# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
###### Abstract  
Tree-counting methods based on computer vision technologies are low-cost and efficient in contrast to the traditional tree counting methods, which are time-consuming, laborious, and humanly infeasible. This study presents a method for detecting and counting tree seedlings in images using a deep learning algorithm with a high economic value and broad application prospects in detecting the type and quantity of tree seedlings. The dataset was built with three types of tree seedlings: dragon spruce, black chokeberries, and Scots pine. The data were augmented via several data augmentation methods to improve the accuracy of the detection model and prevent overfitting. Then a YOLOv5 object detection network was built and trained with three types of tree seedlings to obtain the training weights. The results of the experiments showed that our proposed method could effectively identify and count the tree seedlings in an image. Specifically, the MAP of the dragon spruce, black chokeberries, and Scots pine tree seedlings were 89.8%, 89.1%, and 95.6%, respectively. The accuracy of the detection model reached 95.10% on average (98.58% for dragon spruce, 91.62% for black chokeberries, and 95.11% for Scots pine). The proposed method can provide technical support for the statistical tasks of counting trees. counting tree seedlings; object detection; convolutional neural networks; YOLOv5 202313895.10.  
more transferable regions. The CROPTD approach was evaluated on two large-scale high-resolution satellite images in Peninsula Malaysia. The CROPTD approach improves the detection accuracy by 8.69% in terms of the average F1-score compared with Faster R-CNN. Moreover, CROPTD performs 4.99-2.21% better than the other two domain adaptive object detection approaches. [PERSON] et al. [9] also used high-resolution satellite images as a primary data source and presented an MRCNN model to recognize, count, and categorize date palm trees in the Kingdom of Bahrain. The model was tested on the entire land region of Bahrain's Northern Governorate, which encompassed around 175 square kilometers. As a result, the precise positions and quantities of palm trees across the Northern governance were detected and kept in the agricultural geodatabase; the obtained result can assist in estimating the yearly yield for date palm trees in the Kingdom of Bahrain. [PERSON] et al. [10] presented palm tree classification and counting using remote sensing drone video and the mask R-CNN algorithm with a retuning hyperparameter strategy. The tested area was comprised of the upper and delta coastal area of Myanmar, and over 12,000 aerial images were taken by drone for three types of palm trees (toddy palm, coconut palm, and palm oil); the research result exhibited 80-95% validation accuracy on each palm class. It is deduced that tuning learning will improve the performance of the local palm tree classification approach and can accurately detect and count today palm trees. Recently, many researchers have been focusing on using remote sensing in agriculture due to the benefits of unmanned aerial vehicles (UAVs) [11], which can obtain high-resolution images that are less expensive than other satellites and aircraft that require high altitudes and other capabilities. In contrast, UAVs can fly at lower altitudes, facilitating the easy acquisition of clear images [12]. [PERSON] et al. [13] proposed a method to detect citrus trees from UAV images by a simple convolutional neural network algorithm. The classification approach, which was improved by superpixels that were derived from a simple linear iterative clustering algorithm, has achieved an accuracy of 94.59% and a recall of 97.94%. [PERSON] et al. [14] presented a convolutional neural network approach to estimate the number of citrus trees in highly dense orchards from UAV multispectral images. The dataset was comprised of 2389 images that were acquired using a multispectral camera with four bands; green, red, red-edge, and near-infrared bands.