[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4688":3,"related-4688":72},{"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":22,"tags":24,"primary_topic":9,"secondary_topics":29,"classification":30,"search_phrases":51,"slug":54,"view_count":55,"doi":56,"paper":57,"created_at":71},4688,"USING AERIAL IMAGES FROM AN UNMANNED AERIAL VEHICLE TO CREATE A TERRAIN MAPPING","https:\u002F\u002Fdoi.org\u002F10.55186\u002F25880209_2026_10_5_51","Modern technologies of Earth remote sensing using unmanned aerial vehicles open up new opportunities for operational and high-precision mapping of the area. The article discusses the practical experience of creating cartographic material based on aerial photographs obtained from an unmanned aerial vehicle. The sequence of work is presented: from flight mission planning and aerial photography to photogrammetric processing of images in specialized software and the formation of the final orthophotoplane. The main stages of data processing are described, including image alignment, dense point cloud construction and digital terrain model creation. The advantages and limitations of using UAVs for mapping purposes are identified, recommendations for effective application of this technology are formulated. The obtained results demonstrate that the use of aerial photographs from UAVs allows creating up-to-date and detailed large-scale maps of the area with high accuracy and in a short time.","利用无人机进行地球遥感的现代技术为区域的高效高精度测绘开辟了新的可能性。本文探讨了基于无人机获取的航空影像创建制图材料的实践经验。文中呈现了从飞行任务规划、航空摄影到在专业软件中进行摄影测量处理并生成最终正射影像图的工作流程。描述了数据处理的主要阶段，包括影像对齐、密集点云构建和数字地形模型创建。指出了使用无人机进行测绘的优势与局限性，并提出了有效应用该技术的建议。所得结果表明，利用无人机航空影像能够在短时间内以高精度创建现势性强、细节丰富的大比例尺区域地图。",null,"Mezhdunarodnyi sel skokhozyaistvennyi zhurnal","2026-10-09T00:00:00Z","论文",10,false,52,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":17,"relevant":20,"comment":21},8,14,12,1,"俄文期刊论文，介绍无人机航拍制作地形图的完整流程，方法常规、缺乏新数据，仅对农业遥感测绘有参考价值。",[23],{"name":10,"url":6},[25,26,27,28],"精准农业","无人机遥感","农业测绘","地形建模",[],{"status":31,"locked":14,"version":32,"method":33,"evidence":34},"needs_review","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":14,"reason":50},[36,45],{"code":37,"score":38,"supported":39,"title":40,"abstract":41,"tags":44},"I3",7,true,[],[42,43],"遥感","remote sensing",[26],{"code":46,"score":20,"supported":14,"title":47,"abstract":48,"tags":49},"I1",[],[],[25],"农业相关性需要复核",[52,53],"无人机 航空影像 地形测绘","UAV 正射影像","无人机航空影像地形测绘-4688",0,"10.55186\u002F25880209_2026_10_5_51",{"doi":56,"openalex_id":58,"authors":59,"venue":10,"cited_by_count":55,"oa_url":63,"card":64,"direction":68,"ingested_from":70},"W7221164640",[60],{"name":61,"orcid":62},"Arian Dalbaraev","https:\u002F\u002Forcid.org\u002F0000-0001-8111-5085","https:\u002F\u002Fecience.ru\u002Fen\u002Fnauka\u002Farticle\u002F6ac904c9f4b844b77c0fba72\u002Fpdf",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"利用无人机航拍影像，通过摄影测量处理生成高精度地形图。","无人机航拍、影像对齐、密集点云、数字地形模型、正射影像。","无人机航拍可快速、高精度地制作大比例尺现势地形图。","农业遥感与作物表型","可探索无人机地形图与作物表型、土壤属性结合，服务精准农业管理。","openalex","2026-10-11T23:30:32.995964Z",{"total":73,"page":20,"page_size":73,"items":74},6,[75,151,248,354,408,459],{"id":76,"title":77,"url":78,"summary":79,"summary_zh":9,"content":9,"source_name":80,"source_url":9,"published_at":81,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":82,"score_detail":83,"sources":89,"tags":91,"primary_topic":95,"secondary_topics":101,"classification":107,"search_phrases":136,"slug":139,"view_count":140,"doi":9,"paper":141,"created_at":150},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。","MDPI Agronomy 