[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2522":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":68},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",0,"10.3390\u002Fplants15182819",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":61,"direction":65,"ingested_from":67},"W7212560710",[37,40,43,46,49,52,55,58],{"name":38,"orcid":39},"Irina Marina","https:\u002F\u002Forcid.org\u002F0000-0002-5894-363X",{"name":41,"orcid":42},"Vesna Kandić","https:\u002F\u002Forcid.org\u002F0000-0003-1999-2030",{"name":44,"orcid":45},"Marko Kostić","https:\u002F\u002Forcid.org\u002F0000-0001-9446-994X",{"name":47,"orcid":48},"Nataša Ljubičić","https:\u002F\u002Forcid.org\u002F0000-0001-5982-9401",{"name":50,"orcid":51},"Biljana Bošković","https:\u002F\u002Forcid.org\u002F0000-0003-4977-3170",{"name":53,"orcid":54},"Kosta Gligorević","https:\u002F\u002Forcid.org\u002F0000-0001-5783-4637",{"name":56,"orcid":57},"Miloš Pajić","https:\u002F\u002Forcid.org\u002F0000-0002-8905-3293",{"name":59,"orcid":60},"Milan Dražić","https:\u002F\u002Forcid.org\u002F0000-0002-0416-5174",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"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"]