Using artificial intelligence to predict NDVI/NDRE from standard RGB aerial imagery

The growth of precision agriculture has allowed farmers access to more data and greater efficiency for their farms. With consistently tight profit margins, farmers need ways to take advantage of the advancement of technology to lower their costs or increase their revenue. One area where these advancements can prove beneficial are in the measurement of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE). Currently, an expensive multispectral camera, typically attached to an Unmanned Aerial Vehicle (UAV) during flight, is required for measuring these indices. This makes obtaining NDVI and NDRE somewhat cost prohibitive for most farmers. Color maps representing these vegetation indices can be used to identify problem areas, plant health, or even places where spot applications are needed. This work demonstrates a solution to this cost issue. The solution involves the use of artificial intelligence, or more specifically, the use of a conditional Generative Adversarial Network known as Pix2Pix. By using Pix2Pix along with training data from UAV flights, this research shows the capabilities of predicting accurate NDVI and NDRE with a low-cost Red-Green-Blue (RGB) camera. This thesis explores and assesses a cost-efficient method that can accurately predict these vegetation indices, resulting in cost-savings in the range of $5000.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste