文献类型： 外文期刊

作者： Fan, Liqiang ¹ ; Yang, Jiajie ¹ ; Wang, Xuwen ³ ; Liu, Zhao ⁴ ; Xu, Bowei ¹ ; Liu, Li ³ ; Gao, Chenxu ¹ ; Ai, Xiantao ⁵ ; Li, Fuguang ¹ ; Gao, Lei ¹ ; Yu, Yu ³ ; Yang, Zuoren ¹ ;

作者机构： 1.Chinese Acad Agr Sci, Inst Cotton Res, State Key Lab Cotton Biobreeding & Integrated Util, Anyang 455000, Henan, Peoples R China

2.CAAS, Inst Western Agr, Xinjiang Key Lab Crop Gene Editing & Germplasm Inn, Changji 831100, Xinjiang, Peoples R China

3.Xinjiang Acad Agr & Reclamat Sci, Cotton Res Inst, Northwest Inland Reg Key Lab Cotton Biol & Genet B, Minist Agr, Urumqi, Peoples R China

4.Zhengzhou Univ, Sch Agr Sci, State Key Lab Cotton Biobreeding & Integrated Util, Zhengzhou Res Base, Zhengzhou 450001, Henan, Peoples R China

5.Xinjiang Univ, Res Inst, Coll Smart Agr, Urumqi 830046, Xinjiang, Peoples R China

6.Xinjiang Agr Univ, Coll Agr, Engn Res Ctr Cotton, Minist Educ, 311 Nongda East Rd, Urumqi 830052, Peoples R China

关键词： Plant height; UAV; RGB; LiDAR; Genome-wide association study

期刊名称：PLANT PHENOMICS （ 影响因子：6.4； 五年影响因子：7.1 ）

ISSN： 2643-6515

年卷期： 2025 年 7 卷 1 期

页码：

收录情况： SCI

摘要： 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 1/4 0.934) and RGB (R2 1/4 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.