TY  - JOUR
AU  - Zhang, Xin
AU  - Zhang, Jidong
AU  - Peng, Yunling
AU  - Yu, Xun
AU  - Lu, Lirong
AU  - Liu, Yadong
AU  - Song, Yang
AU  - Yin, Dameng
AU  - Zhao, Shaogeng
AU  - Wang, Hongwu
AU  - Jin, Xiuliang
AU  - Zheng, Jun
PY  - 2026
TI  - QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data
JO  - Journal of Integrative Agriculture (JIA)
SN  - 2095-3119
SP  - 1822
EP  - 1835
VL  - 25
IS  - 5
AB  - Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the world’s growing population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) was employed to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding trials. High-density genetic maps were constructed, and plant height was assessed at both single-plant and row scales across multiple developmental stages and genetic backgrounds. The findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with R2 values of 0.67 vs. 0.56 and RMSE values of 0.12 m vs. 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F1DH and F2DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. This study successfully identified and validated QTLs associated with plant height, thereby revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.
UR  - https://doi.org/10.1016/j.jia.2024.09.004
DO  - 10.1016/j.jia.2024.09.004