耕地实现地块级数字孪生管理，卫星遥感、物联网与地块链数据规模具体，属产业级智慧农业标杆案例，值得入选每日精选。",[77],{"name":67,"url":65},[79,27,28,29,80],"数字乡村","遥感监测",[82,83],"农业物联网 数字乡村 数字孪生 智慧农业","农业物联网 数字乡村","农业物联网数字乡村数字孪生智慧农业-2261","2026-09-13T00:04:03.310614Z",{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":98,"tags":100,"search_phrases":103,"slug":106,"view_count":36,"doi":107,"paper":108,"created_at":128},3615,"RPL-AER: An LSTM-Enhanced Energy-Aware and Secure Routing Protocol for Sustainable Agricultural IoT","https:\u002F\u002Fdoi.org\u002F10.15598\u002Faeee.v24i3.250726","RPL-AER: An LSTM-Enhanced Energy-Aware and Secure Routing Protocol for Sustainable Agricultural IoT。Advances in Electrical and Electronic Engineering","RPL-AER：一种面向可持续农业物联网的LSTM增强型能量感知与安全路由协议。电气与电子工程进展","Advances in Electrical and Electronic Engineering","2026-09-26T00:00:00Z",63,{"impact":21,"substance":19,"depth":17,"authority":74,"freshness":96,"relevant":22,"comment":97},9,"面向农业物联网的LSTM增强能量感知安全路由协议，方法有新意但属细分技术论文，公共影响有限。",[99],{"name":92,"url":89},[27,101,28,102],"农业人工智能","路由协议",[104,105],"RPL-AER 农业物联网 路由协议","LSTM 能量感知 安全路由","RPL-AER农业物联网路由协议-3615","10.15598\u002Faeee.v24i3.250726",{"doi":107,"openalex_id":109,"authors":110,"venue":92,"cited_by_count":36,"oa_url":89,"card":123,"direction":55,"ingested_from":57},"W7214456591",[111,113,115,117,120],{"name":112,"orcid":9},"Madani BELACEL",{"name":114,"orcid":9},"Mohamed BELKHEIR",{"name":116,"orcid":9},"Sofiane BOUKLI HACENE",{"name":118,"orcid":119},"Mehdi Rouissat","https:\u002F\u002Forcid.org\u002F0000-0002-4444-2754",{"name":121,"orcid":122},"Allel Mokaddem","https:\u002F\u002Forcid.org\u002F0000-0002-2874-5923",{"tldr":124,"method":125,"finding":126,"direction":55,"opportunity":127},"提出LSTM增强的RPL路由协议RPL-AER，用于农业物联网节能安全路由。","基于LSTM预测节点能耗与攻击，改进RPL路由选择。","RPL-AER在能耗、寿命和安全性上优于传统RPL。","可探索LSTM预测精度与轻量化部署的平衡，及多攻击场景下的鲁棒性验证。","2026-09-27T23:30:24.650832Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":135,"source_url":132,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":136,"score":137,"score_detail":138,"sources":141,"tags":143,"search_phrases":146,"slug":149,"view_count":36,"doi":150,"paper":151,"created_at":179},3548,"Cybersecurity and Privacy in AI-Enabled Agricultural IoT Ecosystems: A Systematic Review of Threats, Safeguards, and Resilience Gaps","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fa19100827","Agricultural Internet of Things (IoT) ecosystems increasingly connect sensors, drones, edge devices, and cloud platforms to support precision farming, yet cybersecurity, privacy, and the real-world readiness of proposed safeguards remain fragmented across the literature. This study systematically reviewed cybersecurity threats, privacy concerns, AI-driven and traditional safeguards, and evidence gaps in agricultural IoT research published between 2015 and 2025. Following the Kitchenham and Charters methodology, 103 studies were selected from 2535 records retrieved across five databases. STRIDE and LINDDUN were retrospectively applied as complementary frameworks for threat and privacy classification. Because the coding scheme was multi-label, reliability was assessed at the category level