16(18):1853","2026-09-20T00:00:00Z",68,{"impact":19,"substance":84,"depth":85,"authority":86,"freshness":87,"relevant":20,"comment":88},18,16,13,9,"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[90],{"name":80,"url":78},[92,25,26,93,94],"决策支持系统","智慧灌溉","土壤压实",{"code":96,"slug":97,"name":98,"path":99,"parent_code":100},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[102],{"code":103,"slug":104,"name":105,"path":106,"parent_code":100},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":108,"locked":14,"version":32,"method":33,"evidence":109},"classified",{"candidates":110,"agricultural_evidence":39},[111,116,120,126,131],{"code":96,"score":19,"supported":39,"title":112,"abstract":114,"tags":115},[113],"灌溉",[113],[93],{"code":46,"score":19,"supported":39,"title":117,"abstract":118,"tags":119},[25],[25],[25],{"code":103,"score":121,"supported":39,"title":122,"abstract":124,"tags":125},11,[123],"土壤",[123],[],{"code":37,"score":127,"supported":39,"title":128,"abstract":129,"tags":130},4,[],[42],[26],{"code":132,"score":20,"supported":14,"title":133,"abstract":134,"tags":135},"I2",[],[],[92],[137,138],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",2,{"doi":9,"openalex_id":9,"authors":142,"venue":9,"cited_by_count":55,"oa_url":9,"card":143,"direction":147,"ingested_from":149},[],{"tldr":144,"method":145,"finding":146,"direction":147,"opportunity":148},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","智慧农业 \u002F 农业物联网","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":165,"tags":167,"primary_topic":171,"secondary_topics":177,"classification":183,"search_phrases":210,"slug":213,"view_count":127,"doi":214,"paper":215,"created_at":247},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。","Remote Sensing of Environment","2026-09-19T00:00:00Z",84,{"impact":84,"substance":161,"depth":162,"authority":163,"freshness":87,"relevant":20,"comment":164},23,19,15,"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[166],{"name":157,"url":154},[168,169,170,25,26],"智慧农业","农业遥感","棉花",{"code":172,"slug":173,"name":174,"path":175,"parent_code":176},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[178],{"code":37,"slug":179,"name":180,"path":181,"parent_code":182},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":108,"locked":14,"version":32,"method":33,"evidence":184},{"candidates":185,"agricultural_evidence":39},[186,191,196,200,204],{"code":172,"score":87,"supported":39,"title":187,"abstract":189,"tags":190},[188],"cotton",[],[170],{"code":46,"score":17,"supported":39,"title":192,"abstract":193,"tags":195},[],[25,194],"precision agriculture",[168,25],{"code":37,"score":17,"supported":39,"title":197,"abstract":198,"tags":199},[],[42,43],[169,26],{"code":96,"score":73,"supported":39,"title":201,"abstract":202,"tags":203},[],[113,97],[],{"code":132,"score":73,"supported":39,"title":205,"abstract":206,"tags":209},[],[207,208],"深度学习","deep learning",[],[211,212],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014","10.1016\u002Fj.rse.2026.115674",{"doi":214,"openalex_id":216,"authors":217,"venue":157,"cited_by_count":55,"oa_url":154,"card":242,"direction":68,"ingested_from":70},"W7213661690",[218,221,223,225,227,229,232,235,237,240],{"name":219,"orcid":220},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":222,"orcid":9},"Dalei Hao",{"name":224,"orcid":9},"Jan Pisek",{"name":226,"orcid":9},"Zicheng Ji",{"name":228,"orcid":9},"Yanan Wei",{"name":230,"orcid":231},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":233,"orcid":234},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":236,"orcid":9},"Yangmin Feng",{"name":238,"orcid":239},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":241,"orcid":9},"Yelu Zeng",{"tldr":243,"method":244,"finding":245,"direction":68,"opportunity":246},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","2026-09-20T23:30:21.262698Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":9,"source_name":254,"source_url":251,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":256,"score_detail":257,"sources":260,"tags":262,"primary_topic":266,"secondary_topics":270,"classification":277,"search_phrases":329,"slug":332,"view_count":55,"doi":333,"paper":334,"created_at":353},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":84,"substance":258,"depth":84,"authority":86,"freshness":87,"relevant":20,"comment":259},