[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2522":3,"related-2522":109},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":108},2522,"Estimation of Grain Yield and Quality in Awned and Awnless Wheat Genotypes Using UAV and Proximal Multispectral Sensors","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15182819","This study aimed to evaluate the complementary potential of UAV-based and proximal multispectral sensing using the Plant-O-Meter (POM) sensor for the assessment of wheat grain yield and quality across different phenological stages and two growing seasons. The analysis was based on three vegetation indices, the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Red Edge Index (NDRE), and included two morphologically distinct genotype groups, awned and awnless. The study included nine awned and nine awnless genotypes. Vegetation indices showed pronounced seasonal dynamics, with higher values during intensive vegetative development and a decline during later growth stages. The strongest relationship with grain yield was observed for GNDVI-A during the heading to beginning of flowering stage (BBCH 51–61) in Season I (r = 0.85), whereas in Season II, the strongest relationship was observed for NDRE-A during the flag leaf stage (BBCH 37–39) (r = 0.80). Awned genotypes generally showed stronger VI–yield relationships, while associations with grain quality parameters varied among genotype groups and seasons. Linear mixed-effects models showed that both VI-related effects and genotype variability contributed to the variation in grain yield and quality. For yield, marginal R2 was 0.43 in Season I and 0.50 in Season II, while conditional R2 was 0.78 and 0.64, respectively. For protein and wet gluten content, model performance was more variable, with marginal R2 values ranging from 0.30 to 0.39 for protein and from 0.31 to 0.33 for wet gluten. Genotype-level LOGO cross-validation further indicated variation in model performance when genotypes not included in model development were evaluated. Overall, the results indicate that relationships between multispectral vegetation indices and wheat grain yield and quality depend on sensing method, phenological stage, genotype characteristics, and growing season. The findings provide an exploratory basis for the application of multispectral sensing in wheat phenotyping and assessment of grain yield and quality, while further validation across broader genetic and environmental conditions is required.","本研究旨在评估基于无人机与近地多光谱传感（采用Plant-O-Meter（POM）传感器）在不同物候期和两个生长季中对小麦籽粒产量和品质评估的互补潜力。分析基于三个植被指数，即归一化差异植被指数（NDVI）、绿色归一化差异植被指数（GNDVI）和归一化差异红边指数（NDRE），并纳入两个形态学上不同的基因型组，即有芒和无芒。研究包括9个有芒基因型和9个无芒基因型。植被指数表现出明显的季节性动态变化，在旺盛营养发育期数值较高，在生长后期下降。与籽粒产量关系最强的是抽穗至开花初期（BBCH 51–61）第I季的GNDVI-A（r = 0.85），而在第II季，关系最强的是旗叶期（BBCH 37–39）的NDRE-A（r = 0.80）。有芒基因型总体上表现出更强的植被指数-产量关系，而与籽粒品质参数的关联因基因型组和季节而异。线性混合效应模型表明，植被指数相关效应和基因型变异均对籽粒产量和品质的变异有贡献。对于产量，第I季的边际R²为0.43，第II季为0.50，而条件R²分别为0.78和0.64。对于蛋白质和湿面筋含量，模型表现更为多变，蛋白质的边际R²范围为0.30至0.39，湿面筋为0.31至0.33。基因型水平的LOGO交叉验证进一步表明，在评估未纳入模型开发的基因型时，模型表现存在变异。总体而言，结果表明多光谱植被指数与小麦籽粒产量和品质之间的关系取决于传感方法、物候期、基因型特征和生长季。