High-Throughput Phenotyping of Oat Breeding Nurseries Using Unmanned Aerial Systems
Presentation Type
Poster
Student
Yes
Track
Precision Ag/Biological Sciences Application
Abstract
Current strategies for phenotyping thousands of breeding lines under field conditions demand significant investment in both time and labor.Unmanned aerial systems (UAS) can be used to collect Vegetative Index (VI) with high throughput and could provide an efficient way to predict forage yield on thousands of breeding lines. The main objective of the study was to evaluate the use of vegetative indices derived from images for estimating crop biomass. A UAS equipped with a RGB camera was flown over experimental plots in Southshore several times throughout the growing season. A radio-calibration was performed to correct band values on the stitched images before deriving Visual Normalized Difference Vegetation Index (VNDVI). Asignificant positive correlation (r = 0.6) between VNDVI and crop biomass was observed for the last flight before forage harvest. This suggests that UAS could provide an efficient way to measure the forage yield potential of oat breeding lines. However, to confirm this, it will be necessary to repeat this experiment in additional environments.
Start Date
2-5-2019 12:00 PM
End Date
2-5-2019 1:00 PM
High-Throughput Phenotyping of Oat Breeding Nurseries Using Unmanned Aerial Systems
Volstorff A
Current strategies for phenotyping thousands of breeding lines under field conditions demand significant investment in both time and labor.Unmanned aerial systems (UAS) can be used to collect Vegetative Index (VI) with high throughput and could provide an efficient way to predict forage yield on thousands of breeding lines. The main objective of the study was to evaluate the use of vegetative indices derived from images for estimating crop biomass. A UAS equipped with a RGB camera was flown over experimental plots in Southshore several times throughout the growing season. A radio-calibration was performed to correct band values on the stitched images before deriving Visual Normalized Difference Vegetation Index (VNDVI). Asignificant positive correlation (r = 0.6) between VNDVI and crop biomass was observed for the last flight before forage harvest. This suggests that UAS could provide an efficient way to measure the forage yield potential of oat breeding lines. However, to confirm this, it will be necessary to repeat this experiment in additional environments.