Addressing deployability concerns for AI-supported UAV-based High Throuput Phenotyping

Reliable, field-scale indicators of crop water status and plant condition are needed to support plant breeding, precision irrigation and climate adaptation, yet UAV-based monitoring must balance predictive accuracy with deployability. We present an explainable Deep Learning workflow (TriNet) for scalable UAV phenotyping from multispectral time series, aligned with agronomic and breeding practice through high-granularity in situ scoring in accordance with established standards (such as those of the AGES - Österreichische Agentur für Gesundheit und Ernährungssicherheit). TriNet disentangles spatial, temporal, and spectral information and incorporates attention-based interpretability to identify influential inputs and guide efficient acquisition strategies. The framework supports handling multispectral data acquired from comparatively high altitudes with respect to the state of the art (e.g., 60 m with 2.5 cm Ground Sampling Distance (GSD)), and allows the exploration of the trade-off between model performance and GSD. This supports a reduction of flight times and data volumes (e.g., 1.74 GB at 60 m vs. 5.96 GB at 20 m in our reference setup) while maintaining controlled predictive accuracy.

We study the case of winter wheat breeding, and extend this approach with new results for the traits drought stress and plant health and a comprehensive analysis of flight height as an operational design variable, systematically simulating and evaluating acquisitions from 20 to 120 m. Results indicate that predictive accuracy is largely insensitive to flight height across this range, supporting higher-altitude, high-coverage monitoring until the release of larger datasets provide a clear justification for lower-altitude, higher-resolution acquisitions. Finally, we translate these findings into practitioner-oriented operational insights for drone-based High-Throughput Phenotyping.