[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1648":3,"related-1648":105},{"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":9,"secondary_topics":30,"classification":31,"search_phrases":75,"slug":78,"view_count":20,"doi":79,"paper":80,"created_at":104},1648,"Remote sensing for monitoring and managing Eichhornia crassipes: a review of methods, applications, and decision-support frameworks","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15788-y","Eichhornia crassipes (water hyacinth) is among the world's most aggressive aquatic invasive plants, with severe ecological and socioeconomic impacts on freshwater ecosystems. Over the last two decades, remote sensing has emerged as a critical tool for monitoring its spread and supporting management decisions, offering scalable, repeatable, and increasingly precise assessments. This review synthesizes 56 studies published between 2004 and 2025, examining how different platforms (satellites, UAVs, airborne, proximal), sensor types (multispectral, hyperspectral, SAR), and analytical methods (index thresholding, object-based classification, machine learning, deep learning) have been applied to detect, classify, and quantify E. crassipes. We highlight the evolution from early single-platform approaches to recent multimodal frameworks that integrate optical, radar, and UAV data, enabling both large-scale surveillance and fine-resolution diagnostics. Evidence demonstrates that species-level discrimination, phenological tracking, and biomass estimation are now feasible, with promising developments in linking spectral responses to pollutants and stress indicators. Despite these advances, key challenges remain in harmonizing data across platforms, reducing misclassification in mixed vegetation assemblages, and translating remote sensing outputs into decision-relevant information for adaptive management. We propose a decision-support framework built on five strategic pillars: multi-scale sensor fusion, ecologically interpretable classification schemes, scalable analytical ladders, early-warning indicators, and feedback loops linking monitoring to management actions. Together, these directions show how remote sensing can move beyond detection toward more responsive and decision-oriented approaches that support the sustainable management of E. crassipes invasions across diverse aquatic environments.","凤眼莲（Eichhornia crassipes，水葫芦）是全球最具侵略性的水生入侵植物之一，对淡水生态系统造成严重的生态和社会经济影响。过去二十年间，遥感技术已成为监测其扩散并支持管理决策的关键工具，能够提供可扩展、可重复且日益精准的评估。本综述整合了2004年至2025年间发表的56项研究，考察了不同平台（卫星、无人机、机载、近地面）、传感器类型（多光谱、高光谱、合成孔径雷达）以及分析方法（指数阈值法、面向对象分类、机器学习、深度学习）在凤眼莲检测、分类和定量中的应用。我们重点梳理了从早期单一平台方法到近期融合光学、雷达和无人机数据的多模态框架的演进过程，这些框架同时实现了大尺度监测与高分辨率诊断。证据表明，物种级判别、物候追踪和生物量估算现已可行，且在将光谱响应与污染物及胁迫指标关联方面取得了有前景的进展。尽管取得了上述进展，跨平台数据协调、混合植被群落中降低误分类率以及将遥感成果转化为适应管理所需的决策信息等方面仍面临关键挑战。我们提出一个基于五大战略支柱的决策支持框架：多尺度传感器融合、生态可解释的分类体系、可扩展的分析阶梯、早期预警指标以及连接监测与管理行动的反馈回路。综合来看，这些方向展示了遥感如何超越单纯检测，迈向更具响应性和决策导向的方法，从而支持不同水生环境中凤眼莲入侵的可持续管理。",null,"Environmental Monitoring and Assessment","2026-09-02T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,12,3,1,"系统综述遥感监测水葫芦的方法与应用，提出决策支持框架，对农业生态治理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农业遥感","外来入侵物种","生态监测",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":14,"reason":74},[37,47,57,63,69],{"code":38,"score":17,"supported":39,"title":40,"abstract":42,"tags":46},"I3",true,[41],"remote sensing",[43,41,44,45],"遥感","multispectral","hyperspectral",[27],{"code":48,"score":49,"supported":39,"title":50,"abstract":51,"tags":56},"I2",9,[],[52,53,54,55],"深度学习","机器学习","machine learning","deep learning",[],{"code":58,"score":20,"supported":39,"title":59,"abstract":60,"tags":62},"G1",[],[61],"成果转化",[],{"code":64,"score":65,"supported":14,"title":66,"abstract":67,"tags":68},"H6",2,[],[],[28,29],{"code":70,"score":21,"supported":14,"title":71,"abstract":72,"tags":73},"I1",[],[],[26],"农业相关性需要复核",[76,77],"外来入侵物种 农业遥感 智慧农业 生态监测","外来入侵物种 