[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5079":3,"related-5079":83},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":82},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%。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","无人机","农业遥感","区块链","数据安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,57],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[27,50],"smart agriculture",[27],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"I3",[],[],[29],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I6",[],[],[31],[63,64],"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",0,"10.5281\u002Fzenodo.23390977",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":66,"oa_url":6,"card":75,"direction":79,"ingested_from":81},"W7222323013",[71,73],{"name":72,"orcid":9},"B.  Gnanasankari",{"name":74,"orcid":9},"R.  Padmanaban",{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-16T23:30:08.237868Z",{"total":84,"page":22,"page_size":84,"items":85},6,[86,150,197,247,297,352],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":9,"content":9,"source_name":91,"source_url":89,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":101,"tags":103,"primary_topic":107,"secondary_topics":108,"classification":109,"search_phrases":132,"slug":135,"view_count":22,"doi":136,"paper":137,"created_at":149},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","Precision Agriculture","2026-10-14T00:00:00Z",75,{"impact":95,"substance":96,"depth":97,"authority":98,"freshness":99,"relevant":22,"comment":100},16,20,17,14,8,"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。",[102],{"name":91,"url":89},[27,28,104,105,106],"农业人工智能","遥感","大麦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":110},{"candidates":111,"agricultural_evidence":47},[112,118,123,128],{"code":33,"score":113,"supported":47,"title":114,"abstract":115,"tags":117},4,[],[116],"precision agriculture",[27],{"code":119,"score":22,"supported":14,"title":120,"abstract":121,"tags":122},"A5",[],[],[106],{"code":124,"score":22,"supported":14,"title":125,"abstract":126,"tags":127},"I2",[],[],[104],{"code":53,"score":22,"supported":14,"title":129,"abstract":130,"tags":131},[],[],[105],[133,134],"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":136,"openalex_id":138,"authors":139,"venue":91,"cited_by_count":66,"oa_url":9,"card":9,"direction":9,"ingested_from":81},"W7221758985",[140,143,146],{"name":141,"orcid":142},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":144,"orcid":145},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":147,"orcid":148},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":151,"title":152,"url":153,"summary":154,"summary_zh":9,"content":9,"source_name":91,"source_url":153,"published_at":155,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":156,"score_detail":157,"sources":159,"tags":161,"primary_topic":163,"secondary_topics":164,"classification":165,"search_phrases":181,"slug":184,"view_count":113,"doi":185,"paper":186,"created_at":196},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","2026-09-25T00:00:00Z",70,{"impact":17,"substance":19,"depth":97,"authority":98,"freshness":21,"relevant":22,"comment":158},"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。",[160],{"name":91,"url":153},[27,104,29,162],"作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":166},{"candidates":167,"agricultural_evidence":47},[168,172,177],{"code":33,"score":113,"supported":47,"title":169,"abstract":170,"tags":171},[],[116],[27],{"code":53,"score":173,"supported":14,"title":174,"abstract":175,"tags":176},2,[],[],[162,29],{"code":124,"score":22,"supported":14,"title":178,"abstract":179,"tags":180},[],[],[104],[182,183],"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":185,"openalex_id":187,"authors":188,"venue":91,"cited_by_count":66,"oa_url":9,"card":9,"direction":195,"ingested_from":81},"W7214386719",[189,192],{"name":190,"orcid":191},"Nourelhouda Groun","https:\u002F\u002Forcid.org\u002F0000-0002-8099-0627",{"name":193,"orcid":194},"Kheir-eddine Otmani","https:\u002F\u002Forcid.org\u002F0000-0002-7487-0928","农业遥感与作物表型","2026-09-26T23:30:03.186961Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":203,"source_url":200,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":208,"tags":210,"primary_topic":212,"secondary_topics":213,"classification":214,"search_phrases":227,"slug":230,"view_count":173,"doi":231,"paper":232,"created_at":246},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":19,"substance":18,"depth":19,"authority":17,"freshness":173,"relevant":22,"comment":207},"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。",[209],{"name":203,"url":200},[27,28,104,211],"路径优化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":215},{"candidates":216,"agricultural_evidence":47},[217,223],{"code":33,"score":218,"supported":47,"title":219,"abstract":220,"tags":222},15,[116],[221,116],"精准农业",[27],{"code":124,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[104],[228,229],"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":231,"openalex_id":233,"authors":234,"venue":203,"cited_by_count":66,"oa_url":239,"card":240,"direction":195,"ingested_from":81},"W7208744610",[235,237],{"name":236,"orcid":9},"Gigin Durai Chelladurai",{"name":238,"orcid":9},"Lenin Fred