Bean physiology under drought stress: High-throughput field phenotyping using tower-based spectral reflectance and solar-induced fluorescence (SIF)

In light of climate change, drought events are becoming more frequent and intense across many agricultural regions around the world. This may have severe consequences for water conservation, crop productivity and yield. As a result, plant breeders are working to identify lines for drought resilience. However, it is challenging to rapidly phenotype the response of many different genotypes at the field scale, which has traditionally been done with leaf trait measurements (eg. low-throughput methods). Remote sensing offers a promising method for non-destructive high-throughput phenotyping to monitor real time plant function at high spatial and temporal scales. These remote sensing methods include reflectance spectra and solar-induced fluorescence (SIF) which can provide information about vegetation structure, physiology, and function to infer key physiological plant trait information. In this study, we monitored physiology and growth in 16 bean genotypes (12 of common bean, Phaseolus vulgaris, and four of tepary bean, P. acutifolius) under drought stress, in the field, using high-throughput (tower-based canopy reflectance spectra and SIF) and low-throughput phenotyping methods (photosynthetic capacity, leaf water potential, stomatal conductance, canopy light capture by ceptometry). We used regression analysis for SIF and partial least squares regression (PLSR) for the full reflectance spectra to predict physiological traits from the low-throughput methods. Our results show that spectra were able to detect differences in physiological function and structure between the different common bean and tepary bean genotypes under both control and drought conditions. This highlights the potential of remote sensing for high-throughput phenotyping of plant traits to aid in genotype selection for drought resilience.