[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1296":3,"related-1296":96},{"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":18,"tags":20,"primary_topic":22,"secondary_topics":28,"classification":29,"search_phrases":50,"slug":51,"view_count":52,"doi":53,"paper":54,"created_at":95},1296,"A provably secure authentication framework for quantum-safe message transmission in wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-68879-8","Abstract Wireless Sensor Networks (WSNs) have been heavily deployed to facilitate continuous monitoring of their environments as they collect the necessary data which enable autonomous decision making or predictive control. As such, these networks have found applications in fields such as smart agriculture, military, industrial automation, and healthcare. During the message transfers among sensors or between sensors and mobile sinks, public communication channels are deployed. This exposes the collected data to a myriad of security threats such as tampering, eavesdropping, and replay. Although the recent past has witnessed the development of numerous security frameworks for this ecosystem, most of the current solutions are defenseless against threats such as session hijacking, side-channeling, ephemeral secret leakages and quantum attacks. In this paper, we leverage on lattice cryptography to develop an effective authentication framework for the WSNs. Formal security analysis confirms the robustness of our protocol under the Random Oracle Model (ROM). In addition, extensive semantic security analyses demonstrate the resilience of the proposed framework against typical attacks such as denial of service, key compromise and forgery. In terms of performance, we show that the developed framework incurs slightly lower computation and energy overheads when compared with its peers.","无线传感器网络（WSNs）已被广泛部署，以促进对其环境的持续监测，因为它们收集必要的数据，从而实现自主决策或预测控制。因此，这些网络在智能农业、军事、工业自动化和医疗保健等领域得到了应用。在传感器之间或传感器与移动汇聚节点之间的消息传输过程中，采用了公共通信信道。这使得所收集的数据面临多种安全威胁，如篡改、窃听和重放攻击。尽管近年来针对这一生态系统已开发出众多安全框架，但当前大多数解决方案在面对会话劫持、侧信道攻击、临时秘密泄露和量子攻击等威胁时仍显得无能为力。本文利用格密码学为无线传感器网络开发了一种有效的认证框架。形式化安全分析证实了我们的协议在随机预言机模型（ROM）下的稳健性。此外，广泛的语义安全分析表明，所提出的框架能够抵御典型的攻击，如拒绝服务、密钥泄露和伪造攻击。在性能方面，我们展示了所开发的框架与同类框架相比，计算和能量开销略低。",null,"Scientific Reports","2026-08-31T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦无线传感器网络安全，未涉及农业信息化或智慧农业相关主题，不具相关性。",[19],{"name":10,"url":6},[21],"无线传感器网络",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"classified","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":37},[35,43],{"code":23,"score":36,"supported":37,"title":38,"abstract":40,"tags":42},15,true,[39],"wireless sensor",[41,39],"无线传感",[21],{"code":44,"score":45,"supported":37,"title":46,"abstract":47,"tags":49},"I1",3,[],[48],"smart agriculture",[],[],"Aprovablysecureauthenticationframeworkfo-1296",1,"10.1038\u002Fs41598-026-68879-8",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":15,"oa_url":6,"card":87,"direction":93,"ingested_from":94},"W7204792860",[57,60,63,64,67,70,73,76,79,82,85],{"name":58,"orcid":59},"Vincent Omollo Nyangaresi","https:\u002F\u002Forcid.org\u002F0000-0001-6546-8083",{"name":61,"orcid":62},"Zaid Alaa Hussien","https:\u002F\u002Forcid.org\u002F0000-0002-4852-9785",{"name":58,"orcid":59},{"name":65,"orcid":66},"Ali Hasan Ali","https:\u002F\u002Forcid.org\u002F0000-0003-2959-4212",{"name":68,"orcid":69},"Mustafa A. Al