# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
###### Abstract
The result showed high precision (0.95) and low MAE (2.05) compared with Faster R-CNN and RetinaNet algorithm, proposing this approach as a satisfactory and effective strategy to replace the traditional visual inspection method for counting the trees. [PERSON] et al. [15] used computer vision technology to process and analyze visible light images that were obtained by the UAVs in a citrus planting area in Xinhui District of Jiangmen City- China. Their research aims to detect citrus trees in the study area and count the mature trees and saplings. The obtained accuracy rate of the experiment reached more than 95%, which can meet the requirements of fast and accurate counting and planting situation of citrus trees in a planting area at low cost. Furthermore, due to the economic value and ecological impacts of coconut trees, it is vital to detect and monitor them. Thus, many research papers reported on detecting and counting coconut trees [16; 17; 18]. [PERSON] et al. presented a method to detect and count the coconut trees from high-resolution satellite images that were acquired in Tenarunga island in the Pacific Ocean from Google Earth Map [19]. There were three major procedures that were applied: a multi-level region proposal network (RPN), feature extraction, and a large-scale detection workflow. The results showed a high average F1-score of 77.14% that outperformed pure Faster R-CNN with +1.33% in the average F1-score. In summary, it is more practical to use deep learning algorithms to determine the number of trees because it is a fast, low cost, and effective tool [20]. Due to the increase in world population, there is an increasing demand for food and an urgent need to increase tree planting, which is required globally to maintain the water cycle, sequestrate carbon, conserve soils, and protect the continued existence of humans and animals. Moreover, the depletion of forestry resources dramatically is also increasing. Thus, in the agriculture sector, intelligent farming techniques and automated tree-counting approaches play an essential role in the crop management system, achieving rapid and accurate management of forestry resources and empowering the development of many economies worldwide [21]. Moreover, counting tree seedlings helps monitor natural succession and support conservation efforts of forestry resources. This paper presents a new tree seedlings detection method using YOLOv5 with a dataset containing 660 images of three different types of tree seedlings. The dataset images were taken via drone and mobile phone. Then, data augmentation was used to obtain a large amount of data to train the detection model. Finally, the manual counting results are compared with the system. The correctness is evaluated by comparing the manual count results with the system count results. Our detection approach result is satisfactory and is an effective method to replace the traditional human visual inspection method to determine the number of tree seedlings in an area. The proposed method overcame the difficulties of the traditional tree seedlings quantity survey that needs staff to work in the field to obtain information and data, is humanly infeasible, requires high labor costs, and has small effective coverage.