研究结果为多光谱传感在小麦表型分析及籽粒产量和品质评估中的应用提供了探索性依据，但仍需在更广泛的遗传和环境条件下进一步验证。",null,"Plants","2026-09-14T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,21,17,13,9,1,"该研究利用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，方法新颖、数据跨两季，对智慧农业遥感育种有参考价值，但属探索性论文，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","产量预测","小麦育种","作物表型","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,56,62],{"code":33,"score":17,"supported":52,"title":53,"abstract":54,"tags":55},true,[34],[35,34],[29],{"code":40,"score":57,"supported":52,"title":58,"abstract":60,"tags":61},14,[59],"multispectral",[59],[28,30,31],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[27],[68,69],"产量预测 作物表型 小麦育种 智慧农业","产量预测 作物表型","产量预测作物表型小麦育种智慧农业-2522",2,"10.3390\u002Fplants15182819",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":100,"oa_url":6,"card":101,"direction":105,"ingested_from":107},"W7212560710",[76,79,82,85,88,91,94,97],{"name":77,"orcid":78},"Irina Marina","https:\u002F\u002Forcid.org\u002F0000-0002-5894-363X",{"name":80,"orcid":81},"Vesna Kandić","https:\u002F\u002Forcid.org\u002F0000-0003-1999-2030",{"name":83,"orcid":84},"Marko Kostić","https:\u002F\u002Forcid.org\u002F0000-0001-9446-994X",{"name":86,"orcid":87},"Nataša Ljubičić","https:\u002F\u002Forcid.org\u002F0000-0001-5982-9401",{"name":89,"orcid":90},"Biljana Bošković","https:\u002F\u002Forcid.org\u002F0000-0003-4977-3170",{"name":92,"orcid":93},"Kosta Gligorević","https:\u002F\u002Forcid.org\u002F0000-0001-5783-4637",{"name":95,"orcid":96},"Miloš Pajić","https:\u002F\u002Forcid.org\u002F0000-0002-8905-3293",{"name":98,"orcid":99},"Milan Dražić","https:\u002F\u002Forcid.org\u002F0000-0002-0416-5174",0,{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，比较不同生育期和年份的植被指数关系。","无人机与Plant-O-Meter多光谱传感器，NDVI\u002FGNDVI\u002FNDRE，","GNDVI与NDRE在抽穗至开花期和旗叶期与产量相关性最强，有芒基因型关系更强，模型对品质预测较弱。","农业遥感与作物表型","可探索多源遥感融合与基因型分层建模，提升跨年份、跨环境的小麦产量与品质预测泛化能力。","openalex","2026-09-15T23:30:17.124343Z",{"total":110,"page":22,"page_size":110,"items":111},6,[112,182,240,312,360,449],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"primary_topic":131,"secondary_topics":132,"classification":134,"search_phrases":156,"slug":159,"view_count":160,"doi":161,"paper":162,"created_at":181},3328,"Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102588","Reliable assessment of yield models in breeding trials requires evaluation on lines that have not contributed observations to model development. We examined this problem in an advanced spring wheat yield nursery in northern Kazakhstan during the 2024 and 2025 seasons. Of 300 plots, 176 from 22 entries formed the model development set, while 124 plots representing 31 breeding lines were completely excluded from training. Eleven variables were retained from 82 candidates using the training data alone; the candidates were derived from UAV multispectral imagery, ERA5-Land reanalysis, LiDAR-derived static plot microtopography, phenology and maturity group. LightGBM yielded a pooled R² of 0.587 for the excluded lines; resampling breeding lines within season gave a mean R² of 0.564 with a 95 % confidence interval from 0.252 to 0.776. The pooled value partly reflected the strong contrast between seasons rather than discrimination among lines within a season. Random division of plots produced an R² of 0.890, indicating that apparent predictive accuracy depended strongly on validation design. Agreement between observed and predicted line rankings was moderate, with Spearman correlations of 0.612 in 2024 and 0.650 in 2025, although uncertainty was wide because only 14 and 17 independent lines were available. Six modelling approaches produced overlapping uncertainty intervals, with no clear evidence of superiority, and the complete combination of data sources was not separable from simpler configurations retaining the multispectral block. A separate analysis in which screening, selection and fitting used 2024 data alone and evaluation used 2025 data showed a severe loss of performance, with R² = −8.81 and a Spearman correlation of −0.082 that was indistinguishable from zero. Poor transfer persisted among entries grown in both seasons, so genotype novelty alone was insufficient to explain the failure. Breeding lines absent from training could therefore be ranked with moderate consistency when both observed seasonal regimes were represented during model development, whereas prediction in an unobserved season remained unreliable. The study covers one nursery complex and two seasons, so transfer across locations and broader conditions remains to be established.","在育种试验中，要对产量模型进行可靠评估，必须在未参与模型开发的品系上进行评价。