Using UAV-Based Temporal Spectral Indices to Dissect Changes in the Stay-Green Trait in Wheat.

Abstract

Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100 , TracesCS6B03G0356400 , and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.

Plant phenotyping relevance

UAV時系列スペクトル指標を用いてコムギのstay-green（老化動態）を定量化し、RSGS指標と解析パイプラインを開発・適用しており、表現型取得法が研究の中心である。

Code and data availability

The paper's Data Availability statement explicitly deposits the study's genotype and phenotype data (the RSGS stay-green phenotypes and SNP genotypes for the 565-accession wheat panel) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code or raw UAV imagery deposit