Phenotyping cotton leaf chlorophyll via <i>in situ</i> hyperspectral reflectance sensing, spectral vegetation indices, and machine learning.

ABSTRACT: Cotton (Gossypium hirsutum L.) leaf chlorophyll (Chl) has been targeted as a phenotype for breeding selection to improve cotton tolerance to environmental stress. However, high-throughput phenotyping methods based on hyperspectral reflectance sensing are needed to rapidly screen cultivars for chlorophyll in the field. The objectives of this study were to deploy a cart-based field spectroradiometer to measure cotton leaf reflectance in two field experiments over four growing seasons at Maricopa, Arizona and to evaluate 148 spectral vegetation indices (SVI's) and 14 machine learning methods (MLM's) for estimating leaf chlorophyll from spectral information. Leaf tissue was sampled concurrently with reflectance measurements, and laboratory processing provided leaf Chl a, Chl b

SUBMITTER: Thorp KR 

PROVIDER: S-EPMC11617151 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature