[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3471":3,"related-3471":110},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":109},3471,"A secure federated learning framework with blockchain-based authentication for anomaly detection in cooperative smart farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116072","Agriculture is still a crucial global pillar of the world economy, ensuring food security and livelihood. But changing climate conditions and increasing reliance on the Internet of Things (IoT) based precision agriculture have brought new challenges, mainly cybersecurity. Cooperative Smart Farming (CSF) schemes promote resource pooling among small farmers and thus, the adoption of cutting-edge technologies. Although democratizing precision farming also makes it more accessible to cyber threats. To sort out all these problems, the proposed method introduces a secure federated learning framework along with blockchain-based authentication for anomaly detection in CSFs. The proposed method employs a federated transfer learning-based shadow attention mechanism, in conjunction with the Zeiler and Fergus network, to amplify the performance of the anomaly detection technique. This allows farms to detect threats in premises while protecting data privacy, as only model updates that are encrypted are transmitted. A blockchain-backed decentralized authentication scheme based on the proof of authentication consensus protocol is built to provide trust and resistance to tampering. Moreover, the random k-sparsification technique with changing rank adjustment has a positive impact on communication efficiency, which results in the same level of accuracy but with less communication overhead. The proposed method is executed on four datasets, CSE-CIS-IDS2018, MQTTset, ToN-IoT, and Edge IoT, achieving classification accuracies of 99.99%,99.93%,99.94%, and 99.91%, respectively, and it performs better than the current techniques. This comprehensive framework provides a scalable and trusted method for anomaly detection in CSFs, enabling resilient, privacy-preserving, and intelligent smart farming ecosystems.","农业仍然是全球经济的重要支柱，保障着粮食安全和生计。然而，不断变化的气候条件以及日益依赖基于物联网（IoT）的精准农业，带来了新的挑战，主要是网络安全问题。合作智慧农业（Cooperative Smart Farming, CSF）方案促进了小农户之间的资源共享，从而推动了前沿技术的采用。尽管精准农业的民主化使其更加普及，但也使其更容易受到网络威胁。为解决上述问题，所提出的方法引入了一种安全的联邦学习框架，并结合基于区块链的身份验证，用于CSF中的异常检测。所提出的方法采用基于联邦迁移学习的阴影注意力机制，结合Zeiler和Fergus网络，以提升异常检测技术的性能。这使得农场能够在本地检测威胁，同时保护数据隐私，因为仅传输加密的模型更新。构建了一种基于认证证明共识协议的区块链支持的去中心化身份验证方案，以提供信任和防篡改能力。此外，具有变化秩调整的随机k稀疏化技术对通信效率产生了积极影响，在保持相同精度的同时减少了通信开销。所提出的方法在四个数据集CSE-CIS-IDS2018、MQTTset、ToN-IoT和Edge IoT上执行，分别达到了99.99%、99.93%、99.94%和99.91%的分类准确率，且性能优于当前技术。该综合框架为CSF中的异常检测提供了一种可扩展且可信的方法，实现了具有韧性、隐私保护和智能化的智慧农业生态系统。",null,"Engineering Applications of Artificial Intelligence","2026-09-24T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出联邦学习与区块链认证结合的农业异常检测框架，在四个数据集上验证高精度，方法新颖且具产业应用潜力，值得入选每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","联邦学习","区块链","物联网安全",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,59,67,74,80],{"code":45,"score":13,"supported":46,"title":47,"abstract":48,"tags":51},"I1",true,[],[26,49,50],"精准农业","precision agriculture",[26],{"code":32,"score":20,"supported":46,"title":53,"abstract":54,"tags":58},[],[55,56,57],"物联网","internet of things","IoT",[],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"A8",6,[],[64,65],"粮食安全","food security",[],{"code":68,"score":69,"supported":46,"title":70,"abstract":71,"tags":73},"G2",3,[],[72],"小农户",[],{"code":75,"score":69,"supported":46,"title":76,"abstract":77,"tags":79},"H6",[],[78],"农业生态",[],{"code":81,"score":82,"supported":14,"title":83,"abstract":84,"tags":85},"I2",2,[],[],[27,28],[87,88],"联邦学习 