# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
## 3 Results  
The model was tested with 30 images of dragon spruce tree seedlings, 50 images of black chokeberries tree seedlings, and 50 images of Scots pine tree seedlings. The specific detection results of the model before data augmentation are shown in Table 4. The particular detection results of the model after data augmentation are shown in Table 5. Examples of the detection results are shown in Figure 9. As expected, after data augmentation, the model performance was more reliable, and the accuracy of the detection model was improved.  
For the dragon spruce tree seedlings, their morphological characteristics with a pyramidal shape and dense branches led to high detection accuracy. However, some detection is still missed, mainly concentrated in the places where the dragon spruce tree seedlings are relatively dense. In addition, the case of two overlapping tree seedlings will be regarded as one, and the tree seedlings that overlap with another type of tree could be missed from detection. In the case of black chokeberries tree seedlings, the missed detection is mainly  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameter** \\ \hline Experimental environment & server \\ Operating system & Ubuntu 20.04 \\ CPU & Intel(R) Xeon(R) Gold 6128 \\ GPU & NVIDIA GeForce GTX 1080 Ti \\ Deep learning framework & PyTorch 1.7.0 \\ Language & Python 3.6.5 \\ Deep Learning model & YOLOv5s model \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information about Hardware and Software experimental environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Optimization Algorithm** & **Learning Rate** & **Epoch** & **Batch Size** & **Image Size** \\ \hline Value & SGD & 0.001 & 400 & 2 & \(416\times 416\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameter values.  
concentrated in the places where the difference in color and shape of the target and the background is relatively slight, making the model recognize the target as the background. Moreover, the tree seedlings on the edge of the image could be missed from detection. For Scots pine tree seedlings, the more complex crown shape and structure made it challenging to separate the crown of the tree seedlings. Therefore, the model could not estimate tree seedling structure correctly. Moreover, the textured and cluttered backgrounds majorly affect the model's performance. The undetected tree seedlings are labeled by a red ellipse in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Plant** & **TP** & **FN** & **FP** & **P (\%)** & **R (\%)** & \begin{tabular}{c} **F1\_Score** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & **mAP** \\ \hline \begin{tabular}{c} Dragon \\ spruce \\ Black \\ Chokeberries \\ Scots pine \\ \end{tabular} & 319 & 170 & 2 & 99.38 & 65.24 & 78.77 & 64.97 & 87.48\% \\ \
\begin{tabular}{c} Black \\ Chokeberries \\ \end{tabular} & 886 & 224 & 113 & 88.69 & 79.82 & 84.02 & 72.44 & 88.37\% \\
\begin{tabular}{c} Scots pine \\ \end{tabular} & 329 & 29 & 20 & 94.27 & 91.90 & 93.07 & 87.04 & 93.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detection results of the model before augmentation.  
Figure 9: Examples of the detection results for (**a**,**d**) dragon spruce, (**b**,**e**) black chokeberries, and (**c**,**f**) Scots pine.