## 3 Results and Discussion  
### Plant Density Prediction and Validation  
As depicted in first row of Figure 3, RGB images of various GSI components, including trees, grass, soil, and unhealthy trees within the MSU Campus were collected using the drone and extracted from the entire area of GSIs (Supplementary Materials). Subsequently, these images underwent processing and were imported into the Canopeo application, which was developed to quantify canopy cover (or plant density) of green vegetation for any agricultural crop, turf, or grassland based on downward-facing photos. Green live vegetation appears as white pixels and other background elements appear as black pixels in Canopeo, which was the opposite case in code. Results from the Canopeo application in the second row revealed that the plant density percentages were 43.09, 94.69%, 2.51%, and 5.47% for the selected trees, grass, soil, and unhealthy trees, respectively. Additionally, plant density was predicted by self-developed programming codes (Supplementary Materials) in the PyCharm environment. In the last row of Figure 3, the estimated plant densities using the programming code were 42.19%, 93.03%, 2.65%, and 4.96% for the selected trees, grass, soil, and unhealthy trees, respectively. Plant density prediction between these two methods was very similar, with only a slight difference. Furthermore, three images were further analyzed for each component and the results are summarized in Table 2 (These images can be found in Figure S1 of Supplementary Materials). Errors, including ABE, AEE, and R\({}^{2}\) were computed to validate the proposed method based on the computer vision techniques. In this method, the image \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Name** & **GSI Type** & **MD\_North 1** & **MD\_East 1** & \begin{tabular}{c} **Year** \\ **Built** \\ \end{tabular} & \
\begin{tabular}{c} **Coordinates (Latitude, Longitude)** \\ \end{tabular} &
\begin{tabular}{c} **Collection** \\ **Date (2021)** \\ \end{tabular} \\ \hline
17 & CTTH & Green roof & N/A & N/A & 2020 & N/A & 29 May \\
18 & COMM 4 & Pond & 186,716,92100 & 435,964.39900 & 2006 & 39.347958, \(-\)76.582774 & - \\
19 & COMM & Permeable pavement & 186,824.20600 & 435,978.10200 & 2006 & 39.348924, \(-\)76.582609 & - \\
20 & AH 5 & Pond & 185,704.34600 & 435,902.97700 & 1922 & 39.338840, \(-\)76.583540 & - \\ \hline \hline \end{tabular}  
* Note: North and east are geographic points used to locate BMPs. Maryland requires the use of State Plane NAD 83 m for geographic location. Geographic Information Systems (GIS) can be used to provide these coordinates.
* Behavioral Social Science Center (BSC). 3 Legacy Bridge (LB). 4 School of Global Journalism & Communication (COMM). 5 Alumni House (AH).  
\end{table} 
Table 1: _Cont._  
Figure 3: Plant density prediction for GSI components. (**a**) Trees, (**b**) grass, (**c**) soil, and (**d**) unhealthy trees. Note: Vegetation in white pixels, and other media in black pixels in Canopeo and Pycharm results. is initially binarized to identify the green objects, which are then classified into plants based on the geometrical features. The results indicated that self-developed codes for plant density estimation had a high R\({}^{2}\) value of 99% and a relatively low estimation errors of 9.7% for AAE and 3.9% for ABE. Previous studies have concentrated on machine-learning and deep-learning algorithms. However, machine learning may be prone to significant errors due to the lack of representativeness of the training dataset and deep learning may be encounter challenges in labeling very large training datasets [19,22]. This study adopted a computer-vision-based code, offering a simple alternative approach to predicting the plant density. ### Plant Density Prediction and Validation  
A total of 30-50 RGB images were utilized to generate orthogonal images for two different periods.