Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress.

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作者： Zhao,Jiang ^[1] ; Haifu,Tu ^[2] ; Baowei,Bai ^[2] ; Chenghai,Yang ^[3] ; Biquan,Zhao ^[4] ; Ziyue,Guo ^[5] ; Qian,Liu ^[1] ; Hu,Zhao ^[2] ; Wanneng,Yang ^[2] ; Lizhong,Xiong ^[2] ; Jian,Zhang ^[2]

作者单位： Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan, 430070, China. ^[1] National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China. ^[2] Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX, 77845, USA. ^[3] School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583-0988, USA. ^[4] Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68583-0726, USA. ^[5]

关键词 GWAS deep convolutional neural networks (DCNNs)drought stress leaf-rolling score plant water content unmanned aerial vehicle (UAV)

主题词干旱(Droughts);遗传变异(Genetic Variation);全基因组关联研究(Genome-Wide Association Study)

DOI 10.1111/nph.17580

PMID 34165797

发布时间 2021-09-09

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准确一般字义偏差语意偏离译文作用小提交