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

作者：Daniel Smith, Qiaomin Chen, Sean Reynolds Massey-Reed, Andries Potgieter, Scott Chapman · 发表于：Plant Methods · 年份：2024 · DOI：10.1186/s13007-024-01236-w · 被引用次数：17 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Plant Surface Properties and Treatments

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 in the context of late-stage variety selection programs. 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 programs. Results The research uncovered that integrating geometric and spectral traits led to an increase in prediction accuracy, whilst a recursive feature elimination (RFE) based variable selection workflowled to slight reductions in accuracy with the benefit of increased interpretability. Models, tailored to specific experiments were more accurate than those modelling all experiments together, while models trained for broad-growth stages 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 within-season repeatability (w 2 ) 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 potent...