UAV-based LiDAR technology facilitates high-throughput phenotyping in maize breeding trials
Published 29 September, 2026
Maize (Zea mays L.) is 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 and ecological adaptability. However, traditional methods for measuring plant height often lack cost-efficiency and accuracy.
In a study published in Journal of Integrative Agriculture, a team of researchers from China used a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines.
“We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds,” explains corresponding author Jun Zheng at Gansu Agricultural University & Chinese Academy of Agricultural Sciences. “We found 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.”
“We constructed two high-density genetic maps based on SNP markers,” shares co-corresponding author Xiuliang Jin at Chinese Academy of Agricultural Sciences. “In Sanya and Xinxiang, the F1DH and F2DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively.”
Notably, the study 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.
Contact Authors:
Correspondence Jun Zheng, E-mail: zhengjun02@caas.cn; Xiuliang Jin, E-mail: jinxiuliang@caas.cn; Hongwu Wang, E-mail: wanghongwu@caas.cn
Funder:
This research was supported by the National Key Research and Development Program of China (2023YFD1200500), the National Natural Science Foundation of China (32301395, 42071426, and 51922072), the Nanfan Special Project of the Chinese Academy of Agricultural Sciences (YBXM2305), the Central Public-Interest Scientific Institution Basal Research Fund Program, China (Y2020YJ07 and Y2022XK22), the Key Cultivation Program of the Xinjiang Academy of Agricultural Sciences, China (xjkcpy-2020003), and the Open Competition Project of Heilongjiang Province, China (2021ZXJ05A03).
Conflict of Interest:
The authors declare that they have no conflict of interest.
See the Article:
Zhang X et al. 2026. 2026. QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data. Journal of Integrative Agriculture, 25(5): 1822-1835.
https://www.sciencedirect.com/science/article/pii/S2095311924003253