Data from: Leveraging UAV spectral and thermal traits for the genetic improvement of resistance to Dothistroma needle blight in Pinus radiata D.Don

Phenotyping is critical in tree breeding, but traditional methods are
often labour-intensive and not easily scalable. Resistance to biotic and
abiotic stress is a key focus in tree breeding programmes. While heritable
traits derived from spectral remote sensing have been identified in trees,
their application to tree phenotyping remains unexplored. This study
investigates in situ high-throughput hyperspectral and thermal
imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus
radiata D.Don. Using UAV-based hyperspectral and thermal imaging during a
severe DNB outbreak in a clonal trial in New Zealand, we computed
narrow-band hyperspectral indices (NBHIs), canopy temperature indices,
radiative transfer inverted plant traits, and solar-induced fluorescence.
Visual severity scores and remote sensing indices were modelled using
spatially explicit mixed-effect linear models integrating pedigree and
genomic data in a single-step genomic evaluation. Multi-trait models and
sampling simulations were used to evaluate the potential of remote sensing
indices to supplement or replace traditional phenotyping. Remote sensing
indices exhibited narrow-sense heritability values comparable to severity
scores (up to 0.37) and high absolute correlation coefficients with
severity scores (up to 0.79). Carotenoid and chlorophyll-related NBHIs
were the most informative, reflecting the physiological impacts of DNB.
Combining partial visual scoring with NBHIs maintained high estimated
breeding value (EBV) accuracy (0.68) at 50% scoring and moderate accuracy
(0.59) at 20% scoring. EBV correlation with full scoring was above 0.8
even at 20% scoring. Using solely the most heritable NBHI, achieved 0.71
breeding value accuracy and 0.79 absolute EBV correlation with severity
scores, suggesting NBHIs can replace visual scoring with minimal precision
loss. By utilising UAV-based hyperspectral and thermal imaging to capture
single-tree phenotypes related to disease in a forestry trial and pairing
the data to genomic evaluation, this study establishes that remote sensing
data offers an efficient, scalable alternative to traditional phenotyping.
Our approach constitutes a major step towards characterising specific
physiological responses, facilitating the discovery of the genetic
architecture of physiological traits, and significantly enhancing genetic
improvement.