[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2997":3,"related-2997":101},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":52,"search_phrases":85,"slug":88,"view_count":89,"doi":8,"paper":90,"created_at":100},2997,"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方法","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685741381196825088","江苏省农业科学院农业信息研究所梁万杰等联合中国农科院农业环境与可持续发展研究所、湖北粮作所、扬州大学等团队，针对小麦渍害防控提出基于无人机多光谱图像和VGG21模型的快速无损识别方法。在小麦拔节-抽穗和抽穗-灌浆两个阶段开展对照、渍水胁迫、硅肥调控和氨基酸调控4个类别数据集，大疆精灵4多光谱无人机采集小麦冠层多光谱图像，测产评估调控效果。",null,"智慧农业(中英文)2026,8(4):60-69","2026-09-15T12:42:00Z","论文",25,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"多机构协作提出无人机多光谱结合VGG21的小麦渍害无损识别方法，方法新颖、数据扎实，对智慧农业植保监测有参考价值。",[23,24],{"name":9,"url":6},{"name":25,"url":26},"智慧农业(中英文)8(4) 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Sensing","2026-09-15T00:00:00Z",74,{"impact":214,"substance":164,"depth":165,"authority":59,"freshness":135,"relevant":20,"comment":215},15,"中国农科院团队用AutoML融合多光谱、RGB与热红外无人机数据预测冬小麦水分含量，多传感器融合R²达0.876，方法新颖、结论可靠，对精准灌溉有实用价值，值得进入每日精选。",[217],{"name":210,"url":208},[28,29,30,219,220],"冬小麦","多传感器融合",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[223,224],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"code":81,"slug":225,"name":226,"path":227,"parent_code":51},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":53,"locked":13,"version":54,"method":55,"evidence":229},{"candidates":230,"agricultural_evidence":60},[231,238,243,248,254],{"code":47,"score":232,"supported":60,"title":233,"abstract":236,"tags":237},20,[234,235],"遥感","remote sensing",[234],[30],{"code":34,"score":239,"supported":60,"title":240,"abstract":241,"tags":242},12,[36],[36],[219],{"code":81,"score":239,"supported":60,"title":244,"abstract":246,"tags":247},[245],"机器学习",[245],[29],{"code":249,"score":89,"supported":60,"title":250,"abstract":251,"tags":253},"H3",[],[252],"灌溉",[],{"code":76,"score":20,"supported":13,"title":255,"abstract":256,"tags":257},[],[],[28],[259,260],"农业人工智能 多传感器融合 无人机遥感 智慧农业","农业人工智能 多传感器融合","农业人工智能多传感器融合无人机遥感智慧农业-2854",2,{"doi":8,"openalex_id":8,"authors":264,"venue":8,"cited_by_count":92,"oa_url":8,"card":265,"direction":97,"ingested_from":99},[],{"tldr":266,"method":267,"finding":268,"direction":97,"opportunity":269},"用无人机多传感器遥感结合AutoML预测冬小麦水分含量。","无人机多光谱、RGB、热红外图像+地面真值，AutoML回归建模。","灌浆期热红外精度最高，多传感器融合将R²提升至0.876。","可探索不同生育期与品种的泛化性，及将水分预测接入灌溉决策系统。","2026-09-18T00:03:30.758419Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":8,"source_name":277,"source_url":274,"published_at":278,"category":11,"cover_url":8,"hotness":110,"is_selected":13,"score":279,"score_detail":280,"sources":283,"tags":285,"primary_topic":289,"secondary_topics":290,"classification":292,"search_phrases":310,"slug":313,"view_count":314,"doi":315,"paper":316,"created_at":343},3784,"Analysis of the Growth Responses and Multispectral Reflectance Characteristics of Rehmannia glutinosa Plug Seedlings under Drought Stress","https:\u002F\u002Fdoi.org\u002F10.7235\u002Fhort.20260033","Rehmannia glutinosa is a high-value medicinal crop with extreme sensitivity to drought stress.However, non-destructive methods by which to assess the quality of plug seedlings remain limited.This study evaluated the spectral reflectance and vegetation index responses of R. glutinosa plug seedlings to drought stress using multispectral imaging.Drought stress significantly increased the leaf temperature and decreased shoot and root growth parameters compared to the control.NIR (near-infrared) band reflectance declined initially in the early stage of drought stress, with