区块链 异常检测","协同智慧农业 物联网安全","联邦学习区块链异常检测-3471",4,"10.1016\u002Fj.engappai.2026.116072",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7214230618",[95,97,99],{"name":96,"orcid":9},"Ramesh Pandharinath Daund",{"name":98,"orcid":9},"Mohammad Junedul Haque",{"name":100,"orcid":9},"Prof. Umesh B. Pawar",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"提出融合联邦迁移学习与区块链认证的异常检测框架，保障协作智慧农业数据隐私与安全。","联邦迁移学习、影子注意力机制、Zeiler-Fergus网络、区块链认证、随机k","在四个IoT数据集上分类准确率均超99.9%，且通信开销更低，优于现有方法。","智慧农业 \u002F 农业物联网","可探索轻量级区块链共识与联邦学习在资源受限农业边缘设备上的能效与实时性优化。","openalex","2026-09-25T23:30:09.960528Z",{"total":61,"page":21,"page_size":61,"items":111},[112,185,284,356,416,466],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":125,"tags":127,"primary_topic":130,"secondary_topics":134,"classification":136,"search_phrases":156,"slug":159,"view_count":101,"doi":160,"paper":161,"created_at":184},5236,"A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02500-9","The rapid digitization of modern systems has increased the demand for intelligent, adaptive, and data-driven solutions across connected systems. However, these domains face persistent challenges such as heterogeneous datasets, limited labeled data, real-time processing constraints, privacy concerns, model interpretability, and poor generalization across environments. This review examines the application of major Artificial Intelligence (AI) techniques for solving classification, prediction, optimization, anomaly detection, routing, image analysis, and decision-support problems. The empirical patterns across the literature are summarized cautiously across studies. General lessons that emerged across the reviewed domains were also presented. Overall, the review provides researchers with a concise foundation for understanding current AI methods, their practical limitations, and future research directions in the aspect of connected environments. This article has tried to clarify various aspects and overall issues related to the use of AI techniques in computer networks, image processing and computer vision, smart agriculture and related intelligent systems. The authors believe that this is a comprehensive and integrated survey of these AI techniques in a number of application domains, with an emphasis on their practical suitability, their current empirical limitations, and future research directions. Thus, the article is informative to researchers in the field and may be a guide to future research on the development of robust, interpretable, scalable, and context-aware AI-based connected systems.","现代系统的快速数字化增加了对互联系统中智能、自适应和数据驱动解决方案的需求。然而，这些领域面临诸如异构数据集、标注数据有限、实时处理约束、隐私问题、模型可解释性以及跨环境泛化能力差等持续挑战。本综述考察了主要人工智能（Artificial Intelligence, AI）技术在解决分类、预测、优化、异常检测、路由、图像分析和决策支持问题中的应用。本文谨慎地总结了文献中的实证规律。文中还提出了在所综述领域中浮现的一般性经验教训。总体而言，本综述为研究人员提供了一个简明的基础，以理解当前AI方法、其实际局限性以及互联环境方面的未来研究方向。本文试图阐明与AI技术在计算机网络、图像处理与计算机视觉、智慧农业及相关智能系统中的应用有关的各个方面和总体问题。作者认为，这是对若干应用领域中这些AI技术的全面且综合的调查，重点在于其实际适用性、当前实证局限性和未来研究方向。因此，本文对该领域的研究人员具有参考价值，并可为开发稳健、可解释、可扩展且具有情境感知能力的基于AI的互联系统的未来研究提供指导。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",51,{"impact":122,"substance":123,"depth":13,"authority":123,"freshness":20,"relevant":21,"comment":124},8,12,"跨领域AI综述，涉及智慧农业但仅为其一分支，方法层面无突破性新结论，公共价值有限，不宜进入每日精选。",[126],{"name":118,"url":115},[26,27,128,30,129],"计算机视觉","智能系统",{"code":81,"slug":131,"name":132,"path":133,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[135],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":137},{"candidates":138,"agricultural_evidence":46},[139,146,152],{"code":81,"score":140,"supported":46,"title":141,"abstract":143,"tags":145},19,[142],"artificial