[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3784":3,"related-3784":106},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":46,"search_phrases":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":105},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。",null,"Horticultural Science and Technology","2026-09-28T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,16,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","遥感","干旱胁迫","多光谱成像","地黄","种苗质量",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[40],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":47,"locked":14,"version":48,"method":49,"evidence":50},"classified","2026-10-22.2","rules",{"candidates":51,"agricultural_evidence":54},[52,60,65],{"code":34,"score":53,"supported":54,"title":55,"abstract":57,"tags":59},14,true,[56],"drought",[58,56],"干旱",[],{"code":41,"score":20,"supported":54,"title":61,"abstract":63,"tags":64},[62],"multispectral",[62],[30,28],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I1",[],[],[27],[71,72],"地黄 穴盘苗 多光谱","地黄 干旱胁迫 植被指数","地黄穴盘苗多光谱-3784",5,"10.7235\u002Fhort.20260033",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":96,"oa_url":97,"card":98,"direction":102,"ingested_from":104},"W7214608276",[79,82,84,85,87,90,93,95],{"name":80,"orcid":81},"Ji Hye Yun","https:\u002F\u002Forcid.org\u002F0009-0004-9551-5353",{"name":83,"orcid":9},"So Yeong Hwang",{"name":83,"orcid":9},{"name":86,"orcid":9},"Jeong Kil Koo",{"name":88,"orcid":89},"Hyeong Eun Choi","https:\u002F\u002Forcid.org\u002F0009-0009-9318-9845",{"name":91,"orcid":92},"Seong Ha Park","https:\u002F\u002Forcid.org\u002F0009-0005-9189-8972",{"name":94,"orcid":9},"Seung Jae Hwang",{"name":94,"orcid":9},0,"https:\u002F\u002Fwww.hst-j.org\u002Farticles\u002Fpdf\u002Fbz4M\u002Fkshs-2026-044-00-15.pdf",{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"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。","农业遥感与作物表型","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",{"total":107,"page":22,"page_size":107,"items":108},6,[109,208,304,372,422,470],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":123,"tags":125,"primary_topic":128,"secondary_topics":129,"classification":131,"search_phrases":176,"slug":179,"view_count":180,"doi":181,"paper":182,"created_at":207},1425,"Physiological and Spectral Responses of Centella asiatica (L.) to Drought Stress: Implications for Growth, Yield, Stress Monitoring, and Modeling","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jafr.2026.103260","Centella asiatica (L.) is an important medicinal herb valued for its wound-healing, anti-inflammatory, and cognitive-enhancing properties. However, information on its physiological responses to drought stress and the application of hyperspectral remote sensing for drought assessment in this species remains limited. This study evaluated the effects of four soil moisture regimes (control, mild, moderate, and severe drought) on plant growth, physiology, and leaf spectral reflectance in two trials. Drought stress significantly reduced gas exchange, growth, and biomass production. Under severe drought, stomatal conductance decreased by 88%, accompanied by a 37% increase in canopy temperature. Net photosynthetic rate declined by 69% and was positively correlated with soil moisture content (R = 0.80). Marketable fresh weight declined from 6.5 under control to 1.0 kg m -2 under severe drought, while dry biomass decreased from 0.64 to 0.26 kg m -2 in trial 1 and from 0.57 to 0.28 kg m -2 in trial 2. Drought stress also altered leaf spectral reflectance, particularly in the visible region, resulting in significant changes in several vegetation indices that were strongly correlated with key morpho-physiological traits. Additionally, functional relationships between soil moisture and major physiological and growth traits were established through environmental productivity indices, providing a quantitative framework for predicting plant responses to water availability. These findings demonstrate the potential of hyperspectral sensing for early, non-destructive drought detection and provide practical tools for improving irrigation management and precision cultivation of C. asiatica under field and greenhouse conditions.","积雪草（*Centella asiatica* (L.)）