using presence\u002Fabsence decisions on a 20-study sample and observed agreement ranged from 75% to 95% for STRIDE and 95% to 100% for LINDDUN, with interpretable Cohen’s κ values ranging from 0.348 to 0.794 and 0.875 to 1.000, respectively. All included studies also underwent quality appraisal and a supplementary ecological-validity assessment. Denial-of-service, tampering, and spoofing were the most frequently reported threats, concentrated at the device, network, and cloud layers, while the edge layer remained underexamined. AI- and machine-learning-based intrusion detection and privacy-preserving methods such as federated learning emerged as prominent safeguards, but adversarial manipulation of agricultural AI models received limited attention. Privacy research remained oriented toward confidentiality, with 90.3% of studies referencing no applicable regulatory framework. Most importantly, only 8 of 103 studies (7.8%) received a High ecological-validity rating, showing how rarely the evidence base is grounded in real agricultural field conditions. The review identifies field-grounded evaluation, adversarially robust AI, privacy governance, and cyber resilience as priorities for future agricultural IoT security research.","农业物联网（IoT）生态系统日益将传感器、无人机、边缘设备和云平台连接起来，以支持精准农业，然而网络安全、隐私以及所提出保障措施的现实适用性在文献中仍呈现碎片化状态。本研究系统综述了2015年至2025年间发表的农业物联网研究中的网络安全威胁、隐私问题、人工智能驱动及传统保障措施以及证据缺口。遵循Kitchenham和Charters方法论，从五个数据库检索到的2535条记录中筛选出103项研究。STRIDE和LINDDUN被回溯性应用为威胁与隐私分类的互补框架。由于编码方案为多标签，可靠性在类别层面通过20项研究样本的存在\u002F缺失判定进行评估，STRIDE的观察一致率为75%至95%，LINDDUN为95%至100%，可解释的Cohen's κ值分别为0.348至0.794和0.875至1.000。所有纳入研究还接受了质量评价和补充性生态效度评估。拒绝服务、篡改和欺骗是报告最频繁的威胁，集中在设备层、网络层和云层，而边缘层仍未被充分考察。基于人工智能和机器学习的入侵检测以及联邦学习等隐私保护方法成为突出的保障措施，但农业人工智能模型的对抗性操纵受到的关注有限。隐私研究仍以保密性为导向，90.3%的研究未引用任何适用的监管框架。最重要的是，103项研究中仅有8项（7.8%）获得高生态效度评级，表明证据基础鲜有扎根于真实农业田间条件。本综述将田间实证评估、对抗鲁棒人工智能、隐私治理和网络韧性确定为未来农业物联网安全研究的优先事项。","Algorithms",true,82,{"impact":19,"substance":73,"depth":139,"authority":20,"freshness":96,"relevant":22,"comment":140},19,"系统综述103项研究，揭示农业物联网安全证据多脱离田间实际，对智慧农业安全研究有较高参考价值。",[142],{"name":135,"url":132},[27,101,28,144,145],"隐私保护","数据安全",[147,148],"农业物联网 网络安全","农业AI 隐私保护 联邦学习","农业物联网网络安全-3548","10.3390\u002Fa19100827",{"doi":150,"openalex_id":152,"authors":153,"venue":135,"cited_by_count":36,"oa_url":132,"card":174,"direction":55,"ingested_from":57},"W7214403401",[154,157,160,163,166,169,171],{"name":155,"orcid":156},"Emmanuel Kojo Gyamfi","https:\u002F\u002Forcid.org\u002F0009-0002-0441-2830",{"name":158,"orcid":159},"Jess Kropczynski","https:\u002F\u002Forcid.org\u002F0000-0002-7458-6003",{"name":161,"orcid":162},"Jacques Bou Abdo","https:\u002F\u002Forcid.org\u002F0000-0002-3482-9154",{"name":164,"orcid":165},"Joseph Samuel Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-2555-8142",{"name":167,"orcid":168},"Mustapha Awinsongya Yakubu","https:\u002F\u002Forcid.org\u002F0009-0005-6623-0858",{"name":170,"orcid":9},"Anthony Tsetse",{"name":172,"orcid":173},"Gertrude Kaneah