Sibahee","https:\u002F\u002Forcid.org\u002F0000-0002-3943-8101",{"name":71,"orcid":72},"Mohammed Abdulridha Hussain","https:\u002F\u002Forcid.org\u002F0000-0001-5846-6143",{"name":74,"orcid":75},"Iman Qays Abduljaleel","https:\u002F\u002Forcid.org\u002F0000-0002-8068-5710",{"name":77,"orcid":78},"Junchao Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2666-6874",{"name":80,"orcid":81},"Abdulla J. Y. Aldarwish","https:\u002F\u002Forcid.org\u002F0000-0002-1039-6462",{"name":83,"orcid":84},"Dhafer G. Honi","https:\u002F\u002Forcid.org\u002F0000-0002-2052-013X",{"name":86,"orcid":9},"Maryam Mahdi Alhusseini",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"提出基于格密码的WSN认证框架，抵抗量子攻击，保障消息传输安全。","格密码、随机预言机模型、形式化安全分析、性能评估。","框架在ROM下安全，能抵抗典型攻击，计算和能耗略低于同类方案。","其他","可探索将格密码认证框架应用于农业物联网，并优化资源受限节点的能耗与实时性。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T23:30:08.948123Z",{"total":97,"page":52,"page_size":97,"items":98},6,[99,201,254,300,364,431],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":115,"tags":117,"primary_topic":122,"secondary_topics":123,"classification":135,"search_phrases":183,"slug":186,"view_count":52,"doi":187,"paper":188,"created_at":200},4073,"Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.35784\u002Fiapgos.9467","Precision agriculture demands continuous, energy-autonomous soil monitoring capable of detecting critical conditions such as rapid moisture loss or nutrient depletion approaching crop-threatening thresholds. Conventional wireless sensor networks face a fundamental trade-off between multi-year battery life and sub-second response latency, as duty-cycled polling wastes energy on idle queries while always-on communication remains infeasible for battery-powered field deployments spanning hundreds of acres. This work presents a novel integration of TinyML-based edge inference with a custom-designed 40 nm CMOS ultra-low power wake-up receiver (WuRX), enabling event-driven, asynchronous soil condition monitoring without reliance on periodic polling schedules. The proposed three-tier edge-fog-cloud architecture employs sensor nodes measuring moisture, temperature, pH, nitrogen, phosphorus, and potassium, with quantized neural networks trained and validated on 64,800 labelled samples collected from instrumented corn and soybean field deployments over 180 days, achieving a classification accuracy of 91.3% and F1-score of 93.0% at an inference energy cost of 0.87 mJ per cycle. Circuit-level simulations of the 40 nm CMOS WuRX confirm an input return loss of -15.8 dB, transmission gain of +18.2 dB, and power-added efficiency of 45.3% at -60 dBm sensitivity, with continuous standby consumption of 45–53 µW. System-level power budget analysis validates battery lifetimes of 200–300 days using 240 mAh lithium cells augmented by a 5 cm² monocrystalline solar harvester. Key limitations include the restriction of field validation to two crop types and dependence on adequate solar irradiance for energy-neutral operation. The proposed architecture reduces communication overhead by 80–90% relative to threshold-based systems, providing a practical framework for scalable, resource-constrained precision agriculture deployments.","精准农业需要能够持续、能源自主地监测土壤，以检测诸如水分快速流失或养分耗竭接近威胁作物阈值等关键状况。传统无线传感器网络面临多年电池寿命与亚秒级响应延迟之间的根本性权衡，因为占空比轮询会在空闲查询上浪费能量，而始终在线的通信对于覆盖数百英亩的电池供电田间部署而言仍不可行。本研究提出了一种新颖的集成方案，将基于TinyML的边缘推理与定制设计的40 nm CMOS超低功耗唤醒接收器（WuRX）相结合，实现事件驱动的异步土壤状况监测，而无需依赖周期性轮询调度。