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: JIANG, ZHAO - Huazhong Agricultural University; TU, HAIFU - Huazhong Agricultural University; BAI, BAOWEI - Huazhong Agricultural University; Yang, Chenghai; ZHAO, BIQUAN - University Of Nebraska; GUO, ZIYUE - Huazhong Agricultural University; LIU, QIAN - Huazhong Agricultural University; ZHAO, HU - Huazhong Agricultural University; YANG, WANNENG - Huazhong Agricultural University; XIAON, LIZHONG - Huazhong Agricultural University; ZHANG, JIAN - Huazhong Agricultural University

Submitted to: New Phytologist
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/17/2021
Publication Date: 9/1/2021
Citation: Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z., Liu, Q., Zhao, H., Yang, W., Xiaon, L., Zhang, J. 2021. 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. New Phytologist. 232(1):440-455. https://doi.org/10.1111/nph.17580.
DOI: https://doi.org/10.1111/nph.17580

Interpretive Summary: 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. This study employed high-efficiency and high-frequent image acquisition by an unmanned aerial vehicle to quantify the dynamic drought response of a rice population under field conditions. Deep learning and canopy height models were applied to extract highly correlated phenotypic traits. The models achieved high accuracy to monitor the drought resistance of rice accessions. The results from this study showed that unmanned aerial aquired imagery analyzed with deep learning techniques provide for effective phenotyping for more complete genetic dissection of rice dynamic responses to drought and exploration of valuable alleles for drought resistance improvement.

Technical 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.