Assessing an Unmanned Aerial System Equipped for Aerial RGB Photography Collection and Chlorophyll a Fluorescence for Detecting Water-Induced Differences in Canopy Development and Yield in Cotton

Characterizing canopy development in the past using Normalized Difference Vegetation Index (NDVI) was demonstrated to be an effective tool. However, digital conventional cameras that detect red, green, and blue (RGB) channels are incredibly common today and require a minimal investment compared to conventional NDVI equipment. These cameras are small enough to be lifted by current hobby grade Unmanned Aerial Systems (UAS), known commonly as drones, which are also becoming much more affordable. Furthermore, vegetation indices, such as the Green-Red Vegetation Index (GRVI) can be easily calculated from RGB images. The goal of our project was to evaluate the ability of a drone carrying a RGB camera to assess the utility of RGB-derived indices and chlorophyll fluorescence methodologies to detect water-induced differences in canopy development as well as yield data were collected from two separate projects in 2014 and 2015 conducted at both the University of Georgia’s Stripling Irrigation Research Park (UGA SIRP) as well as the Lang-Rigdon research farm. This study included a total of eight irrigation treatments and 56 replicate plots. Infield physiological data (plant height, nodes above white flower, and mainstem nodes) were collected biweekly, while remote sensing data was collected weekly and included Normalized Difference Vegetation Index (NDVI), chlorophyll fluorescence fast-transient analysis (OJIP), and aerial RGB photography. RGB images were converted with Imagej into vegetation index images and vegetation indices were derived for each plot.