Unmanned aerial vehicles (UAVs) and deep learning are important tools at the forefront of automated forest monitoring research, where classification of individual tree species is a critical forest management goal. Near-infrared (NIR) information provided by specialized UAV sensors may improve classification accuracy at the cost of added operational complexity; however, this potential for improvement is context-dependent and, therefore, may not be necessary. We assessed the performance of conventional red-green-blue (RGB) versus NIR imagery when classifying regenerating lodgepole pine and white spruce crowns automatically delineated by a trained deep learning algorithm. Models trained on NIR imagery slightly outperformed those trained on RGB imagery. Models trained on spectral bands outperformed those trained on spectral indices. The minor difference in performance between the two sets of imagery showed that accurate classification of lodgepole pine and white spruce can be carried-out using conventional RGB imagery.