Manual assessment of emergence is a time-consuming practice that must occur within a short time-frame of the emergence stage in canola (Brassica napus). Unmanned aerial vehicles (UAV) may allow for a more thorough assessment of canola emergence by covering a wider scope of the field and in a more timely manner than in-person evaluations. This research aims to calibrate the relationship between emerging plant population count and the ground cover. The field trial took place at the University of Saskatchewan Kernen Research Farm in the 2021 growing season. The study design combined six row spacing treatments, and eight seeding density treatments to factor in growth variabilities. The design of the experiment was an RCBD with four replicates and 192 plots in total. At emergence, the two center rows of each plot underwent a manual plant population count. The same day each plot was imaged from the height of two meters with a Mavic 2 Pro UAV using a RGB camera. The manual count and UAV imaging were repeated a week later to account for delayed emergence. The low altitude, high resolution imagery was used to calculate emergence ground cover using the excess green index. The UAV imagery was also used in plant population counts derived from deep learning software. Several model architectures using different sized models were compared. Accuracy of count and model efficiency were used to select the model to be applied to the whole dataset of images for the generation of plant population counts. Comparing emergence ground cover to computer generated and manual emergence plant population counts may express the importance of ground cover values being gathered at emergence and the use of UAV imagery in emergence scouting.