## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance
#### 3.2.2 Comparison of Camera Types  
To find the effectiveness of camera types in segmentation, we compared the performance of each camera based on the average and maximum values of the metrics of all segmentation algorithms. The comparisons of average and maximum Recall, Precision, and F1 score for each camera type are given in Tables 8-10, respectively. A comparison of daytime, nighttime, and the whole day was also conducted, and the results are given in the tables. For straightforward reading, the highest values in the tables are given in gray. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Isodata**} & \multicolumn{2}{c}{**Li**} & \multicolumn{2}{c}{**Local**} & \multicolumn{2}{c}{**Mean**} \\ \hline TP & FN & TP & FN & TP & FN & TP & FN \\ \hline
23.50\% & 0.23\% & 23.36\% & 0.37\% & 12.62\% & 11.11\% & 22.96\% & 0.72\% \\ \hline FP & TN & FP & TN & FP & TN & FP & TN \\ \hline FP & TN & FP & TN & FP & TN & FP & TN \\ \hline TP & FN & TP & FN & TP & FN & & \\
23.47\% & 0.26\% & 23.46\% & 0.30\% & 17.69\% & 6.06\% & & \\ \hline FP & TN & FP & TN & FP & TN & & \\
55.41\% & 20.86\% & 53.76\% & 22.49\% & 28.38\% & 47.86\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normalized confusion tables of segmentation results using thermal camera (nighttime). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Isodata**} & \multicolumn{2}{c}{**Li**} & \multicolumn{2}{c}{**Local**} & \multicolumn{2}{c}{**Mean**} \\ \hline TP & FN & TP & FN & TP & FN & TP & FN \\ \hline
23.02\% & 294\% & 21.83\% & 41.2\% & 14.01\% & 11.94\% & 22.16\% & 3.80\% \\ \hline FP & TN & FP & TN & FP & TN & FP & TN \\
48.72\% & 25.32\% & 44.25\% & 29.79\% & 40.54\% & 33.51\% & 41.59\% & 32.45\% \\ \hline \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Otsu**} & \multicolumn{2}{c}{**Multi-Otsu**} & \multicolumn{2}{c}{**Triangle**} \\ \hline TP & FN & TP & FN & TP & FN & TP & FN \\
6.67\% & 19.29\% & 23.05\% & 291\% & 21.10\% & 4.85\% & 11.79\% & 14.17\% \\ \hline FP & TN & FP & TN & FP & TN & FP & TN \\
15.20\% & 58.84\% & 48.86\% & 25.19\% & 39.28\% & 34.76\% & 26.91\% & 47.14\% \\ \hline \multicolumn{2}{c}{**Yen**} & \multicolumn{2}{c}{**K-Means (K = 2)**} & \multicolumn{2}{c}{**K-Means (K = 3)**} & & \\ \hline TP & FN & TP & FN & TP & FN & & \\
23.72\% & 2.24\% & 22.53\% & 3.43\% & 17.23\% & 8.73\% & & \\ \hline FP & TN & FP & TN & FP & TN & & \\
50.51\% & 23.53\% & 46.19\% & 27.86\% & 26.67\% & 47.37\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Normalized confusion tables of segmentation results using thermal camera (daytime and nighttime). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Camera Type**} & \multicolumn{3}{c}{**Average Recall**} & \multicolumn{3}{c}{**Maximum Recall**} \\ \cline{2-7}  & **Daytime** & **Nighttime** & **Whole Day** & **Daytime** & **Nighttime** & **Whole Day** \\ \hline Visible camera & 0.794 & NA & NA & 0.934 & NA & NA \\ \hline NoIR camera & 0.761 & 0.405 & 0.574 & 0.917 & 0.937 & 0.868 \\ \hline Thermal camera & 0.647 & 0.831 & 0.740 & 0.863 & 0.990 & 0.926 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average and maximum Recall for each camera type. Table 8 shows that the visible camera achieved the best performance for average and maximum Recall on daytime images with values of 0.794 and 0.934, respectively. The thermal camera achieved the best performance for average daytime (whole day) images with values of 0.831 (0.740) and 0.990 (0.926), respectively. Table 9 shows that the visible camera achieved the best performance for average and maximum Precision on daytime images, with values of 0.663 and 0.751, respectively. The NoIR camera achieved the best performance for average and maximum Precision on whole day images, with the values of 0.443 and 0.575, respectively, and maximum Precision of 0.572 on nighttime images. The thermal camera achieved the best performance for average Precision on nighttime images with a value of 0.418.