Improving drone-based uncalibrated estimates of wheat canopy temperature in plot experiments by accounting for confounding factors in a multi-view analysis

Canopy temperature (CT) is an integrative trait, indicative of the relative fitness of a plant genotype to the environment. Lower CT is associated with higher yield, biomass and generally a higher performing genotype. In view of changing climatic conditions, measuring CT is becoming increasingly important in breeding and variety testing. Ideally, CTs should be measured as simultaneously as possible in all genotypes to avoid any bias resulting from changes in environmental conditions. The use of thermal cameras mounted on drones allows to measure large experiments in a short time. Uncooled thermal cameras are sufficiently lightweight to be mounted on drones. However, such cameras are prone to thermal drift, where the measured temperature changes with the conditions the sensor is exposed to. Thermal drift and changing environmental conditions impede precise and consistent thermal measurements with uncooled cameras. Furthermore, the viewing geometry of images affects the ratio between pixels showing soil or plants. Particularly for row crops such as wheat, changing viewing geometries will increase CT uncertainties. Restricting the range of viewing geometries can potentially reduce these effects. In this study, sequences of repeated thermal images were analyzed in a multi-view approach which allowed to extract information on trigger timing and viewing geometry for individual measurements. We propose a mixed model approach that can account for temporal drift and viewing geometry by including temporal and geometric covariates. This approach allowed to improve consistency and genotype specificity of CT measurements compared to approaches relying on orthomosaics in a two-year field variety testing trial with winter wheat. The correlations between independent measurements taken within 20 min reached 0.99, and heritabilities 0.95. Selecting measurements with oblique viewing geometries for analysis can reduce the influence of soil background. The proposed workflow provides a lean phenotyping method to collect high-quality CT measurements in terms of ranking consistency and heritability with an affordable thermal camera by incorporating available additional information from drone-based mapping flights in a post-processing step.