[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2960":3,"related-2960":68},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":49,"slug":52,"view_count":43,"doi":53,"paper":54,"created_at":67},2960,"Мониторинг трансформации руслового рельефа при селевых воздействиях с применением беспилотных авиационных систем","https:\u002F\u002Fdoi.org\u002F10.46698\u002Fvnc.2026.73.60.001","В работе показан опыт применения цифровых методов дистанционного зондирования для количественной оценки морфометрических последствий схода селевого потока в русле горной реки. По материалам разновременной БПЛА-аэрофотосъёмки участка р. Нальчик у с. Хасанья (до и после события) построены ортофотопланы и цифровые модели местности, на основе которых рассчитаны объёмы денудации и аккумуляции грунта. Вынос грунта в зоне разрушения автодороги составил 4,3 тыс. м³, накопление наносов выше автодорожного моста – 4,6 тыс. м³. Полученные данные интегрированы в базу расчётов для разработки инженерно-защитных мероприятий. Показана высокая эффективность метода для оперативного мониторинга горных территорий при минимальных затратах. The paper shows the experience of using digital remote sensing methods to quantify the morphometric consequences of a mudflow in a mountain riverbed. Based on multi-temporal UAV aerial surveys of a section of the Nalchik River near the village of Khasanya conducted before and after the event, orthophotomaps and digital terrain models (DTMs) were generated, on the basis of which the volumes of denudation and sediment accumulation were calculated. The volume of material in the area where the highway was destroyed amounted to 4.3 thousand m3 , while sediment accumulation upstream of the highway bridge amounted to 4.6 thousand m3 . The data obtained were integrated into a calculation database for developing engineering protection measures. The study demonstrates the high efficiency of the method for operational monitoring of mountainous areas at relatively low cost. Keywords: digital terrain model, orthophotomap, mudflow, riverchannel, morphometry, unmanned aerial system, remote sensing","本文展示了应用数字遥感方法对山区河流河床泥石流（селевой поток）地貌形态后果进行定量评估的实践。基于对哈萨尼亚村附近纳利奇克河某河段在事件前后开展的无人机（БПЛА）多时相航空摄影资料，构建了正射影像图和数字地形模型，并据此计算了土壤剥蚀量和堆积量。公路破坏区的土体输出量为4300立方米，公路桥上游的泥沙堆积量为4600立方米。所得数据已整合至工程防护措施设计计算数据库。研究表明，该方法在以最低成本对山区进行实时监测方面具有高效性。",null,"Вестник Владикавказского научного центра","2026-09-18T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究山地泥石流河床地形变化的无人机遥感监测，属于地质灾害与遥感测绘领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"无人机遥感","泥石流监测","山地灾害","数字地形模型",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":48},[32,41],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I3",7,true,[],[38,39],"遥感","remote sensing",[21],{"code":42,"score":43,"supported":35,"title":44,"abstract":45,"tags":47},"H2",3,[],[46],"土壤",[],"农业相关性需要复核",[50,51],"Нальчик 泥石流 无人机","泥石流 河床 地形监测","Нальчик泥石流无人机-2960","10.46698\u002Fvnc.2026.73.60.001",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":15,"oa_url":6,"card":59,"direction":65,"ingested_from":66},"W7213550177",[57],{"name":58,"orcid":9},"З.