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

Authors

Zhao Jiang, Huazhong Agricultural University, Wuhan
Haifu Tu, Huazhong Agricultural University, Wuhan
Baowei Bai, Huazhong Agricultural University, Wuhan
Chenghai Yang, USDA-Agricultural Research Service, College Station, TX
Biquan Zhao, University of Nebraska-Lincoln
Ziyue Guo, Huazhong Agricultural University, Wuhan
Qian Liu, Huazhong Agricultural University, Wuhan
Hu Zhao, Huazhong Agricultural University, Wuhan
Wanneng Yang, Huazhong Agricultural University, Wuhan
Lizhong Xiong, Huazhong Agricultural University, WuhanFollow
Jian Zhang, Huazhong Agricultural UniversityFollow

ORCID IDs

Zhao Jiang https://orcid.org/0000-0001-8686-2784

Haifu Tu https://orcid.org/0000-0003-4051-7605

Baowei Bai https://orcid.org/0000-0002-7032-9018

Chenghai Yang https://orcid.org/0000-0002-9898-628X

Biquan Zhao https://orcid.org/0000-0002-9710-744X

Ziyue Guo https://orcid.org/0000-0003-3368-1482

Qian Liu https://orcid.org/0000-0003-1137-0026

Hu Zhao https://orcid.org/0000-0001-5046-6632

Wanneng Yang https://orcid.org/0000-0003-1095-1355

Lizhong Xiong https://orcid.org/0000-0003-0490-1474

Jian Zhang https://orcid.org/0000-0003-3364-1544

Date of this Version

2021

Citation

New Phytologist (2021)

doi:10.1111/nph.17580

Comments

U.S. government work

Abstract

Accurate and high-throughput phenotyping of the dynamic response of a large rice population to drought stress in the field is a bottleneck for genetic dissection and breeding of drought resistance.

Here, high-efficiency and high-frequent image acquisition by an unmanned aerial vehicle (UAV) was utilized to quantify the dynamic drought response of a rice population under field conditions. Deep convolutional neural networks (DCNNs) and canopy height models were applied to extract highly correlated phenotypic traits including UAV-based leaf-rolling score (LRS_uav), plant water content (PWC_uav) and a new composite trait, drought resistance index by UAV (DRI_uav).

The DCNNs achieved high accuracy (correlation coefficient R = 0.84 for modeling set and R = 0.86 for test set) to replace manual leaf-rolling rating. PWC_uav values were precisely estimated (correlation coefficient R = 0.88) and DRI_uav was modeled to monitor the drought resistance of rice accessions dynamically and comprehensively. A total of 111 significantly associated loci were detected by genome-wide association study for the three dynamic traits, and 30.6% of them were not detected in previous mapping studies using nondynamic drought response traits.

Unmanned aerial vehicle and deep learning are confirmed effective phenotyping techniques for more complete genetic dissection of rice dynamic responses to drought and exploration of valuable alleles for drought resistance improvement.