intelligence",[128,142,144],"computer vision",[27,128],{"code":45,"score":147,"supported":46,"title":148,"abstract":150,"tags":151},15,[149],"smart agriculture",[26,149],[26],{"code":32,"score":122,"supported":46,"title":153,"abstract":154,"tags":155},[57],[],[],[157,158],"AI 物联网安全 智慧农业","农业人工智能 物联网安全 计算机视觉 智慧农业","AI物联网安全智慧农业-5236","10.1007\u002Fs44163-026-02500-9",{"doi":160,"openalex_id":162,"authors":163,"venue":118,"cited_by_count":101,"oa_url":115,"card":179,"direction":106,"ingested_from":108},"W7222847048",[164,167,170,173,176],{"name":165,"orcid":166},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":168,"orcid":169},"Mohammed Dahiru Buhari","https:\u002F\u002Forcid.org\u002F0000-0002-1076-216X",{"name":171,"orcid":172},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":174,"orcid":175},"Enerst Edozie","https:\u002F\u002Forcid.org\u002F0000-0002-7616-0962",{"name":177,"orcid":178},"Asaba Shabiluh","https:\u002F\u002Forcid.org\u002F0000-0001-6342-9099",{"tldr":180,"method":181,"finding":182,"direction":106,"opportunity":183},"综述AI在物联网安全、计算机网络、图像处理与智慧农业中的应用与局限。","跨领域文献综述，归纳分类、预测、优化、异常检测等方法。","各领域普遍受限于异构数据、标注少、实时性与可解释性不足。","可聚焦农业物联网中少标注、跨环境泛化的可解释实时AI模型研究。","2026-10-18T23:30:07.908595Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":193,"is_selected":14,"score":194,"score_detail":195,"sources":198,"tags":202,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":252,"slug":255,"view_count":90,"doi":256,"paper":257,"created_at":283},4016,"THE IMPACT OF MODERN TECHNOLOGICAL INNOVATIONS ON FOOD","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34037571.v1","This study empirically examines how modern technological innovations affect food security in Nigerian technology-based farming organizations. The goal of this study is to ascertain the nature of connections between modern technological innovation and food security. It’s also aimed at identifying modern technological innovations that guarantee food security and determining their effectiveness and profitability for the food security business. This study used a cross-sectional survey research design to examine a few technology-based and digital-farming organizations in Delta State that were reachable by the study population. From the managers who were the study objects, 60 of the remaining managerial personnel were reported. To assess the data from the respondents, Spearman's rank-order correlation coefficient was used. The findings demonstrated a strong positive connection between food security and modern technological innovations. The study identified the modern technological innovations that guarantees food security such as nanotechnology in agriculture, Artificial Intelligence (AI) in Quality Control, Robotics in Food Processing, The Blockchain Technology and Supply chain management, The Use of Drones in Agriculture, Internet of Things (IoT) in Food Safety, Climate-Resilient Crops, Automated Harvesting using Robot and Post-Harvest Technologies, digital farming, responsible innovation, Vertical Farming, Precision Agriculture, micro-innovation. This study concludes that all the identified modern technological innovations are effective and profitable for the food security business. The study recommends that farmers adopt modern technological innovations to ensure food security and, to a greater extent, sustain the economy.","本研究实证考察了现代技术创新如何影响尼日利亚技术型农业组织的粮食安全。本研究旨在厘清现代技术创新与粮食安全之间联系的性质，同时识别能够保障粮食安全的现代技术创新，并确定其对粮食安全产业的有效性和盈利性。本研究采用横断面调查研究设计，对三角洲州若干可被研究总体接触到的技术型和数字农业组织进行了考察。研究对象为管理者，报告了其余60名管理人员的情况。为评估受访者的数据，采用了斯皮尔曼等级相关系数。研究结果表明，粮食安全与现代技术创新之间存在强烈的正相关关系。本研究识别出能够保障粮食安全的现代技术创新，包括农业中的纳米技术、质量控制中的人工智能（AI）、食品加工中的机器人技术、区块链技术与供应链管理、农业中无人机的使用、食品安全中的物联网（IoT）、气候适应性作物、利用机器人进行的自动化收割与采后技术、数字农业、负责任创新、垂直农业、精准农业、微创新。