Prediction accuracy and heritability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location

Abstract

Abstract Background This study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing and breeding programs. Results The research uncovered that integrating geometric and spectral traits with a partial least squares regression (PLSR) based variable selection workflow notably enhanced biomass prediction accuracy. A key finding was that models, tailored to specific maturity stages (vegetative, flowering, and grain-fill) were more accurate than those modelling the entire growth season for estimation of biomass at corresponding stages. However, experiment specific models did not significantly increase accuracy. The comparison between a permanent and a precise region of interest (ROI) within the plot showed negligible differences in biomass prediction accuracy, indicating the robustness of the approach across different sampling locations within the plot. Significant differences in the broad-sense heritability (H2) of biomass predictions across different experiments highlighted the need for further investigation into the optimal timing of measurement for prediction. Conclusions The study highlights the promising potential of UAV technology in biomass prediction for wheat at a small plot scale. It suggests that the accuracy of biomass predictions can be significantly improved through optimizing analytical and modelling protocols (i.e., variable selection, algorithm selection, stage-specific model development). Future work should focus on exploring the applicability of these findings under a wider variety of conditions and from a more diverse set of genotypes.