[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3480":3,"related-3480":51},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":50},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",null,"Research Square","2026-09-24T00:00:00Z","论文",10,false,47,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":16,"relevant":20,"comment":21},8,12,13,6,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业物联网","精准农业","无线传感器网络","芒果种植",[31,32],"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",0,"10.21203\u002Frs.3.rs-9696926\u002Fv1",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":34,"oa_url":6,"card":8,"direction":48,"ingested_from":49},"W7214233683",[39,42,45],{"name":40,"orcid":41},"Wen‐Tsai Sung","https:\u002F\u002Forcid.org\u002F0000-0001-9045-9090",{"name":43,"orcid":44},"Indra Griha Tofik Isa","https:\u002F\u002Forcid.org\u002F0000-0002-7437-6751",{"name":46,"orcid":47},"Sung‐Jung Hsiao","https:\u002F\u002Forcid.org\u002F0000-0002-0723-1632","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:15.526882Z",{"total":19,"page":20,"page_size":19,"items":52},[53,106,139,175,207,240],{"id":54,"title":55,"url":56,"summary":57,"summary_zh":58,"content":8,"source_name":59,"source_url":56,"published_at":60,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":61,"score_detail":62,"sources":68,"tags":70,"search_phrases":73,"slug":76,"view_count":34,"doi":77,"paper":78,"created_at":105},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":19,"substance":63,"depth":64,"authority":65,"freshness":66,"relevant":20,"comment":67},16,15,4,9,"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。",[69],{"name":59,"url":56},[25,71,26,27,72],"农业人工智能","农业教育",[74,75],"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":77,"openalex_id":79,"authors":80,"venue":59,"cited_by_count":34,"oa_url":56,"card":99,"direction":48,"ingested_from":49},"W7214071608",[81,84,87,90,93,96],{"name":82,"orcid":83},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":85,"orcid":86},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":88,"orcid":89},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":91,"orcid":92},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":94,"orcid":95},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":97,"orcid":98},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"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":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":8,"source_name":112,"source_url":109,"published_at":113,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":114,"score_detail":115,"sources":118,"tags":120,"search_phrases":122,"slug":125,"view_count":34,"doi":126,"paper":127,"created_at":138},3161,"Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.59256\u002Fijire.20260705005","Plant diseases are a major challenge in modern agriculture, as they can significantly reduce crop yield, crop quality, and economic productivity. Traditional plant disease detection methods mainly depend on visual inspection and expert knowledge, which can be time-consuming, subjective, and difficult to apply across large agricultural fields. The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), and Computer Vision, has created new opportunities for automated and efficient crop disease detection and crop health monitoring. AI-based plant disease detection systems can analyze plant and leaf images to identify disease-related characteristics such as leaf discoloration, spots, lesions, texture variations, and abnormal growth patterns. Advanced techniques, including Convolutional Neural Networks (CNNs), transfer learning, image processing, image segmentation, and object detection, can be used for plant disease classification and identification of affected