我们在2024年和2025年生长季于哈萨克斯坦北部的一个高级春小麦产量圃中考察了这一问题。在300个小区中，来自22个品系的176个小区构成模型开发集，而代表31个育种品系的124个小区则完全排除在训练之外。仅使用训练数据，从82个候选变量中保留了11个变量；这些候选变量来源于无人机多光谱影像、ERA5-Land再分析数据、LiDAR衍生的静态小区微地形、物候和成熟期组。LightGBM对被排除品系给出的合并R²为0.587；在生长季内对育种品系进行重采样得到的平均R²为0.564，95%置信区间为0.252至0.776。合并值部分反映了生长季之间的强烈差异，而非生长季内品系之间的区分能力。对小区进行随机划分得到的R²为0.890，表明表观预测精度在很大程度上取决于验证设计。观测品系排名与预测品系排名之间的一致性为中等，2024年和2025年的Spearman相关系数分别为0.612和0.650，但由于仅有14个和17个独立品系可用，不确定性范围较宽。六种建模方法产生了相互重叠的不确定性区间，没有明确证据表明哪一种更优，并且完整的数据源组合与保留多光谱模块的较简单配置无法区分。另一项分析中，筛选、选择和拟合仅使用2024年数据，而评估使用2025年数据，结果显示性能严重下降，R² = −8.81，Spearman相关系数为−0.082，与零无法区分。在两个生长季均种植的品系之间，较差的迁移性依然存在，因此仅用品系新颖性不足以解释这种失败。因此，当模型开发过程中涵盖了所观测到的两种生长季情形时，未参与训练的育种品系可以以中等一致性进行排名，而在未观测生长季中的预测仍然不可靠。本研究仅涵盖一个圃系复合体和两个生长季，因此跨地点和更广泛条件下的迁移性仍有待确立。","Smart Agricultural Technology","2026-09-22T00:00:00Z",74,{"impact":122,"substance":123,"depth":124,"authority":20,"freshness":21,"relevant":22,"comment":125},12,22,18,"基于无人机多光谱与气象数据的春小麦育种品系产量预测研究，验证设计严谨、结论审慎，对智慧育种与遥感估产有参考价值，但属单点试验、地域性强，未达重大突破层级。",[127],{"name":118,"url":115},[27,28,129,29,130],"机器学习","遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[133],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":135},{"candidates":136,"agricultural_evidence":52},[137,141,147,151],{"code":33,"score":17,"supported":52,"title":138,"abstract":139,"tags":140},[34],[35,34],[29],{"code":40,"score":142,"supported":52,"title":143,"abstract":144,"tags":146},8,[],[145,59],"无人机多光谱",[28,130],{"code":63,"score":22,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[27],{"code":152,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},"I2",[],[],[129],[157,158],"哈萨克斯坦 春小麦 产量预测","UAV 多光谱 育种试验","哈萨克斯坦春小麦产量预测-3328",4,"10.1016\u002Fj.atech.2026.102588",{"doi":161,"openalex_id":163,"authors":164,"venue":118,"cited_by_count":100,"oa_url":115,"card":176,"direction":105,"ingested_from":107},"W7214044328",[165,168,171,174],{"name":166,"orcid":167},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":169,"orcid":170},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":172,"orcid":173},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":175,"orcid":9},"Bakhtiyar Zhanzakov",{"tldr":177,"method":178,"finding":179,"direction":105,"opportunity":180},"评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。","无人机多光谱、ERA5-Land、LiDAR与物候数据，LightGBM等六种模","两季均参与训练时品系排名中等一致，但预测未观测季节完全失效。","需研究跨地点、跨年份可迁移的表型预测模型与验证设计，避免随机划分高估精度。","2026-09-24T23:30:03.201775Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":9,"content":9,"source_name":187,"source_url":9,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":192,"tags":194,"primary_topic":197,"secondary_topics":198,"classification":206,"search_phrases":227,"slug":230,"view_count":22,"doi":9,"paper":231,"created_at":239},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":124,"substance":123,"depth":124,"authority":57,"freshness":13,"relevant":22,"comment":191},"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。",[193],{"name":187,"url":185},[27,195,30,145,196],"农业遥感","小麦渍害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[199,200],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":207},{"candidates":208,"agricultural_evidence":52},[209,214,218,223],{"code":33,"score":210,"supported":52,"title":211,"abstract":212,"tags":213},11,[35],[35],[],{"code":40,"score":210,"supported":52,"title":215,"abstract":216,"tags":217},[145],[],[30,195,145],{"code":201,"score":142,"supported":52,"title":219,"abstract":221,"tags":222},[220],"渍害",[],[],{"code":63,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[27],[228,229],"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":232,"venue":9,"cited_by_count":100,"oa_url":9,"card":233,"direction":105,"ingested_from":238},[],{"tldr":234,"method":235,"finding":236,"direction":105,"opportunity":237},"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":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":9,"source_name":118,"source_url":243,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":248,"sources":251,"tags":253,"primary_topic":257,"secondary_topics":258,"classification":260,"search_phrases":283,"slug":286,"view_count":100,"doi":287,"paper":288,"created_at":311},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","2026-10-17T00:00:00Z",77,{"impact":249,"substance":123,"depth":124,"authority":20,"freshness":142,"relevant":22,"comment":250},16,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[252],{"name":118,"url":243},[27,254,31,255,256],"无人机","冬小麦","生物量估算",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[259],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":261},{"candidates":262,"agricultural_evidence":52},[263,269,274,279],{"code":33,"score":264,"supported":52,"title":265,"abstract":267,"tags":268},26,[266,34],"winter wheat",[35,266,34],[255],{"code":40,"score":249,"supported":52,"title":270,"abstract":272,"tags":273},[271],"remote sensing",[130,271],[256,31],{"code":152,"score":110,"supported":52,"title":275,"abstract":276,"tags":278},[],[129,277],"machine learning",[],{"code":63,"score":22,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[27],[284,285],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":287,"openalex_id":289,"authors":290,"venue":118,"cited_by_count":100,"oa_url":243,"card":306,"direction":105,"ingested_from":107},"W7222806066",[291,293,295,297,300,302,304],{"name":292,"orcid":9},"Guangtao Wang",{"name":294,"orcid":9},"Sumei Yao",{"name":296,"orcid":9},"Yanzheng Xie",{"name":298,"orcid":299},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":301,"orcid":9},"Xiangdong Chen",{"name":303,"orcid":9},"Yuhao Zhang",{"name":305,"orcid":9},"Lina Jiang",{"tldr":307,"method":308,"finding":309,"direction":105,"opportunity":310},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":9,"content":9,"source_name":317,"source_url":9,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":325,"secondary_topics":326,"classification":329,"search_phrases":348,"slug":351,"view_count":100,"doi":9,"paper":352,"created_at":359},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":249,"substance":18,"depth":19,"authority":57,"freshness":142,"relevant":22,"comment":321},"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。",[323],{"name":317,"url":315},[27,195,30,145,196],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[327,328],{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":330},{"candidates":331,"agricultural_evidence":52},[332,336,340,344],{"code":33,"score":210,"supported":52,"title":333,"abstract":334,"tags":335},[35],[35],[],{"code":201,"score":210,"supported":52,"title":337,"abstract":338,"tags":339},[220],[220],[],{"code":40,"score":210,"supported":52,"title":341,"abstract":342,"tags":343},[145],[],[30,195,145],{"code":63,"score":160,"supported":52,"title":345,"abstract":346,"tags":347},[],[27],[27],[349,350],"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":353,"venue":9,"cited_by_count":100,"oa_url":9,"card":354,"direction":105,"ingested_from":238},[],{"tldr":355,"method":356,"finding":357,"direction":105,"opportunity":358},"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":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":52,"score":368,"score_detail":369,"sources":372,"tags":374,"primary_topic":378,"secondary_topics":379,"classification":381,"search_phrases":413,"slug":416,"view_count":142,"doi":417,"paper":418,"created_at":448},4087,"Improving