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Leaf-level functional trait predictions in maize crops using UAV-based hyperspectral imaging
  • +3
  • Karoll Quijano,
  • John Couture,
  • Michael Mickelbart,
  • Elisabeth Joll,
  • Mitchell Tuinstra,
  • Melba Crawford
Karoll Quijano
Department of Agricultural and Biological Engineering
John Couture
Department of Entomology, Department of Forestry and Natural Resources

Corresponding Author:[email protected]

Author Profile
Michael Mickelbart
Department of Botany
Elisabeth Joll
Department of Forestry and Natural Resources
Mitchell Tuinstra
Department of Agronomy
Melba Crawford
Department of Agricultural and Biological Engineering, Department of Agronomy, School of Civil Engineering, Purdue University

Abstract

Plant functional traits capture essential morphological, physiological, or phenological characteristics of plants that influence growth, resource allocation, and survival. These traits can be used to identify plant adaptations to changes in the environment. Functional traits that are indicators of water status in plants can be used to identify adaptative mechanisms to overcome drought. Measuring functional traits using standard approaches is costly, time consuming, and destructive. Vegetation spectroscopy has been shown to estimate a wide variety of plant functional traits, but still can involve extensive field work. Using proximal spectral measurements as a training input for unpiloted aerial vehicle (UAV) collections can potentially bridge spatial gaps between point-based reference and proximal measurements and whole-field UAV measurements. This research proposes a non-destructive approach to transition from proximal spectroscopy to high resolution UAV imagery to predict photosynthetic and water relation traits in maize hybrids grown in two different environments under varying levels of water availability. Both proximal and UAV collected spectral measurements covered the visible, near infrared, and shortwave infrared regions. Partial least squares regression (PLSR) models were developed for both proximal spectra, using reference measurements, and UAV spectra, using proximal predictions as response variable. Many UAV-based PLSR models, including chlorophyll concentration, CO2 assimilation, osmotic potential, and succulence, performed well with goodness of fit statistics over 0.60. These preliminary results highlight the opportunity to advance the capabilities of UAV-based hyperspectral imaging to rapidly and non-destructively predict leaf-level functional traits related to drought, improving breeding approaches and genotype selection.
31 Oct 2023Submitted to NAPPN 2024
31 Oct 2023Published in NAPPN 2024