regions with high accuracy. This chapter introduces the fundamental concepts of AI-based plant disease detection, covering image acquisition, image preprocessing, feature extraction, model development, disease classification, and performance evaluation. It also examines the applications of AI in precision agriculture, smart agriculture, mobile-based plant disease diagnosis, drone-assisted crop monitoring, IoT-enabled farming, and edge-based agricultural systems. Furthermore, the chapter discusses important challenges such as limited and imbalanced datasets, environmental variations, similar disease symptoms, model generalization, computational requirements, and the need for explainable AI in agricultural applications. Finally, emerging trends and future opportunities are discussed, with emphasis on integrating AI with IoT, remote sensing, agricultural robotics, and multimodal agricultural data. The chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.","植物病害是现代农业面临的一项重大挑战，因为它们会显著降低作物产量、作物品质和经济生产力。传统的植物病害检测方法主要依赖视觉检查和专家知识，这种方式耗时、主观性强，且难以在大规模农田中应用。人工智能（AI）的快速发展，尤其是机器学习（ML）、深度学习（DL）和计算机视觉，为自动化、高效的作物病害检测和作物健康监测创造了新的机遇。基于AI的植物病害检测系统可以分析植物和叶片图像，以识别与病害相关的特征，如叶片变色、斑点、病斑、纹理变化和异常生长模式。包括卷积神经网络（CNN）、迁移学习、图像处理、图像分割和目标检测在内的先进技术，可用于植物病害分类和受影响区域的高精度识别。本章介绍了基于AI的植物病害检测的基本概念，涵盖图像采集、图像预处理、特征提取、模型开发、病害分类和性能评估。本章还探讨了AI在精准农业、智慧农业、基于移动端的植物病害诊断、无人机辅助作物监测、物联网（IoT）赋能农业和边缘农业系统中的应用。此外，本章讨论了重要挑战，如数据集有限且不平衡、环境变化、相似病害症状、模型泛化、计算需求，以及农业应用中可解释AI的需求。最后，讨论了新兴趋势和未来机遇，重点强调将AI与物联网、遥感、农业机器人和多模态农业数据相结合。本章为理解人工智能用于植物病害检测如何支持早期病害识别、减少作物损失、优化农业资源，并促进可持续和智能农业实践提供了基础。","International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z",59,{"impact":17,"substance":116,"depth":64,"authority":12,"freshness":16,"relevant":20,"comment":117},14,"系统综述AI在植物病害检测中的应用，内容全面但属教科书式介绍，方法新颖性与数据规模有限，可作为智慧农业主题聚合素材。",[119],{"name":112,"url":109},[25,71,26,27,121],"植物病害检测",[123,124],"AI 植物病害检测","无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":126,"openalex_id":128,"authors":129,"venue":112,"cited_by_count":34,"oa_url":8,"card":132,"direction":48,"ingested_from":49},"W7213950095",[130],{"name":131,"orcid":8},"Jamuna Ratcha",{"tldr":133,"method":134,"finding":135,"direction":136,"opportunity":137},"综述AI在植物病害检测中的应用，涵盖图像采集到模型评估全流程及未来趋势。","综述CNN、迁移学习、图像分割与目标检测在叶片病害识别中的应用。","AI可高精度识别病害，但受限于数据集不足、环境变化与模型泛化能力。","农业人工智能与决策模型","可探索多模态数据融合与可解释AI，提升复杂田间环境下病害检测的泛化能力。","2026-09-22T23:30:11.209653Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":8,"source_name":145,"source_url":142,"published_at":146,"category":11,"cover_url":8,"hotness":147,"is_selected":13,"score":148,"score_detail":149,"sources":152,"tags":156,"search_phrases":159,"slug":162,"view_count":34,"doi":163,"paper":164,"created_at":174},2932,"Wireless Sensor Network Intrusion Detection: A Review of Statistical, Machine Learning, and Federated Methods","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22823847","Wireless Sensor Networks have become essential infrastructure across environmental monitoring, healthcare, smart agriculture, industrial automation, and smart city applications. However, their distributed, resource-constrained, and physically accessible design creates serious security risks. This paper surveys cybersecurity challenges and intrusion detection in WSNs, tracing the field from statistical methods to machine learning, deep learning, explainable AI, and federated learning. Our analysis shows that while signature-based and anomaly-based detection provide foundational security, they face significant limitations in dynamic, resource-limited WSN environments. Machine learning and deep learning achieve strong results, Random Forest reaching 98–99.5% accuracy and deep learning architectures exceeding 99% in some studies, but these figures depend heavily on dataset, preprocessing, and