regional winter wheat yield estimation by assimilating cross-scale heterogeneity information from UAV and satellite remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115701","Accurate high-resolution winter wheat yield estimation remains challenging in heterogeneous agricultural systems. Conventional yield estimation approaches remain limited by high uncertainty (process-based crop models) or weak transferability due to insufficient physical knowledge and scarce sample sizes (statistical models based on remote sensing data). Data assimilation offers a promising approach to overcome these limitations, but its performance is constrained by the neglect of heterogeneous information within assimilation grids, particularly in low-intensity agricultural systems. Therefore, we developed a novel cross-scale data assimilation framework to enhance winter wheat yield estimation by integrating unmanned aerial vehicle (UAV) and satellite remote sensing with the WOFOST crop model. First, UAV-derived crop-soil parameters, including leaf area index (LAI), aboveground biomass (AGB), and soil moisture at 10 and 20 cm depths (SMC-10 and SMC-20), served as a bridge linking in situ sampling data with Sentinel satellite images. Next, a mixture density network (MDN) was employed to construct an upscaling model for predicting probability distributions, thereby obtaining the distribution of crop-soil parameters within each satellite pixel as field heterogeneity information. Finally, these heterogeneity distributions were incorporated into WOFOST through an improved particle filter to generate high-resolution yield (10 m × 10 m) estimates at the regional scale. The results showed that robust crop-soil parameter inversion models were constructed using random forest with multispectral and thermal UAV images (R 2 : 0.73–0.88; NRMSE: 12.30%–22.83%). The MDN quantified within-pixel heterogeneity of crop-soil parameters through a UAV-satellite upscaling approach. The joint assimilation of LAI, AGB, and SMC-10 achieved optimal winter wheat yield estimation performance for MDN-based cross-scale data assimilation (R 2 = 0.83, NRMSE = 15.70% in 2024–2025), outperforming satellite-based and mean-based cross-scale assimilation. The proposed framework showed stable interannual performance in Xianyang (RMSE ≤758 kg ha −1 , NRMSE ≤17.20% in 2017–2023) and reasonable transferability in two external winter wheat regions (RMSE ≤943 kg ha −1 ; NRMSE ≤18.29% in Baoding and Changji). Overall, the cross-scale data assimilation framework exhibited strong performance in wheat yield estimation and would provide scientific support for intensive agricultural development.","在异质性农业系统中，实现准确的高分辨率冬小麦产量估算仍具挑战性。传统产量估算方法受限于较高不确定性（基于过程的作物模型）或可迁移性较弱（基于遥感数据的统计模型），后者源于物理知识不足和样本量稀缺。数据同化为克服这些局限提供了一种有前景的途径，但其性能受到同化网格内异质性信息被忽略的制约，尤其是在低强度农业系统中。因此，我们开发了一种新的跨尺度数据同化框架，通过将无人机（UAV）和卫星遥感与WOFOST作物模型相结合，提升冬小麦产量估算。首先，无人机获取的作物-土壤参数，包括叶面积指数（LAI）、地上生物量（AGB）以及10和20 cm深度土壤水分（SMC-10和SMC-20），作为连接原位采样数据与Sentinel卫星影像的桥梁。接下来，采用混合密度网络（MDN）构建升尺度模型以预测概率分布，从而获得每个卫星像元内作物-土壤参数的分布，作为田块异质性信息。最后，通过改进的粒子滤波器将这些异质性分布纳入WOFOST，以生成区域尺度的高分辨率产量（10 m × 10 m）估算。结果表明，利用随机森林结合多光谱和热红外无人机影像构建了稳健的作物-土壤参数反演模型（R²：0.73–0.88；NRMSE：12.30%–22.83%）。