所提出的三层边缘-雾-云架构采用测量水分、温度、pH、氮、磷和钾的传感器节点，其量化神经网络在180天内从仪器化玉米和大豆田间部署中采集的64,800个标记样本上进行训练和验证，在每次推理周期能耗为0.87 mJ的情况下，实现了91.3%的分类准确率和93.0%的F1分数。对40 nm CMOS WuRX的电路级仿真证实，在-60 dBm灵敏度下，输入回波损耗为-15.8 dB，传输增益为+18.2 dB，功率附加效率为45.3%，连续待机功耗为45–53 µW。系统级功率预算分析验证了使用240 mAh锂电池并辅以5 cm²单晶硅太阳能收集器时，电池寿命可达200–300天。主要局限性包括田间验证仅限于两种作物类型，以及能量中性运行依赖充足的太阳辐照度。与基于阈值的系统相比，所提出的架构将通信开销降低了80–90%，为可扩展、资源受限的精准农业部署提供了实用框架。","Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","2026-09-30T00:00:00Z",80,{"impact":109,"substance":110,"depth":111,"authority":112,"freshness":113,"relevant":52,"comment":114},18,22,19,13,8,"将TinyML边缘推理与40nm CMOS超低功耗唤醒接收机结合，实现事件驱动型土壤监测，实测数据扎实、能耗指标明确，对精准农业物联网部署有参考价值。",[116],{"name":105,"url":102},[118,119,120,21,121],"智慧农业","农业人工智能","精准农业","土壤监测",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[124,130],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":131,"slug":132,"name":133,"path":134,"parent_code":129},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":30,"locked":14,"version":31,"method":32,"evidence":136},{"candidates":137,"agricultural_evidence":37},[138,142,148,153,158,165,172,178],{"code":23,"score":36,"supported":37,"title":139,"abstract":140,"tags":141},[39],[41,39],[21],{"code":125,"score":143,"supported":37,"title":144,"abstract":145,"tags":147},14,[126],[146,126],"土壤",[],{"code":131,"score":113,"supported":37,"title":149,"abstract":151,"tags":152},[150],"sustainable agriculture",[],[],{"code":44,"score":113,"supported":37,"title":154,"abstract":155,"tags":157},[],[120,156],"precision agriculture",[118,120],{"code":159,"score":97,"supported":37,"title":160,"abstract":161,"tags":164},"A3",[],[162,163],"玉米","corn",[],{"code":166,"score":97,"supported":37,"title":167,"abstract":168,"tags":171},"A4",[],[169,170],"大豆","soybean",[],{"code":173,"score":45,"supported":37,"title":174,"abstract":175,"tags":177},"F5",[],[176],"harvester",[],{"code":179,"score":52,"supported":14,"title":180,"abstract":181,"tags":182},"I2",[],[],[119],[184,185],"TinyML 土壤监测","超低功耗 唤醒接收机 精准农业","TinyML土壤监测-4073","10.35784\u002Fiapgos.9467",{"doi":187,"openalex_id":189,"authors":190,"venue":105,"cited_by_count":15,"oa_url":194,"card":195,"direction":93,"ingested_from":94},"W7214927134",[191],{"name":192,"orcid":193},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":196,"method":197,"finding":198,"direction":93,"opportunity":199},"提出TinyML边缘推理与超低功耗唤醒接收机集成的土壤监测系统，实现事件驱动异步监测。","TinyML量化神经网络、40nm CMOS唤醒接收机、三层边缘-雾-云架构，6","分类准确率91.3%，通信开销降低80-90%，电池寿命200-300天。","可探索多作物多土壤类型泛化、无光照或弱光下能量收集与事件触发协同优化。","2026-10-03T23:30:17.793958Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":215,"tags":217,"primary_topic":220,"secondary_topics":221,"classification":222,"search_phrases":239,"slug":242,"view_count":52,"doi":9,"paper":243,"created_at":253},4017,"Energy-Aware Reinforcement-Driven Clustering Framework with Adaptive Processing for Sustainable IoT Sensor Networks","https:\u002F\u002Fcigrjournal.org\u002Findex.php\u002FEjounral\u002Farticle\u002Fview\u002F10455","Energy saving and sustainable behavior of the system is a vital problem, especially for a resource-limited agricultural monitoring environment in WSNs. In this paper, we introduce a Reinforcement-driven Energy-aware Clustering scheme with Adaptive Processing (RECAP) for extending lifetime, conserving energy, and maintaining data fidelity in field situations. RECAP adopts a hierarchical architecture comprising an Adaptive Duty-Cycling Engine (ADCE), an