Research Article | Open Access

Volume 2023 |Article ID 0021 | https://doi.org/10.34133/plantphenomics.0021

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean

Christopher YS Wong,¹ Matthew E Gilbert,¹ Marshall A Pierce,¹ Travis A Parker,¹ Antonia Palkovic,¹ Paul Gepts,¹ Troy S Magney ,^1,2 Tepary Bean^1,2

¹Department of Plant Sciences, University of California,Davis, Davis, CA 95616, USA
²Joint senior authors

Abstract

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.