[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2932":3,"related-2932":53},{"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,"search_phrases":34,"slug":37,"view_count":38,"doi":39,"paper":40,"created_at":52},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",[35,36],"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",0,"10.5281\u002Fzenodo.22823847",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":38,"oa_url":6,"card":45,"direction":49,"ingested_from":51},"W7213580003",[43],{"name":44,"orcid":9},"Jennifer Kyari-Ayele, Joseph Mom M, Iorkyase Ephraim T",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"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":54,"page":22,"page_size":54,"items":55},6,[56,97,148,198,231,269],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":63,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":64,"score_detail":65,"sources":69,"tags":71,"search_phrases":74,"slug":77,"view_count":38,"doi":78,"paper":79,"created_at":96},2764,"An energy-aware federated intelligence framework for sustainable WSN-IoT ecosystems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02291-x","With the rapid development of Internet of Things (IoT), the deployment of Wireless Sensor Networks (WSNs) as vital infrastructure for environmental monitoring, industrial automation, precision agriculture, and smart city applications has increased significantly. Despite this, WSN-IoT ecosystems are still hindered by the issues of limited energy, communication overhead, scalability, preserving privacy, and learning in an intelligent decentralized fashion. To overcome these challenges, this study suggests a federated energy-aware intelligence framework namely EcoSense-AI for sustainable WSN-IoT ecosystems. It combines Energy-Weighted Federated Learning, Adaptive Differential Privacy, Energy-Aware Graph Neural Network (EA-GNN)-based topology modeling, and multi-objective resource optimization, to facilitate secure, efficient, and adaptive edge-cloud collaborative intelligence. The effectiveness of EcoSense-AI was tested with a simulated WSN-IoT network of heterogeneous sensor nodes operating with different residual energy levels, communication ranges and moving network conditions. Distributed environmental sensing data in the device, edge and cloud layer were subjected to experimental analysis. The proposed framework was compared with FedAvg and FedProx for equal training rounds, communication setup and in energy constrained deployment settings. The accuracy, energy efficiency, privacy score, communication cost reduction and network lifetime extension were used as evaluation metrics to measure performance. The experimental results show that the proposed method, EcoSense-AI outperforms the baseline methods with 98.56% accuracy, 93.2% energy efficiency, and 0.97 privacy score, and increased network lifetime by 58.9% and decreased communication cost. The proposed framework for sustainable intelligent sensing and decentralized learning in next-generation WSN-IoT applications is proved to be effective through the results.","随着物联网（IoT）的快速发展，无线传感器网络（WSN）作为环境监测、工业自动化、精准农业和智慧城市应用的重要基础设施，其部署规模显著增长。尽管如此，WSN-IoT生态系统仍然受到能量受限、通信开销、可扩展性、隐私保护以及智能去中心化学习等问题的制约。为克服这些挑战，本研究提出了一种面向可持续WSN-IoT生态系统的联邦能量感知智能框架，即EcoSense-AI。该框架融合了能量加权联邦学习、自适应差分隐私、基于能量感知图神经网络（EA-GNN）的拓扑建模以及多目标资源优化，以实现安全、高效且自适应的边云协同智能。EcoSense-AI的有效性通过一个模拟WSN-IoT网络进行了验证，该网络由具有不同剩余能量水平、通信范围和动态网络条件的异构传感器节点组成。对设备层、边缘层和云层中的分布式环境感知数据进行了实验分析。在相同训练轮次、通信设置和能量受限部署条件下，将所提框架与FedAvg和FedProx进行了对比。采用准确率、能量效率、隐私评分、通信成本降低和网络寿命延长作为评价指标来衡量性能。