evaluation choices. Explainable AI techniques such as SHAP and LIME address the \"black-box\" problem by enabling transparent security decisions, while federated learning enables privacy-preserving, communication-efficient collaborative detection. Significant gaps remain: integrated frameworks that detect both data theft and eavesdropping are lacking, passive attacks receive limited attention, developing-region contexts are under-addressed, and energy-aware adaptive security remains aspirational. We identify future directions including lightweight deep models, energy-aware adaptive security, hybrid architectures, and robust, poisoning-resilient federated mechanisms.","无线传感器网络（Wireless Sensor Networks, WSNs）已成为环境监测、医疗健康、智慧农业、工业自动化和智慧城市应用中的关键基础设施。然而，其分布式、资源受限且物理可访问的设计带来了严重的安全风险。本文综述了无线传感器网络中的网络安全挑战与入侵检测，梳理了该领域从统计方法到机器学习、深度学习、可解释人工智能（Explainable AI）和联邦学习（Federated Learning）的发展脉络。我们的分析表明，尽管基于签名和基于异常的检测提供了基础性安全保障，但它们在动态且资源受限的无线传感器网络环境中面临显著局限。机器学习和深度学习方法取得了良好效果，随机森林（Random Forest）达到98–99.5%的准确率，深度学习架构在部分研究中超过99%，但这些数据在很大程度上取决于数据集、预处理和评估方式的选择。SHAP和LIME等可解释人工智能技术通过实现透明的安全决策，缓解了“黑箱”问题，而联邦学习则支持隐私保护且通信高效的协作检测。目前仍存在显著空白：缺乏同时检测数据窃取和窃听的集成框架，被动攻击受到的关注有限，发展中地区的应用场景研究不足，能量感知的自适应安全仍停留在设想阶段。我们指出了未来研究方向，包括轻量级深度模型、能量感知自适应安全、混合架构以及鲁棒且抗投毒的联邦机制。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-18T00:00:00Z",25,61,{"impact":16,"substance":150,"depth":63,"authority":12,"freshness":66,"relevant":20,"comment":151},18,"综述系统梳理WSN入侵检测从统计方法到联邦学习的演进，对农业物联网安全有参考价值，但属通用技术综述、非农业专属突破。",[153,154],{"name":145,"url":142},{"name":145,"url":155},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22823848",[25,26,157,28,158],"联邦学习","入侵检测",[160,161],"无线传感器网络 入侵检测 联邦学习","无线传感器网络 农业物联网 入侵检测 智慧农业","无线传感器网络入侵检测联邦学习-2932","10.5281\u002Fzenodo.22823847",{"doi":163,"openalex_id":165,"authors":166,"venue":145,"cited_by_count":34,"oa_url":142,"card":169,"direction":48,"ingested_from":49},"W7213580003",[167],{"name":168,"orcid":8},"Jennifer Kyari-Ayele, Joseph Mom M, Iorkyase Ephraim T",{"tldr":170,"method":171,"finding":172,"direction":48,"opportunity":173},"综述无线传感器网络入侵检测从统计到机器学习、深度学习、可解释AI与联邦学习的方法演进与挑战。","文献综述，对比统计、机器学习、深度学习、可解释AI和联邦学习方法及性能。","机器学习检测精度高但依赖数据集，缺乏同时检测数据窃取与窃听的集成框架，被动攻击研究不足。","面向农业WSN的轻量级、能量感知自适应安全与抗投毒联邦检测框架尚属空白。","2026-09-19T23:30:11.466192Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":8,"source_name":145,"source_url":178,"published_at":181,"category":11,"cover_url":8,"hotness":147,"is_selected":13,"score":182,"score_detail":183,"sources":185,"tags":189,"search_phrases":191,"slug":194,"view_count":34,"doi":195,"paper":196,"created_at":206},2866,"Upcoming Technologies for Agriculture: Innovations Shaping the Future of Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816586","Agriculture has always been a cornerstone of human civilization, providing food, raw materials, and employment. However, with the global population projected to reach nearly 10 billion by 2050, the demand for food production is expected to increase substantially (Godfray et al., 2010). Traditional farming methods are increasingly challenged by climate change, resource limitations, and environmental concerns. To address these challenges, upcoming technologies in agriculture promise to revolutionize farming practices by enhancing productivity, sustainability, and resilience. This article reviews key emerging technologies, including precision agriculture, artificial intelligence (AI), gene editing, drone and robotic systems, Internet of Things (IoT) sensors, and sustainable farming innovations. The integration of these technologies is expected to transform agriculture into a more efficient, data-driven, and environmentally friendly sector.","农业一直是人类文明的基石，为人类提供食物、原材料和就业机会。然而，随着全球人口预计到2050年将接近100亿，粮食生产需求预计将大幅增加（Godfray等，2010）。