Alfred","https:\u002F\u002Fjournal.csrp-pub.com\u002Findex.php\u002FJBRHA\u002Farticle\u002Fdownload\u002F139\u002F138",{"tldr":241,"method":242,"finding":243,"direction":244,"opportunity":245},"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。","2026-09-05T23:30:27.619181Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":9,"content":9,"source_name":91,"source_url":250,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":257,"tags":259,"primary_topic":262,"secondary_topics":263,"classification":264,"search_phrases":276,"slug":279,"view_count":66,"doi":280,"paper":281,"created_at":296},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":95,"substance":255,"depth":19,"authority":98,"freshness":13,"relevant":22,"comment":256},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。",[258],{"name":91,"url":250},[27,104,221,260,261],"机器视觉","作物行检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":265},{"candidates":266,"agricultural_evidence":47},[267,272],{"code":33,"score":268,"supported":47,"title":269,"abstract":270,"tags":271},5,[],[116],[27,221],{"code":124,"score":173,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[104,260],[277,278],"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":280,"openalex_id":282,"authors":283,"venue":91,"cited_by_count":66,"oa_url":9,"card":9,"direction":9,"ingested_from":81},"W7223117828",[284,287,290,293],{"name":285,"orcid":286},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":288,"orcid":289},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":291,"orcid":292},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":294,"orcid":295},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":9,"source_name":10,"source_url":300,"published_at":303,"category":12,"cover_url":9,"hotness":304,"is_selected":14,"score":305,"score_detail":306,"sources":308,"tags":312,"primary_topic":317,"secondary_topics":318,"classification":319,"search_phrases":335,"slug":338,"view_count":66,"doi":339,"paper":340,"created_at":351},5397,"The Relationship Between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23469780","Relationship between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study explores the relationship between nutrient concentrations and the optical characteristics of banana leaves using quantitative image analysis. The study focuses on measurable leaf features, including colour intensity, RGB values, and other image-derived parameters, to identify patterns associated with plant nutrient status. Digital images of banana leaves can provide valuable information about variations in leaf appearance that may reflect differences in nutrient availability. By analysing these characteristics alongside nutrient concentration measurements, the study aims to establish quantitative relationships that can support plant nutrient assessment. This approach may contribute to the development of cost-effective, non-destructive, and image-based techniques for monitoring banana plant health. The findings can be useful for precision agriculture, crop management, and future research on digital image processing for agricultural applications.","香蕉叶片养分浓度与可测量光学特征之间的关系：一项基于定量图像的研究探讨了利用定量图像分析研究香蕉叶片养分浓度与光学特征之间的关系。该研究聚焦于可测量的叶片特征，包括颜色强度、RGB值及其他图像衍生参数，以识别与植物养分状况相关的模式。香蕉叶片的数字图像可提供关于叶片外观变化的有价值信息，这些变化可能反映养分有效性的差异。通过将这些特征与养分浓度测量结果一并分析，该研究旨在建立可支持植物养分评估的定量关系。该方法可能有助于开发低成本、非破坏性且基于图像的香蕉植株健康监测技术。研究结果可用于精准农业、作物管理以及未来农业应用数字图像处理的研究。","2026-10-19T00:00:00Z",25,66,{"impact":17,"substance":19,"depth":218,"authority":17,"freshness":21,"relevant":22,"comment":307},"该研究基于图像定量分析香蕉叶片营养状况，方法新颖且面向无损监测，对精准农业有参考价值，但属单作物探索性研究，影响范围有限。",[309,310],{"name":10,"url":300},{"name":10,"url":311},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[27,313,314,315,316],"图像分析","香蕉","无损监测","作物营养",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":320},{"candidates":321,"agricultural_evidence":47},[322,326,331],{"code":33,"score":46,"supported":47,"title":323,"abstract":324,"tags":325},[],[221,116],[27],{"code":327,"score":22,"supported":14,"title":328,"abstract":329,"tags":330},"B2",[],[],[314],{"code":124,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[313],[336,337],"香蕉叶片 营养浓度 图像分析","香蕉 无损监测 精准农业","香蕉叶片营养浓度图像分析-5397","10.5281\u002Fzenodo.23469780",{"doi":339,"openalex_id":341,"authors":342,"venue":10,"cited_by_count":66,"oa_url":300,"card":345,"direction":350,"ingested_from":81},"W7222941290",[343],{"name":344,"orcid":9},"Anantjit Publication",{"tldr":346,"method":347,"finding":348,"direction":195,"opportunity":349},"用定量图像分析探究香蕉叶片养分浓度与可测光学特征的关系。","采集香蕉叶片数字图像，提取颜色强度、RGB值等参数，与养分浓度做定量关联分析。","叶片图像光学特征与养分浓度存在可量化的关联，可支撑无损养分评估。","可扩展到多品种、多生育期与田间光照校正，并构建图像-养分反演模型与便携诊断工具。","数字乡村与农业信息化","2026-10-20T23:30:22.619634Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":9,"content":9,"source_name":357,"source_url":355,"published_at":303,"category":12,"cover_url":9,"hotness":304,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":371,"search_phrases":379,"slug":382,"view_count":66,"doi":383,"paper":384,"created_at":396},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex",37,{"impact":99,"substance":84,"depth":99,"authority":84,"freshness":21,"relevant":22,"comment":360},"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[362,363],{"name":357,"url":355},{"name":357,"url":364},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[366,27,367,368],"数字农业","农业信息化","中非",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":372},{"candidates":373,"agricultural_evidence":47},[374],{"code":33,"score":98,"supported":47,"title":375,"abstract":377,"tags":378},[376],"digital agriculture",[376],[367,366,27],[380,381],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":383,"openalex_id":385,"authors":386,"venue":9,"cited_by_count":66,"oa_url":355,"card":9,"direction":350,"ingested_from":81},"W7222930115",[387,389,391,394],{"name":388,"orcid":9},"Anani Ogou",{"name":390,"orcid":9},"Henry Chotangui",{"name":392,"orcid":393},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":395,"orcid":9},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z"]