[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4973":3,"related-4973":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":66,"slug":69,"view_count":21,"doi":70,"paper":71,"created_at":85},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture",null,"Precision Agriculture","2026-10-14T00:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,20,17,14,8,1,"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","无人机","农业人工智能","遥感","大麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56,61],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},4,true,[],[49],"precision agriculture",[26],{"code":52,"score":21,"supported":13,"title":53,"abstract":54,"tags":55},"A5",[],[],[30],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"I2",[],[],[28],{"code":62,"score":21,"supported":13,"title":63,"abstract":64,"tags":65},"I3",[],[],[29],[67,68],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":70,"openalex_id":72,"authors":73,"venue":9,"cited_by_count":83,"oa_url":8,"card":8,"direction":8,"ingested_from":84},"W7221758985",[74,77,80],{"name":75,"orcid":76},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":78,"orcid":79},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":81,"orcid":82},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231",0,"openalex","2026-10-15T23:30:02.421798Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,144,200,250,296,347],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":8,"source_name":95,"source_url":92,"published_at":96,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":97,"score_detail":98,"sources":104,"tags":106,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":123,"slug":126,"view_count":102,"doi":127,"paper":128,"created_at":143},1737,"A hawk-inspired metaheuristic for trajectory optimization of UAVs in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.65746\u002Fjbrha139","Unmanned Aerial Vehicles (UAVs) have become central to precision agriculture, where efficient trajectory planning over large fields directly determines mission feasibility, energy use, and the quality of acquired data. We address the problem of generating an optimal, fully covering trajectory for a single UAV operating over a plantation field modeled as a graph of waypoints. The problem is a constrained variant of the Travelling Salesman Problem in which all waypoints must be visited, total path length and turning cost must be minimized, and an energy budget must be respected. Existing metaheuristics such as Particle Swarm Optimization, Grey Wolf Optimizer, and Harris’s Hawks Optimization struggle to balance exploration with the strict coverage constraint of plantation scenarios, and tend to converge prematurely on irregular layouts. We propose the Hawk-Inspired Trajectory Optimizer (HITO), a new population-based metaheuristic whose search behavior is inspired by the soaring, patrolling, and stooping phases of hawks. HITO introduces a thermal-soaring exploration operator, a territorial-patrol transition operator, and a stoop-dive exploitation operator, supported by a coverage-repair mechanism that guarantees feasibility at every iteration. We provide a Markov-chain argument for convergence and an O(N·n²) per-iteration complexity bound, where N is population size and n are the number of waypoints. HITO is benchmarked on simulated plantation fields of 1, 3, and 5 hectares against seven baselines, and on twelve CEC 2017 benchmark functions. Across thirty independent runs, HITO reduces path length by 12.4 % on average over the best baseline, lowers energy consumption by 14.1 %, improves coverage robustness, and converges in fewer generations. Statistical significance is confirmed using Wilcoxon signed-rank and Friedman tests.","无人机（UAV）已成为精准农业的核心工具，在大面积农田上高效的轨迹规划直接决定了任务可行性、能源消耗以及所采集数据的质量。