是一种重要的药用植物，因其具有伤口愈合、抗炎和增强认知功能的特性而备受重视。然而，关于该物种对干旱胁迫的生理响应以及利用高光谱遥感技术评估其干旱状况的信息仍然有限。本研究通过两项试验，评估了四种土壤水分条件（对照、轻度、中度和重度干旱）对植物生长、生理特性及叶片光谱反射率的影响。干旱胁迫显著降低了气体交换、生长和生物量产量。在重度干旱条件下，气孔导度下降了88%，同时冠层温度升高了37%。净光合速率下降了69%，并与土壤含水量呈正相关（R = 0.80）。商品鲜重从对照条件下的6.5 kg m⁻²降至重度干旱条件下的1.0 kg m⁻²，而干生物量在试验1中从0.64 kg m⁻²降至0.26 kg m⁻²，在试验2中从0.57 kg m⁻²降至0.28 kg m⁻²。干旱胁迫还改变了叶片光谱反射率，尤其是在可见光区域，导致多个植被指数发生显著变化，这些指数与关键形态-生理性状密切相关。此外，通过环境生产力指数建立了土壤水分与主要生理及生长性状之间的函数关系，为预测植物对水分可用性的响应提供了定量框架。这些发现证明了高光谱传感技术在早期、非破坏性干旱检测方面的潜力，并为改善田间和温室条件下积雪草的灌溉管理和精准栽培提供了实用工具。","Journal of Agriculture and Food Research","2026-09-01T00:00:00Z",69,{"impact":119,"substance":18,"depth":120,"authority":119,"freshness":121,"relevant":22,"comment":122},12,18,7,"研究利用高光谱遥感监测药用植物干旱胁迫，为精准灌溉提供新工具，兼具科研与应用价值。",[124],{"name":115,"url":112},[27,28,126,29,127],"精准灌溉","积雪草",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[130],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":132},{"candidates":133,"agricultural_evidence":54},[134,138,144,151,158,165,172],{"code":34,"score":53,"supported":54,"title":135,"abstract":136,"tags":137},[56],[58,56],[],{"code":41,"score":13,"supported":54,"title":139,"abstract":140,"tags":143},[],[28,141,142],"remote sensing","hyperspectral",[28],{"code":145,"score":121,"supported":54,"title":146,"abstract":147,"tags":150},"H3",[],[148,149],"灌溉","irrigation",[126],{"code":152,"score":107,"supported":54,"title":153,"abstract":154,"tags":157},"B7",[],[155,156],"温室","greenhouse",[],{"code":159,"score":107,"supported":54,"title":160,"abstract":161,"tags":164},"H2",[],[162,163],"土壤","soil",[],{"code":166,"score":167,"supported":54,"title":168,"abstract":169,"tags":171},"B6",3,[],[170],"药用植物",[],{"code":66,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[27],[177,178],"干旱胁迫 智慧农业 精准灌溉 积雪草","干旱胁迫 智慧农业","干旱胁迫智慧农业精准灌溉积雪草-1425",4,"10.1016\u002Fj.jafr.2026.103260",{"doi":181,"openalex_id":183,"authors":184,"venue":115,"cited_by_count":96,"oa_url":201,"card":202,"direction":102,"ingested_from":104},"W7204869219",[185,187,190,192,195,198],{"name":186,"orcid":9},"Naflath Thenveettil",{"name":188,"orcid":189},"CLEDIANE ALENCAR DA SILVA","https:\u002F\u002Forcid.org\u002F0009-0006-4333-5128",{"name":191,"orcid":9},"Navneet Kaur",{"name":193,"orcid":194},"Nuwan K. Wijewardane","https:\u002F\u002Forcid.org\u002F0000-0001-8962-9451",{"name":196,"orcid":197},"Krishna N. Reddy","https:\u002F\u002Forcid.org\u002F0000-0002-2807-834X",{"name":199,"orcid":200},"K. Raja Reddy","https:\u002F\u002Forcid.org\u002F0000-0002-7906-7755","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666154326006319\u002Fpdf",{"tldr":203,"method":204,"finding":205,"direction":102,"opportunity":206},"研究干旱胁迫对积雪草生长、生理和光谱响应的影响，并建立预测模型。","设置四种土壤水分梯度，测量气体交换、生长和叶片光谱反射率，建立环境生产力指数模型","干旱显著降低光合和生物量，光谱指数与性状强相关，可用于非破坏性干旱监测。","可延伸至其他药用植物或经济作物的干旱胁迫光谱监测模型，结合无人机遥感实现田间精准灌溉管理。","2026-09-02T23:30:23.204664Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":216,"sources":218,"tags":220,"primary_topic":224,"secondary_topics":229,"classification":232,"search_phrases":276,"slug":279,"view_count":74,"doi":280,"paper":281,"created_at":303},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":119,"substance":120,"depth":19,"authority":119,"freshness":17,"relevant":22,"comment":217},"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。",[219],{"name":214,"url":211},[27,221,28,222,223],"农业人工智能","GIS","干旱监测",{"code":225,"slug":226,"name":227,"path":228,"parent_code":45},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[230,231],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":233},{"candidates":234,"agricultural_evidence":54},[235,243,248,252,262,266,272],{"code":225,"score":120,"supported":54,"title":236,"abstract":237,"tags":242},[222],[238,239,222,240,241],"地理信息","空间分析","spatial analysis","geographic