实验结果表明，所提方法EcoSense-AI优于基线方法，准确率达到98.56%，能量效率为93.2%，隐私评分为0.97，网络寿命延长了58.9%，并降低了通信成本。实验结果证明了该框架在下一代WSN-IoT应用中实现可持续智能感知和去中心化学习的有效性。","Peer-to-Peer Networking and Applications","2026-09-17T00:00:00Z",78,{"impact":19,"substance":66,"depth":18,"authority":67,"freshness":20,"relevant":22,"comment":68},21,13,"提出面向WSN-IoT的联邦能效智能框架EcoSense-AI，在精度、能效、隐私与网络寿命上均有量化提升，对农业环境监测与精准农业的可持续感知具有参考价值。",[70],{"name":62,"url":59},[29,30,72,31,73],"边缘计算","数据隐私",[75,76],"农业物联网 数据隐私 智慧农业 联邦学习","农业物联网 数据隐私","农业物联网数据隐私智慧农业联邦学习-2764","10.1007\u002Fs12083-026-02291-x",{"doi":78,"openalex_id":80,"authors":81,"venue":62,"cited_by_count":38,"oa_url":59,"card":91,"direction":49,"ingested_from":51},"W7169722039",[82,84,87,89],{"name":83,"orcid":9},"G Elumalai",{"name":85,"orcid":86},"Dhanalakshmi Gopal","https:\u002F\u002Forcid.org\u002F0009-0000-4123-176X",{"name":88,"orcid":9},"Jayaprakash Chinnadurai",{"name":90,"orcid":9},"V.V. Teresa",{"tldr":92,"method":93,"finding":94,"direction":49,"opportunity":95},"提出EcoSense-AI联邦智能框架，解决WSN-IoT能耗、隐私与通信瓶颈。","能量加权联邦学习、自适应差分隐私、能量感知GNN与多目标优化仿真。","准确率98.56%、能效93.2%、隐私0.97，网络寿命延长58.9%。","可将该框架迁移至农田异构传感网，验证真实农业场景下的能效与隐私权衡。","2026-09-17T23:30:10.020578Z",{"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,"search_phrases":115,"slug":118,"view_count":38,"doi":119,"paper":120,"created_at":147},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",[116,117],"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":119,"openalex_id":121,"authors":122,"venue":103,"cited_by_count":38,"oa_url":100,"card":142,"direction":49,"ingested_from":51},"W7212387370",[123,126,129,132,135,137,139],{"name":124,"orcid":125},"Amina Khacha","https:\u002F\u002Forcid.org\u002F0009-0009-3299-8622",{"name":127,"orcid":128},"Zibouda Aliouat","https:\u002F\u002Forcid.org\u002F0000-0002-7007-7607",{"name":130,"orcid":131},"Yasmine Harbi","https:\u002F\u002Forcid.org\u002F0000-0001-6731-7895",{"name":133,"orcid":134},"Chirihane Gherbi","https:\u002F\u002Forcid.org\u002F0000-0002-0551-3978",{"name":136,"orcid":9},"Rafika Saadouni",{"name":138,"orcid":9},"Ado Adamou ABBA ARI",{"name":140,"orcid":141},"Hakim Mabed","https:\u002F\u002Forcid.org\u002F0000-0001-8358-4029",{"tldr":143,"method":144,"finding":145,"direction":49,"opportunity":146},"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":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":154,"source_url":151,"published_at":155,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":156,"score_detail":157,"sources":161,"tags":163,"search_phrases":165,"slug":168,"view_count":38,"doi":169,"paper":170,"created_at":197},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":158,"substance":66,"depth":159,"authority":67,"freshness":17,"relevant":22,"comment":160},12,17,"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。",[162],{"name":154,"url":151},[29,113,30,32,164],"能量采集",[166,167],"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":169,"openalex_id":171,"authors":172,"venue":154,"cited_by_count":38,"oa_url":151,"card":192,"direction":49,"ingested_from":51},"W7212168596",[173,175,177,179,181,183,185,187,190],{"name":174,"orcid":9},"Md. Atik Hasan Nishat",{"name":176,"orcid":9},"Prithwiraj Biswas Pallab",{"name":178,"orcid":9},"Nowrin Jannat",{"name":180,"orcid":9},"Saleha Nasrin Mishu",{"name":182,"orcid":9},"Md Fahad Ullah Utsho",{"name":184,"orcid":9},"Md. Bipul Islam",{"name":186,"orcid":9},"Riaz Uddin Mondal",{"name":188,"orcid":189},"Md. Firoz Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2721-0596",{"name":191,"orcid":9},"M. Hasnat