MDN通过无人机-卫星升尺度方法量化了作物-土壤参数的像元内异质性。LAI、AGB和SMC-10的联合同化使基于MDN的跨尺度数据同化取得了最优的冬小麦产量估算性能（2024–2025年R² = 0.83，NRMSE = 15.70%），优于基于卫星和基于均值的跨尺度同化。所提出的框架在咸阳表现出稳定的年际性能（2017–2023年RMSE ≤758 kg ha⁻¹，NRMSE ≤17.20%），并在两个外部冬小麦区域具有合理的可迁移性（保定和昌吉RMSE ≤943 kg ha⁻¹；NRMSE ≤18.29%）。总体而言，该跨尺度数据同化框架在小麦产量估算中表现出较强性能，可为集约化农业发展提供科学支持。","Remote Sensing of Environment","2026-10-01T00:00:00Z",83,{"impact":124,"substance":370,"depth":124,"authority":17,"freshness":21,"relevant":22,"comment":371},23,"该研究提出跨尺度数据同化框架，融合无人机与卫星遥感提升区域冬小麦估产精度，方法新颖、验证充分，对精准农业与农业遥感应用具有较高参考价值。",[373],{"name":366,"url":363},[27,375,31,376,377],"作物模型","数据同化","小麦产量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[380],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":382},{"candidates":383,"agricultural_evidence":52},[384,389,393,400,404,408],{"code":33,"score":385,"supported":52,"title":386,"abstract":387,"tags":388},25,[266,34],[35,266,34],[],{"code":40,"score":124,"supported":52,"title":390,"abstract":391,"tags":392},[271],[130,271,59],[31],{"code":394,"score":110,"supported":52,"title":395,"abstract":396,"tags":399},"H2",[],[397,398],"土壤","soil",[],{"code":63,"score":22,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[27],{"code":152,"score":22,"supported":14,"title":405,"abstract":406,"tags":407},[],[],[375],{"code":409,"score":22,"supported":14,"title":410,"abstract":411,"tags":412},"I5",[],[],[376],[414,415],"无人机 卫星 遥感 冬小麦 估产","WOFOST 数据同化 小麦","无人机卫星遥感冬小麦估产-4087","10.1016\u002Fj.rse.2026.115701",{"doi":417,"openalex_id":419,"authors":420,"venue":366,"cited_by_count":100,"oa_url":363,"card":443,"direction":105,"ingested_from":107},"W7215051418",[421,423,425,427,430,432,435,437,439,441],{"name":422,"orcid":9},"Zhikai Cheng",{"name":424,"orcid":9},"Xiaobo Gu",{"name":426,"orcid":9},"Yang Xu",{"name":428,"orcid":429},"Xiaohai Fang","https:\u002F\u002Forcid.org\u002F0000-0003-1322-1366",{"name":431,"orcid":9},"Zhengtao Zhang",{"name":433,"orcid":434},"Penglin Li","https:\u002F\u002Forcid.org\u002F0009-0005-2706-1326",{"name":436,"orcid":9},"Yupeng Li",{"name":438,"orcid":9},"Shikun Sun",{"name":440,"orcid":9},"Yadan Du",{"name":442,"orcid":9},"Huanjie Cai",{"tldr":444,"method":445,"finding":446,"direction":105,"opportunity":447},"构建跨尺度数据同化框架，融合无人机与卫星遥感提升区域冬小麦产量估算精度。","UAV-Sentinel数据、混合密度网络升尺度、改进粒子滤波同化WOFOST模","联合同化LAI、AGB与SMC-10最优，R²=0.83，跨年与跨区表现稳定。","可探索异质农田下多源遥感与作物模型同化的普适性及低成本无人机采样优化。","2026-10-03T23:30:33.709814Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":10,"source_url":452,"published_at":455,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":456,"score_detail":457,"sources":459,"tags":461,"primary_topic":462,"secondary_topics":463,"classification":465,"search_phrases":485,"slug":488,"view_count":71,"doi":489,"paper":490,"created_at":517},1307,"Estimation of the Comprehensive High Photosynthetic-Efficiency Phenotypic Index in Winter Wheat Based on UAV Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15172674","Accurate monitoring of photosynthetic phenotypes is fundamental for breeding high photosynthetic-efficiency wheat cultivars, and UAV remote sensing provides an effective approach for their large-scale identification. However, single photosynthetic parameters are limited in comprehensively evaluating crop photosynthetic efficiency. This study integrated multiple photosynthetic phenotypic parameters using principal component analysis (PCA) and the CRITIC objective weighting method (PCA-CRITIC) to construct a Comprehensive High Photosynthetic-Efficiency Phenotypic Index (CHPPI) for winter wheat. Concurrently, multispectral vegetation indices (MSVI), RGB vegetation indices (RGBVI), texture