information",[222],{"code":34,"score":244,"supported":54,"title":245,"abstract":246,"tags":247},15,[56],[58,56],[223],{"code":41,"score":244,"supported":54,"title":249,"abstract":250,"tags":251},[141],[28,141],[28],{"code":253,"score":13,"supported":54,"title":254,"abstract":255,"tags":261},"I2",[],[256,257,258,259,260],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[221],{"code":159,"score":107,"supported":54,"title":263,"abstract":264,"tags":265},[],[162,163],[],{"code":145,"score":107,"supported":54,"title":267,"abstract":268,"tags":271},[],[269,270],"水资源","evapotranspiration",[],{"code":66,"score":22,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[27],[277,278],"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":280,"openalex_id":282,"authors":283,"venue":214,"cited_by_count":96,"oa_url":211,"card":298,"direction":102,"ingested_from":104},"W7214156857",[284,286,288,290,292,294,296],{"name":285,"orcid":9},"V. Dhanalakshmi",{"name":287,"orcid":9},"N. Manikandan",{"name":289,"orcid":9},"V. S. Jinsy",{"name":291,"orcid":9},"K. V. Sumesh",{"name":293,"orcid":9},"P. Nideesh",{"name":295,"orcid":9},"P. S. Manju",{"name":297,"orcid":9},"N. Gopika",{"tldr":299,"method":300,"finding":301,"direction":102,"opportunity":302},"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":305,"title":306,"url":307,"summary":308,"summary_zh":9,"content":9,"source_name":309,"source_url":9,"published_at":310,"category":12,"cover_url":9,"hotness":311,"is_selected":14,"score":312,"score_detail":313,"sources":316,"tags":321,"primary_topic":324,"secondary_topics":330,"classification":333,"search_phrases":359,"slug":362,"view_count":167,"doi":9,"paper":363,"created_at":371},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。","智慧农业(中英文)2026,8(4):60-69","2026-09-15T12:42:00Z",25,78,{"impact":120,"substance":314,"depth":120,"authority":53,"freshness":107,"relevant":22,"comment":315},22,"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。",[317,318],{"name":309,"url":307},{"name":319,"url":320},"智慧农业(中英文)8(4) 60-69(2026)","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml",[27,221,322,323,30],"无人机遥感","小麦渍害",{"code":325,"slug":326,"name":327,"path":328,"parent_code":329},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[331,332],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":47,"locked":14,"version":48,"method":49,"evidence":334},{"candidates":335,"agricultural_evidence":54},[336,341,346,351,355],{"code":41,"score":20,"supported":54,"title":337,"abstract":339,"tags":340},[338],"无人机多光谱",[338],[30,322],{"code":325,"score":342,"supported":54,"title":343,"abstract":344,"tags":345},11,[327],[327],[],{"code":34,"score":342,"supported":54,"title":347,"abstract":349,"tags":350},[348],"渍害",[348],[],{"code":66,"score":22,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[27],{"code":253,"score":22,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[221],[360,361],"江苏省农科院 小麦渍害 无人机多光谱","VGG21 小麦 渍害识别","江苏省农科院小麦渍害无人机多光谱-2997",{"doi":9,"openalex_id":9,"authors":364,"venue":9,"cited_by_count":96,"oa_url":9,"card":365,"direction":102,"ingested_from":370},[],{"tldr":366,"method":367,"finding":368,"direction":102,"opportunity":369},"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多光谱无人机采集冠层图像，构建VGG21分类模型。","该方法可快速无损识别渍害及硅肥、氨基酸调控效果。","可探索多光谱与深度学习结合评估其他逆境调控措施，并迁移至多作物场景。","agent","2026-09-20T00:03:07.858899Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":9,"content":9,"source_name":377,"source_url":9,"published_at":378,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":387,"secondary_topics":388,"classification":391,"search_phrases":410,"slug":413,"view_count":22,"doi":9,"paper":414,"created_at":421},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","2026-10-21T00:00:00Z",82,{"impact":120,"substance":314,"depth":120,"authority":53,"freshness":13,"relevant":22,"comment":381},"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。",[383],{"name":377,"url":375},[27,385,386,338,323],"农业遥感","作物表型",{"code":325,"slug":326,"name":327,"path":328,"parent_code":329},[389,390],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":47,"locked":14,"version":48,"method":49,"evidence":392},{"candidates":393,"agricultural_evidence":54},[394,398,402,406],{"code":325,"score":342,"supported":54,"title":395,"abstract":396,"tags":397},[327],[327],[],{"code":41,"score":342,"supported":54,"title":399,"abstract":400,"tags":401},[338],[],[386,385,338],{"code":34,"score":17,"supported":54,"title":403,"abstract":404,"tags":405},[348],[],[],{"code":66,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[27],[411,412],"