Stand Characterization of Rain-Fed Cotton Via Uav

This research entails utilizing UAV to characterize cotton stands in rain-fed cotton production settings. Re-plant decision making is challenging and can be particularly important in rain-fed cotton farming. We were able to analyze virtual sample spaces in UAV collected RGB image data and extract the precise pixel locations of all pixel representing biological material in the field. This data allowed for these stands to be classified based on spatial relationships between plants. These classifications were then associated with yield outcomes. Results to be presented.