Researchers examine drought tolerance traits in beans using hyperspectral remote sensing

Bean hyperspectral reflectance from ground-based (A and C) and tower-based instruments (B and D) for control (blue) and drought (red) treatments. Credit: Christopher YS Wong et al. from the University of California, Davis

Crops adapt to climatic and environmental changes by presenting some modified biological characteristics. For example, plants that grow in deserts show resistance to drought. However, from time to time, plant breeding becomes necessary to ensure optimal crop yields, stress response and water use. High-throughput phenotyping tools are then used to cost-effectively and rapidly screen for desired biological traits.

However, such monitoring becomes laborious and time-consuming. It can also lead to subjective interpretation and crop destruction. A research team recently made an attempt to overcome this limitation using rapid hyperspectral remote sensing. This work was published in Phenomena of Plants.

Says lead author Christopher YS Wong of the Department of Plant Sciences at the University of California, Davis, “We estimated physiological (predawn and midday stomatal conductance and water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to assess drought response in 12 common bean and 4 broad bean genotypes in 3 field campaigns (1 pre-drought and 2 post-drought).

The research team harnessed the power of hyperspectral imaging—extracting data related to specific crop characteristics from different regions of the electromagnetic spectrum using advanced imaging techniques—with the help of a portable device and tower-based equipment. The bean plantation was irrigated or left unirrigated to mimic normal and drought conditions respectively. The collected data was then analyzed with the help of a machine learning based technique called Partial Least Squares Regression (PLSR).

PLSR modeling was able to specifically examine two physiological traits in common and tepear beans – stomatal conductance and leaf water potential (LWP). Both stomatal conductance and LWP are indicators of plant water status and are often used to assess drought tolerance.

Senior author Thomas N. Buckley, Associate Professor in the Department of Plant Sciences, observes, “Native to semiarid and arid environments, common beans are generally more drought tolerant than common beans. We explore these common and tepover bean genotypes in a field experiment with irrigated (control) and terminal drought treatments.”

The research team also developed unmanned aerial vehicles (drones) to further facilitate remote measurements. A comparison was then made to assess the effectiveness of the ground and tower methods. For example, the team observed that the ground-based method generally outperformed the tower-based method for all 3 traits—oral conductance, predawn LWP, and midday LWP. The researchers then used heat map clustering—used primarily to highlight drought response—to characterize drought response phenotypes.

The hyperspectral data were able to successfully predict the bean characteristics under investigation. Furthermore, there was good agreement between terrestrial and physiological measurements, thus validating the technique. According to the authors, this new modern agricultural technique based on remote sensing can also be used to predict crop characteristics in well-irrigated and drought-prone geographies.

“This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response among genotypes for vegetation monitoring and breeding population control,” concludes corresponding author Troy S. Magney.

More information:
Christopher YS Wong et al, Hyperspectral Remote Sensing to Phenotype the Physiological Response to Drought of Common and Tepary Bean, Phenomena of Plants (2022). DOI: 10.34133/plantphenomics.0021

Provided by NanJing Agricultural University

Reference: Researchers examine drought resistance traits in beans using hyperspectral remote sensing (2023, March 9) Retrieved March 9, 2023 from html

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