Abagale","https:\u002F\u002Forcid.org\u002F0009-0009-1502-3188",{"tldr":175,"method":176,"finding":177,"direction":55,"opportunity":178},"系统综述2015-2025年农业物联网的网络安全、隐私威胁与防护措施及证据缺口。","Kitchenham系统综述法，筛选103项研究，用STRIDE与LINDDUN","拒绝服务、篡改、欺骗威胁最多，边缘层研究不足，仅7.8%研究具高生态效度。","农业AI模型的对抗鲁棒性、边缘层安全、隐私治理与真实田间条件下的韧性评估是明显空白。","2026-09-26T23:30:15.415998Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":187,"sources":191,"tags":193,"search_phrases":197,"slug":200,"view_count":36,"doi":201,"paper":202,"created_at":215},3536,"Functional photonic and optoelectronic materials and devices for climate-resilient smart agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmats.2026.1943334","The variation in the temperature, precipitation, and extreme events has rendered climate change very threatening to crop production and the overall world food security. The prospects of sustainable and climate resilient agricultural production is brought up with the use of photonics and optoelectronics coupled with smart farming technology. The paper offers an overall foundation of smart agricultural systems that are assisted by photonics and optoelectronics that combine the state-of-the-art optical sensors, Internet of Things (IoT) communications, and machine learning algorithms in the crop and soil monitoring. Due to optical sensing technologies including fiber optic sensors, LiDAR, fluorescence spectroscopy, and hyperspectral imaging, optical sensors can also be used in high resolution and non-destructive measurement of physiological properties of plants, nutrient status, water stress and disease incidence. With the help of optoelectronic devices, it is possible to carry out the accurate signal processing, data collection, automated control of irrigation, fertigation and microclimate regulating systems. When sensor-based information is incorporated with any decision support systems the quality of early stress detection is improved, wastage of resources is minimized and the effect on the environment is minimized. Multispectral and temporal data based on machine learning models enhance forecasting of climate risk, diseases, and pest outbreaks forecasting, and crop yield forecasting. The system will aid in making the contemporary crop production systems more sustainable and resilient in the long-term due to the fact that it will provide scalable and flexible solutions across the agroecological regions.","温度、降水及极端事件的变化使气候变化对作物生产和全球粮食安全构成严重威胁。光子学与光电子学结合智慧农业技术，为实现可持续且气候韧性的农业生产带来了前景。本文提供了由光子学和光电子学辅助的智慧农业系统的总体基础，该系统将先进的光学传感器、物联网（IoT）通信和机器学习算法结合应用于作物与土壤监测。借助光纤传感器、激光雷达（LiDAR）、荧光光谱和高光谱成像等光学传感技术，光学传感器还可用于高分辨率、非破坏性地测量植物生理特性、养分状况、水分胁迫和病害发生情况。借助光电子器件，可以实现精确的信号处理、数据采集以及灌溉、施肥和微气候调节系统的自动控制。当基于传感器的信息与决策支持系统相结合时，早期胁迫检测的质量得以提高，资源浪费降至最低，对环境的影响也降至最小。基于机器学习模型的多光谱和时间序列数据增强了气候风险、病虫害暴发预测以及作物产量预测的能力。该系统将有助于使当代作物生产系统在长期内更具可持续性和韧性，因为它将为各农业生态区域提供可扩展且灵活的解决方案。","Frontiers in Materials",{"impact":19,"substance":188,"depth":189,"authority":20,"freshness":21,"relevant":22,"comment":190},20,17,"系统综述光子与光电子技术在气候韧性智慧农业中的应用，方法覆盖全面、结论可靠，对农业信息化领域有较高参考价值。",[192],{"name":186,"url":183},[27,28,194,195,196],"机器学习","农业传感器","光谱遥感",[198,199],"光子学 光电材料 智慧农业","光纤传感 高光谱成像 作物监测","光子学光电材料智慧农业-3536","10.3389\u002Ffmats.2026.1943334",{"doi":201,"openalex_id":203,"authors":204,"venue":186,"cited_by_count":36,"oa_url":183,"card":210,"direction":55,"ingested_from":57},"W7214293156",[205,207],{"name":206,"orcid":9},"Karthika