Kabir",{"tldr":193,"method":194,"finding":195,"direction":49,"opportunity":196},"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":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":10,"source_url":201,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":204,"score_detail":205,"sources":208,"tags":212,"search_phrases":215,"slug":218,"view_count":38,"doi":219,"paper":220,"created_at":230},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。",68,{"impact":18,"substance":108,"depth":206,"authority":67,"freshness":17,"relevant":22,"comment":207},15,"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。",[209,210],{"name":10,"url":201},{"name":10,"url":211},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[29,113,30,213,214],"精准农业","基因编辑",[216,217],"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":219,"openalex_id":221,"authors":222,"venue":10,"cited_by_count":38,"oa_url":201,"card":225,"direction":49,"ingested_from":51},"W7213515489",[223],{"name":224,"orcid":9},"Zorawar Singh",{"tldr":226,"method":227,"finding":228,"direction":49,"opportunity":229},"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":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":63,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":238,"score_detail":239,"sources":241,"tags":243,"search_phrases":247,"slug":250,"view_count":38,"doi":251,"paper":252,"created_at":268},2779,"INFORMATION AND COMMUNICATION TECHNOLOGIES IN THE MANAGEMENT OF RISKS IN FAMILY AGRICULTURE: A SYSTEMATIC REVIEW OF THE LITERATURE","https:\u002F\u002Fdoi.org\u002F10.23900\u002Fartefactum.v25i9.4236","Family farming is relevant to food production, income generation and sustainable development, but it remains exposed to climatic, production, economic and operational risks. In this context, Information and Communication Technologies (ICTs) provide resources for monitoring, automation and decision support. This study analyzes the contributions of ICTs to risk management in family farming through a Systematic Literature Review conducted according to PRISMA 2020. Searches were carried out in Google Scholar, SciELO, the CAPES Periodicals Portal and the Brazilian Digital Library of Theses and Dissertations, considering publications from 2018 to 2026. After applying the inclusion and exclusion criteria, 14 studies composed the review corpus. The results indicate that the Internet of Things, sensors, automated systems and digital platforms contribute to monitoring, risk prevention, productive efficiency and sustainability. Limitations related to infrastructure, rural connectivity, implementation costs and farmer training persist. It is concluded that ICTs are strategic instruments for risk management in family farming, although integrated models combining innovation, cooperation, monitoring and decision support are still needed.","家庭农业与粮食生产、收入创造和可持续发展密切相关，但仍面临气候、生产、经济和经营风险。在此背景下，信息与通信技术（ICTs）为监测、自动化和决策支持提供了资源。本研究通过依据PRISMA 2020开展的系统性文献综述，分析ICTs对家庭农业风险管理的贡献。检索在Google Scholar、SciELO、CAPES期刊门户和巴西数字论文与学位论文图书馆中进行，考虑2018年至2026年的出版物。在应用纳入和排除标准后，14项研究构成了综述语料库。结果表明，物联网、传感器、自动化系统和数字平台有助于监测、风险预防、生产效率提升和可持续发展。与基础设施、农村连通性、实施成本和农民培训相关的局限仍然存在。