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[261],{"name":254,"url":251},[168,263,264,25,265],"农业人工智能","物联网","气候韧性",{"code":132,"slug":267,"name":268,"path":269,"parent_code":182},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[271,276],{"code":272,"slug":273,"name":274,"path":275,"parent_code":182},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":96,"slug":97,"name":98,"path":99,"parent_code":100},{"status":108,"locked":14,"version":32,"method":33,"evidence":278},{"candidates":279,"agricultural_evidence":39},[280,289,293,300,305,312,316,323],{"code":132,"score":84,"supported":39,"title":281,"abstract":283,"tags":288},[282],"artificial intelligence",[207,284,285,286,208,287],"机器学习","计算机视觉","machine learning","computer vision",[263],{"code":46,"score":85,"supported":39,"title":290,"abstract":291,"tags":292},[194],[25,194],[168,25],{"code":272,"score":13,"supported":39,"title":294,"abstract":295,"tags":299},[],[264,296,297,298],"边缘计算","IoT","edge computing",[264],{"code":96,"score":87,"supported":39,"title":301,"abstract":302,"tags":304},[],[113,303,97],"水资源",[],{"code":306,"score":38,"supported":39,"title":307,"abstract":308,"tags":311},"H7",[],[309,310],"气候变化","climate change",[265],{"code":103,"score":73,"supported":39,"title":313,"abstract":314,"tags":315},[],[123,104],[],{"code":317,"score":318,"supported":39,"title":319,"abstract":320,"tags":322},"A7",3,[],[321],"间作",[],{"code":324,"score":318,"supported":39,"title":325,"abstract":326,"tags":328},"F3",[],[327],"养分管理",[],[330,331],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":333,"openalex_id":335,"authors":336,"venue":254,"cited_by_count":55,"oa_url":251,"card":347,"direction":147,"ingested_from":70},"W7223121456",[337,340,342,344],{"name":338,"orcid":339},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":341,"orcid":9},"Oluwadare OYEBODE",{"name":343,"orcid":9},"Iretiayo Adelaiye",{"name":345,"orcid":346},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":348,"method":349,"finding":350,"direction":351,"opportunity":352},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":9,"content":9,"source_name":359,"source_url":357,"published_at":360,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":365,"tags":367,"primary_topic":370,"secondary_topics":374,"classification":375,"search_phrases":387,"slug":390,"view_count":55,"doi":391,"paper":392,"created_at":407},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","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":85,"substance":363,"depth":84,"authority":18,"freshness":13,"relevant":20,"comment":364},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。",[366],{"name":359,"url":357},[168,263,25,368,369],"机器视觉","作物行检测",{"code":46,"slug":371,"name":372,"path":373,"parent_code":182},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":108,"locked":14,"version":32,"method":33,"evidence":376},{"candidates":377,"agricultural_evidence":39},[378,383],{"code":46,"score":379,"supported":39,"title":380,"abstract":381,"tags":382},5,[],[194],[168,25],{"code":132,"score":140,"supported":14,"title":384,"abstract":385,"tags":386},[],[],[263,368],[388,389],"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":391,"openalex_id":393,"authors":394,"venue":359,"cited_by_count":55,"oa_url":9,"card":9,"direction":9,"ingested_from":70},"W7223117828",[395,398,401,404],{"name":396,"orcid":397},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":399,"orcid":400},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":402,"orcid":403},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":405,"orcid":406},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":409,"title":410,"url":411,"summary":412,"summary_zh":9,"content":9,"source_name":413,"source_url":9,"published_at":414,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":415,"score_detail":416,"sources":419,"tags":421,"primary_topic":424,"secondary_topics":425,"classification":427,"search_phrases":447,"slug":450,"view_count