Energy-Aware Clustering Module (EACM), a Reinforcement Learning Controller (RLC), and an Edge Processing and Communication Layer (EPCL). Every Cluster Head tunes their work dynamically by a lightweight Q-learning model that adapts cluster formation, transmission scheduling, and node sleeping cycles according to local energy and topology states. The architecture uses a hybrid energy draining model through context-aware duty cycling and data aggregation to avoid redundant transmissions. Simulation results show that RECAP reduces 68% energy consumption, achieves event-triggered data collection with 98% accuracy and low computation overhead, and overcomes the latency problem than existing hybrid models like LEACH-CNN, PSO-LSTM, and base GA-GRU. These results validate RECAP for sustainable IoT-based precision agriculture and other mission-critical WSN deployments.","系统的节能与可持续行为是一个至关重要的问题，尤其是在无线传感器网络（WSNs）中资源有限的农业监测环境下。本文提出了一种面向强化驱动的能量感知自适应处理分簇方案（RECAP），用于在田间场景中延长网络寿命、节约能量并保持数据保真度。RECAP采用分层架构，包括自适应占空比引擎（ADCE）、能量感知分簇模块（EACM）、强化学习控制器（RLC）以及边缘处理与通信层（EPCL）。每个簇头通过轻量级Q学习模型动态调整其工作方式，根据局部能量和拓扑状态自适应地调整簇形成、传输调度和节点睡眠周期。该架构通过上下文感知的占空比调节和数据聚合采用混合能量消耗模型，以避免冗余传输。仿真结果表明，与LEACH-CNN、PSO-LSTM和基础GA-GRU等现有混合模型相比，RECAP降低了68%的能量消耗，以98%的准确率实现事件触发式数据采集且计算开销低，并克服了延迟问题。这些结果验证了RECAP在可持续的基于物联网的精准农业及其他任务关键型WSN部署中的适用性。","Agricultural Engineering International: CIGR Journal","2026-09-29T00:00:00Z",71,{"impact":211,"substance":212,"depth":213,"authority":112,"freshness":113,"relevant":52,"comment":214},12,21,17,"提出强化学习驱动的自适应分簇节能框架，仿真显示能耗降低68%、数据精度98%，对农业物联网可持续部署有参考价值，但尚属仿真阶段、非产业级突破。",[216],{"name":207,"url":204},[118,218,120,219,21],"农业物联网","强化学习",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":223},{"candidates":224,"agricultural_evidence":37},[225,231,235],{"code":23,"score":111,"supported":37,"title":226,"abstract":228,"tags":230},[227],"IoT",[229,41,227],"物联网",[218,21],{"code":44,"score":113,"supported":37,"title":232,"abstract":233,"tags":234},[],[120,156],[118,120],{"code":179,"score":52,"supported":14,"title":236,"abstract":237,"tags":238},[],[],[219],[240,241],"RECAP 无线传感器网络 节能聚类","CIGR Journal 精准农业 物联网","RECAP无线传感器网络节能聚类-4017",{"doi":9,"openalex_id":244,"authors":245,"venue":207,"cited_by_count":15,"oa_url":9,"card":248,"direction":93,"ingested_from":94},"W7215257154",[246],{"name":247,"orcid":9},"Shwetha G N",{"tldr":249,"method":250,"finding":251,"direction":93,"opportunity":252},"提出RECAP框架，用强化学习动态聚类与自适应处理延长农业物联网传感器网络寿命。","轻量Q学习控制器、自适应占空比引擎、能量感知聚类与边缘处理，仿真对比LEACH-","能耗降低68%，事件触发数据收集准确率达98%，延迟优于现有混合模型。","可探索将RECAP与真实农田异构节点、多跳路由及能量收集结合，验证长期可持续性。","2026-10-02T23:30:16.406584Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":9,"content":9,"source_name":259,"source_url":257,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":264,"tags":266,"primary_topic":268,"secondary_topics":269,"classification":270,"search_phrases":281,"slug":284,"view_count":285,"doi":286,"paper":287,"created_at":299},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":113,"substance":211,"depth":112,"authority":97,"freshness":113,"relevant":52,"comment":263},"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。",[265],{"name":259,"url":257},[118,218,120,21,267],"芒果种植",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":271},{"candidates":272,"agricultural_evidence":37},[273,277],{"code":44,"score":112,"supported":37,"title":274,"abstract":275,"tags":276},[156],[156],[118,120],{"code":23,"score":112,"supported":37,"title":278,"abstract":279,"tags":280},[39],[39],[218,21],[282,283],"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",4,"10.21203\u002Frs.3.rs-9696926\u002Fv1",{"doi":286,"openalex_id":288,"authors":289,"venue":259,"cited_by_count":15,"oa_url":257,"card":9,"direction":93,"ingested_from":94},"W7214233683",[290,293,296],{"name":291,"orcid":292},"Wen‐Tsai