## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance
#### 3.2.3 Comparison of Segmentation Algorithms  
To find the effectiveness of the segmentation algorithm, we compared the performance of each algorithm based on the average and maximum values of the metrics for the three cameras. The comparisons of average and maximum Recall, Precision, and F1 score for each algorithm are given in Tables 11-13, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Camera** \\ **Type** \\ \end{tabular} } & \multicolumn{3}{c}{**Average F1 Score**} & \multicolumn{3}{c}{**Maximum F1 Score**} \\ \cline{2-7}  & **Daytime** & **Nighttime** & **Whole Day** & **Daytime** & **Nighttime** & **Whole Day** \\ \hline Visible camera & 0.584 & NA & NA & 0.794 & NA & NA \\ \hline NoIR camera & 0.466 & 0.333 & 0.396 & 0.560 & 0.636 & 0.599 \\ \hline Thermal camera & 0.319 & 0.282 & 0.301 & 0.405 & 0.383 & 0.394 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average and maximum F1 score for each camera type. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Camera** \\ **Type** \\ \end{tabular} } & \multicolumn{3}{c}{**Average Precision**} & \multicolumn{3}{c}{**Maximum Precision**} \\ \cline{2-7}  & **Daytime** & **Nighttime** & **Whole Day** & **Daytime** & **Nighttime** & **Whole Day** \\ \hline Visible camera & 0.663 & NA & NA & 0.751 & NA & NA \\ \hline NoIR camera & 0.568 & 0.329 & 0.443 & 0.599 & 0.572 & 0.575 \\ \hline Thermal camera & 0.419 & 0.418 & 0.418 & 0.493 & 0.505 & 0.494 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average and maximum Precision for each camera type. Table 11 shows that on daytime images, the best performance for average Recall was achieved by Mean thresholding, with a value of 0.820, and for maximum Recall Triangle thresholding performed best, with a value of 0.934. K-Means (K = 3) achieved the best performance on nighttime and whole day images, with average Recall values of 0.963 and 0.876, respectively. The best performance on nighttime images for maximum Recall was achieved by Isodata and Otsu thresholding, with a value of 0.990. Yen thresholding achieved the best performance for maximum Recall on whole day images with a value of 0.926. Table 12 shows that K-Means (K = 3) performed best in Precision for all conditions. The average Precision values for daytime, nighttime, and whole day images are 0.603, 0.539, and 0.534, respectively. The maximum Precisions for daytime, nighttime, and whole day images are 0.751, 0.572, and 0.575, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Segmentation**} & \multicolumn{4}{c}{**Average Recall**} & \multicolumn{4}{c}{**Maximum Recall**} \\ \cline{2-7}  & **Daytime** & **Nighttime** & **Whole Day** & **Daytime** & **Nighttime** & **Whole Day** \\ \hline Isodata & 0.575 & 0.279 & 0.403 & 0.650 & 0.454 & 0.472 \\ \hline Li & 0.593 & 0.506 & 0.522 & 0.702 & 0.545 & 0.571 \\ \hline Local & 0.479 & 0.168 & 0.319 & 0.597 & 0.324 & 0.347 \\ \hline Mean & 0.592 & 0.520 & 0.532 & 0.689 & 0.546 & 0.570 \\ \hline Min & 0.538 & 0.277 & 0.366 & 0.699 & 0.545 & 0.571 \\ \hline Otsu & 0.575 & 0.276 & 0.401 & 0.649 & 0.454 & 0.471 \\ \hline Multi-Otsu & 0.598 & 0.496 & 0.513 & 0.731 & 0.503 & 0.538 \\ \hline Triangle & 0.411 & 0.299 & 0.290 & 0.655 & 0.480 & 0.332 \\ \hline Yen & 0.514 & 0.367 & 0.400 & 0.669 & 0.457 & 0.474 \\ \hline K-Means (K = 2) & 0.570 & 0.379 & 0.456 & 0.630 & 0.462 & 0.477 \\ \hline K-Means (K = 3) & 0.603 & 0.539 & 0.534 & 0.751 & 0.572 & 0.575 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Average and maximum Precision for each segmentation method.