[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2932":3,"related-2932":94},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":46,"search_phrases":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":93},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-18T00:00:00Z","论文",25,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,10,9,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22823848",[29,30,31,32,33],"智慧农业","农业物联网","联邦学习","无线传感器网络","入侵检测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[41],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":47,"locked":14,"version":48,"method":49,"evidence":50},"classified","2026-10-22.2","rules",{"candidates":51,"agricultural_evidence":53},[52,61,67],{"code":35,"score":18,"supported":53,"title":54,"abstract":56,"tags":60},true,[55],"machine learning",[57,58,55,59],"深度学习","机器学习","deep learning",[31],{"code":42,"score":19,"supported":53,"title":62,"abstract":64,"tags":66},[63],"wireless sensor",[65,63],"无线传感",[30,32],{"code":68,"score":69,"supported":53,"title":70,"abstract":71,"tags":73},"I1",7,[],[29,72],"smart agriculture",[29],[75,76],"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",2,"10.5281\u002Fzenodo.22823847",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":90,"ingested_from":92},"W7213580003",[83],{"name":84,"orcid":9},"Jennifer Kyari-Ayele, Joseph Mom M, Iorkyase Ephraim T",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-19T23:30:11.466192Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,178,253,315,376,432],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":105,"score_detail":106,"sources":110,"tags":112,"primary_topic":115,"secondary_topics":116,"classification":118,"search_phrases":145,"slug":148,"view_count":78,"doi":149,"paper":150,"created_at":177},2279,"Communication-efficient Federated Transfer Learning for real-time intrusion detection in agricultural IoT systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112411","The rapid deployment of Agricultural Internet of Things (Ag-IoT) networks has introduced significant security challenges, mainly due to their limited computational resources and operation in remote environments. These constraints, combined with the critical nature of agricultural data and operations, make Ag-IoT systems particularly vulnerable to cyber threats, thus requiring robust, efficient security mechanisms. In this context, Intrusion Detection Systems (IDS) play a crucial role in monitoring network activities and detecting malicious behavior. However, conventional IDS solutions often suffer from high computational overhead, increased latency, and substantial communication costs. Although Federated Learning (FL) enhances data privacy by enabling distributed model training, its efficiency remains constrained in resource-limited Ag-IoT scenarios. To overcome these limitations, we propose a novel IDS based on Federated Transfer Learning (FTL) for multiclass network intrusion classification, which integrates FL with lightweight pretrained image models. The key idea of our approach is to transform network traffic data into grayscale images, allowing the reuse of efficient computer vision models. This transformation reduces data complexity, which is expected to support low-resource training and inference on edge devices. The FTL framework ensures that only model updates are exchanged, minimizing communication overhead while preserving data privacy. Experimental results, validated over three independent runs (Mean ± SD), demonstrate that our approach achieves 99.96% ± 0.00% accuracy on the Edge-IIoTset and 99.96% ± 0.01% accuracy on the Farm-Flow dataset. These findings highlight the strong theoretical potential of our FTL-based IDS as a promising candidate to enable intrusion detection in real-time, resource-efficient, and privacy-preserving Ag-IoT networks.","农业物联网(Ag-IoT)网络的快速部署带来了显著的安全挑战，这主要源于其有限的计算资源和在偏远环境中的运行条件。