Sung","https:\u002F\u002Forcid.org\u002F0000-0001-9045-9090",{"name":294,"orcid":295},"Indra Griha Tofik Isa","https:\u002F\u002Forcid.org\u002F0000-0002-7437-6751",{"name":297,"orcid":298},"Sung‐Jung Hsiao","https:\u002F\u002Forcid.org\u002F0000-0002-0723-1632","2026-09-25T23:30:15.526882Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":308,"is_selected":14,"score":309,"score_detail":310,"sources":314,"tags":318,"primary_topic":321,"secondary_topics":325,"classification":327,"search_phrases":347,"slug":350,"view_count":351,"doi":352,"paper":353,"created_at":363},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":113,"substance":109,"depth":311,"authority":13,"freshness":312,"relevant":52,"comment":313},16,9,"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。",[315,316],{"name":306,"url":303},{"name":306,"url":317},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22823848",[118,218,319,21,320],"联邦学习","入侵检测",{"code":179,"slug":322,"name":323,"path":324,"parent_code":27},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[326],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":328},{"candidates":329,"agricultural_evidence":37},[330,338,342],{"code":179,"score":109,"supported":37,"title":331,"abstract":333,"tags":337},[332],"machine learning",[334,335,332,336],"深度学习","机器学习","deep learning",[319],{"code":23,"score":311,"supported":37,"title":339,"abstract":340,"tags":341},[39],[41,39],[218,21],{"code":44,"score":343,"supported":37,"title":344,"abstract":345,"tags":346},7,[],[118,48],[118],[348,349],"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":352,"openalex_id":354,"authors":355,"venue":306,"cited_by_count":15,"oa_url":303,"card":358,"direction":93,"ingested_from":94},"W7213580003",[356],{"name":357,"orcid":9},"Jennifer Kyari-Ayele, Joseph Mom M, Iorkyase Ephraim T",{"tldr":359,"method":360,"finding":361,"direction":93,"opportunity":362},"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":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":372,"sources":374,"tags":376,"primary_topic":378,"secondary_topics":379,"classification":381,"search_phrases":398,"slug":401,"view_count":351,"doi":402,"paper":403,"created_at":430},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",{"impact":211,"substance":212,"depth":213,"authority":112,"freshness":113,"relevant":52,"comment":373},"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。",[375],{"name":370,"url":367},[118,119,218,21,377],"能量采集",{"code":179,"slug":322,"name":323,"path":324,"parent_code":27},[380],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":382},{"candidates":383,"agricultural_evidence":37},[384,388,394],{"code":179,"score":36,"supported":37,"title":385,"abstract":386,"tags":387},[332],[335,332],[119],{"code":23,"score":389,"supported":37,"title":390,"abstract":391,"tags":393},11,[],[229,392,41,227,39],"农业传感",[218,21],{"code":44,"score":343,"supported":37,"title":395,"abstract":396,"tags":397},[],[120,156],[118],[399,400],"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":402,"openalex_id":404,"authors":405,"venue":370,"cited_by_count":15,"oa_url":367,"card":425,"direction":93,"ingested_from":94},"W7212168596",[406,408,410,412,414,416,418,420,423],{"name":407,"orcid":9},"Md. Atik