Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of red edge based vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) content of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to the lab for N content analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVI red edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS), Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N content compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N content. Empirical relationships between VIs and plant N content were cross-validated using test data sets for each growth stage. At Feekes 5, the plant N content estimated based on NDVIred edge showed one to one correlation with measured N content. At Feekes 10, the estimated and measured N content were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N content. The observed high correlations between red edge based VIs derived from UAV and the plant N content suggests the significance of red edge based VIs deriving from UAVs for within-season N content monitoring of agricultural crops such as spring wheat.