本文针对单一无人机在 plantation 场景（以航点图建模）中生成最优全覆盖轨迹的问题展开研究。该问题属于旅行商问题（TSP）的一种受约束变体，要求访问所有航点，同时最小化总路径长度和转弯成本，并满足能量预算约束。现有元启发式算法，如粒子群优化（PSO）、灰狼优化器（GWO）和哈里斯鹰优化（HHO），在平衡探索能力与 plantation 场景严格覆盖约束方面存在困难，且在不规则布局下容易过早收敛。为此，我们提出了一种新型基于种群的元启发式算法——鹰启发轨迹优化器（HITO），其搜索行为受鹰的翱翔、巡逻和俯冲三个阶段启发。HITO 引入了热气流翱翔探索算子、领地巡逻过渡算子以及俯冲下击开发算子，并辅以覆盖修复机制，确保每次迭代的可行性。我们提供了基于马尔可夫链的收敛性论证，并给出了每迭代复杂度上界 O(N·n²)，其中 N 为种群规模，n 为航点数量。HITO 在模拟的 1、3 和 5 公顷 plantation 农田上，与七种基线算法进行了对比测试，并在十二个 CEC 2017 基准函数上进行了验证。在三十次独立运行中，HITO 相较于最佳基线算法，平均路径长度减少了 12.4%，能耗降低了 14.1%，覆盖鲁棒性得到提升，且收敛所需的迭代次数更少。统计显著性通过 Wilcoxon 符号秩检验和 Friedman 检验得到确认。","Journal of Biological Regulators and Homeostatic Agents\u002FJournal of Biological Regulators & Homeostatic Agents","2026-09-04T00:00:00Z",72,{"impact":99,"substance":100,"depth":99,"authority":101,"freshness":102,"relevant":21,"comment":103},18,22,12,2,"提出新型元启发式算法优化无人机植保轨迹，显著提升效率，对精准农业有实质贡献。",[105],{"name":95,"url":92},[26,27,28,107],"路径优化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":111},{"candidates":112,"agricultural_evidence":46},[113,119],{"code":32,"score":114,"supported":46,"title":115,"abstract":116,"tags":118},15,[49],[117,49],"精准农业",[26],{"code":57,"score":21,"supported":13,"title":120,"abstract":121,"tags":122},[],[],[28],[124,125],"农业人工智能 智慧农业 路径优化 无人机","农业人工智能 智慧农业","农业人工智能智慧农业路径优化无人机-1737","10.65746\u002Fjbrha139",{"doi":127,"openalex_id":129,"authors":130,"venue":95,"cited_by_count":83,"oa_url":135,"card":136,"direction":142,"ingested_from":84},"W7208744610",[131,133],{"name":132,"orcid":8},"Gigin Durai Chelladurai",{"name":134,"orcid":8},"Lenin Fred Alfred","https:\u002F\u002Fjournal.csrp-pub.com\u002Findex.php\u002FJBRHA\u002Farticle\u002Fdownload\u002F139\u002F138",{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"提出鹰启发式元启发算法HITO，优化无人机在精准农业中的全覆盖轨迹，降低路径长度和能耗。","模拟鹰的翱翔、巡逻、俯冲阶段，设计热上升探索、领地巡逻转换、俯冲开发算子及覆盖修","HITO相比最优基线平均路径缩短12.4%，能耗降低14.1%，收敛更快，统计显著。","农业人工智能与决策模型","可探索将HITO扩展到多无人机协同、动态障碍物环境，或结合实时传感器数据实现自适应路径规划。","农业遥感与作物表型","2026-09-05T23:30:27.619181Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":151,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":152,"score_detail":153,"sources":157,"tags":159,"primary_topic":161,"secondary_topics":162,"classification":163,"search_phrases":178,"slug":181,"view_count":21,"doi":182,"paper":183,"created_at":199},1403,"CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1896392","Precise segmentation of small and dense field crop pests in UAV images remains challenging due to limited long-range dependency modeling in CNNs and quadratic complexity in ViTs. Although VM-UNet captures global context efficiently, it lacks local texture detail and multi-scale feature integration for small targets. To address these limitations, this paper proposes CFAEVM-UNet (CrossFusion Attention Enhanced VM-UNet), which incorporates three core modules: a Visual State Space (VSS) block for efficient long-range dependency modeling, a Local-Global Spatial-Channel Gated Attention (LG-SCGA) module for multi-scale gated attention fusion, and a Multi-Stage Channel-Wise Attention (MSCWA) module for cross-stage channel reweighting. Extensive experiments on an integrated dataset (IP102 subset combined with UAV-captured field images) demonstrate that CFAEVM-UNet achieves state-of-the-art performance, attaining an mIoU of 81.23% and a DSC of 83.67%, outperforming U-Net by 8.89% in mIoU. This work provides an effective and practical solution for automated pest monitoring in precision agriculture.","无人机图像中密集小尺度农作物害虫的精确分割仍面临挑战，原因在于卷积神经网络（CNN）中长距离依赖建模能力有限，以及视觉Transformer（ViT）中的二次复杂度问题。