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.
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The objective of this study is to develop InDel molecular markers for Island cotton, which is characterized by its superior fiber quality, particularly the fiber tensile strength-a key indicator of cotton fiber quality. The study aims to validate these markers using RIL (Recombinant Inbred Line) populations and resource materials, thereby providing a theoretical foundation for breeding new varieties of Island cotton with enhanced fiber quality.
Utilizing a previously established population of 213 Pima S-7 and 5917 F5:6 RILs, we conducted QTL (Quantitative Trait Locus) mapping to identify the locus regulating fiber strength in Island cotton, designated qFS-chr17-1. InDel markers were designed based on whole genome sequencing (WGS) data of the parental lines, followed by the identification of polymorphic markers. Preliminary validation of these markers was performed using 40 extreme family materials selected based on phenotypic data. Genotyping was carried out on both the 213 RIL population and the 213 Island cotton resource population, alongside multi-year fiber quality data to assess the markers' effectiveness.
The genotyping of the RIL and Island cotton resource populations with the two developed InDel markers indicated a close linkage to fiber strength phenotypic data, with significant differences observed in fiber strength traits among the differentiated materials. The analysis of genotypic combinations revealed an upward trend in fiber strength across four combination types, with materials exhibiting the Hap3 (B/A) and Hap4 (B/B) genotypes demonstrating significantly greater fiber strength than those with Hap1 (A/A) and Hap2 (A/B). Furthermore, the InDel-3L2 marker showed significant correlations with fiber length, fiber uniformity, and spinning consistency index, consistent with the observed phenotypic trends. Analysis of multi-year fiber quality data from two experimental sites revealed environmental variability in fiber quality, while temperature data indicated that the developed molecular markers are minimally influenced by environmental factors. Clustering analysis of fiber quality data from 213 Island cotton resource materials, combined with molecular marker genotyping, identified eight materials exhibiting superior fiber quality.
This study successfully developed two InDel molecular markers closely linked to the fiber strength QTL (qFS-chr17-1), which maintain their effectiveness upon combination. The InDel-3L2 marker demonstrates significant correlations with fiber length, fiber uniformity, and spinning consistency index. These markers can efficiently and accurately identify high-strength fiber resources in Island cotton, contributing to the breeding of improved fiber quality. Additionally, eight materials with excellent fiber quality have been identified.
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