Abstract: Abstract: An accurate and rapid counting of the seedlings is of great significance to evaluate the yield and quality in the nursery for the decision making on reasonable production plans. The traditional way is the manual operation to count the seedlings one by one by workers. It is time-consuming and labor-intensive, particularly with the slow data update. It is a high demand for the new counting of the seedlings to meet the actual needs. Taking spruce as the research object, this study aims to develop automatic, accurate, and fast counting, where the images were captured by the unmanned aerial vehicle (UAV) as the experimental data. At first, some attempts were made to count the seedlings using density map regression with deep learning, especially for the higher accuracy of the adhesion forest seedlings. Specifically, the density map regression (CSRNet) was selected as the base model, due to the excellent robustness of the seedling adhesion. In the CSRNet model, the coarse ground-truth density map was normally generated by the point annotations using a Gaussian kernel as the supervised signal training, leading to low counting accuracy. A Bayesian CSRNet model was designed to directly use the manually annotated point label data as the supervised signal, where the Bayesian loss function was also introduced to improve the counting accuracy of the CSRNet model. Then, the 558 acquired spruce images were divided into the training set (392 images) and test set (166 images). The brightness was also adjusted to add the random noise, in order to process 392 spruce images from the training set. Among them, the brightness adjustment mainly simulated the spruce images under different light intensities, whereas, the addition of random noise was the spruce images under different environmental noises. After data processing, the 392 spruce images in the training set were expanded to 1 176 images. After that, the Bayesian CSRNet model was trained on the 1 176 spruce images and tested on the 166 test set. The results showed that the Bayesian CSRNet model can be used to accurately and quickly count the spruce in the UAV aerial images. The mean counting accuracy (MCA), mean absolute error (MAE), and mean square error (MSE) of the spruce counting in the test set images were 99.19%, 1.42, and 2.80, respectively. The Bayesian CSRNet model took only 248 ms to count a single spruce image, and the model size was only 62 Mb after training. Finally, an experiment was designed to compare the counting of 166 spruce images in the test set by the YOLOv3, the improved YOLOv3, and the CSRNet model. It was found that the Bayesian CSRNet model presented an outstanding performance, in terms of MCA, MAE, and MSE. The MCA values of the Bayesian CSRNet model were 3.43, 1.44, and 1.13 percentage points higher than those of the YOLOv3 model, the improved YOLOv3 model, and the CSRNet model, respectively. The MAEs of the Bayesian CSRNet model were 6.8, 2.9, and 1.67 lower than those of the YOLOv3 model, the improved YOLOv3 model, and the CSRNet model, respectively. The MSEs of the Bayesian CSRNet model were 101.74, 23.48, and 8.57 lower than the YOLOv3 model, the improved YOLOv3 model and the CSRNet model, respectively. In terms of MCT and MS, the Bayesian CSRNet model was the same as the CSRNet model, but outperformed the YOLOv3 and improved the YOLOv3 model. The MCT of the Bayesian CSRNet model was 60 and 103 ms lower than the YOLOv3 and improved YOLOv3 model, respectively. The MS of the Bayesian CSRNet model was 173 and 206 Mb lower than the YOLOv3 and improved YOLOv3 model, respectively. Therefore, the Bayesian CSRNet model can be expected to serve as an effective way to realize automatic, accurate, and rapid statistics on the number of seedlings in UAV aerial images.