尽管VM-UNet能够高效捕获全局上下文，但其在局部纹理细节和小目标多尺度特征整合方面存在不足。为解决上述局限，本文提出CFAEVM-UNet（CrossFusion Attention Enhanced VM-UNet），该模型融合了三个核心模块：用于高效长距离依赖建模的视觉状态空间（VSS）模块、用于多尺度门控注意力融合的局部-全局空间-通道门控注意力（LG-SCGA）模块，以及用于跨阶段通道重加权的多阶段通道注意力（MSCWA）模块。在综合数据集（IP102子集结合无人机拍摄的田间图像）上进行的大量实验表明，CFAEVM-UNet达到了最先进的性能，其平均交并比（mIoU）为81.23%，Dice相似系数（DSC）为83.67%，相较于U-Net在mIoU上提升了8.89%。本研究为精准农业中的自动化害虫监测提供了一种有效且实用的解决方案。","Frontiers in Plant Science","2026-09-01T00:00:00Z",78,{"impact":99,"substance":100,"depth":99,"authority":154,"freshness":155,"relevant":21,"comment":156},13,7,"提出针对无人机图像小目标害虫分割的新模型，性能显著提升，对精准农业有实用价值。",[158],{"name":150,"url":147},[26,28,29,160],"病虫害防治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":164},{"candidates":165,"agricultural_evidence":46},[166,170,174],{"code":32,"score":155,"supported":46,"title":167,"abstract":168,"tags":169},[],[117,49],[26],{"code":57,"score":21,"supported":13,"title":171,"abstract":172,"tags":173},[],[],[28],{"code":62,"score":21,"supported":13,"title":175,"abstract":176,"tags":177},[],[],[29],[179,180],"农业人工智能 病虫害防治 智慧农业 遥感","农业人工智能 病虫害防治","农业人工智能病虫害防治智慧农业遥感-1403","10.3389\u002Ffpls.2026.1896392",{"doi":182,"openalex_id":184,"authors":185,"venue":150,"cited_by_count":83,"oa_url":192,"card":193,"direction":198,"ingested_from":84},"W7204879529",[186,188,190],{"name":187,"orcid":8},"Guohong Qi",{"name":189,"orcid":8},"Jing Zhang",{"name":191,"orcid":8},"Shanwen Zhang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1896392\u002Fpdf",{"tldr":194,"method":195,"finding":196,"direction":140,"opportunity":197},"提出CFAEVM-UNet模型，用于无人机图像中密集小害虫的精确分割。","结合VSS块、LG-SCGA模块和MSCWA模块，增强多尺度特征融合与通道注意力","在集成数据集上mIoU达81.23%，DSC达83.67%，比U-Net高8.89%。","可探索模型在更多作物害虫种类和不同无人机高度下的泛化性，以及轻量化设计以适配边缘设备。","智慧农业 \u002F 农业物联网","2026-09-02T23:30:10.832021Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":8,"content":8,"source_name":9,"source_url":203,"published_at":205,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":206,"score_detail":207,"sources":210,"tags":212,"primary_topic":215,"secondary_topics":216,"classification":217,"search_phrases":229,"slug":232,"view_count":83,"doi":233,"paper":234,"created_at":249},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":16,"substance":208,"depth":99,"authority":19,"freshness":12,"relevant":21,"comment":209},21,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[211],{"name":9,"url":203},[26,28,117,213,214],"机器视觉","作物行检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,225],{"code":32,"score":221,"supported":46,"title":222,"abstract":223,"tags":224},5,[],[49],[26,117],{"code":57,"score":102,"supported":13,"title":226,"abstract":227,"tags":228},[],[],[28,213],[230,231],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":233,"openalex_id":235,"authors":236,"venue":9,"cited_by_count":83,"oa_url":8,"card":8,"direction":8,"ingested_from":84},"W7223117828",[237,240,243,246],{"name":238,"orcid":239},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":241,"orcid":242},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":244,"orcid":245},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":247,"orcid":248},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":8,"source_name":256,"source_url":253,"published_at":257,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":206,"score_detail":258,"sources":261,"tags":263,"primary_topic":266,"secondary_topics":267,"classification":268,"search_phrases":280,"slug":283,"view_count":83,"doi":284,"paper":285,"created_at":295},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",{"impact":99,"substance":100,"depth":99,"authority":101,"freshness":259,"relevant":21,"comment":260},9,"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[262],{"name":256,"url":253},[26,28,264,117,265],"数字孪生","植物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":269},{"candidates":270,"agricultural_evidence":46},[271,276],{"code":32,"score":17,"supported":46,"title":272,"abstract":274,"tags":275},[273],"digital