本研究得出结论，所有已识别的现代技术创新对粮食安全产业均具有有效性和盈利性。研究建议农民采用现代技术创新以保障粮食安全，并在更大程度上维持经济可持续发展。","Figshare","2026-10-01T00:00:00Z",25,45,{"impact":122,"substance":19,"depth":196,"authority":90,"freshness":122,"relevant":21,"comment":197},11,"论文主题契合农业信息化，但样本仅60人、来源为预印本平台，权威性与影响层级有限，暂不建议进入每日精选。",[199,200],{"name":191,"url":188},{"name":191,"url":201},"https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34037571",[26,203,27,49,29],"无人机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":46},[209,214,218,222,229,236,240,246],{"code":45,"score":196,"supported":46,"title":210,"abstract":211,"tags":213},[],[49,212,50],"数字农业",[26,49],{"code":32,"score":20,"supported":46,"title":215,"abstract":216,"tags":217},[],[55,56,57],[],{"code":60,"score":61,"supported":46,"title":219,"abstract":220,"tags":221},[],[64,65],[],{"code":223,"score":61,"supported":46,"title":224,"abstract":225,"tags":228},"E1",[],[226,227],"食品加工","food processing",[],{"code":230,"score":61,"supported":46,"title":231,"abstract":232,"tags":235},"E4",[],[233,234],"食品安全","food safety",[],{"code":81,"score":90,"supported":46,"title":237,"abstract":238,"tags":239},[],[142],[27],{"code":241,"score":69,"supported":46,"title":242,"abstract":243,"tags":245},"B7",[],[244],"垂直农业",[],{"code":247,"score":69,"supported":46,"title":248,"abstract":249,"tags":251},"H7",[],[250],"气候适应",[],[253,254],"尼日利亚 数字农业 粮食安全","农业人工智能 智慧农业 精准农业 区块链","尼日利亚数字农业粮食安全-4016","10.6084\u002Fm9.figshare.34037571.v1",{"doi":256,"openalex_id":258,"authors":259,"venue":191,"cited_by_count":101,"oa_url":188,"card":277,"direction":106,"ingested_from":108},"W7215054315",[260,263,266,268,270,272,274],{"name":261,"orcid":262},"Ernest Jebolise Chukwuka","https:\u002F\u002Forcid.org\u002F0000-0002-7876-9974",{"name":264,"orcid":265},"Clara Dumebi Moemeke","https:\u002F\u002Forcid.org\u002F0000-0003-1848-0623",{"name":267,"orcid":9},"Ugboh Onyemaechi",{"name":269,"orcid":9},"Nwaka Rita Nneka",{"name":271,"orcid":9},"Okpako Abugor Ejaita",{"name":273,"orcid":9},"Glory Ewere Chukwuka",{"name":275,"orcid":276},"Apaokueze Tessy Nkechi","https:\u002F\u002Forcid.org\u002F0000-0002-6581-0384",{"tldr":278,"method":279,"finding":280,"direction":281,"opportunity":282},"实证研究尼日利亚技术型农场中现代技术创新与粮食安全的关系。","对60名管理者进行横断面问卷调查，采用斯皮尔曼等级相关分析。","现代技术创新与粮食安全呈强正相关，所列技术均有效且有利可图。","数字乡村与农业信息化","可拓展至不同国家或小农户场景，量化各技术对粮食安全的贡献差异与成本效益。","2026-10-02T23:30:16.060799Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":9,"source_name":290,"source_url":287,"published_at":291,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":292,"sources":294,"tags":296,"primary_topic":299,"secondary_topics":300,"classification":302,"search_phrases":323,"slug":326,"view_count":82,"doi":327,"paper":328,"created_at":355},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",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":293},"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。",[295],{"name":290,"url":287},[26,27,297,28,298],"农业物联网","网络安全",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[301],{"code":81,"slug":131,"name":132,"path":133,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":303},{"candidates":304,"agricultural_evidence":46},[305,309,313,319],{"code":32,"score":17,"supported":46,"title":306,"abstract":307,"tags":308},[57],[55,56,57],[297],{"code":81,"score":122,"supported":46,"title":310,"abstract":311,"tags":312},[],[128,144],[27,28],{"code":314,"score":90,"supported":46,"title":315,"abstract":316,"tags":318},"I6",[],[317],"agricultural