  Soybean production is susceptible to biotic and abiotic stresses, exacerbated
by extreme weather events. Water limiting stress, i.e. drought, emerges as a
significant risk for soybean production, underscoring the need for advancements
in stress monitoring for crop breeding and production. This project combines
multi-modal information to identify the most effective and efficient automated
methods to investigate drought response. We investigated a set of diverse
soybean accessions using multiple sensors in a time series high-throughput
phenotyping manner to: (1) develop a pipeline for rapid classification of
soybean drought stress symptoms, and (2) investigate methods for early
detection of drought stress. We utilized high-throughput time-series
phenotyping using UAVs and sensors in conjunction with machine learning (ML)
analytics, which offered a swift and efficient means of phenotyping. The
red-edge and green bands were most effective to classify canopy wilting stress.
The Red-Edge Chlorophyll Vegetation Index (RECI) successfully differentiated
susceptible and tolerant soybean accessions prior to visual symptom
development. We report pre-visual detection of soybean wilting using a
combination of different vegetation indices. These results can contribute to
early stress detection methodologies and rapid classification of drought
responses in screening nurseries for breeding and production applications.
