## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance  
#### 3.2.1 Comparison of Camera Types and Segmentation Algorithms  
Figure 13 shows the segmentation performance of the visible camera during the day. The figure shows that the segmentation performance (Recall, Precision, and F1 score) of 11 algorithms with the visible camera varied. The highest Recall of 0.934, Precision of 0.751, and F1 score of 0.794 were achieved by Triangle thresholding, K-Means algorithm (K = 3), and Multi-Otsu thresholding. Local thresholding, as predicted from one example image discussed in Section 3.1, achieved the lowest performance. Figure 11: Image segmentation results of NoIR images: (**a**) original tested image; (**b**) Isodata; (**c**) Li; (**d**) Local; (**e**) Mean; (**f**) Minimum; (**g**) Otsu; (**h**) Multi-Otsu; (**i**) Triangle; (**j**) Yen; (**k**) K-Means (K = 2); (**l**) K-Means (K = 3). Figure 12: Image segmentation results of thermal images: (**a**) original tested image; (**b**) Isodata; (**c**) Li; (**d**) Local; (**e**) Mean; (**f**) Minimum; (**g**) Otsu; (**h**) Multi-Otsu; (**i**) Triangle; (**j**) Yen; (**k**) K-Means (K = 2); (**l**) K-Means (K = 3). Figure 13: Segmentation performance of visible camera (daytime). and K-Means (K = 3). This suggested that for the given tested images, non-leaf objects can be extracted properly by considering the multi-class segmentation. Figure 14 and Table 2 show the segmentation performance and normalized confusion tables of segmentation results using the NoIR camera during the day, respectively. Figure 14 shows that most algorithms had similar performance to the NoIR camera (daytime). Significantly, Precision was very close, except for Yen thresholding. Similar to the visible camera, Triangle thresholding achieved the highest Recall. It is notable that Multi-Otsu thresholding and K-Means (K = 3) achieved the highest F1 scores. This suggests that by employing three classes/clusters, both methods can balance Recall and Precision, thus increasing the Precision of the NoIR camera (daytime). These results are confirmed by Table 2, where normalized confusion tables in the figure show that Triangle thresholding achieved the highest TP and lowest FP, and Multi-Otsu and K-Means (K-3) achieved the highest TN and lowest FP, similarly to Table 1.  
Figure 15 and Table 3 show the segmentation performance and normalized confusion tables of segmentation results using the NoIR camera at night, respectively. Figure 15 shows that the performance of algorithms with the NoIR camera (nighttime) is low. It can be seen from Figure 7b that the leaf objects partially appeared due to the low power of infrared LEDs used in this experiment. This condition produced a lower TP for most algorithms, as shown in the normalized confusion tables in Table 3. However, the approach to using a NoIR camera for night measurement is promising due to the low cost and small size of the infrared LEDs embedded in the camera module. Further, it is non-visible light; thus, it does not disrupt the natural conditions in the plant environments at night. It can be noted from the figure that K-Means (K = 2) achieved high Recall but low Precision and F1 score. From the experimental results, the high Recall was caused by an incorrect segmentation method that produced a wide white section (foreground), thus producing very high Recall and very low Precision. The results suggest that Recall and Precision should be considered when evaluating segmentation performance. Figure 16 and Table 4 show the segmentation performance and normalized confusion tables of segmentation results using the NoIR camera over a whole day, respectively. Figure 16 shows that similar to the visible camera, the performance of 11 algorithms with the NoIR camera (daytime and nighttime) also varied. The figure shows that the Multi-Otsu and K-Means (K = 3) achieved the highest Precisions, however, the values are lower than the visible camera. Table 4 shows that TP values are lower and FN values are higher compared to the ones in Table 1 (visible camera).