Virtual StatsPD@Waite meeting

Every month, the professional development meetings of statisticians and data scientists at Waite, known as StatsPD@Waite, bring together specialists in various aspects of data sciences in agriculture from Waite, Roseworthy and Adelaide.

Please join us for the next StatsPD@Waite seminar where Daniel Smith from the School of Agriculture and Food Sustainability, The University of Queensland will present on “Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials”.

Also please note that the StatsPD@Waite meetings are recorded. If you have a question to the speaker but had rather not be recorded, please send your question via chat during the meeting and it will be asked on your behalf.

Please email Sam Rogers with questions or for details of the Zoom meeting.

Title: Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials

Presenter: Daniel Smith from the School of Agriculture and Food Sustainability, The University of Queensland

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.

The research found that integrating geometric and spectral traits notably enhanced biomass prediction 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 (w²) of biomass predictions across different experiments highlighted the need for further investigation into the optimal timing of measurement for prediction.

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). 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.