Multi-Sensor and Multi-temporal High-Throughput Phenotyping for
  Monitoring and Early Detection of Water-Limiting Stress in Soybean
Sarah E. Jones Timilehin Ayanlade Benjamin Fallen Talukder Z. Jubery Arti Singh Baskar Ganapathysubramanian Soumik Sarkar Asheesh K. Singh
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.