农业遥感","外来入侵物种农业遥感智慧农业生态监测-1648","10.1007\u002Fs10661-026-15788-y",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":95,"oa_url":96,"card":97,"direction":101,"ingested_from":103},"W7204996866",[83,85,88,90,92],{"name":84,"orcid":9},"Daniel H.C. Salim",{"name":86,"orcid":87},"Caio Mello","https:\u002F\u002Forcid.org\u002F0000-0001-7492-7237",{"name":89,"orcid":9},"Gabriel Pereira",{"name":91,"orcid":9},"Raian V. Maretto",{"name":93,"orcid":94},"Camila C. Amorim","https:\u002F\u002Forcid.org\u002F0000-0001-6132-0866",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10661-026-15788-y.pdf",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"综述遥感监测水葫芦的方法、应用及决策支持框架，涵盖2004-2025年56项研究。","综述56项研究，分析卫星、无人机等平台及多光谱、高光谱、SAR数据，结合机器学习","遥感可实现物种识别、物候追踪和生物量估算，但需解决数据融合和分类误差，提出五支柱决策框架。","农业遥感与作物表型","可探索多源遥感数据融合与生态解释性分类，开发早期预警指标，将监测结果直接用于入侵物种的适应性管理。","openalex","2026-09-04T23:30:33.784091Z",{"total":106,"page":21,"page_size":106,"items":107},6,[108,208,271,322,394,454],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":124,"primary_topic":128,"secondary_topics":134,"classification":146,"search_phrases":176,"slug":179,"view_count":95,"doi":180,"paper":181,"created_at":207},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":118,"substance":119,"depth":17,"authority":120,"freshness":13,"relevant":21,"comment":121},16,22,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[123],{"name":114,"url":111},[26,125,27,126,127],"农业人工智能","氮素监测","紫花苜蓿",{"code":129,"slug":130,"name":131,"path":132,"parent_code":133},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[135,140],{"code":38,"slug":136,"name":137,"path":138,"parent_code":139},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":141,"slug":142,"name":143,"path":144,"parent_code":145},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":147,"locked":14,"version":33,"method":34,"evidence":148},"classified",{"candidates":149,"agricultural_evidence":39},[150,154,161,167,172],{"code":38,"score":17,"supported":39,"title":151,"abstract":152,"tags":153},[45],[43,41,45],[27],{"code":129,"score":155,"supported":39,"title":156,"abstract":158,"tags":160},15,[157],"alfalfa",[159,157],"苜蓿",[127],{"code":141,"score":13,"supported":39,"title":162,"abstract":163,"tags":166},[],[164,159,165],"饲草","forage",[127],{"code":48,"score":13,"supported":39,"title":168,"abstract":169,"tags":171},[],[52,53,54,55,170],"decision support",[125],{"code":70,"score":21,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[26],[177,178],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":180,"openalex_id":182,"authors":183,"venue":114,"cited_by_count":95,"oa_url":111,"card":202,"direction":101,"ingested_from":103},"W7223154576",[184,186,188,190,192,195,197,200],{"name":185,"orcid":9},"Shuai Fu",{"name":187,"orcid":9},"Yunhao Li",{"name":189,"orcid":9},"Chunli Miao",{"name":191,"orcid":9},"Jie Liu",{"name":193,"orcid":194},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":196,"orcid":9},"Yongjia Hu",{"name":198,"orcid":199},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":201,"orcid":9},"吴彩霞",{"tldr":203,"method":204,"finding":205,"direction":101,"opportunity":206},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":9,"content":9,"source_name":213,"source_url":9,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":217,"tags":219,"primary_topic":223,"secondary_topics":228,"classification":236,"search_phrases":258,"slug":261,"view_count":21,"doi":9,"paper":262,"created_at":270},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":17,"substance":119,"depth":17,"authority":120,"freshness":13,"relevant":21,"comment":216},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[218],{"name":213,"url":211},[26,27,220,221,222],"作物表型","无人机多光谱","小麦渍害",{"code":224,"slug":225,"name":226,"path":227,"parent_code":133},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[229,230],{"code":38,"slug":136,"name":137,"path":138,"parent_code":139},{"code":231,"slug":232,"name":233,"path":234,"parent_code":235},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":147