data",[298],{"code":45,"score":21,"supported":14,"title":320,"abstract":321,"tags":322},[],[],[26],[324,325],"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":327,"openalex_id":329,"authors":330,"venue":290,"cited_by_count":101,"oa_url":287,"card":350,"direction":106,"ingested_from":108},"W7212387370",[331,334,337,340,343,345,347],{"name":332,"orcid":333},"Amina Khacha","https:\u002F\u002Forcid.org\u002F0009-0009-3299-8622",{"name":335,"orcid":336},"Zibouda Aliouat","https:\u002F\u002Forcid.org\u002F0000-0002-7007-7607",{"name":338,"orcid":339},"Yasmine Harbi","https:\u002F\u002Forcid.org\u002F0000-0001-6731-7895",{"name":341,"orcid":342},"Chirihane Gherbi","https:\u002F\u002Forcid.org\u002F0000-0002-0551-3978",{"name":344,"orcid":9},"Rafika Saadouni",{"name":346,"orcid":9},"Ado Adamou ABBA ARI",{"name":348,"orcid":349},"Hakim Mabed","https:\u002F\u002Forcid.org\u002F0000-0001-8358-4029",{"tldr":351,"method":352,"finding":353,"direction":106,"opportunity":354},"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":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":363,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":364,"score_detail":365,"sources":368,"tags":370,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":394,"slug":397,"view_count":82,"doi":398,"paper":399,"created_at":415},1723,"Bridging challenges with technology: A scientometric and systematic study on sustainability in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.59953\u002Fpaperasia.v42i4b.1489","The rapid evolution of agricultural practices driven by population growth, climate change, and technological advancements has necessitated the integration of smart technologies to enhance productivity and sustainability. This study aims to achieve two research objectives: (1) to provide a comprehensive overview of research patterns and key contributors in the domain of technology integration in smart agriculture, and (2) to evaluate the advantages of integrating technology in smart agriculture to address existing challenges. A scientometric analysis and a systematic literature review (SLR) were employed to examine the research landscape and synthesize current evidence. Following the PRISMA screening process, 18 articles were retained for the final systematic review. The findings reveal the transformative role of the Internet of Things (IoT), artificial intelligence (AI), blockchain, and unmanned aerial vehicles (UAVs) in improving resource management, reducing labour costs, enhancing real-time monitoring, and strengthening farm safety. The scientometric analysis further highlights global research collaborations, influential contributors, and emerging technological trends shaping the field of smart agriculture. These findings demonstrate the significant potential of advanced technologies to address contemporary agricultural challenges while promoting sustainable and resilient farming practices worldwide.","人口增长、气候变化和技术进步推动农业实践快速演变，这要求整合智能技术以提升生产力和可持续性。本研究旨在实现两个研究目标：（1）全面概述智能农业技术整合领域的研究模式及主要贡献者；（2）评估智能农业中技术整合的优势，以应对现有挑战。采用科学计量分析和系统文献综述（SLR）来考察研究格局并综合现有证据。经过PRISMA筛选流程，最终纳入18篇文章进行系统综述。研究结果揭示了物联网（IoT）、人工智能（AI）、区块链和无人机（UAVs）在改善资源管理、降低劳动力成本、增强实时监测及强化农场安全方面的变革性作用。科学计量分析进一步凸显了全球研究合作、有影响力的贡献者以及塑造智能农业领域的新兴技术趋势。