":55,"doi":9,"paper":451,"created_at":458},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":85,"substance":363,"depth":417,"authority":87,"freshness":73,"relevant":20,"comment":418},17,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[420],{"name":413,"url":411},[168,263,422,26,423],"目标检测","棉花病害",{"code":172,"slug":173,"name":174,"path":175,"parent_code":176},[426],{"code":132,"slug":267,"name":268,"path":269,"parent_code":182},{"status":108,"locked":14,"version":32,"method":33,"evidence":428},{"candidates":429,"agricultural_evidence":39},[430,435,439,443],{"code":132,"score":84,"supported":39,"title":431,"abstract":433,"tags":434},[207,432],"YOLO",[],[263,422],{"code":172,"score":121,"supported":39,"title":436,"abstract":437,"tags":438},[170],[188],[],{"code":46,"score":20,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[168],{"code":37,"score":20,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[26],[448,449],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":9,"openalex_id":9,"authors":452,"venue":9,"cited_by_count":55,"oa_url":9,"card":453,"direction":68,"ingested_from":149},[],{"tldr":454,"method":455,"finding":456,"direction":68,"opportunity":457},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","2026-10-22T00:07:09.816790Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":465,"source_url":462,"published_at":466,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":471,"tags":473,"primary_topic":478,"secondary_topics":484,"classification":485,"search_phrases":531,"slug":534,"view_count":55,"doi":535,"paper":536,"created_at":568},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":258,"substance":469,"depth":84,"authority":86,"freshness":17,"relevant":20,"comment":470},20,"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[472],{"name":465,"url":462},[168,25,474,475,476,477],"基因编辑","植物病害","生物防治","纳米技术",{"code":479,"slug":480,"name":481,"path":482,"parent_code":483},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":108,"locked":14,"version":32,"method":33,"evidence":486},{"candidates":487,"agricultural_evidence":39},[488,492,497,504,508,512,516,522,526],{"code":46,"score":17,"supported":39,"title":489,"abstract":490,"tags":491},[],[25,194],[168,25],{"code":479,"score":38,"supported":39,"title":493,"abstract":494,"tags":496},[],[495,476],"农药",[476],{"code":498,"score":73,"supported":39,"title":499,"abstract":500,"tags":503},"A8",[],[501,502],"粮食安全","food security",[],{"code":103,"score":73,"supported":39,"title":505,"abstract":506,"tags":507},[],[123,104],[],{"code":306,"score":73,"supported":39,"title":509,"abstract":510,"tags":511},[],[309,310],[],{"code":37,"score":73,"supported":39,"title":513,"abstract":514,"tags":515},[],[42,43],[],{"code":517,"score":318,"supported":39,"title":518,"abstract":519,"tags":521},"H6",[],[520],"农业生态",[],{"code":132,"score":318,"supported":39,"title":523,"abstract":524,"tags":525},[],[282],[],{"code":527,"score":20,"supported":14,"title":528,"abstract":529,"tags":530},"F2",[],[],[474],[532,533],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":535,"openalex_id":537,"authors":538,"venue":465,"cited_by_count":55,"oa_url":462,"card":563,"direction":147,"ingested_from":70},"W7223044776",[539,542,545,548,551,554,557,560],{"name":540,"orcid":541},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":543,"orcid":544},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":546,"orcid":547},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":549,"orcid":550},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":552,"orcid":553},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":555,"orcid":556},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":558,"orcid":559},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":561,"orcid":562},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":564,"method":565,"finding":566,"direction":147,"opportunity":567},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z"]