,"locked":14,"version":33,"method":34,"evidence":237},{"candidates":238,"agricultural_evidence":39},[239,244,248,254],{"code":224,"score":240,"supported":39,"title":241,"abstract":242,"tags":243},11,[226],[226],[],{"code":38,"score":240,"supported":39,"title":245,"abstract":246,"tags":247},[221],[],[220,27,221],{"code":231,"score":249,"supported":39,"title":250,"abstract":252,"tags":253},8,[251],"渍害",[],[],{"code":70,"score":21,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[26],[259,260],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":9,"openalex_id":9,"authors":263,"venue":9,"cited_by_count":95,"oa_url":9,"card":264,"direction":101,"ingested_from":269},[],{"tldr":265,"method":266,"finding":267,"direction":101,"opportunity":268},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":9,"content":9,"source_name":276,"source_url":9,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":283,"tags":285,"primary_topic":286,"secondary_topics":287,"classification":290,"search_phrases":310,"slug":313,"view_count":95,"doi":9,"paper":314,"created_at":321},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":118,"substance":280,"depth":281,"authority":120,"freshness":249,"relevant":21,"comment":282},21,17,"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[284],{"name":276,"url":274},[26,27,220,221,222],{"code":224,"slug":225,"name":226,"path":227,"parent_code":133},[288,289],{"code":231,"slug":232,"name":233,"path":234,"parent_code":235},{"code":38,"slug":136,"name":137,"path":138,"parent_code":139},{"status":147,"locked":14,"version":33,"method":34,"evidence":291},{"candidates":292,"agricultural_evidence":39},[293,297,301,305],{"code":224,"score":240,"supported":39,"title":294,"abstract":295,"tags":296},[226],[226],[],{"code":231,"score":240,"supported":39,"title":298,"abstract":299,"tags":300},[251],[251],[],{"code":38,"score":240,"supported":39,"title":302,"abstract":303,"tags":304},[221],[],[220,27,221],{"code":70,"score":306,"supported":39,"title":307,"abstract":308,"tags":309},4,[],[26],[26],[311,312],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":9,"openalex_id":9,"authors":315,"venue":9,"cited_by_count":95,"oa_url":9,"card":316,"direction":101,"ingested_from":269},[],{"tldr":317,"method":318,"finding":319,"direction":101,"opportunity":320},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":337,"secondary_topics":338,"classification":343,"search_phrases":366,"slug":369,"view_count":21,"doi":370,"paper":371,"created_at":393},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems",69,{"impact":19,"substance":18,"depth":118,"authority":19,"freshness":49,"relevant":21,"comment":331},"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[333],{"name":328,"url":325},[26,27,52,335,336],"油棕","病害诊断",{"code":38,"slug":136,"name":137,"path":138,"parent_code":139},[339],{"code":48,"slug":340,"name":341,"path":342,"parent_code":139},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":147,"locked":14,"version":33,"method":34,"evidence":344},{"candidates":345,"agricultural_evidence":39},[346,350,355,362],{"code":38,"score":155,"supported":39,"title":347,"abstract":348,"tags":349},[41],[43,41],[27],{"code":48,"score":19,"supported":39,"title":351,"abstract":352,"tags":354},[55],[353],"YOLO",[52],{"code":356,"score":106,"supported":39,"title":357,"abstract":358,"tags":361},"B5",[],[359,360],"蘑菇","mushroom",[],{"code":70,"score":21,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[26],[367,368],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":370,"openalex_id":372,"authors":373,"venue":328,"cited_by_count":95,"oa_url":325,"card":388,"direction":101,"ingested_from":103},"W7222626135",[374,377,380,382,385],{"name":375,"orcid":376},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":378,"orcid":379},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":381,"orcid":9},"Jin Yuan Chan",{"name":383,"orcid":384},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":386,"orcid":387},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":389,"method":390,"finding":391,"direction":101,"opportunity":392},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":406,"tags":408,"primary_topic":412,"secondary_topics":416,"classification":417,"search_phrases":435,"slug":438,"view_count":95,"doi":439,"paper":440,"created_at":453},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%。