[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3524":3,"related-3524":79},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":61,"slug":64,"view_count":44,"doi":65,"paper":66,"created_at":78},3524,"A hybrid PCA and 1D-CNN approach with regional priors for accurate agricultural monitoring: a case study with EuroCropsML","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10449-z","A hybrid PCA and 1D-CNN approach with regional priors for accurate agricultural monitoring: a case study with EuroCropsML。Precision Agriculture",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,70,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,17,14,9,1,"方法类论文，提出PCA与一维CNN结合并引入区域先验，在EuroCropsML数据集上验证，对作物遥感监测有参考价值，但属细分方法进展，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","农业人工智能","农业遥感","作物分类",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,56],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},4,true,[],[48],"precision agriculture",[26],{"code":51,"score":52,"supported":13,"title":53,"abstract":54,"tags":55},"I3",2,[],[],[29,28],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"I2",[],[],[27],[62,63],"EuroCropsML 作物分类","PCA 1D-CNN 农业监测","EuroCropsML作物分类-3524","10.1007\u002Fs11119-026-10449-z",{"doi":65,"openalex_id":67,"authors":68,"venue":9,"cited_by_count":75,"oa_url":8,"card":8,"direction":76,"ingested_from":77},"W7214386719",[69,72],{"name":70,"orcid":71},"Nourelhouda Groun","https:\u002F\u002Forcid.org\u002F0000-0002-8099-0627",{"name":73,"orcid":74},"Kheir-eddine Otmani","https:\u002F\u002Forcid.org\u002F0000-0002-7487-0928",0,"农业遥感与作物表型","openalex","2026-09-26T23:30:03.186961Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,134,181,236,290,343],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":8,"content":8,"source_name":9,"source_url":85,"published_at":87,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":93,"tags":95,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":113,"slug":116,"view_count":75,"doi":117,"paper":118,"created_at":133},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":90,"substance":91,"depth":17,"authority":19,"freshness":12,"relevant":21,"comment":92},16,21,"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。",[94],{"name":9,"url":85},[26,27,96,97,98],"精准农业","机器视觉","作物行检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":102},{"candidates":103,"agricultural_evidence":45},[104,109],{"code":31,"score":105,"supported":45,"title":106,"abstract":107,"tags":108},5,[],[48],[26,96],{"code":57,"score":52,"supported":13,"title":110,"abstract":111,"tags":112},[],[],[27,97],[114,115],"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":117,"openalex_id":119,"authors":120,"venue":9,"cited_by_count":75,"oa_url":8,"card":8,"direction":8,"ingested_from":77},"W7223117828",[121,124,127,130],{"name":122,"orcid":123},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":125,"orcid":126},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":128,"orcid":129},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":131,"orcid":132},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":8,"source_name":140,"source_url":137,"published_at":141,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":142,"sources":145,"tags":147,"primary_topic":150,"secondary_topics":151,"classification":152,"search_phrases":165,"slug":168,"view_count":75,"doi":169,"paper":170,"created_at":180},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":17,"substance":143,"depth":17,"authority":16,"freshness":20,"relevant":21,"comment":144},22,"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。",[146],{"name":140,"url":137},[26,27,148,96,149],"数字孪生","植物表型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":153},{"candidates":154,"agricultural_evidence":45},[155,161],{"code":31,"score":156,"supported":45,"title":157,"abstract":159,"tags":160},20,[158],"digital twin",[96,148,48],[148,26,96],{"code":57,"score":21,"supported":13,"title":162,"abstract":163,"tags":164},[],[],[27],[166,167],"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":169,"openalex_id":171,"authors":172,"venue":140,"cited_by_count":75,"oa_url":137,"card":175,"direction":76,"ingested_from":77},"W7222885652",[173],{"name":174,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":176,"method":177,"finding":178,"direction":76,"opportunity":179},"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":182,"title":183,"url":184,"summary":185,"summary_zh":8,"content":8,"source_name":186,"source_url":184,"published_at":187,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":188,"score_detail":189,"sources":192,"tags":194,"primary_topic":196,"secondary_topics":197,"classification":198,"search_phrases":215,"slug":218,"view_count":52,"doi":219,"paper":220,"created_at":235},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","2026-10-14T00:00:00Z",67,{"impact":16,"substance":90,"depth":18,"authority":19,"freshness":190,"relevant":21,"comment":191},8,"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。",[193],{"name":186,"url":184},[26,27,96,195],"生物防治",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":199},{"candidates":200,"agricultural_evidence":45},[201,206,211],{"code":31,"score":202,"supported":45,"title":203,"abstract":204,"tags":205},13,[48],[48],[26,96],{"code":207,"score":21,"supported":13,"title":208,"abstract":209,"tags":210},"F4",[],[],[195],{"code":57,"score":21,"supported":13,"title":212,"abstract":213,"tags":214},[],[],[27],[216,217],"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":219,"openalex_id":221,"authors":222,"venue":186,"cited_by_count":75,"oa_url":8,"card":8,"direction":234,"ingested_from":77},"W7221746877",[223,226,229,231],{"name":224,"orcid":225},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":227,"orcid":228},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":230,"orcid":8},"İsmail