Evaluation of UAV multispectral cameras for yield and biomass prediction in wheat under different sun elevation angles and phenological stages
Sahameh Shafiee, Tomasz Mróz, I. Burud, Morten Lillemo
Abstract
Quantitative trait prediction using multispectral UAV imagery is gaining popularity in field trials. However, the reliability of models is influenced by the type of camera and its consistency under different light conditions. In this study, we investigate the comparability of two popular multispectral cameras, the Phantom 4 Multispectral (P4M) and the Micasense RedEdge-M (RedEdge-M), for wheat yield and biomass prediction under varying sun angles and phenological changes. Our results indicate that the P4M camera produces more stable values for wavelength bands and derived vegetation indices (VIs) than the RedEdge-M camera under different sun angles. However, the Green Normalized Difference Vegetation Index (GNDVI) showed minimal anisotropic reflectance effect and was consistently correlated with yield, regardless of the camera type. Both cameras were found suitable for yield and biomass prediction, with P4M being more robust against sun angle and providing more stable results. The maximum R2 values for yield were 0.71 and 0.68 in season 2020, and 0.66 and 0.64 in season 2021 for RedEdge-M and P4M, respectively. For biomass prediction, the maximum R2 values were 0.73 and 0.71 for dry biomass and 0.62 and 0.57 for fresh biomass for RedEdge-M and P4M, respectively. These findings have implications for researchers and practitioners using multispectral UAV imagery for quantitative trait prediction in field trials.