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",74,{"impact":19,"substance":119,"depth":17,"authority":404,"freshness":49,"relevant":21,"comment":405},13,"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。",[407],{"name":400,"url":397},[26,409,27,410,411],"无人机","区块链","数据安全",{"code":70,"slug":413,"name":414,"path":415,"parent_code":139},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":147,"locked":14,"version":33,"method":34,"evidence":418},{"candidates":419,"agricultural_evidence":39},[420,426,430],{"code":70,"score":421,"supported":39,"title":422,"abstract":423,"tags":425},7,[],[26,424],"smart agriculture",[26],{"code":38,"score":21,"supported":14,"title":427,"abstract":428,"tags":429},[],[],[27],{"code":431,"score":21,"supported":14,"title":432,"abstract":433,"tags":434},"I6",[],[],[411],[436,437],"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":439,"openalex_id":441,"authors":442,"venue":400,"cited_by_count":95,"oa_url":397,"card":447,"direction":451,"ingested_from":103},"W7222323013",[443,445],{"name":444,"orcid":9},"B.  Gnanasankari",{"name":446,"orcid":9},"R.  Padmanaban",{"tldr":448,"method":449,"finding":450,"direction":451,"opportunity":452},"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。","2026-10-16T23:30:08.237868Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":462,"sources":464,"tags":466,"primary_topic":469,"secondary_topics":474,"classification":475,"search_phrases":535,"slug":538,"view_count":95,"doi":539,"paper":540,"created_at":560},4987,"Smart Agriculture Research in Date Palm (Phoenix dactylifera L.) Production: A Comprehensive Bibliometric Study","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15203137","The date palm (Phoenix dactylifera L.) is fundamental to food security, livelihoods, and agro-ecosystem sustainability across the Arabian Peninsula, yet its production is increasingly constrained by water scarcity, salinity, climate change, and biotic stresses. Smart agriculture technologies offer opportunities to improve crop monitoring, resource-use efficiency, and decision-making; however, their application in date palm production remains fragmented and insufficiently validated. This study evaluates the development, geographic distribution, technological focus, research approaches, and thematic structure of smart agriculture research on date palms in the Arabian Peninsula and other major date-producing countries. A bibliometric analysis was conducted using peer-reviewed English-language articles and reviews indexed in Scopus and Web of Science from 2016 to 2026, with 2026 representing a partial year. Following screening and duplicate removal, 164 published studies were retained. Publication output increased gradually, although the trend was not statistically significant (β = 1.25 articles year−1, p = 0.068; Mann–Kendall τ = 0.489, p = 0.060). The literature was concentrated in the Gulf Cooperation Council (GCC) region, with Saudi Arabia contributing 54 publications (32.9%). General agronomy\u002Fother topics accounted for 46.3% of publications, while remote sensing\u002FUAVs (17.1%), GIS\u002Fgeospatial technologies (14.0%), IoT\u002Fsensors (13.4%), and AI\u002Fmachine learning (9.8%) represented the leading specific technology categories. Water and irrigation management dominated thematic applications (46.3%), whereas Smart Farming\u002FIoT accounted for only 11.0%. Observational and monitoring studies comprised 50.0% of publications, compared with only 6.1% involving field experiments, indicating limited field-scale validation. Internationally co-authored