Investigating the Use of Unmanned Aerial Vehicles and High-Resolution Multispectral Imagery to Characterize Grain Sorghum Senescence Patterns

Grain sorghum is important to producers around the world. In precipitation-limited environments, sorghum is the grain of choice because it is able to produce grain yields with limited precipitation. Plant breeders place a priority on breeding for a characterized form of post-flowering drought-tolerance, known as stay-green (SG). Assessing thousands of plots for this trait can be labor intensive and time consuming, so the goal of this study was to use unmanned aircraft vehicles (UAVs) equipped with high resolution cameras to characterize and quantify senescence patterns in grain sorghum. A field experiment with 20 hybrids was planted in Manhattan, KS. The UAV used was a Matrice 200 equipped with a MicaSense RedEdge-MX camera, and data was collected at four different sorghum growth stages. Vegetative indices (VIs) were computed from the multispectral data, including the normalized difference vegetative index (NDVI), normalized difference red edge (NDRE) index, the simple ratio (SR), green chlorophyll index (GCI), and the red edge chlorophyll index (RECI). Correlation and regression analyses were conducted to determine both the relationship of ground-measured senescence scores and the depth of senescence detection into the canopy. Results showed weak to no VI correlation with ground-truth senescence scores. Significant R2 coefficients were shown between VIs and ground-truth senescence ratings at physiological maturity with the first 7 leaves of the canopy. We therefore conclude that the Mica- Sense RedEdge-MX may not be the most effective camera to determine grain sorghum senescence patterns