Ж. Гергокова",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"用无人机多期航测量化泥石流对山区河床地形的侵蚀与堆积变化。","前后两期无人机航拍生成正射影像与数字地形模型，计算侵蚀堆积体积。","公路破坏区冲出泥沙4.3千立方米，桥上游堆积4.6千立方米，方法高效低成本。","农业遥感与作物表型","可将无人机多期地形监测拓展至农业流域泥沙输移与农田保护工程评估。","数字乡村与农业信息化","openalex","2026-09-19T23:30:45.161114Z",{"total":69,"page":70,"page_size":69,"items":71},6,1,[72,146,200,251,301,347],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":9,"content":9,"source_name":77,"source_url":9,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":86,"tags":88,"primary_topic":93,"secondary_topics":99,"classification":106,"search_phrases":133,"slug":136,"view_count":15,"doi":9,"paper":137,"created_at":145},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":81,"substance":82,"depth":83,"authority":84,"freshness":69,"relevant":70,"comment":85},16,21,17,9,"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。",[87],{"name":77,"url":75},[89,90,91,21,92],"智慧农业","农业人工智能","目标检测","棉花病害",{"code":94,"slug":95,"name":96,"path":97,"parent_code":98},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[100],{"code":101,"slug":102,"name":103,"path":104,"parent_code":105},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":107,"locked":14,"version":28,"method":29,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":35},[110,117,124,129],{"code":101,"score":111,"supported":35,"title":112,"abstract":115,"tags":116},18,[113,114],"深度学习","YOLO",[],[90,91],{"code":94,"score":118,"supported":35,"title":119,"abstract":121,"tags":123},11,[120],"棉花",[122],"cotton",[],{"code":125,"score":70,"supported":14,"title":126,"abstract":127,"tags":128},"I1",[],[],[89],{"code":33,"score":70,"supported":14,"title":130,"abstract":131,"tags":132},[],[],[21],[134,135],"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":138,"venue":9,"cited_by_count":15,"oa_url":9,"card":139,"direction":63,"ingested_from":144},[],{"tldr":140,"method":141,"finding":142,"direction":63,"opportunity":143},"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。","agent","2026-10-22T00:07:09.816790Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":9,"content":9,"source_name":151,"source_url":9,"published_at":78,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":158,"tags":160,"primary_topic":163,"secondary_topics":168,"classification":169,"search_phrases":188,"slug":191,"view_count":15,"doi":9,"paper":192,"created_at":199},5362,"深度语义分割模型的时序泛化评估:无人机小麦倒伏监测 5 年跨年测试","https:\u002F\u002Fbioengineer.org\u002Fai-models-trained-on-one-year-of-wheat-fail-the-next-five-year-drone-study-shows\u002F","Zhang G., Wang X., Wang D., Sun D., Zhang Y., Zhang D. (2026) 利用 5 年田间试验评估深度语义分割模型在无人机(UAV)小麦倒伏监测中的时序泛化能力。研究发现:同年内高精度不能保证跨年可靠性,模型从训练年份迁移到其他年份时性能显著下降,基于注意力的模型通常比全卷积网络更稳健,可能因为层级窗口自注意力能整合更长范围上下文,而卷积强调局部模式;即便最优架构在大时序漂移下也出现严重退化。强调时序泛化测试应成为农业 AI 模型部署前的必备环节。DOI: 10.1016\u002Fj.atech.2026.102631。","Smart Agricultural Technology Vol.15 Article 102631 2026-10-16",79,{"impact":111,"substance":154,"depth":111,"authority":155,"freshness":156,"relevant":70,"comment":157},22,13,8,"基于5年跨年田间试验揭示农业AI模型时序泛化瓶颈，结论扎实且具部署指导价值，值得入选。",[159],{"name":151,"url":149},[89,90,21,161,162],"小麦倒伏","模型泛化",{"code":164,"slug":165,"name":166,"path":167,"parent_code":98},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[],{"status":107,"locked":14,"version":28,"method":29,"evidence":170},{"candidates":171,"agricultural_evidence":35},[172,176,180,184],{"code":164,"score":118,"supported":35,"title":173,"abstract":174,"tags":175},[166],[166],[],{"code":125,"score":70,"supported":14,"title":177,"abstract":178,"tags":179},[],[],[89],{"code":101,"score":70,"supported":14,"title":181,"abstract":182,"tags":183},[],[],[90],{"code":33,"score":70,"supported":14,"title":185,"abstract":186,"tags":187},[],[],[21],[189,190],"无人机 