UAV-borne hyperspectral and thermal imagery integration empowers genetic dissection of wheat stomatal conductance

Abstract

Implementing advanced approaches such as marker-assisted selection into classic breeding programs is critical for increasing genetic gain and meeting the population’s ever-growing food demand. Genome-wide association studies (GWAS) is a well-known method for detecting genetic markers related to various morphological and physiological traits. However, the ability to collect phenotypic data in large panels often limits the feasibility of genetic studies. This study aimed to assess the potential of UAV-borne thermal and hyperspectral imaging for estimating key wheat traits and identifying their genetic architecture. A diversity panel (300 genotypes) was characterized under well-watered and terminal-drought conditions in a rainout shelter facility. Stomatal conductance, leaf area index, and total chlorophyll content were estimated across two growing seasons. A support vector machine model that integrates canopy spectral reflectance and temperature emittance from UAV-borne imagery reduced the root mean square error of stomatal conductance estimation by 28% compared to using canopy reflectance alone. The models were further used to estimate the traits in the entire panel and to detect genomic markers associated with them and their dynamics throughout the season. Altogether, 16 genetic markers associated with alleles conferring these traits were detected, and the most promising markers were validated during an additional growing season. In the validation experiment, both the spectral estimation models and the allelic effect of the markers were consistent with the previous season. This study introduces, for the first time, the use of stomatal conductance estimation based on combining UAV hyperspectral and thermal imagery for genomic mapping. Implementing this integrated approach could promote the development of new climate-resilience wheat varieties to ensure food security worldwide by screening for stomatal conductance, which is not practical with manual measurements.

Plant phenotyping relevance

UAV熱・ハイパースペクトル画像を統合し、気孔コンダクタンス等の植物形質を推定するモデルを開発・検証しており、フェノタイピング手法が研究の中心である。

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