Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials

Abstract An important aspect of reliable cultivar development is good field trial evaluations. Unoccupied aircraft systems (UAS also known as drones or UAVs) are a popular high‐throughput phenotyping tool that has been used to successfully evaluate plant stress and other canopy characteristics in a field. In precision agriculture applications, UAS imagery has been used to identify spatial variability in field settings. Here, we use UAS spectral imagery to improve field trial spatial analysis, better control spatial variability, and reduce errors for more reliable selections. UAS imagery data were collected across 47 breeding trials planted in an augmented complete block design (ACBD) or alpha‐lattice replicated designs from 2020 through 2023. Trials were evaluated using three spatial analysis strategies: linear models incorporating block effect, row‐column effect, or 2D splines. UAS‐derived spectral reflectance indices (SRI) were combined with each model as covariates. Modeling strategies were used across all trials and evaluated for autocorrelation, model fitness, and coefficient of variation (CV). Akaike information criterion (AIC) was used to assess model fitness. For spatial analysis trials, SRIs significantly lowered model AIC by an average of 38.4 for alpha‐lattice trials and 69.1 for ACBD trials. CV scores were also lowered when SRIs were utilized, with average CV values being 2.6 lower for alpha‐lattice and 2.1 for ACBD trials. This study highlights the potential for SRIs to improve the analyses of field breeding trials despite extreme environmental variables and climate conditions, improving experiment reliability and changing the way breeders plan and implement breeding experiments.