Phenomic prediction I: Plot‐level prediction of lodging severity in sorghum breeding trials using UAV‐based photogrammetric height data

Abstract

Abstract Lodging in sorghum ( Sorghum bicolor (L.) Moench) presents a significant challenge for plant breeders due to the trade‐off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time‐consuming, expensive, and error‐prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)‐derived metrics provide a potential high‐throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot‐level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot‐level lodging from UAV imagery across 2675 sorghum breeding plots. Multi‐temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV‐derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, nonparametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80–0.84 and lowest root mean square error of 16%–18%, explaining 64%–70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot‐level lodging assessment. This study demonstrates that integrated multi‐temporal UAV imagery offers a practical alternative to labor‐intensive manual evaluation methods by enabling high‐throughput lodging assessment suitable for implementation in sorghum breeding programs.

The authors' abstract, as published at the source. The Plant Phenome Journal, 2026 · DOI ↗