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":415,"venue":9,"cited_by_count":96,"oa_url":9,"card":416,"direction":102,"ingested_from":370},[],{"tldr":417,"method":418,"finding":419,"direction":102,"opportunity":420},"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。","2026-10-22T00:07:10.070916Z",{"id":423,"title":424,"url":320,"summary":425,"summary_zh":9,"content":9,"source_name":426,"source_url":9,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":428,"score_detail":429,"sources":433,"tags":435,"primary_topic":436,"secondary_topics":437,"classification":440,"search_phrases":459,"slug":462,"view_count":96,"doi":9,"paper":463,"created_at":469},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》","《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":19,"substance":430,"depth":431,"authority":53,"freshness":17,"relevant":22,"comment":432},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。",[434],{"name":426,"url":320},[27,385,386,338,323],{"code":325,"slug":326,"name":327,"path":328,"parent_code":329},[438,439],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":441},{"candidates":442,"agricultural_evidence":54},[443,447,451,455],{"code":325,"score":342,"supported":54,"title":444,"abstract":445,"tags":446},[327],[327],[],{"code":34,"score":342,"supported":54,"title":448,"abstract":449,"tags":450},[348],[348],[],{"code":41,"score":342,"supported":54,"title":452,"abstract":453,"tags":454},[338],[],[386,385,338],{"code":66,"score":180,"supported":54,"title":456,"abstract":457,"tags":458},[],[27],[27],[460,461],"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":464,"venue":9,"cited_by_count":96,"oa_url":9,"card":465,"direction":102,"ingested_from":370},[],{"tldr":366,"method":466,"finding":467,"direction":102,"opportunity":468},"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":471,"title":472,"url":473,"summary":474,"summary_zh":475,"content":9,"source_name":476,"source_url":473,"published_at":477,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":478,"score_detail":479,"sources":482,"tags":484,"primary_topic":488,"secondary_topics":489,"classification":495,"search_phrases":536,"slug":539,"view_count":540,"doi":541,"paper":542,"created_at":561},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":120,"substance":314,"depth":480,"authority":53,"freshness":17,"relevant":22,"comment":481},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[483],{"name":476,"url":473},[27,485,257,486,487],"粮食安全","遥感监测","农业干旱",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[490,491],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":253,"slug":492,"name":493,"path":494,"parent_code":45},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":47,"locked":14,"version":48,"method":49,"evidence":496},{"candidates":497,"agricultural_evidence":54},[498,502,506,510,516,520,526,532],{"code":41,"score":244,"supported":54,"title":499,"abstract":500,"tags":501},[141],[28,141],[486],{"code":34,"score":53,"supported":54,"title":503,"abstract":504,"tags":505},[56],[58,56],[],{"code":253,"score":13,"supported":54,"title":507,"abstract":508,"tags":509},[],[256,257,258,259,260],[257],{"code":511,"score":121,"supported":54,"title":512,"abstract":513,"tags":515},"A8",[],[485,514],"food security",[485],{"code":159,"score":107,"supported":54,"title":517,"abstract":518,"tags":519},[],[162,163],[],{"code":521,"score":167,"supported":54,"title":522,"abstract":523,"tags":525},"D",[],[524],"水产",[],{"code":527,"score":167,"supported":54,"title":528,"abstract":529,"tags":531},"H6",[],[530],"农业生态",[],{"code":66,"score":22,"supported":14,"title":533,"abstract":534,"tags":535},[],[],[27],[537,538],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":541,"openalex_id":543,"authors":544,"venue":476,"cited_by_count":96,"oa_url":554,"card":555,"direction":560,"ingested_from":104},"W7221081979",[545,548,551],{"name":546,"orcid":547},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":549,"orcid":550},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":552,"orcid":553},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":556,"method":557,"finding":558,"direction":102,"opportunity":559},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:14.933078Z"]