小麦倒伏 监测","深度语义分割 时序泛化","无人机小麦倒伏监测-5362",{"doi":9,"openalex_id":9,"authors":193,"venue":9,"cited_by_count":15,"oa_url":9,"card":194,"direction":63,"ingested_from":144},[],{"tldr":195,"method":196,"finding":197,"direction":63,"opportunity":198},"用5年田间试验评估深度语义分割模型在无人机小麦倒伏监测中的跨年泛化能力。","5年无人机小麦倒伏影像，对比注意力模型与全卷积网络的跨年迁移表现。","同年内高精度不能保证跨年可靠，注意力模型比全卷积网络更稳健，但大时序漂移下仍严重退化。","可研究时序域自适应与跨年增量学习，建立农业AI模型部署前的时序泛化评测基准。","2026-10-20T02:47:54.289085Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":9,"content":9,"source_name":205,"source_url":203,"published_at":206,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":207,"score_detail":208,"sources":213,"tags":215,"primary_topic":219,"secondary_topics":223,"classification":224,"search_phrases":237,"slug":240,"view_count":15,"doi":241,"paper":242,"created_at":250},5135,"AutoPlot grid: development and quantitative evaluation of an RTK-GNSS-based tool for automated breeding-plot delineation and UAV phenotyping data extraction","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10457-z","AutoPlot grid: development and quantitative evaluation of an RTK-GNSS-based tool for automated breeding-plot delineation and UAV phenotyping data extraction。Precision Agriculture","Precision Agriculture","2026-10-15T00:00:00Z",71,{"impact":209,"substance":210,"depth":83,"authority":211,"freshness":156,"relevant":70,"comment":212},12,20,14,"该论文提出基于RTK-GNSS的育种小区自动划分工具，并定量评估了无人机表型数据提取效果，方法新颖、结论可靠，但属于细分领域工具开发，产业影响有限，适合作为专业精选而非头条。",[214],{"name":205,"url":203},[89,21,216,217,218],"表型组学","育种表型","RTK-GNSS",{"code":125,"slug":220,"name":221,"path":222,"parent_code":105},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":107,"locked":14,"version":28,"method":29,"evidence":225},{"candidates":226,"agricultural_evidence":35},[227,233],{"code":125,"score":228,"supported":35,"title":229,"abstract":230,"tags":232},4,[],[231],"precision agriculture",[89],{"code":33,"score":70,"supported":14,"title":234,"abstract":235,"tags":236},[],[],[21],[238,239],"无人机遥感 智慧农业 育种表型 表型组学","无人机遥感 智慧农业","无人机遥感智慧农业育种表型表型组学-5135","10.1007\u002Fs11119-026-10457-z",{"doi":241,"openalex_id":243,"authors":244,"venue":205,"cited_by_count":15,"oa_url":9,"card":9,"direction":63,"ingested_from":66},"W7222190582",[245,247],{"name":246,"orcid":9},"Aashvi Dua",{"name":248,"orcid":249},"Ajay Sharda","https:\u002F\u002Forcid.org\u002F0000-0001-5161-4123","2026-10-17T23:30:02.059850Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":9,"content":9,"source_name":256,"source_url":9,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":79,"score_detail":258,"sources":260,"tags":262,"primary_topic":265,"secondary_topics":271,"classification":272,"search_phrases":289,"slug":292,"view_count":70,"doi":9,"paper":293,"created_at":300},5131,"Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.19154","Yanben Shen等评估了17份豌豆种质资源和7个加拿大品种在爱荷华州的农艺和籽粒产量表现。研究发现PI种质资源试验在物候学和冠层性状上表现出遗传多样性,而加拿大优良品种在爱荷华条件下显示出较大的产量潜力。最优产量加拿大品种在两个试验年中平均产量为1961公斤\u002F公顷。为综合评估田间豌豆表现,研究提取了时间序列无人机(UAV)衍生的性状,评估了它们与多个农艺性状和产量的相关性。