## 3 Results and Discussion
### Experimental Results Example on Plant's Identification and Localization
Table 5 presents the findings related to the calculated total projected leaf area through experimentation for the 15 iterations involving six baby lettuce, as compared to the area deduced from the manual tracing on the same images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Plant No.** & **Plant Localization Steps (Mean/Std)** \\ \hline A & \(2624\pm 23.8\) \\ B & \(6833\pm 10.3\) \\ C & \(17,428\pm 32.3\) \\ D & \(27,092\pm 10.3\) \\ E & \(42,294\pm 17.2\) \\ F & \(47,277\pm 28.6\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated mean and standard deviation for each plant localization (in steps). Figure 15: Plant identification and localization example of the 6 lettuce plants (marked with the letters A through F in the Figure) for the 15 experimental iterations conducted in the laboratory. In column (**a**), the cropped images of the 6 lettuce seedlings are shown in a single pass (out of the 15 iterations). In columns (**b**,**c**), intermediate results of the morphological operations are presented. The final outcomes of each plant contour found (in blue) along with its centroid (small cyan circle), but also the estimated minimum enclosing circle plotted with each center (in red), are depicted in column (**d**). In column (**e**), the manually traced plant contours (in yellow squares), along with the respective centroid overlaid in each figure as a small blue circle, are presented for a direct visual comparison (colored figure online). The evaluation metrics IoU, Precision, Recall, and F1 score are utilized to compare these outcomes with the corresponding plant features obtained through manual tracing of the lettuces' contours. During the whole run, the RGB and NIR cameras capture images with a resolution of 1920 \(\times\) 1080 pixels (original images). As mentioned previously the RGB camera is utilized for the plant identification and localization task. Nevertheless, since the calibration parameters are known, a process can be conducted to correct lens distortions in both cameras. Additionally, a rectification process can be applied to the pair of stereo images, enabling the computation of a disparity map for depth estimations and 3D reconstruction or stitching alignment procedures. However, this latter aspect is reserved for future investigations. At present, the identification of plants is performed using the Identify Plant function (Figure 10), which is an ongoing iterative procedure aimed at successfully recognizing different plants. To enhance the efficiency of the image processing process for plant identification, only the RGB images are undistorted prior to being cropped. The color cropped images, which were further analyzed and processed in the specific experiment, had dimensions of 360 \(\times\) 360 pixels. All images were saved in a PNG format and organized automatically into dedicated folders for each iteration. At the present time, the system logs the original images of both cameras together with all the intermediate (morphologically processed) images until the final, undistorted, cropped colored image outcomes, with the plant centered and circled. Furthermore, the system automatically logged and stored plant growth-related data measurements, along with the datalogging information, both locally and remotely. On average, the entire process, starting from the initial image acquisition at the home position to the identification and localization of the six baby lettuce seedlings, as well as saving all the relevant results, took approximately 6.5 min per iteration. Moreover, the system successfully identified and localized each plant within an average time frame of 55 s.