这些限制，加之农业数据和操作的关键性，使Ag-IoT系统特别容易受到网络威胁，因此需要稳健、高效的安全机制。在此背景下，入侵检测系统(IDS)在监控网络活动和检测恶意行为方面发挥着至关重要的作用。然而，传统IDS解决方案往往面临高计算开销、延迟增加和大量通信成本的问题。尽管联邦学习(FL)通过支持分布式模型训练增强了数据隐私，但其效率在资源受限的Ag-IoT场景中仍然受到制约。为克服这些局限，我们提出了一种基于联邦迁移学习(FTL)的新型IDS，用于多类网络入侵分类，该方案将FL与轻量级预训练图像模型相结合。我们方法的核心思想是将网络流量数据转换为灰度图像，从而能够复用高效的计算机视觉模型。这种转换降低了数据复杂度，有望支持边缘设备上的低资源训练和推理。FTL框架确保仅交换模型更新，在保持数据隐私的同时最大限度地减少通信开销。实验结果表明，在三次独立运行验证下(均值±标准差)，我们的方法在Edge-IIoTset上达到99.96%±0.00%的准确率，在Farm-Flow数据集上达到99.96%±0.01%的准确率。这些发现凸显了基于FTL的IDS在实现实时、资源高效且隐私保护的Ag-IoT网络入侵检测方面具有强大的理论潜力。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",81,{"impact":18,"substance":107,"depth":18,"authority":108,"freshness":21,"relevant":22,"comment":109},22,14,"提出联邦迁移学习入侵检测方法，将流量转为灰度图复用轻量视觉模型，在Edge-IIoTset与Farm-Flow上达99.96%精度，兼顾实时性、低通信开销与隐私保护，对农业物联网安全有较强参考价值。",[111],{"name":103,"url":100},[29,113,30,31,114],"农业人工智能","网络安全",{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},[117],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":119},{"candidates":120,"agricultural_evidence":53},[121,128,134,141],{"code":42,"score":18,"supported":53,"title":122,"abstract":124,"tags":127},[123],"IoT",[125,126,123],"物联网","internet of things",[30],{"code":35,"score":17,"supported":53,"title":129,"abstract":130,"tags":133},[],[131,132],"计算机视觉","computer vision",[113,31],{"code":135,"score":136,"supported":53,"title":137,"abstract":138,"tags":140},"I6",4,[],[139],"agricultural data",[114],{"code":68,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[29],[146,147],"农业人工智能 农业物联网 智慧农业 网络安全","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业网络安全-2279","10.1016\u002Fj.compag.2026.112411",{"doi":149,"openalex_id":151,"authors":152,"venue":103,"cited_by_count":85,"oa_url":100,"card":172,"direction":90,"ingested_from":92},"W7212387370",[153,156,159,162,165,167,169],{"name":154,"orcid":155},"Amina Khacha","https:\u002F\u002Forcid.org\u002F0009-0009-3299-8622",{"name":157,"orcid":158},"Zibouda Aliouat","https:\u002F\u002Forcid.org\u002F0000-0002-7007-7607",{"name":160,"orcid":161},"Yasmine Harbi","https:\u002F\u002Forcid.org\u002F0000-0001-6731-7895",{"name":163,"orcid":164},"Chirihane Gherbi","https:\u002F\u002Forcid.org\u002F0000-0002-0551-3978",{"name":166,"orcid":9},"Rafika Saadouni",{"name":168,"orcid":9},"Ado Adamou ABBA ARI",{"name":170,"orcid":171},"Hakim Mabed","https:\u002F\u002Forcid.org\u002F0000-0001-8358-4029",{"tldr":173,"method":174,"finding":175,"direction":90,"opportunity":176},"提出联邦迁移学习入侵检测方法，将流量转灰度图复用轻量视觉模型，实现农业物联网实时检测。","联邦迁移学习+轻量预训练图像模型，流量转灰度图，Edge-IIoTset与Far","在Edge-IIoTset和Farm-Flow上均达99.96%准确率，通信开销低且保护隐私。","可探索真实Ag-IoT边缘设备部署与动态异构流量下的联邦迁移学习鲁棒性及通信压缩优化。","2026-09-13T23:30:01.631534Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":186,"score_detail":187,"sources":193,"tags":195,"primary_topic":197,"secondary_topics":198,"classification":200,"search_phrases":220,"slug":223,"view_count":78,"doi":224,"paper":225,"created_at":252},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":188,"substance":189,"depth":190,"authority":191,"freshness":17,"relevant":22,"comment":192},12,21,17,13,"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。",[194],{"name":184,"url":181},[29,113,30,32,196],"能量采集",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[199],{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":201},{"candidates":202,"agricultural_evidence":53},[203,208,214],{"code":35,"score":204,"supported":53,"title":205,"abstract":206,"tags":207},15,[55],[58,55],[113],{"code":42,"score":209,"supported":53,"title":210,"abstract":211,"tags":213},11,[],[125,212,65,123,63],"农业传感",[30,32],{"code":68,"score":69,"supported":53,"title":215,"abstract":216,"tags":219},[],[217,218],"精准农业","precision