features (TF), and their combination, Comprehensive Multispectral-Visible-Texture Features (CMVTF), were extracted from UAV multimodal remote sensing data. Based on these features, four feature selection methods, namely PCC, VIP, SPA, and UVE, were evaluated in combination with five machine learning algorithms, including KNN, DT, SVR, RF, and XGBoost, to precisely estimate the CHPPI across key growth stages. The results demonstrated that the CHPPI exhibited significant cultivar variations across growth stages. Cultivars SH06144 and Liangxing19 consistently maintained high photosynthetic efficiency levels. The fused feature set CMVTF derived from multimodal remote sensing data outperformed individual feature categories, and the feature subset selected by the UVE method most significantly enhanced model accuracy. Regarding algorithms, the ensemble-based XGBoost and RF models markedly surpassed traditional machine learning algorithms. Specifically, the CMVTF-UVE-XGBoost model achieved the optimal comprehensive performance, yielding an R2 of 0.862, an RMSE of 0.032, and an MAE of 0.027 on the testing set, with its spatial distribution estimations highly consistent with measured values. This study provides robust technical support for the rapid and large-scale screening of high photosynthetic-efficiency wheat breeding materials.","光合表型精准监测是培育高光效小麦品种的基础，无人机遥感为其大规模鉴定提供了有效途径。然而，单一光合参数难以全面评价作物光合效率。本研究利用主成分分析(PCA)与CRITIC客观赋权法(PCA-CRITIC)整合多个光合表型参数，构建了冬小麦高光效综合表型指数(CHPPI)。同时，从无人机多模态遥感数据中提取多光谱植被指数(MSVI)、RGB植被指数(RGBVI)、纹理特征(TF)及其融合特征——多光谱-可见光-纹理综合特征(CMVTF)。基于上述特征，评估了PCC、VIP、SPA和UVE四种特征选择方法，并结合KNN、DT、SVR、RF和XGBoost五种机器学习算法，对关键生育期的CHPPI进行精准估算。结果表明，CHPPI在不同生育期表现出显著的品种间差异，品种SH06144和良星19始终保持较高的光合效率水平。基于多模态遥感数据构建的融合特征集CMVTF优于单一特征类别，且UVE方法筛选的特征子集对模型精度的提升最为显著。在算法方面，基于集成的XGBoost和RF模型明显优于传统机器学习算法。具体而言，CMVTF-UVE-XGBoost模型取得了最优的综合性能，在测试集上R²为0.862，RMSE为0.032，MAE为0.027，其空间分布估算结果与实测值高度一致。本研究为高光效小麦育种材料的快速大规模筛选提供了有力的技术支撑。","2026-08-31T00:00:00Z",73,{"impact":124,"substance":123,"depth":124,"authority":20,"freshness":71,"relevant":22,"comment":458},"基于无人机多模态遥感构建小麦光合效率综合指数，方法新颖且模型精度高，对育种筛选有实用价值。",[460],{"name":10,"url":452},[27,129,29,130],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[464],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":466},{"candidates":467,"agricultural_evidence":52},[468,472,476,481],{"code":33,"score":264,"supported":52,"title":469,"abstract":470,"tags":471},[266,34],[35,266,34],[29],{"code":40,"score":124,"supported":52,"title":473,"abstract":474,"tags":475},[271],[130,271,59],[130],{"code":152,"score":477,"supported":52,"title":478,"abstract":479,"tags":480},7,[],[129,277],[129],{"code":63,"score":22,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[27],[486,487],"小麦育种 智慧农业 机器学习 遥感","小麦育种 智慧农业","小麦育种智慧农业机器学习遥感-1307","10.3390\u002Fplants15172674",{"doi":489,"openalex_id":491,"authors":492,"venue":10,"cited_by_count":100,"oa_url":452,"card":512,"direction":105,"ingested_from":107},"W7204845072",[493,495,497,499,501,503,505,507,509],{"name":494,"orcid":9},"Ning Yang",{"name":496,"orcid":9},"Dayong Cui",{"name":498,"orcid":9},"Songming Lin",{"name":500,"orcid":9},"Changliang Du",{"name":502,"orcid":9},"Liwen Wang",{"name":504,"orcid":9},"Fei Zhang",{"name":506,"orcid":9},"Zhu Shi",{"name":508,"orcid":9},"Bingqian Hou",{"name":510,"orcid":511},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"tldr":513,"method":514,"finding":515,"direction":105,"opportunity":516},"基于无人机多模态遥感数据，构建综合高光效表型指数并精准估算，用于小麦育种材料筛选。","PCA-CRITIC构建CHPPI，提取多模态特征，结合UVE特征选择与XGBo","CMVTF-UVE-XGBoost模型表现最优，测试集R²=0.862，可快速大规模筛选高光效小麦。","可探索将CHPPI估算模型推广至其他作物或不同环境，并引入深度学习或时序数据提升泛化能力。","2026-09-01T23:30:16.317443Z"]