studies received significantly more citations than domestically authored studies (16.55 vs. 8.68; p = 0.0007). Thematic analysis identified four major clusters: geospatial monitoring, water management, pest control, and sustainability, with AI\u002Fmachine learning showing marked growth after 2022. Altogether, the field is expanding but remains fragmented, highlighting the need for integrated IoT and sensor networks, rigorous field trials, AI-driven decision-support systems, and stronger regional and international collaboration to enable scalable, context-specific smart agriculture for date palm production.","海枣（Phoenix dactylifera L.）对阿拉伯半岛的粮食安全、生计和农业生态系统可持续性至关重要，然而其生产日益受到水资源短缺、盐碱化、气候变化和生物胁迫的制约。智慧农业技术为改善作物监测、资源利用效率和决策提供了机遇；然而，其在海枣生产中的应用仍然零散且缺乏充分验证。本研究评估了阿拉伯半岛及其他主要海枣生产国海枣智慧农业研究的发展、地理分布、技术重点、研究方法和主题结构。采用文献计量学分析方法，基于Scopus和Web of Science数据库中2016年至2026年索引的同行评审英文论文和综述，其中2026年为部分年份。经过筛选和去重后，共保留164篇已发表研究。发文量逐渐增长，但该趋势未达到统计学显著性（β = 1.25篇\u002F年，p = 0.068；Mann–Kendall τ = 0.489，p = 0.060）。文献集中于海湾合作委员会（GCC）地区，其中沙特阿拉伯贡献了54篇论文（32.9%）。一般农学\u002F其他主题占论文总数的46.3%，而遥感\u002F无人机（17.1%）、GIS\u002F地理空间技术（14.0%）、物联网\u002F传感器（13.4%）和人工智能\u002F机器学习（9.8%）为主要的具体技术类别。水资源与灌溉管理在主题应用中占主导地位（46.3%），而智慧农业\u002F物联网仅占11.0%。观测与监测类研究占论文总数的50.0%，而涉及田间试验的仅占6.1%，表明田间尺度的验证有限。国际合著论文的引用次数显著高于国内独著论文（16.55 vs. 8.68；p = 0.0007）。主题分析识别出四大聚类：地理空间监测、水资源管理、病虫害防治和可持续性，其中人工智能\u002F机器学习在2022年后呈现显著增长。总体而言，该领域正在扩展但仍显零散，凸显了整合物联网与传感器网络、严格的田间试验、人工智能驱动的决策支持系统以及加强区域和国际合作的必要性，以推动可规模化、因地制宜的海枣生产智慧农业。","Plants","2026-10-14T00:00:00Z",{"impact":17,"substance":119,"depth":17,"authority":120,"freshness":249,"relevant":21,"comment":463},"基于Scopus与WoS的164篇文献计量分析，揭示枣椰树智慧农业研究碎片化、田间验证不足，对干旱区智慧农业布局有参考价值。",[465],{"name":460,"url":457},[26,125,27,467,468],"农业物联网","枣椰树",{"code":470,"slug":471,"name":472,"path":473,"parent_code":235},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[],{"status":147,"locked":14,"version":33,"method":34,"evidence":476},{"candidates":477,"agricultural_evidence":39},[478,486,490,496,500,504,511,518,524,529],{"code":479,"score":281,"supported":39,"title":480,"abstract":482,"tags":485},"I7",[481],"bibliometric",[483,484,481],"文献计量","研究方法",[],{"code":70,"score":155,"supported":39,"title":487,"abstract":488,"tags":489},[424],[26,424],[26],{"code":470,"score":49,"supported":39,"title":491,"abstract":492,"tags":495},[],[493,494,471],"灌溉","水资源",[],{"code":48,"score":421,"supported":39,"title":497,"abstract":498,"tags":499},[],[53,54],[125],{"code":38,"score":421,"supported":39,"title":501,"abstract":502,"tags":503},[],[43,41],[27],{"code":505,"score":421,"supported":39,"title":506,"abstract":507,"tags":510},"I5",[],[508,509],"物联网","IoT",[467],{"code":512,"score":106,"supported":39,"title":513,"abstract":514,"tags":517},"A8",[],[515,516],"粮食安全","food security",[],{"code":231,"score":106,"supported":39,"title":519,"abstract":520,"tags":523},[],[521,522],"气候变化","climate change",[],{"code":64,"score":20,"supported":39,"title":525,"abstract":526,"tags":528},[],[527],"农业生态",[],{"code":530,"score":20,"supported":39,"title":531,"abstract":532,"tags":534},"I4",[],[533],"GIS",[],[536,537],"枣椰树 智慧农业 文献计量","农业人工智能 农业物联网 农业遥感 智慧农业","枣椰树智慧农业文献计量-4987","10.3390\u002Fplants15203137",{"doi":539,"openalex_id":541,"authors":542,"venue":460,"cited_by_count":95,"oa_url":457,"card":555,"direction":451,"ingested_from":103},"W7221786893",[543,546,549,552],{"name":544,"orcid":545},"Sajid Safeer","https:\u002F\u002Forcid.org\u002F0000-0002-0120-5900",{"name":547,"orcid":548},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":550,"orcid":551},"Sajid Maqsood","https:\u002F\u002Forcid.org\u002F0000-0003-2099-8392",{"name":553,"orcid":554},"Cataldo Pulvento","https:\u002F\u002Forcid.org\u002F0000-0002-2650-1204",{"tldr":556,"method":557,"finding":558,"direction":451,"opportunity":559},"用文献计量分析2016-2026年枣椰树智慧农业研究，发现领域增长但碎片化、田间验证不足。","Scopus与Web of Science文献计量分析，164篇论文，含趋势检验","研究集中于海湾地区，遥感与灌溉为主，田间试验仅6.1%，国际合作论文引用更高。","枣椰树智慧农业缺乏IoT传感器集成与田间验证，AI决策支持系统是待填补的研究空白。","2026-10-15T23:30:08.558630Z"]