传统耕作方式日益受到气候变化、资源限制和环境问题的挑战。为应对这些挑战，农业领域的新兴技术有望通过提高生产力、可持续性和韧性来彻底变革耕作方式。本文综述了关键新兴技术，包括精准农业、人工智能（AI）、基因编辑、无人机与机器人系统、物联网（IoT）传感器以及可持续农业创新。这些技术的融合有望将农业转变为一个更高效、数据驱动且环境友好的产业。","2026-09-17T00:00:00Z",68,{"impact":150,"substance":116,"depth":64,"authority":18,"freshness":16,"relevant":20,"comment":184},"综述性论文系统梳理精准农业、AI、基因编辑等前沿技术，时效性尚可，但缺乏新数据与独家结论，适合作为主题聚合素材而非每日精选头条。",[186,187],{"name":145,"url":178},{"name":145,"url":188},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[25,71,26,27,190],"基因编辑",[192,193],"精准农业 人工智能 无人机","农业物联网 传感器 机器人","精准农业人工智能无人机-2866","10.5281\u002Fzenodo.22816586",{"doi":195,"openalex_id":197,"authors":198,"venue":145,"cited_by_count":34,"oa_url":178,"card":201,"direction":48,"ingested_from":49},"W7213515489",[199],{"name":200,"orcid":8},"Zorawar Singh",{"tldr":202,"method":203,"finding":204,"direction":48,"opportunity":205},"综述精准农业、AI、基因编辑、无人机、物联网等新兴技术如何重塑未来农业。","文献综述，整合精准农业、AI、基因编辑、无人机、IoT等关键技术。","技术融合将推动农业向高效、数据驱动和环境友好方向转型。","可聚焦多技术集成落地中的成本、数据标准与农户采纳障碍等实证研究空白。","2026-09-18T23:30:14.975692Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":8,"source_name":213,"source_url":210,"published_at":214,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":215,"score_detail":216,"sources":219,"tags":221,"search_phrases":224,"slug":227,"view_count":34,"doi":228,"paper":229,"created_at":239},2652,"ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fijhlt\u002F1\u002F2\u002F001","Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.","高效灌溉管理对可持续农业至关重要，尤其是在淡水日益稀缺、优化作物生产力需求不断增长的背景下。传统灌溉实践主要依赖固定时间表或人工田间评估，这常常导致过度灌溉、根区养分淋失、灌溉不足引起的水分胁迫以及严重的资源低效。本文提出了一种智能灌溉管理系统，该系统集成了物联网（IoT）感知架构、多参数环境遥测以及监督式机器学习（ML）算法，以实现动态、数据驱动和自动化的灌溉控制。所提出的系统通过由ESP32微控制器管道管理的部署传感器节点，持续采集实时田间数据——包括土壤湿度、环境温度、相对湿度、土壤温度和降雨量。遥测数据流通过低功耗通信信道传输至集中处理引擎，其中随机森林分类模型评估多维土壤-环境相互作用，以预测即时灌溉需求。预测状态输入自动执行层，直接调节电磁阀和水泵继电器，形成闭环反馈管道。与传统基于阈值和时间表驱动的方法相比，所提出的IoT-ML框架展现出更优的运行响应能力，最大限度地减少了不必要的灌溉用水，并为现代精准农业提供了一种稳健、可扩展的架构模板。","INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)","2026-09-15T00:00:00Z",72,{"impact":63,"substance":150,"depth":217,"authority":18,"freshness":16,"relevant":20,"comment":218},17,"论文提出IoT与随机森林融合的闭环智能灌溉系统，方法完整、数据驱动，对节水农业有参考价值，但标题与摘要主题不符需核实。",[220],{"name":213,"url":210},[25,26,222,223,27],"机器学习","智能灌溉",[225,226],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-2652","10.65725\u002Fijhlt\u002F1\u002F2\u002F001",{"doi":228,"openalex_id":230,"authors":231,"venue":213,"cited_by_count":34,"oa_url":8,"card":234,"direction":48,"ingested_from":49},"W7213277724",[232],{"name":233,"orcid":8},"M. Rathamani",{"tldr":235,"method":236,"finding":237,"direction":48,"opportunity":238},"提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。","ESP32传感器节点采集土壤温湿度等数据，随机森林分类预测灌溉需求。","相比传统定时或阈值方法，该系统响应更优、减少不必要灌溉，可扩展性强。","可探索多模态数据融合与边缘智能，提升灌溉决策的实时性与泛化能力。","2026-09-16T23:30:16.194086Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":8,"source_name":246,"source_url":243,"published_at":247,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":248,"score_detail":249,"sources":252,"tags":254,"search_phrases":256,"slug":259,"view_count":34,"doi":260,"paper":261,"created_at":288},2154,"Ambient RF energy harvesting using voltage doubler rectifier for battery-free agricultural sensor networks: Design, statistical analysis, and machine learning validation","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0350236","The growing adoption of precision agriculture requires sustainable power sources for wireless sensor networks (WSNs) deployed in remote and resource-constrained environments. Conventional battery-powered systems are constrained by limited-service life, maintenance requirements, and environmental concerns. This study presents the design, experimental validation, statistical evaluation, and machine learning (ML)-based