twin",[117,264,49],[264,26,117],{"code":57,"score":21,"supported":13,"title":277,"abstract":278,"tags":279},[],[],[28],[281,282],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":284,"openalex_id":286,"authors":287,"venue":256,"cited_by_count":83,"oa_url":253,"card":290,"direction":142,"ingested_from":84},"W7222885652",[288],{"name":289,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":291,"method":292,"finding":293,"direction":142,"opportunity":294},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":8,"content":8,"source_name":301,"source_url":299,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":302,"score_detail":303,"sources":305,"tags":307,"primary_topic":309,"secondary_topics":310,"classification":311,"search_phrases":327,"slug":330,"view_count":102,"doi":331,"paper":332,"created_at":346},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology",67,{"impact":101,"substance":16,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":304},"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[306],{"name":301,"url":299},[26,28,117,308],"生物防治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":312},{"candidates":313,"agricultural_evidence":46},[314,318,323],{"code":32,"score":154,"supported":46,"title":315,"abstract":316,"tags":317},[49],[49],[26,117],{"code":319,"score":21,"supported":13,"title":320,"abstract":321,"tags":322},"F4",[],[],[308],{"code":57,"score":21,"supported":13,"title":324,"abstract":325,"tags":326},[],[],[28],[328,329],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":331,"openalex_id":333,"authors":334,"venue":301,"cited_by_count":83,"oa_url":8,"card":8,"direction":198,"ingested_from":84},"W7221746877",[335,338,341,343],{"name":336,"orcid":337},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":339,"orcid":340},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":342,"orcid":8},"İsmail Alper Susurluk",{"name":344,"orcid":345},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":8,"source_name":256,"source_url":350,"published_at":353,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":363,"secondary_topics":364,"classification":365,"search_phrases":382,"slug":385,"view_count":83,"doi":386,"paper":387,"created_at":399},5079,"Lightweight Blockchain Based Authentication and Secure Data Sharing in UAV Networks","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23390977","Unmanned Aerial Vehicle (UAV) networks, oper- ating within Flying Ad-Hoc Networks (FANETs), play an in- dispensable role in contemporary surveillance, border patrol, smart agriculture, disaster recovery, and intelligent transporta- tion systems. Owing to their open wireless propagation channels, dynamic line-of-sight flight paths, and severe computational and battery resource constraints, UAVs are exceptionally susceptible to eavesdropping, message forgery, node impersonation, physical node capture, and denial-of-service (DoS) attacks. Conventional public key infrastructure (PKI) frameworks and centralized Certificate Authorities (CAs) exhibit heavy cryptographic over- head, substantial communication latency, and single-point-of- failure vulnerabilities that render them unsuitable for mission- critical aerial swarms. In this paper, we propose LBAS-UAV, a robust, lightweight, consortium blockchain-assisted mutual