Hasan Nishat",{"name":409,"orcid":9},"Prithwiraj Biswas Pallab",{"name":411,"orcid":9},"Nowrin Jannat",{"name":413,"orcid":9},"Saleha Nasrin Mishu",{"name":415,"orcid":9},"Md Fahad Ullah Utsho",{"name":417,"orcid":9},"Md. Bipul Islam",{"name":419,"orcid":9},"Riaz Uddin Mondal",{"name":421,"orcid":422},"Md. Firoz Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2721-0596",{"name":424,"orcid":9},"M. Hasnat Kabir",{"tldr":426,"method":427,"finding":428,"direction":93,"opportunity":429},"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":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":9,"source_name":437,"source_url":434,"published_at":438,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":442,"tags":444,"primary_topic":448,"secondary_topics":453,"classification":456,"search_phrases":511,"slug":514,"view_count":15,"doi":515,"paper":516,"created_at":540},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":109,"substance":110,"depth":109,"authority":211,"freshness":312,"relevant":52,"comment":441},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[443],{"name":437,"url":434},[118,119,445,446,447],"遥感","作物水分胁迫","精准灌溉",{"code":449,"slug":450,"name":451,"path":452,"parent_code":129},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[454,455],{"code":179,"slug":322,"name":323,"path":324,"parent_code":27},{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":457},{"candidates":458,"agricultural_evidence":37},[459,464,469,474,480,486,493,498,505],{"code":449,"score":36,"supported":37,"title":460,"abstract":461,"tags":463},[450],[462,450],"灌溉",[447],{"code":179,"score":13,"supported":37,"title":465,"abstract":466,"tags":468},[],[335,332,467],"artificial intelligence",[119],{"code":23,"score":312,"supported":37,"title":470,"abstract":471,"tags":473},[],[229,472,227],"internet of things",[],{"code":44,"score":343,"supported":37,"title":475,"abstract":476,"tags":479},[],[477,478],"数字孪生","digital twin",[118],{"code":481,"score":343,"supported":37,"title":482,"abstract":483,"tags":485},"I3",[],[445,484],"remote sensing",[445],{"code":487,"score":97,"supported":37,"title":488,"abstract":489,"tags":492},"A2",[],[490,491],"小麦","wheat",[],{"code":159,"score":97,"supported":37,"title":494,"abstract":495,"tags":497},[],[162,496],"maize",[],{"code":499,"score":97,"supported":37,"title":500,"abstract":501,"tags":504},"A5",[],[502,503],"马铃薯","potato",[],{"code":506,"score":45,"supported":37,"title":507,"abstract":508,"tags":510},"B2",[],[509],"葡萄",[],[512,513],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":515,"openalex_id":517,"authors":518,"venue":437,"cited_by_count":15,"oa_url":434,"card":534,"direction":93,"ingested_from":94},"W7223088921",[519,522,525,528,531],{"name":520,"orcid":521},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":523,"orcid":524},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":526,"orcid":527},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":529,"orcid":530},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":532,"orcid":533},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":535,"method":536,"finding":537,"direction":538,"opportunity":539},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z"]