Pre-harvest defoliation of cotton is a key agricultural measure to improve mechanical harvesting efficiency and raw cotton purity. Collecting data on cotton defoliation traits for genetic localization and thus breeding defoliation-prone varieties is an essential alternative to traditional defoliant spraying. Nevertheless, it is hampered by low throughput and artificial error in manual field surveys. In this study, a framework for collecting high-throughput defoliation data in large fields was established. Three spectral indices (MTCI, VDVI, CI) and leaf area index (LAI) were first screened as core predictors through hierarchical segmentation analysis in three levels: leaf number (LN), leaf number difference (LND), and defoliation rate (DR). Four deep learning architectures (CNN, BiGRU, CNN-BiGRU, and CNN-BiGRU-Attention) were developed, and the CNN-BiGRU-Attention hybrid model demonstrated superior performance at all three levels, with R² values exceeding 0.85. Importantly, the inversion accuracy of this model at the LN and LND levels was superior to that at the DR level, which was also confirmed by the results of the genome-wide association study (GWAS). We combined GWAS and transcriptome results to identify a new gene, GhDR_UAV1, associated with defoliation traits. The overexpression of GhDR_UAV1 significantly promoted the wilting of cotton leaves, indicating that GhDR_UAV1 plays a positive regulatory role in cotton defoliation. This study proposed a strategy to invert cotton defoliation data at three levels using deep learning fusion of UAV remote sensing data and LAI data and confirmed that LND can provide accurate phenotypic data for GWAS analysis. This study provides a new theoretical basis for cotton defoliation regulation and genetic improvement by integrating cotton high-throughput defoliation phenomics and genomics from an innovative perspective.

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.

Cotton (Gossypium spp.) is the most important natural textile fiber crop in the world. The ideal plant architecture of cotton is suitable for mechanical harvesting and productivity in modern agricultural production. However, cotton genes regulating plant development and architecture have not been fully identified. We identified a basic helix-loop-helix (bHLH) transcription factor, GhPAS1 (PAGODA1 SUPPRESSOR 1) in G. hirsutum (Upland cotton). GhPAS1 was located in the nucleus and showed a strong transcription activation effect. Tissue-specific expression patterns showed that GhPAS1 was highly expressed in floral organs, followed by high expression in early stages of ovule development and rapid fiber elongation. GhPAS1 overexpression in Arabidopsis and BRZ (brassinazole, BR biosynthesis inhibitor) treatment indicated that GhPAS1 positively regulates and responds to the BR (brassinosteroid) signaling pathway and promotes cell elongation. GhPAS1 overexpression in Arabidopsis mediated plant development in addition to increasing plant biomass. Virus-induced gene silencing of GhPAS1 indicated that down-regulation of GhPAS1 inhibited cotton growth and development, as plant height, fruit branch length, and boll size of silenced plants were lower than in control plants. Fiber length and seed yield were also lower in silenced plants. We conclude that GhPAS1, a bHLH transcription factor, regulates plant development and architecture in cotton. These findings may help breeders and researchers develop cotton cultivars with desirable agronomic characteristics.