结果发现UAV衍生的时间序列冠层面积是所有UAV衍生冠层特征、植被指数和人工测量中与产量最相关的性状(r=0.63)。","arXiv 2610.19154 2026 年 10 月 13 日","2026-10-13T00:00:00Z",{"impact":209,"substance":82,"depth":83,"authority":118,"freshness":156,"relevant":70,"comment":259},"预印本以时间序列无人机表型揭示冠层面积与豌豆产量强相关，方法新颖但属区域育种研究，影响力有限。",[261],{"name":256,"url":254},[89,263,21,216,264],"产量预测","豌豆育种",{"code":266,"slug":267,"name":268,"path":269,"parent_code":270},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[],{"status":107,"locked":14,"version":28,"method":29,"evidence":273},{"candidates":274,"agricultural_evidence":35},[275,280,285],{"code":266,"score":43,"supported":35,"title":276,"abstract":277,"tags":279},[],[278],"种质资源",[],{"code":33,"score":281,"supported":14,"title":282,"abstract":283,"tags":284},2,[],[],[263,21],{"code":125,"score":70,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[89],[290,291],"爱荷华州 豌豆 无人机表型","豌豆种质资源 产量 冠层面积","爱荷华州豌豆无人机表型-5131",{"doi":9,"openalex_id":9,"authors":294,"venue":9,"cited_by_count":15,"oa_url":9,"card":295,"direction":63,"ingested_from":144},[],{"tldr":296,"method":297,"finding":298,"direction":63,"opportunity":299},"评估17份豌豆种质与7个加拿大品种在爱荷华的表现，并用无人机时间序列表型关联产量。","田间试验结合时间序列无人机冠层成像与植被指数提取，关联农艺性状和产量。","无人机时间序列冠层面积是与产量最相关的性状（r=0.63），优于其他植被指数和人工测量。","可探索多时相无人机冠层面积预测产量的通用模型，并拓展到其他豆科作物育种筛选。","2026-10-17T00:13:44.990816Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":308,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":207,"score_detail":309,"sources":311,"tags":313,"primary_topic":9,"secondary_topics":318,"classification":319,"search_phrases":327,"slug":330,"view_count":15,"doi":331,"paper":332,"created_at":346},5100,"Scale‐space image analysis for mapping spotted knapweed in grassland ecosystems","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faps3.70086","Abstract Premise This study investigates the value of scale‐space representations of spatial features for the identification and mapping of invasive plants using high‐resolution multispectral imagery. Scale‐space representations combine the two‐dimensional spatial domain with a third dimension representing spatial scale, such that features can be represented across both space and image spatial resolution. Methods Gaussian pyramids were constructed to create representations of the features across five image resolution levels (between 3 and 46 cm) within the scale space. A case study was employed to evaluate the importance of features across the scale space for the identification and mapping of spotted knapweed in a grassland ecosystem using multispectral imagery acquired using a remotely piloted aircraft system (RPAS). Classification was performed using a random forest classifier that was constructed using a process that included feature and hyperparameter optimization. Results The results of this case study show that classifiers constructed with only high spatial resolution features were associated with better classification performance than those constructed with only lower spatial resolution features; however, the best classification is achieved by leveraging features from throughout the feature scale spaces. Discussion These findings suggest that optimization of the spatial scale at which features are computed can substantially improve remote sensing image analysis and potentially inform RPAS flight planning to improve mission efficiency.","摘要 前提 本研究探讨了空间特征的尺度空间表示在高分辨率多光谱影像中用于入侵植物识别与制图的价值。