## 3 Experimental Results and Discussion
### Performance of Leaf Temperature Measurement  
The objective of evaluating the leaf temperature measurement performance is to compare the proposed leaf temperature measurement technique based on the Otsu thresholding with the one obtained by manual segmentation. In the manual segmentation, the location of the leaf area is defined manually by observing the thermal image. Fortunately, since the resolution of a thermal image is only \(8\times 8\) pixels, it can be carried out quickly. After the leaf area is defined, the leaf temperature is calculated by averaging the temperature values on the leaf area. The leaf temperature measurement results of Plant-1 and Plant-2 are depicted in Figures 14 and 15, respectively. The figures show the thermal images (heat-map color), the proposed segmented images with their respective leaf temperature, and the manually segmented images with their respective leaf temperature. The first, second, and third rows represent the measurement at 21:00 h, 11:00 h, and 15:00 h, respectively. In the proposed segmented images, the white color represents the pixels whose thermal values are below the optimal threshold (\(T_{opt}\)) obtained by the Otsu thresholding. In the manually segmented images, the white color represents the leaf pixels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Collection Time**} & \multicolumn{4}{c}{**Data Recording Success Rate (DRSR)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Raspberry Pi Zero W**} & \multicolumn{4}{c}{**Haiwell IoT Cloud HMI**} \\ \cline{2-7}  & **Plant-1 (DH22,** & **Plant-2** & **Plant-1 (DH22,** & **Plant-2** & **Plant-2** & **Plant-1** & **Plant-2** \\  & **AMG8833 \#1)** & **(AMG8833 \#2)** & **AMG8833 \#1)** & **(AMG8833)** & **(JXCT)** & **(JXCT)** \\ \hline Day-1 (15:13–23:59 h) & 85.6\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline Day-2 (00:00–23:59 h) & 88.6\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline Day-3 (00:00–23:59 h) & 86.3\% & 99.1\% & 100\% & 100\% & 100\% & 100\% \\ \hline Day-4 (00:00–23:59 h) & 88.1\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline Day-5 (00:00–08:33 h) & 83.9\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline Average & 86.5\% & 99.8\% & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Data recording success rate (DRSR). Figure 14: Leaf temperature measurement of Plant-1: (**a**,**d**,**g**) Thermal images (heat-map color) at 21:00 h, 11:00 h, 15:00 h; (**b**,**e**,**h**) Segmented images and leaf temperatures of (**a**,**d**,**g**); (**c**,**f**,**i**) Manually segmented images and leaf temperatures of (**a**,**d**,**g**). Figure 15: Leaf temperature measurement of Plant-2: (**a**,**d**,**g**) Thermal images (heat-map color) at 21:00 h, 11:00 h, 15:00 h; (**b**,**e**,**h**) Segmented images and leaf temperatures of (**a**,**d**,**g**); (**c**,**f**,**i**) Manually segmented images and leaf temperatures of (**a**,**d**,**g**). Figures 14 and 15 show that the areas of white pixels in the proposed segmented images differ from the ones of manually segmented images. However, the measured leaf temperatures of the proposed method are close to the manual ones. This is caused by the algorithm that computes the leaf temperature as the average temperature on the white pixels, whose values are almost the same due to the similar temperature. Table 12 shows the leaf temperature measurement error of Plant-1 and Plant-2 in Figures 14 and 15. Observing Figure 14, Figure 15 and Table 12, we find that the discrepancy between the segmented pixels of proposed and manual methods does not reflect the leaf temperature measurement error. For instance, the discrepancy of segmented images in Figure 15h,i results in a small measurement error of 0.03%. It differs from the regular usage of the thresholding method in matching or detecting the object where the segmented object discrepancy yields higher error detection. Our proposed method takes benefit of the similarity of temperature values in the leaf and background.