这些发现表明，先进技术在应对当代农业挑战、同时促进全球可持续和韧性农业实践方面具有巨大潜力。","PaperAsia","2026-09-04T00:00:00Z",56,{"impact":123,"substance":17,"depth":366,"authority":122,"freshness":82,"relevant":21,"comment":367},16,"系统综述与科学计量分析，梳理智慧农业技术应用与挑战，但时效性较低。",[369],{"name":362,"url":359},[26,203,27,55,29],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":374},{"candidates":375,"agricultural_evidence":46},[376,380,384,390],{"code":45,"score":123,"supported":46,"title":377,"abstract":378,"tags":379},[149],[149],[26],{"code":32,"score":13,"supported":46,"title":381,"abstract":382,"tags":383},[],[55,56,57],[55],{"code":247,"score":61,"supported":46,"title":385,"abstract":386,"tags":389},[],[387,388],"气候变化","climate change",[],{"code":81,"score":90,"supported":46,"title":391,"abstract":392,"tags":393},[],[142],[27],[395,396],"农业人工智能 智慧农业 区块链 无人机","农业人工智能 智慧农业","农业人工智能智慧农业区块链无人机-1723","10.59953\u002Fpaperasia.v42i4b.1489",{"doi":398,"openalex_id":400,"authors":401,"venue":362,"cited_by_count":101,"oa_url":359,"card":410,"direction":106,"ingested_from":108},"W7208757733",[402,404,406,408],{"name":403,"orcid":9},"Nor Suzylah Sohaimi",{"name":405,"orcid":9},"Nor Syahidah Ishak",{"name":407,"orcid":9},"Rozaimi Majid",{"name":409,"orcid":9},"Siti Noor Zilawati Mingat@Minhad",{"tldr":411,"method":412,"finding":413,"direction":106,"opportunity":414},"通过科学计量和系统综述，评估智能农业中技术整合的研究格局与优势。","科学计量分析结合PRISMA系统文献综述，筛选18篇文章。","IoT、AI、区块链和无人机提升资源管理、降低成本、增强监测与安全。","可深入探究区块链与AI在农业中的协同应用，或针对特定作物\u002F区域的实证研究。","2026-09-05T23:30:09.377361Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":9,"source_name":422,"source_url":419,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":424,"score_detail":425,"sources":427,"tags":429,"primary_topic":430,"secondary_topics":431,"classification":432,"search_phrases":449,"slug":451,"view_count":452,"doi":453,"paper":454,"created_at":465},1419,"Smart Agriculture with Quantum Intelligence and Blockchain Security","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84736","Selecting energy-efficient, trustworthy cluster heads (CHs) remains a key bottleneck in agricultural wireless sensor and Internet of Things (IoT) networks, where heuristic protocols such as LEACH often converge to unstable configurations and cannot verify whether a selected node is trustworthy. This paper proposes a framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) for cluster head selection with a permissioned blockchain trust-verification layer. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed method encodes candidate cluster head assignments as qubit chromosomes and evolves them through quantum rotation-gate updates, treating cluster formation as a combinatorial optimization problem rather than a classification task. Verified assignments and node reputation scores are recorded as immutable blockchain transactions validated through a lightweight Byzantine fault-tolerant consensus, enabling automatic exclusion of low-trust nodes without a central authority. A discrete-event simulation comparing the proposed framework against LEACH and a classical genetic algorithm baseline shows comparable or improved energy retention and reliable detection of malicious nodes across a range of attack ratios. The results indicate that combining quantum-inspired combinatorial optimization with blockchain-verified trust offers a practical, hardware-independent pathway toward energy-aware, tamperresistant clustering for precision agriculture","在农业无线传感器与物联网（IoT）网络中，选择节能且可信的簇头（CH）仍是关键瓶颈，诸如LEACH等启发式协议往往收敛于不稳定配置，且无法验证所选节点是否可信。本文提出一个框架，将量子启发遗传算法（QIGA）用于簇头选择，并融合许可型区块链信任验证层。与以往将量子分类器应用于传感器数据分类的研究不同，本方法将候选簇头分配编码为量子比特染色体，并通过量子旋转门更新进行演化，将簇形成视为组合优化问题而非分类任务。验证后的分配结果与节点信誉评分被记录为不可篡改的区块链交易，并通过轻量级拜占庭容错共识机制加以验证，从而在无中心权威的情况下自动排除低信任节点。将所提框架与LEACH及经典遗传算法基线进行离散事件仿真对比，结果表明，在不同攻击比率下，该框架在能量保持方面表现相当或更优，并能可靠检测恶意节点。