Alper Susurluk",{"name":232,"orcid":233},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","智慧农业 \u002F 农业物联网","2026-10-16T23:30:09.556034Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":8,"source_name":140,"source_url":239,"published_at":242,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":243,"score_detail":244,"sources":246,"tags":248,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":272,"slug":275,"view_count":75,"doi":276,"paper":277,"created_at":289},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":16,"substance":143,"depth":17,"authority":202,"freshness":20,"relevant":21,"comment":245},"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。",[247],{"name":140,"url":239},[26,249,28,250,251],"无人机","区块链","数据安全",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":255},{"candidates":256,"agricultural_evidence":45},[257,263,267],{"code":31,"score":258,"supported":45,"title":259,"abstract":260,"tags":262},7,[],[26,261],"smart agriculture",[26],{"code":51,"score":21,"supported":13,"title":264,"abstract":265,"tags":266},[],[],[28],{"code":268,"score":21,"supported":13,"title":269,"abstract":270,"tags":271},"I6",[],[],[251],[273,274],"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":276,"openalex_id":278,"authors":279,"venue":140,"cited_by_count":75,"oa_url":239,"card":284,"direction":234,"ingested_from":77},"W7222323013",[280,282],{"name":281,"orcid":8},"B.  Gnanasankari",{"name":283,"orcid":8},"R.  Padmanaban",{"tldr":285,"method":286,"finding":287,"direction":234,"opportunity":288},"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",{"id":291,"title":292,"url":293,"summary":294,"summary_zh":8,"content":8,"source_name":9,"source_url":293,"published_at":187,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":295,"score_detail":296,"sources":298,"tags":300,"primary_topic":303,"secondary_topics":304,"classification":305,"search_phrases":325,"slug":328,"view_count":21,"doi":329,"paper":330,"created_at":342},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",75,{"impact":90,"substance":156,"depth":18,"authority":19,"freshness":190,"relevant":21,"comment":297},"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。",[299],{"name":9,"url":293},[26,249,27,301,302],"遥感","大麦",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":306},{"candidates":307,"agricultural_evidence":45},[308,312,317,321],{"code":31,"score":44,"supported":45,"title":309,"abstract":310,"tags":311},[],[48],[26],{"code":313,"score":21,"supported":13,"title":314,"abstract":315,"tags":316},"A5",[],[],[302],{"code":57,"score":21,"supported":13,"title":318,"abstract":319,"tags":320},[],[],[27],{"code":51,"score":21,"supported":13,"title":322,"abstract":323,"tags":324},[],[],[301],[326,327],"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":329,"openalex_id":331,"authors":332,"venue":9,"cited_by_count":75,"oa_url":8,"card":8,"direction":8,"ingested_from":77},"W7221758985",[333,336,339],{"name":334,"orcid":335},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":337,"orcid":338},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":340,"orcid":341},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":8,"content":8,"source_name":348,"source_url":346,"published_at":349,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":143,"score_detail":350,"sources":353,"tags":355,"primary_topic":357,"secondary_topics":358,"classification":359,"search_phrases":370,"slug":373,"view_count":75,"doi":374,"paper":375,"created_at":392},4659,"Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22742.61769","Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation。OpenAlex","OpenAlex","2026-10-10T00:00:00Z",{"impact":351,"substance":52,"depth":52,"authority":80,"freshness":20,"relevant":21,"comment":352},3,"仅有标题与来源、无实质摘要内容的论文条目，信息增量极低，不建议进入每日精选。",[354],{"name":348,"url":346},[26,27,356],"科研平台",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":360},{"candidates":361,"agricultural_evidence":45},[362,366],{"code":31,"score":16,"supported":45,"title":363,"abstract":364,"tags":365},[261],[261],[26],{"code":57,"score":21,"supported":13,"title":367,"abstract":368,"tags":369},[],[],[27],[371,372],"农业人工智能 智慧农业 科研平台","农业人工智能 智慧农业","农业人工智能智慧农业科研平台-4659","10.13140\u002Frg.2.2.22742.61769",{"doi":374,"openalex_id":376,"authors":377,"venue":8,"cited_by_count":75,"oa_url":346,"card":8,"direction":234,"ingested_from":77},"W7221337880",[378,380,382,384,386,388,390],{"name":379,"orcid":8},"Ing José Antonio",{"name":381,"orcid":8},"Valles Romero",{"name":383,"orcid":8},"Gerardo Manuel",{"name":385,"orcid":8},"Alonzo Medina",{"name":387,"orcid":8},"Emilio Raymundo Morales Maldonado",{"name":389,"orcid":8},"Ing José",{"name":391,"orcid":8},"Antonio Valles","2026-10-11T23:30:07.700109Z"]