Assessment of the Genetic Architecture at Early-Stage Drought Tolerance in Wheat Using UAV-Based Multispectral Imaging

Abstract

Abstract Recent advances in unmanned aerial vehicles (UAVs) and multispectral sensor technologies have transformed high-throughput phenotyping as an efficient alternative to traditional approaches. In this study, we employed UAV-based multispectral imaging to monitor early-stage drought stress in wheat. A panel of 221 historical spring wheat cultivars from Pakistan, representing over a century of breeding history, were evaluated under irrigated and drought conditions. UAV flights were conducted twice at early growth stages to capture multispectral imagery, which was processed in Pix4D mapper for image alignment and orthomosaic generation. Plot segmentation and trait extraction were performed in QGIS. Eight drought-responsive vegetative indices (VIs) were analyzed to assess genotypic variation. Significant to highly significant differences were observed among genotypes, treatments, and their interactions across all VIs. Broad-sense heritability estimates were moderate to high for most traits, with predominantly additive gene action. Vegetative indices such as NDVI, GNDVI, EVI, and SAVI showed strong correlations with each other and effectively detected early canopy stress under drought. Principal component analysis based on 23.897K SNPs indicated a mixed genetic population. Genome-wide association studies identified 115 significant QTNs linked to VIs under both conditions, corresponding to 74 loci, including 26 pleiotropic loci. Of these, 10 pleiotropic loci detected under drought were annotated and six putative candidate genes were identified which showed expression in multiple tissues based on transcriptome data. Two genes i.e., TraesCS1D01G217600.1 ( HSP70 ) and TraesCS6D01G254000.1 ( ZmMDAR3 ), showed differential expression pattern among control and drought treatment. These findings highlight the potential of UAV-based phenotyping for early drought detection and provide candidate genes for improving drought tolerance in wheat. Future research should focus on the functional roles of these genes under drought stress.

Plant phenotyping relevance

UAVマルチスペクトル画像から植生指数を抽出し、コムギの干ばつストレスという植物状態を評価するフェノタイピング手法が研究の主要な基盤であり、単なる補助的測定ではない。

Code and data availability

The supplied blocks describe UAV multispectral phenotyping, VI extraction in Pix4D/QGIS, and GWAS workflows, but contain no data or code availability statement, no public deposit of imagery, VI trait tables, or analysis scripts. The only URLs mentioned (Ensembl Plants BioMart, wheat-expression.com, barleyexp.com) are外部

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