结论认为，ICTs是家庭农业风险管理的战略性工具，尽管仍需要结合创新、合作、监测和决策支持的整合模式。","Artefactum",62,{"impact":158,"substance":18,"depth":206,"authority":17,"freshness":21,"relevant":22,"comment":240},"系统综述梳理物联网、传感器与数字平台在家庭农业风险管理中的作用，结论与局限清晰，但样本仅14篇且来源为行业平台，属细分领域参考性成果。",[242],{"name":237,"url":234},[244,29,30,245,246],"数字乡村","家庭农场","农业风险管理",[248,249],"农业风险管理 农业物联网 家庭农场 数字乡村","农业风险管理 农业物联网","农业风险管理农业物联网家庭农场数字乡村-2779","10.23900\u002Fartefactum.v25i9.4236",{"doi":251,"openalex_id":253,"authors":254,"venue":237,"cited_by_count":38,"oa_url":234,"card":262,"direction":49,"ingested_from":51},"W7213473081",[255,258,260],{"name":256,"orcid":257},"Vilson Gruber","https:\u002F\u002Forcid.org\u002F0000-0003-4092-8578",{"name":259,"orcid":9},"Samantha Ferreira da Silva",{"name":261,"orcid":9},"Dairce Londero",{"tldr":263,"method":264,"finding":265,"direction":266,"opportunity":267},"系统综述2018-2026年ICT在家庭农业风险管理中的贡献与局限。","PRISMA 2020系统文献综述，检索4个数据库，纳入14篇研究。","物联网、传感器与数字平台助力监测、风险预防与效率，但基础设施、连通性、成本与培训仍是瓶颈。","数字乡村与农业信息化","可研究面向小农户的低成本、低连通依赖的集成式风险监测与决策支持模型。","2026-09-17T23:30:20.104369Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":276,"published_at":277,"category":12,"cover_url":9,"hotness":20,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"search_phrases":287,"slug":290,"view_count":38,"doi":291,"paper":292,"created_at":312},2778,"DS2-Based Cross-Data-Space Interoperability for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.17185","Despite the strategies of modern precision agriculture to leverage the integration of legacy agricultural systems, the challenges of IoT data fragmentation, farmers' sovereignty preservation, and limited interoperability still persist. This paper presents our work, conducted within the Horizon Europe DS2 project (DataSpace, DataShare 2.0), that applies an interoperability-oriented framework supporting participants of different agricultural data spaces to share data products and services under secure, sovereign, and transparent methods. The suggested methodology follows a layered reference architecture, where each layer consists of independent operational modules that facilitate the inter-sector data exchange between DigiAgro and AgroScience Data Spaces. The result of this work is an automated ecosystem for sharing diverse farm IoT measurements, satellite images and metrics, weather forecasts, and analytics services across distinct data spaces, aiming to generate accurate recommendations on crop practices, such as irrigation schedules, that farmers and agronomists will rely on to increase crop production while maintaining sustainability.","尽管现代精准农业策略致力于整合传统农业系统，但物联网数据碎片化、农民主权保护以及互操作性有限等挑战依然存在。本文介绍了我们在Horizon Europe DS2项目（DataSpace，DataShare 2.0）中开展的工作，该工作应用了一个面向互操作性的框架，支持不同农业数据空间的参与者在安全、主权和透明的方法下共享数据产品与服务。所建议的方法遵循分层参考架构，其中每一层由独立的操作模块组成，促进DigiAgro与AgroScience数据空间之间的跨部门数据交换。这项工作的成果是一个自动化生态系统，用于在不同数据空间之间共享多样化的农场物联网测量数据、卫星图像与指标、天气预报以及分析服务，旨在生成关于作物实践（如灌溉计划）的准确建议，农民和农艺师将依赖这些建议来提高作物产量，同时保持可持续性。","arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.17185","2026-09-15T00:00:00Z",77,{"impact":18,"substance":66,"depth":159,"authority":67,"freshness":17,"relevant":22,"comment":280},"欧盟DS2项目下跨数据空间互操作框架，方法新颖且面向精准农业落地，对农业数据要素流通有参考价值。",[282],{"name":275,"url":276},[29,30,284,285,286],"遥感","农业数据空间","数据互操作",[288,289],"农业数据空间 农业物联网 数据互操作 智慧农业","农业数据空间 农业物联网","农业数据空间农业物联网数据互操作智慧农业-2778","10.48550\u002Farxiv.2609.17185",{"doi":291,"openalex_id":293,"authors":294,"venue":275,"cited_by_count":38,"oa_url":306,"card":307,"direction":49,"ingested_from":51},"W7213417676",[295,297,300,302,304],{"name":296,"orcid":9},"Katerina Kyriakou",{"name":298,"orcid":299},"Ilias Syrigos","https:\u002F\u002Forcid.org\u002F0000-0002-4392-6036",{"name":301,"orcid":9},"Ioannis Moutsinas",{"name":303,"orcid":9},"Panagiotis Tzimotoudis",{"name":305,"orcid":9},"Thanasis Korakis","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.17185",{"tldr":308,"method":309,"finding":310,"direction":49,"opportunity":311},"提出基于DS2的跨数据空间互操作框架，实现农业数据安全共享与精准推荐。","分层参考架构，连接DigiAgro与AgroScience数据空间，集成IoT、","自动化生态系统可跨数据空间共享数据并生成灌溉等精准农事建议。","可研究跨数据空间互操作中的语义对齐、信任机制与实时决策优化。","2026-09-17T23:30:18.248884Z"]