Abstract: Abstract: A seedling quantity is a key indicator to predict the actual production, supply, and demand for the operation and management of a nursery. The manual visualization has still dominated the statistics for the number of seedlings in complete plots. However, the application needs cannot be fully met in recent years, such as high cost, low efficiency, and slow data update. Therefore, it is necessary to fast and accurately estimate the number of seedlings in the whole plots. Taking the spruce as the research object, this study aims to propose a quantity statistics approach under Unmanned Aerial Vehicle (UAV) aerial videos using YOLOv3 and SORT. The specific procedure included the data acquisition, YOLOv3 detection model, SORT tracking, and cross-line counting. Two areas were divided for the image and video acquisition, each with 6 complete test plots. In the stage of data acquisition, 558 images and 6 videos were captured by a DJI Phantom 4 (UAV). The quantity statistics dataset was then constructed with the acquired images and videos, where the training dataset contained 558 images, and the test dataset contained 6 videos. Subsequently, a YOLOv3 model was selected to detect the spruce, while a SORT model was to track the spruce, and the cross-line counting to count the number of spruce. The performance of the combined YOLOv3+SORT was also quantitatively evaluated using Mean Count Accuracy (MCA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Frame Rate (FR). It was found that the MCA of 92.30%, MAE of 72, RMSE of 98.85, and FR of 11.5 frames/s for the test dataset in the quantity statistics. The experimental results showed that quick and accurate counting was achieved for the number of spruce in the complete plots. The YOLOv3+SORT was also compared with the SSD+SORT and Faster R-CNN+SORT, in order to further verify the performance of the model. The results showed that the YOLOv3+SORT performed over the SSD+SORT in all four evaluation indexes. Particularly, the YOLOv3+SORT was much faster with higher guaranteed accuracy, with 1.33 percentage points lower MCA, and 10.1 frames/s higher FR, compared with the Faster R-CNN+SORT. In summary, the quantity statistics using YOLOv3 and SORT can be widely expected to serve as an effective way to rapidly and accurately count the number of seedlings in the whole plots. This study can also offer promising potential support to the seedling quantity statistics from the perspective of UAV aerial videos.