尺度空间表示将二维空间域与表示空间尺度的第三维相结合，从而使特征能够在空间和影像空间分辨率两个维度上得到表示。方法 构建高斯金字塔，以在尺度空间内生成跨越五个影像分辨率层级（3至46厘米）的特征表示。通过一个案例研究，评估尺度空间中的特征在使用由遥控驾驶航空器系统（RPAS）获取的多光谱影像对草地生态系统中斑点矢车菊进行识别与制图时的重要性。分类使用随机森林分类器完成，该分类器通过包含特征优化和超参数优化的流程构建。结果 本案例研究的结果表明，仅使用高空间分辨率特征构建的分类器，其分类性能优于仅使用较低空间分辨率特征构建的分类器；然而，最佳分类效果是通过综合利用整个特征尺度空间中的特征实现的。讨论 这些发现表明，优化特征计算所采用的空间尺度可以显著改进遥感影像分析，并有可能为RPAS飞行规划提供依据，以提高任务效率。","Applications in Plant Sciences","2026-10-14T00:00:00Z",{"impact":209,"substance":82,"depth":83,"authority":155,"freshness":156,"relevant":70,"comment":310},"方法新颖、结论可靠，但属细分领域基础研究，产业影响有限，适合作为技术前沿收录而非头条精选。",[312],{"name":307,"url":304},[21,314,315,316,317],"遥感监测","多光谱影像","入侵植物","草地生态",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":320},{"candidates":321,"agricultural_evidence":14,"reason":48},[322],{"code":33,"score":118,"supported":35,"title":323,"abstract":324,"tags":326},[],[38,39,325],"multispectral",[21,314],[328,329],"斑点矢车菊 无人机 遥感","尺度空间 多光谱 草地","斑点矢车菊无人机遥感-5100","10.1002\u002Faps3.70086",{"doi":331,"openalex_id":333,"authors":334,"venue":307,"cited_by_count":15,"oa_url":340,"card":341,"direction":63,"ingested_from":66},"W7222056923",[335,337],{"name":336,"orcid":9},"Shohreh Sahebi",{"name":338,"orcid":339},"David Hill","https:\u002F\u002Forcid.org\u002F0000-0002-4530-3131","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faps3.70086",{"tldr":342,"method":343,"finding":344,"direction":63,"opportunity":345},"利用尺度空间表示多光谱影像特征，结合随机森林分类器，实现草地生态系统中斑点矢车菊的识别与制图。","构建高斯金字塔生成5个分辨率层级（3-46cm）的尺度空间特征，用随机森林分类并","仅用高分辨率特征的分类器优于仅用低分辨率特征，但融合整个尺度空间特征时分类效果最佳。","可探索尺度空间特征优化在入侵植物早期监测中的应用，并指导无人机飞行规划以提升效率。","2026-10-16T23:30:31.287501Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":308,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":363,"secondary_topics":368,"classification":373,"search_phrases":398,"slug":401,"view_count":228,"doi":402,"paper":403,"created_at":426},4998,"Unmanned aerial vehicle time‐series phenotyping illuminates genotype‐specific responses to off‐target dicamba exposure in soybean","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70118","Abstract Dicamba, a synthetic auxin herbicide, is widely used for postemergence weed control. However, the high potential for volatility and spray drift raises concerns about damage to nontarget dicot crops lacking the dicamba monooxygenase transgene, which provides bioengineered resistance. Our objective was to evaluate the practical application of unmanned aerial vehicle (UAV)‐based multispectral imaging for quantifying off‐target dicamba injury, using non‐genetically modified (GM) soybean ( Glycine max (L.) Merr.) genotypes with known tolerant (T) and sensitive (S) responses to provide a reference framework. We conducted field experiments in 2021 and 2022 using varying dicamba rates. Temporal visual injury estimates (0–5 scale) were recorded approximately every 5 days for 6–8 weeks. Significant differences were detected between T and S lines, especially with 1\u002F500 single and weekly repeated treatments, which provided the largest separation. To augment the precision of visual injury assessment, UAV‐based multispectral imagery from each of multiple flights was used to calculate four vegetation indices (VIs): green leaf index (GLI), normalized excess green index (ExG2), normalized green red difference index (NGRDI), and