agriculture",[29],[221,222],"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":224,"openalex_id":226,"authors":227,"venue":184,"cited_by_count":85,"oa_url":181,"card":247,"direction":90,"ingested_from":92},"W7212168596",[228,230,232,234,236,238,240,242,245],{"name":229,"orcid":9},"Md. Atik Hasan Nishat",{"name":231,"orcid":9},"Prithwiraj Biswas Pallab",{"name":233,"orcid":9},"Nowrin Jannat",{"name":235,"orcid":9},"Saleha Nasrin Mishu",{"name":237,"orcid":9},"Md Fahad Ullah Utsho",{"name":239,"orcid":9},"Md. Bipul Islam",{"name":241,"orcid":9},"Riaz Uddin Mondal",{"name":243,"orcid":244},"Md. Firoz Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2721-0596",{"name":246,"orcid":9},"M. Hasnat Kabir",{"tldr":248,"method":249,"finding":250,"direction":90,"opportunity":251},"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",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":260,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":261,"score_detail":262,"sources":264,"tags":266,"primary_topic":269,"secondary_topics":270,"classification":272,"search_phrases":289,"slug":292,"view_count":85,"doi":293,"paper":294,"created_at":314},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":19,"substance":189,"depth":18,"authority":188,"freshness":21,"relevant":22,"comment":263},"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。",[265],{"name":259,"url":256},[29,113,30,267,268],"大语言模型","传感器标准化",{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},[271],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":273},{"candidates":274,"agricultural_evidence":53},[275,279,285],{"code":42,"score":18,"supported":53,"title":276,"abstract":277,"tags":278},[123],[125,126,123],[30],{"code":35,"score":19,"supported":53,"title":280,"abstract":282,"tags":284},[281],"decision support",[283,281],"large language model",[113,267],{"code":68,"score":69,"supported":53,"title":286,"abstract":287,"tags":288},[],[29,72],[29],[290,291],"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":293,"openalex_id":295,"authors":296,"venue":259,"cited_by_count":85,"oa_url":256,"card":309,"direction":90,"ingested_from":92},"W7222920424",[297,299,301,303,305,307],{"name":298,"orcid":9},"Leqi Lai",{"name":300,"orcid":9},"Langtao Duan",{"name":302,"orcid":9},"Yuhao Chen",{"name":304,"orcid":9},"Yuhang Zheng",{"name":306,"orcid":9},"Jinyu Yang",{"name":308,"orcid":9},"Mingyao Wen",{"tldr":310,"method":311,"finding":312,"direction":90,"opportunity":313},"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":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":9,"source_name":321,"source_url":318,"published_at":322,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":334,"search_phrases":357,"slug":360,"view_count":361,"doi":362,"paper":363,"created_at":375},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":188,"substance":18,"depth":19,"authority":191,"freshness":21,"relevant":22,"comment":325},"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。",[327],{"name":321,"url":318},[29,113,30,329,330],"遥感监测","作物估产",{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},[333],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":335},{"candidates":336,"agricultural_evidence":53},[337,343,347,352],{"code":42,"score":18,"supported":53,"title":338,"abstract":339,"tags":342},[123],[125,340,126,123,341],"边缘计算","edge computing",[30],{"code":68,"score":204,"supported":53,"title":344,"abstract":345,"tags":346},[72],[217,218],[29],{"code":35,"score":20,"supported":53,"title":348,"abstract":349,"tags":351},[],[57,58,55,59,350],"artificial intelligence",[113],{"code":353,"score":78,"supported":14,"title":354,"abstract":355,"tags":356},"I3",[],[],[330,329],[358,359],"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",3,"10.1007\u002Fs12083-026-02299-3",{"doi":362,"openalex_id":364,"authors":365,"venue":321,"cited_by_count":85,"oa_url":318,"card":370,"direction":90,"ingested_from":92},"W7221560555",[366,368],{"name":367,"orcid":9},"Keerthana