this followed by a significant decrease in visible bands (blue, green, red) as stress intensified.Among the five vegetation indices, the NGRDI (normalized green-red difference index) responded earliest at the early stage of drought stress, while the GNDVI (green normalized difference vegetation index), PSRI (plant senescence reflectance index), and NDRE (normalized difference red edge index) showed significant changes as stress intensified.The NDVI (normalized difference vegetation index) showed a delayed response and was significantly lower in the drought-stressed seedlings than in the control only after rewatering.These results suggest that multispectral imaging is a promising nondestructive tool for early drought stress detection and seedling quality assessments in medicinal plug seedling production systems.","地黄是一种高价值药用作物，对干旱胁迫极为敏感。然而，目前用于评估穴盘苗质量的无损方法仍然有限。本研究利用多光谱成像技术，评估了地黄穴盘苗对干旱胁迫的光谱反射率和植被指数响应。与对照相比，干旱胁迫显著提高了叶片温度，并降低了地上部和根系的生长参数。近红外（NIR）波段反射率在干旱胁迫早期首先下降，随后随着胁迫加剧，可见光波段（蓝、绿、红）反射率显著降低。在5个植被指数中，归一化绿红差异指数（NGRDI）在干旱胁迫早期响应最早，而绿归一化差异植被指数（GNDVI）、植物衰老反射指数（PSRI）和归一化红边差异指数（NDRE）随着胁迫加剧表现出显著变化。归一化差异植被指数（NDVI）响应滞后，仅在复水后干旱胁迫幼苗才显著低于对照。这些结果表明，多光谱成像在药用穴盘苗生产系统中用于干旱胁迫早期检测和种苗质量评估具有良好的无损应用前景。","Horticultural Science and Technology","2026-09-28T00:00:00Z",66,{"impact":135,"substance":232,"depth":163,"authority":59,"freshness":281,"relevant":20,"comment":282},9,"多光谱成像用于药用作物穴盘苗干旱胁迫早期无损检测，方法新颖、结论可靠，但属细分领域研究，产业影响有限。",[284],{"name":277,"url":274},[28,234,286,32,287,288],"干旱胁迫","地黄","种苗质量",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[291],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"status":53,"locked":13,"version":54,"method":55,"evidence":293},{"candidates":294,"agricultural_evidence":60},[295,301,306],{"code":41,"score":18,"supported":60,"title":296,"abstract":298,"tags":300},[297],"drought",[299,297],"干旱",[],{"code":47,"score":59,"supported":60,"title":302,"abstract":304,"tags":305},[303],"multispectral",[303],[32,234],{"code":76,"score":20,"supported":13,"title":307,"abstract":308,"tags":309},[],[],[28],[311,312],"地黄 穴盘苗 多光谱","地黄 干旱胁迫 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Hwang",{"name":334,"orcid":8},"https:\u002F\u002Fwww.hst-j.org\u002Farticles\u002Fpdf\u002Fbz4M\u002Fkshs-2026-044-00-15.pdf",{"tldr":338,"method":339,"finding":340,"direction":97,"opportunity":341},"用多光谱成像无损检测地黄穴盘苗干旱胁迫下的生长与光谱响应。","多光谱成像测反射率与5种植被指数，结合生长参数和叶温。","NGRDI最早响应早期干旱，GNDVI、PSRI、NDRE随胁迫加剧变化，NDVI复水后才显著。","可构建基于早期敏感植被指数的药用植物穴盘苗干旱预警与质量分级模型。","openalex","2026-09-29T23:30:21.147972Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":8,"source_name":350,"source_url":347,"published_at":351,"category":11,"cover_url":8,"hotness":110,"is_selected":13,"score":279,"score_detail":352,"sources":354,"tags":356,"primary_topic":359,"secondary_topics":364,"classification":367,"search_phrases":411,"slug":414,"view_count":314,"doi":415,"paper":416,"created_at":438},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",{"impact":239,"substance":16,"depth":163,"authority":239,"freshness":135,"relevant":20,"comment":353},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[355],{"name":350,"url":347},[28,29,234,357,358],"GIS","干旱监测",{"code":360,"slug":361,"name":362,"path":363,"parent_code":51},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[365,366],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"status":53,"locked":13,"version":54,"method":55,"evidence":368},{"candidates":369,"agricultural_evidence":60},[370,378,382,386,394,401,407],{"code":360,"score":16,"supported":60,"title":371,"abstract":372,"tags":377},[357],[373,374,357,375,376],"地理信息","空间分析","spatial