red edge normalized difference vegetation index (RENDVI). These VIs captured differences between T and S lines, indicating dicamba injury can be quantified through remote sensing across contrasting soybean genotypes. Strong negative correlations were observed between visual injury scores and VI scores across treatments at each flight date, with overall correlations for GLI, ExG2, NGRDI, and RENDVI being −0.43, −0.43, −0.44, and −0.39 in 2021 and −0.54, −0.54, −0.64, and −0.57 in 2022. These results support the utility of UAV‐derived VIs for detecting and quantifying off‐target herbicide injury in soybean, facilitating scientific research efforts to develop non‐GM‐tolerant genotypes.","摘要 麦草畏（dicamba）是一种合成生长素类除草剂，广泛用于苗后杂草防除。然而，其高挥发性和喷雾飘移潜力引发了人们对非靶标双子叶作物的担忧，这些作物缺乏提供生物工程抗性的麦草畏单加氧酶（dicamba monooxygenase）转基因。本研究旨在评估基于无人机（UAV）的多光谱成像在量化非靶标麦草畏药害中的实际应用，利用对麦草畏具有已知耐受（T）和敏感（S）响应的非转基因（GM）大豆（Glycine max (L.) Merr.）基因型作为参考框架。我们于2021年和2022年开展了田间试验，使用不同的麦草畏剂量。在约6–8周内，每约5天记录一次时序视觉药害评估（0–5级）。在T和S品系之间检测到显著差异，尤其是在1\u002F500单次和每周重复处理下，差异最为明显。为提高视觉药害评估的精度，利用多次飞行获取的无人机多光谱影像计算了4种植被指数（VIs）：绿叶指数（GLI）、归一化超绿指数（ExG2）、归一化绿红差异指数（NGRDI）和红边归一化差异植被指数（RENDVI）。这些植被指数捕捉到了T和S品系之间的差异，表明麦草畏药害可通过遥感在不同大豆基因型间进行量化。在各飞行日期的所有处理中，视觉药害评分与植被指数评分之间均观察到强负相关，2021年GLI、ExG2、NGRDI和RENDVI的总体相关系数分别为−0.43、−0.43、−0.44和−0.39，2022年分别为−0.54、−0.54、−0.64和−0.57。这些结果支持无人机衍生植被指数在大豆非靶标除草剂药害检测和量化中的实用性，有助于推动开发非转基因耐受基因型的科学研究工作。","The Plant Phenome Journal",77,{"impact":81,"substance":154,"depth":111,"authority":155,"freshness":156,"relevant":70,"comment":356},"该研究利用无人机多光谱时序表型定量评估大豆非靶标除草剂药害，方法新颖、数据扎实，对智慧农业遥感监测具有参考价值。",[358],{"name":353,"url":350},[89,360,21,361,362],"大豆","植被指数","除草剂药害",{"code":364,"slug":365,"name":366,"path":367,"parent_code":98},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[369],{"code":33,"slug":370,"name":371,"path":372,"parent_code":105},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":107,"locked":14,"version":28,"method":29,"evidence":374},{"candidates":375,"agricultural_evidence":35},[376,382,387,394],{"code":364,"score":377,"supported":35,"title":378,"abstract":380,"tags":381},15,[379],"soybean",[360,379],[360],{"code":33,"score":118,"supported":35,"title":383,"abstract":384,"tags":386},[],[38,385,39,325],"无人机多光谱",[21,361],{"code":388,"score":69,"supported":35,"title":389,"abstract":390,"tags":393},"F4",[],[391,392],"除草","weed control",[],{"code":125,"score":70,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[89],[399,400],"无人机 多光谱 大豆 药害","麦草畏 飘移 大豆 遥感","无人机多光谱大豆药害-4998","10.1002\u002Fppj2.70118",{"doi":402,"openalex_id":404,"authors":405,"venue":353,"cited_by_count":15,"oa_url":350,"card":421,"direction":63,"ingested_from":66},"W7221759072",[406,409,412,415,418],{"name":407,"orcid":408},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":410,"orcid":411},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":413,"orcid":414},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":416,"orcid":417},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":419,"orcid":420},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":422,"method":423,"finding":424,"direction":63,"opportunity":425},"用无人机多光谱时序表型量化非转基因大豆受除草剂飘移药害的基因型差异。","2021-2022田间试验，无人机多光谱影像计算GLI、ExG2、NGRDI、R","植被指数与目测药害评分显著负相关，能区分耐性与敏感基因型，2022年相关性更强。","可拓展到多作物、多除草剂飘移的无人机早期诊断，并融合机器学习构建药害分级预警模型。","2026-10-15T23:30:20.537887Z"]