T",{"name":369,"orcid":9},"Janarthanan R",{"tldr":371,"method":372,"finding":373,"direction":90,"opportunity":374},"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":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":387,"tags":391,"primary_topic":393,"secondary_topics":394,"classification":396,"search_phrases":411,"slug":414,"view_count":22,"doi":9,"paper":415,"created_at":431},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","2026-10-08T00:00:00Z",47,{"impact":17,"substance":108,"depth":191,"authority":95,"freshness":95,"relevant":22,"comment":386},"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。",[388,389],{"name":382,"url":379},{"name":382,"url":390},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[29,113,30,217,392],"低功耗通信",{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},[395],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":397},{"candidates":398,"agricultural_evidence":53},[399,403,407],{"code":42,"score":18,"supported":53,"title":400,"abstract":401,"tags":402},[123],[125,340,126,123,341],[30],{"code":35,"score":20,"supported":53,"title":404,"abstract":405,"tags":406},[],[58,55,350],[113],{"code":68,"score":17,"supported":53,"title":408,"abstract":409,"tags":410},[],[217,218],[29,217],[412,413],"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":416,"authors":417,"venue":382,"cited_by_count":85,"oa_url":379,"card":426,"direction":90,"ingested_from":92},"W7221414266",[418,420,422,424],{"name":419,"orcid":9},"Shital Chavan",{"name":421,"orcid":9},"Anam Bedrekar",{"name":423,"orcid":9},"Sanika Babar",{"name":425,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":427,"method":428,"finding":429,"direction":90,"opportunity":430},"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",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":382,"source_url":435,"published_at":383,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":15,"score_detail":438,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":451,"classification":454,"search_phrases":480,"slug":483,"view_count":85,"doi":9,"paper":484,"created_at":500},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。",{"impact":188,"substance":19,"depth":204,"authority":188,"freshness":95,"relevant":22,"comment":439},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[441],{"name":382,"url":435},[29,113,443,30,444],"精准施肥","智能农机",{"code":446,"slug":447,"name":448,"path":449,"parent_code":450},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[452,453],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":42,"slug":43,"name":44,"path":45,"parent_code":39},{"status":47,"locked":14,"version":48,"method":49,"evidence":455},{"candidates":456,"agricultural_evidence":53},[457,463,467,471,475],{"code":446,"score":204,"supported":53,"title":458,"abstract":460,"tags":462},[459],"fertilizer",[461,459],"肥料",[443],{"code":35,"score":20,"supported":53,"title":464,"abstract":465,"tags":466},[],[58,55,350],[113],{"code":42,"score":20,"supported":53,"title":468,"abstract":469,"tags":470},[],[125,126,123],[30],{"code":68,"score":69,"supported":53,"title":472,"abstract":473,"tags":474},[],[217,218],[29],{"code":476,"score":22,"supported":14,"title":477,"abstract":478,"tags":479},"F5",[],[],[444],[481,482],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":9,"openalex_id":485,"authors":486,"venue":382,"cited_by_count":85,"oa_url":435,"card":495,"direction":90,"ingested_from":92},"W7221394563",[487,489,491,493],{"name":488,"orcid":9},"Anjali Manoj Kumbhar",{"name":490,"orcid":9},"Shruti Prashant Kulkarni",{"name":492,"orcid":9},"Snehal Santosh Khune",{"name":494,"orcid":9},"B. B. Godbole",{"tldr":496,"method":497,"finding":498,"direction":90,"opportunity":499},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z"]