研究结果表明，将量子启发组合优化与区块链验证信任相结合，为实现精准农业中能量感知、防篡改的分簇提供了一条实用且不依赖硬件的路径。","International Journal for Research in Applied Science and Engineering Technology","2026-08-31T00:00:00Z",59,{"impact":123,"substance":17,"depth":366,"authority":13,"freshness":69,"relevant":21,"comment":426},"提出量子启发遗传算法与区块链结合的簇头选择框架，方法新颖，但尚处仿真阶段，影响有限。",[428],{"name":422,"url":419},[26,27,55,29],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":433},{"candidates":434,"agricultural_evidence":46},[435,439,445],{"code":45,"score":147,"supported":46,"title":436,"abstract":437,"tags":438},[149],[49,50],[26],{"code":32,"score":13,"supported":46,"title":440,"abstract":441,"tags":444},[],[55,442,56,57,443],"无线传感","wireless sensor",[55],{"code":81,"score":21,"supported":14,"title":446,"abstract":447,"tags":448},[],[],[27],[450,396],"农业人工智能 智慧农业 区块链 物联网","农业人工智能智慧农业区块链物联网-1419",5,"10.22214\u002Fijraset.2026.84736",{"doi":453,"openalex_id":455,"authors":456,"venue":422,"cited_by_count":101,"oa_url":419,"card":459,"direction":106,"ingested_from":108},"W7204875790",[457],{"name":458,"orcid":9},"Munta Padmavathi",{"tldr":460,"method":461,"finding":462,"direction":463,"opportunity":464},"提出量子启发遗传算法与区块链结合，用于农业物联网中可信且节能的簇头选择。","量子启发遗传算法（QIGA）优化簇头选择，结合许可区块链和拜占庭容错共识验证信任","相比LEACH和经典遗传算法，QIGA+区块链在能量保持和恶意节点检测上相当或更优。","其他","可探索将量子启发优化与区块链用于动态作物监测场景，或扩展到边缘计算与真实硬件部署验证。","2026-09-02T23:30:17.512521Z",{"id":467,"title":468,"url":469,"summary":470,"summary_zh":471,"content":9,"source_name":472,"source_url":469,"published_at":473,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":474,"score_detail":475,"sources":477,"tags":479,"primary_topic":483,"secondary_topics":489,"classification":492,"search_phrases":550,"slug":553,"view_count":101,"doi":554,"paper":555,"created_at":579},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":17,"substance":18,"depth":17,"authority":123,"freshness":20,"relevant":21,"comment":476},"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。",[478],{"name":472,"url":469},[26,27,480,481,482],"遥感","作物水分胁迫","精准灌溉",{"code":484,"slug":485,"name":486,"path":487,"parent_code":488},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[490,491],{"code":81,"slug":131,"name":132,"path":133,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":493},{"candidates":494,"agricultural_evidence":46},[495,500,506,510,517,523,530,537,544],{"code":484,"score":147,"supported":46,"title":496,"abstract":497,"tags":499},[485],[498,485],"灌溉",[482],{"code":81,"score":13,"supported":46,"title":501,"abstract":502,"tags":505},[],[503,504,142],"机器学习","machine learning",[27],{"code":32,"score":20,"supported":46,"title":507,"abstract":508,"tags":509},[],[55,56,57],[],{"code":45,"score":511,"supported":46,"title":512,"abstract":513,"tags":516},7,[],[514,515],"数字孪生","digital twin",[26],{"code":518,"score":511,"supported":46,"title":519,"abstract":520,"tags":522},"I3",[],[480,521],"remote sensing",[480],{"code":524,"score":61,"supported":46,"title":525,"abstract":526,"tags":529},"A2",[],[527,528],"小麦","wheat",[],{"code":531,"score":61,"supported":46,"title":532,"abstract":533,"tags":536},"A3",[],[534,535],"玉米","maize",[],{"code":538,"score":61,"supported":46,"title":539,"abstract":540,"tags":543},"A5",[],[541,542],"马铃薯","potato",[],{"code":545,"score":69,"supported":46,"title":546,"abstract":547,"tags":549},"B2",[],[548],"葡萄",[],[551,552],"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":554,"openalex_id":556,"authors":557,"venue":472,"cited_by_count":101,"oa_url":469,"card":573,"direction":106,"ingested_from":108},"W7223088921",[558,561,564,567,570],{"name":559,"orcid":560},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":562,"orcid":563},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":565,"orcid":566},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":568,"orcid":569},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":571,"orcid":572},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":574,"method":575,"finding":576,"direction":577,"opportunity":578},"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"]