Vishnu Priya Kathula",{"name":208,"orcid":209},"Murugesan Mohana Keerthi","https:\u002F\u002Forcid.org\u002F0000-0002-2018-0200",{"tldr":211,"method":212,"finding":213,"direction":55,"opportunity":214},"综述光子与光电子材料器件结合物联网和机器学习，支撑气候韧性智慧农业的作物与土壤监测。","综述光纤传感、LiDAR、荧光光谱、高光谱成像与IoT、机器学习融合方案。","光学传感与光电器件可实现无损高分辨监测，提升早期胁迫检测并减少资源浪费。","可探索低成本光子传感器与轻量ML模型在田间边缘端的集成及跨生态区泛化验证。","2026-09-26T23:30:09.644423Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":9,"content":9,"source_name":221,"source_url":219,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":226,"tags":228,"search_phrases":232,"slug":235,"view_count":36,"doi":236,"paper":237,"created_at":249},3480,"Constructing the Comprehensive Intelligent Monitoring System based on Wireless Sensor Networks (WSNs) for Mango Crop Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-9696926\u002Fv1","Constructing the Comprehensive Intelligent Monitoring System based on Wireless Sensor Networks (WSNs) for Mango Crop Precision Agriculture。Research Square","Research Square","2026-09-24T00:00:00Z",47,{"impact":21,"substance":74,"depth":20,"authority":60,"freshness":21,"relevant":22,"comment":225},"预印本论文，将WSN用于芒果精准农业监测，方法有一定新意但尚未经同行评审，产业影响有限。",[227],{"name":221,"url":219},[27,28,229,230,231],"精准农业","无线传感器网络","芒果种植",[233,234],"芒果 精准农业 无线传感器网络","WSN 芒果 智能监测","芒果精准农业无线传感器网络-3480","10.21203\u002Frs.3.rs-9696926\u002Fv1",{"doi":236,"openalex_id":238,"authors":239,"venue":221,"cited_by_count":36,"oa_url":219,"card":9,"direction":55,"ingested_from":57},"W7214233683",[240,243,246],{"name":241,"orcid":242},"Wen‐Tsai Sung","https:\u002F\u002Forcid.org\u002F0000-0001-9045-9090",{"name":244,"orcid":245},"Indra Griha Tofik Isa","https:\u002F\u002Forcid.org\u002F0000-0002-7437-6751",{"name":247,"orcid":248},"Sung‐Jung Hsiao","https:\u002F\u002Forcid.org\u002F0000-0002-0723-1632","2026-09-25T23:30:15.526882Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":263,"tags":265,"search_phrases":267,"slug":270,"view_count":36,"doi":271,"paper":272,"created_at":299},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",50,{"impact":60,"substance":17,"depth":260,"authority":261,"freshness":96,"relevant":22,"comment":262},15,4,"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。",[264],{"name":256,"url":253},[27,101,28,229,266],"农业教育",[268,269],"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":271,"openalex_id":273,"authors":274,"venue":256,"cited_by_count":36,"oa_url":253,"card":293,"direction":55,"ingested_from":57},"W7214071608",[275,278,281,284,287,290],{"name":276,"orcid":277},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":279,"orcid":280},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":282,"orcid":283},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":285,"orcid":286},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":288,"orcid":289},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":291,"orcid":292},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":294,"method":295,"finding":296,"direction":297,"opportunity":298},"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"]