Research Article | Open Access

Volume 2025 |Article ID 100026 | https://doi.org/10.1016/j.plaphe.2025.100026

Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Gossypium hirsutum)

Liqiang Fan,^1,2,6,7 Jiajie Yang,^1,2,7 Xuwen Wang,^3,7 Zhao Liu,⁴ Bowei Xu,^1,2 Li Liu,³ Chenxu Gao,¹ Xiantao Ai,⁵ Fuguang Li,^1,2,4 Lei Gao ,^1,2 Yu Yu ,³ Zuoren Yang ^1,2,4,5

¹State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China
²Xinjiang Key Laboratory of Crop Gene Editing and Germplasm Innovation, Institute of Western Agricultural of CAAS, Changji, 831100, Xinjiang, China
³Cotton Research Institute, Xinjiang Academy Agricultural and Reclamation Science/Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding (Xinjiang), Ministry of Agriculture, China
⁴Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, China
⁵College of Smart Agriculture (Research Institute), Xinjiang University, Urumqi, 830046, Xinjiang, China
⁶Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
⁷These authors contributed equally to this work.

Abstract

Plant height (PH) is a key agronomic trait influencing plant architecture. Suitable PH values for cotton are important for lodging resistance, high planting density, and mechanized harvesting, making it crucial to elucidate the mechanisms of the genetic regulation of PH. However, traditional field PH phenotyping largely relies on manual measurements, limiting its large-scale application. In this study, a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field. Different strategies were used to extract PH values from two sets of sensor data, and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions. These predictions were consistent with manual measurements of the PH for the LiDAR (R2 = 0.934) and RGB (R2 = 0.914) data. The predicted PH values were used for GWAS analysis, and 34 PH-related genes, two of which have been demonstrated to regulate PH in cotton, namely, GhPH1 and GhUBP15, were identified. We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G. hirsutum cultivar ZM24 harvested on the 15th, 35th, and 70th days after sowing compared with those from a dwarf mutant (pag1), which presented shortened stem and internode phenotypes. The overexpression of GhPH_UAV1 significantly promoted cotton stem development, whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth, suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH. This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations, which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.