Smith Daniel T L, Chen Qiaomin, Massey-Reed Sean Reynolds, Potgieter Andries B, Chapman Scott C
School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, QLD, 4072, Australia.
Center for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
Plant Methods. 2024 Aug 20;20(1):129. doi: 10.1186/s13007-024-01236-w.
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.
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) 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, 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.
本研究探讨了使用无人机（UAV）估算小麦生物量，重点关注表型分析和分析方案在后期品种选择计划中的影响。它强调了在预测生物量时变量选择、模型特异性和实验区内采样位置的重要性，旨在改进基于无人机的估算技术，以提高品种测试计划中的选择准确性和通量。
研究发现，整合几何和光谱特征可提高预测准确性，而基于递归特征消除（RFE）的变量选择工作流程虽导致准确性略有降低，但具有更高的可解释性。针对特定实验定制的模型比将所有实验一起建模的模型更准确，而针对广泛生长阶段训练的模型并未显著提高准确性。实验区内永久和精确感兴趣区域（ROI）之间的比较表明，生物量预测准确性的差异可忽略不计，这表明该方法在实验区内不同采样位置具有稳健性。不同实验中生物量预测的季节内重复性（w）存在显著差异，这突出了进一步研究预测最佳测量时间的必要性。
该研究突出了无人机技术在小地块尺度小麦生物量预测方面的巨大潜力。研究表明，通过优化分析和建模方案（即变量选择、算法选择、特定阶段模型开发），生物量预测的准确性可显著提高。未来的工作应侧重于探索这些发现在更广泛条件下和更多样化基因型中的适用性。