au- thentication, key agreement, and secure data sharing framework tailored specifically for resource-constrained UAV environments. LBAS-UAV integrates elliptic curve cryptography (ECC), physi- cal unclonable functions (PUFs), and keyed cryptographic hash functions to achieve mutual authentication without high-overhead bilinear pairings or certificate management. Dynamic UAV pseudonyms protect flight-path privacy and user untraceability. A consortium blockchain, maintained jointly by Ground Control Stations (GCS) and mobile edge servers under Practical Byzan- tine Fault Tolerance (PBFT) consensus, provides decentralized public-key validation, non-repudiation, and tamper-proof access auditing. To eliminate on-chain storage bottlenecks, high-volume aerial sensory telemetry is encrypted via AES-128-GCM and stored off-chain in the InterPlanetary File System (IPFS), while only 32-byte cryptographic content addresses and fine-grained access policies are registered on-chain. Formal security verification using the Real-Or-Random (ROR) oracle model, Burrows-Abadi- Needham (BAN) logic, and the automated AVISPA tool confirms that LBAS-UAV guarantees session key confidentiality and re- sists physical capture, replay, man-in-the-middle, and ephemeral secret leakage attacks. Extensive hardware benchmarking and network simulations demonstrate that LBAS-UAV demands only 0.318 ms of cryptographic execution time and 206 bytes of communication overhead on the UAV, outperforming recent state- of-the-art schemes by up to 38.4% in computation and 25.3% in energy efficiency.","无人机网络在飞行自组网(FANETs)中运行，在当代监视、边境巡逻、智慧农业、灾难恢复和智能交通系统中发挥着不可或缺的作用。由于其开放的无线传播信道、动态的视距飞行路径以及严重的计算和电池资源限制，无人机极易受到窃听、消息伪造、节点冒充、物理节点捕获和拒绝服务(DoS)攻击。传统的公钥基础设施(PKI)框架和集中式证书颁发机构(CAs)存在沉重的密码学开销、显著的通信延迟和单点故障漏洞，使其不适用于任务关键的空中集群。本文提出了LBAS-UAV，一种鲁棒的、轻量级的、联盟区块链辅助的相互认证、密钥协商和安全数据共享框架，专为资源受限的无人机环境量身定制。LBAS-UAV集成了椭圆曲线密码学(ECC)、物理不可克隆函数(PUFs)和带密钥的密码学哈希函数，无需高开销的双线性配对或证书管理即可实现相互认证。动态无人机假名保护飞行路径隐私和用户不可追踪性。由地面控制站(GCS)和移动边缘服务器在实用拜占庭容错(PBFT)共识下共同维护的联盟区块链，提供去中心化的公钥验证、不可否认性和防篡改的访问审计。为消除链上存储瓶颈，大容量空中传感遥测数据通过AES-128-GCM加密并存储在星际文件系统(IPFS)的链下，而仅将32字节的密码学内容地址和细粒度访问策略注册在链上。使用Real-Or-Random(ROR)预言机模型、Burrows-Abadi-Needham(BAN)逻辑和自动化AVISPA工具进行的形式化安全验证确认，LBAS-UAV保证会话密钥机密性，并能抵抗物理捕获、重放、中间人和临时秘密泄露攻击。广泛的硬件基准测试和网络仿真表明，LBAS-UAV在无人机上仅需0.318毫秒的密码学执行时间和206字节的通信开销，在计算方面优于近期最先进的方案高达38.4%，在能效方面优于25.3%。","2026-10-15T00:00:00Z",74,{"impact":101,"substance":100,"depth":99,"authority":154,"freshness":259,"relevant":21,"comment":356},"面向资源受限无人机网络的轻量级区块链认证与安全数据共享方案，方法新颖、验证充分，对智慧农业无人机应用有参考价值，但属通用技术论文，农业场景关联较间接。",[358],{"name":256,"url":350},[26,27,360,361,362],"农业遥感","区块链","数据安全",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":366},{"candidates":367,"agricultural_evidence":46},[368,373,377],{"code":32,"score":155,"supported":46,"title":369,"abstract":370,"tags":372},[],[26,371],"smart agriculture",[26],{"code":62,"score":21,"supported":13,"title":374,"abstract":375,"tags":376},[],[],[360],{"code":378,"score":21,"supported":13,"title":379,"abstract":380,"tags":381},"I6",[],[],[362],[383,384],"UAV 区块链 认证 数据共享","LBAS-UAV 无人机 网络安全","UAV区块链认证数据共享-5079","10.5281\u002Fzenodo.23390977",{"doi":386,"openalex_id":388,"authors":389,"venue":256,"cited_by_count":83,"oa_url":350,"card":394,"direction":198,"ingested_from":84},"W7222323013",[390,392],{"name":391,"orcid":8},"B.  Gnanasankari",{"name":393,"orcid":8},"R.  Padmanaban",{"tldr":395,"method":396,"finding":397,"direction":198,"opportunity":398},"提出轻量级联盟链认证与安全数据共享框架LBAS-UAV，用于资源受限无人机网络。","融合ECC、PUF与哈希函数，PBFT联盟链结合IPFS链下存储，AES-128","仅需0.318ms计算与206字节通信开销，计算性能提升38.4%，可抵御多种攻击。","可将该轻量认证框架迁移至农业无人机植保与农田监测场景，研究能耗与实时性权衡。","2026-10-16T23:30:08.237868Z"]