modeling of an ambient radio-frequency (RF) energy harvesting system for agricultural monitoring applications. The proposed system harvests RF energy from AM\u002FFM broadcasting (558 kHz–108 MHz) and cellular communication bands (800–2100 MHz) using a frequency-selective broadband antenna, an integrated diplexer, switchable L-section impedance-matching networks, and a Villard voltage-doubler rectifier based on low forward-voltage OA79 germanium diodes. Experimental measurements from 30 independent repeated trials yielded a mean open-circuit output voltage of 4.07 ± 0.08 V (95% CI: 4.04–4.10 V) and a loaded output voltage of 2.11 ± 0.11 V, corresponding to a harvested power of approximately 44.5 μW across a 100 kΩ load. One-way analysis of variance (ANOVA) demonstrated significant differences among diode types (F (2,87) = 687.4, p \u003C 0.001), while Tukey’s post hoc test confirmed the superior performance of OA79 diodes compared with 1N4148 and 1N5819 alternatives. Among the evaluated ML models, Gradient Boosting regression achieved the highest predictive accuracy, with R² = 0.963 and RMSE = 0.071 V. SHapley Additive exPlanations (SHAP) analysis identified diode forward voltage and ambient RF power density as the most influential factors affecting output voltage prediction. The results indicate the potential applicability of ambient multi-band RF energy harvesting as a supplementary energy source for low-power agricultural sensing applications operating under duty-cycled conditions. By combining broadband energy harvesting, statistical validation, and predictive modeling, the proposed framework establishes a systematic methodology for evaluating self-powered agricultural IoT systems operating under variable RF environments.","精准农业的日益普及，对部署在偏远且资源受限环境中的无线传感器网络（WSN）提出了可持续供电的需求。传统电池供电系统受限于使用寿命有限、维护需求以及环境问题。本研究介绍了一种面向农业监测应用的环境射频（RF）能量收集系统的设计、实验验证、统计评估以及基于机器学习（ML）的建模。所提出的系统利用频率选择性宽带天线、集成双工器、可切换L型阻抗匹配网络以及基于低正向电压OA79锗二极管的Villard倍压整流器，从AM\u002FFM广播频段（558 kHz–108 MHz）和蜂窝通信频段（800–2100 MHz）收集射频能量。来自30次独立重复试验的实验测量结果得出，平均开路输出电压为4.07 ± 0.08 V（95% CI：4.04–4.10 V），负载输出电压为2.11 ± 0.11 V，对应在100 kΩ负载上收集的功率约为44.5 μW。单因素方差分析（ANOVA）表明，不同二极管类型之间存在显著差异（F (2,87) = 687.4，p \u003C 0.001），而Tukey事后检验证实，与1N4148和1N5819替代方案相比，OA79二极管具有更优越的性能。在评估的机器学习模型中，梯度提升回归取得了最高的预测精度，R² = 0.963，RMSE = 0.071 V。SHapley加性解释（SHAP）分析确定，二极管正向电压和环境射频功率密度是影响输出电压预测的最重要因素。结果表明，环境多频段射频能量收集作为在占空比条件下运行的低功耗农业传感应用的补充能源，具有潜在适用性。通过将宽带能量收集、统计验证和预测建模相结合，所提出的框架建立了一种系统化方法，用于评估在多变射频环境下运行的自供电农业物联网系统。","PLoS ONE","2026-09-10T00:00:00Z",71,{"impact":17,"substance":250,"depth":217,"authority":18,"freshness":16,"relevant":20,"comment":251},21,"面向无电池农业传感网络的射频能量采集研究，方法完整、统计与机器学习验证扎实，但属实验室原型阶段，产业影响有限。",[253],{"name":246,"url":243},[25,71,26,28,255],"能量采集",[257,258],"无线传感器网络 农业人工智能 农业物联网 智慧农业","无线传感器网络 农业人工智能","无线传感器网络农业人工智能农业物联网智慧农业-2154","10.1371\u002Fjournal.pone.0350236",{"doi":260,"openalex_id":262,"authors":263,"venue":246,"cited_by_count":34,"oa_url":243,"card":283,"direction":48,"ingested_from":49},"W7212168596",[264,266,268,270,272,274,276,278,281],{"name":265,"orcid":8},"Md. Atik Hasan Nishat",{"name":267,"orcid":8},"Prithwiraj Biswas Pallab",{"name":269,"orcid":8},"Nowrin Jannat",{"name":271,"orcid":8},"Saleha Nasrin Mishu",{"name":273,"orcid":8},"Md Fahad Ullah Utsho",{"name":275,"orcid":8},"Md. Bipul Islam",{"name":277,"orcid":8},"Riaz Uddin Mondal",{"name":279,"orcid":280},"Md. Firoz Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2721-0596",{"name":282,"orcid":8},"M. Hasnat Kabir",{"tldr":284,"method":285,"finding":286,"direction":48,"opportunity":287},"设计多频段环境RF能量收集系统，为无电池农业传感器网络供电并验证。","Villard倍压整流器、宽带天线、ANOVA统计与梯度提升\u002FSHAP机器学习建","OA79锗二极管性能最优，输出约44.5μW，梯度提升预测R²达0.963。","可探索多源环境能量混合收集与自适应功率管理，提升农业物联网节点长期自持能力。","2026-09-11T23:30:16.081985Z"]