Accurately estimate soybean growth stages from UAV imagery by accounting for spatial heterogeneity and climate factors across multiple environments

•Developed Field Spatial-Correction Model: Developed a field spatial-correction model to quantify environmental covariates, resulting in improved estimation accuracy.•Dynamic Growth Curves: Created genotype-specific dynamic growth curves that accurately fit observed target curves, applicable to vari...

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Veröffentlicht in:	Computers and electronics in agriculture 2024-10, Vol.225, p.109313, Article 109313
Hauptverfasser:	Che, Yingpu, Gu, Yongzhe, Bai, Dong, Li, Delin, Li, Jindong, Zhao, Chaosen, Wang, Qiang, Qiu, Hongmei, Huang, Wen, Yang, Chunyan, Zhao, Qingsong, Liu, Like, Wang, Xing, Xing, Guangnan, Hu, Guoyu, Shan, Zhihui, Wang, Ruizhen, Li, Ying-hui, Jin, Xiuliang, Qiu, Li-juan
Format:	Artikel
Sprache:	eng
Schlagworte:	agriculture algorithms climate cultivars electronics germplasm growth and development growth curves heritability latitude Multi-environment trials photosynthesis Photothermal accumulation area precision agriculture Soybean growth stages soybeans Spatial heterogeneity spatial variation temporal variation Unmanned aircraft vehicle vegetation
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