analysis","geographic information",[357],{"code":41,"score":214,"supported":60,"title":379,"abstract":380,"tags":381},[297],[299,297],[358],{"code":47,"score":214,"supported":60,"title":383,"abstract":384,"tags":385},[235],[234,235],[234],{"code":81,"score":110,"supported":60,"title":387,"abstract":388,"tags":393},[],[389,245,390,391,392],"深度学习","machine learning","deep learning","artificial intelligence",[29],{"code":395,"score":19,"supported":60,"title":396,"abstract":397,"tags":400},"H2",[],[398,399],"土壤","soil",[],{"code":249,"score":19,"supported":60,"title":402,"abstract":403,"tags":406},[],[404,405],"水资源","evapotranspiration",[],{"code":76,"score":20,"supported":13,"title":408,"abstract":409,"tags":410},[],[],[28],[412,413],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":415,"openalex_id":417,"authors":418,"venue":350,"cited_by_count":92,"oa_url":347,"card":433,"direction":97,"ingested_from":342},"W7214156857",[419,421,423,425,427,429,431],{"name":420,"orcid":8},"V. Dhanalakshmi",{"name":422,"orcid":8},"N. Manikandan",{"name":424,"orcid":8},"V. S. Jinsy",{"name":426,"orcid":8},"K. V. Sumesh",{"name":428,"orcid":8},"P. Nideesh",{"name":430,"orcid":8},"P. S. Manju",{"name":432,"orcid":8},"N. Gopika",{"tldr":434,"method":435,"finding":436,"direction":97,"opportunity":437},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":8,"source_name":445,"source_url":442,"published_at":211,"category":11,"cover_url":8,"hotness":110,"is_selected":13,"score":446,"score_detail":447,"sources":449,"tags":451,"primary_topic":453,"secondary_topics":454,"classification":457,"search_phrases":487,"slug":490,"view_count":89,"doi":491,"paper":492,"created_at":516},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing",80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":135,"relevant":20,"comment":448},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[450],{"name":445,"url":442},[28,29,36,234,452],"精准灌溉",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[455,456],{"code":47,"slug":48,"name":49,"path":50,"parent_code":51},{"code":81,"slug":225,"name":226,"path":227,"parent_code":51},{"status":53,"locked":13,"version":54,"method":55,"evidence":458},{"candidates":459,"agricultural_evidence":60},[460,466,470,474,480],{"code":34,"score":461,"supported":60,"title":462,"abstract":464,"tags":465},26,[463,35],"winter wheat",[36,463,35],[36],{"code":47,"score":16,"supported":60,"title":467,"abstract":468,"tags":469},[235],[234,235,303],[234],{"code":81,"score":214,"supported":60,"title":471,"abstract":472,"tags":473},[390],[245,390],[29],{"code":249,"score":110,"supported":60,"title":475,"abstract":476,"tags":479},[],[252,404,477,478],"irrigation","water resource",[452],{"code":76,"score":481,"supported":60,"title":482,"abstract":483,"tags":486},7,[],[484,485],"精准农业","precision agriculture",[28],[488,489],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":491,"openalex_id":493,"authors":494,"venue":445,"cited_by_count":92,"oa_url":442,"card":511,"direction":97,"ingested_from":342},"W7213246708",[495,498,500,502,505,508],{"name":496,"orcid":497},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":499,"orcid":8},"Qian Cheng",{"name":501,"orcid":8},"Fuyi Duan",{"name":503,"orcid":504},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":506,"orcid":507},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":509,"orcid":510},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":512,"method":513,"finding":514,"direction":97,"opportunity":515},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z"]