## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance
#### 3.2.5 Comparison of Segmentation Algorithms Using Well-Known Image Dataset  
To verify the performance of segmentation techniques (thresholding and K-means), used in this work for leaf segmentation, we evaluated 11 segmentation algorithms using a well-known image dataset, namely Ara2012 [16, 17, 51], consisting of 120 _Arabidopsis thaliana_ plant images. Examples of tested images are shown in Figure 20. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Segmentation Method**} & \multicolumn{4}{c}{**Average F1 Score**} & \multicolumn{4}{c}{**Maximum F1 Score**} \\ \cline{2-7}  & \multirow{2}{*}{**Daytime**} & \multirow{2}{*}{**Nighttime**} & **Whole Day** & \multirow{2}{*}{**Daytime**} & **Nighttime** & **Whole Day** \\ \hline Isodata & 0.460 & 0.238 & 0.321 & 0.558 & 0.295 & 0.325 \\ \hline Li & 0.468 & 0.398 & 0.406 & 0.578 & 0.489 & 0.476 \\ \hline Local & 0.375 & 0.122 & 0.241 & 0.462 & 0.233 & 0.257 \\ \hline Mean & 0.469 & 0.410 & 0.413 & 0.574 & 0.489 & 0.478 \\ \hline Min & 0.434 & 0.252 & 0.308 & 0.574 & 0.489 & 0.476 \\ \hline Otsu & 0.460 & 0.235 & 0.319 & 0.558 & 0.294 & 0.324 \\ \hline Multi-Otsu & 0.581 & 0.436 & 0.455 & 0.794 & 0.546 & 0.553 \\ \hline Triangle & 0.314 & 0.205 & 0.208 & 0.508 & 0.319 & 0.244 \\ \hline Yen & 0.418 & 0.335 & 0.341 & 0.562 & 0.372 & 0.359 \\ \hline K-Means (K = 2) & 0.459 & 0.240 & 0.324 & 0.546 & 0.302 & 0.332 \\ \hline K-Means (K = 3) & 0.585 & 0.509 & 0.496 & 0.792 & 0.636 & 0.599 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Average and maximum F1 score for each segmentation method. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Segmentation Algorithm**} & \multirow{2}{*}{**Isodata**} & \multirow{2}{*}{**Li**} & \multirow{2}{*}{**Local**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Minimum**} & **Otsu** & **Multi-Otsu** & **Triangle** & **Yen** & **K-Means (K = 3)** \\ \hline Execution time (s) & 0.174 & 0.210 & 4.992 & 0.175 & 0.342 & 0.183 & 0.205 & 0.167 & 0.176 & 2.463 & 3.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Execution time of segmentation algorithms. Figure 21 shows that K-Means (K = 3) achieved the highest F1 score of 0.917, while values of Recall and Precision are 0.952 and 0.931, respectively. The second highest F1 score was achieved by Multi-Otsu with a value of 0.813, while values of Recall and Precision are 0.894 and 0.845, respectively. Referring to Sections 3.2.1 and 3.2.3, K-Means (K-3) and Multi-Otsu also show the best performance, even though it is lower than for the Ara2012 dataset. This result implies that leaf segmentation using multi-clusters provides a better solution for achieving high Recall, Precision, and F1 score. Unfortunately, due to the complicated backgrounds in our image dataset, this approach cannot achieve high performance. Furthermore, by comparing our image dataset and the Ara2012 dataset, we can observe an interesting result, as described in the following. Comparing F1 scores of the Otsu and Triangle thresholding techniques between our dataset and the Ara2012 dataset, it was seen that Otsu thresholding shows better performance than Triangle thresholding in our dataset, however, the Triangle thresholding shows better performance than Otsu thresholding in the Ara2012 dataset. This result could be seen from the image histograms shown in Figure 22, where Figure 22a,b are the image histograms of our dataset and the Ara2012 dataset, respectively. It is clear from the figures that the Triangle thresholding works better when the shape of the histogram is bi-modal, as shown in  
NoIR camera was suitable for nighttime images. The K-Means algorithm achieved the maximum Precision and F1 score in all conditions; meanwhile, no single segmentation algorithm showed superior Recall in all conditions. Based on the results, a technique for combining camera types and segmentation techniques will be developed in future work for better performance of the leaf segmentation system.