## 4 Discussion
Table 2 compares the measured (average, minimum, maximum, and standard deviation) leaf thickness information, whereas Table 3 provides numerical information (average, minimum, maximum, and standard deviation) of three types of angular assessments as a comparison between healthy and infected leaf specimens. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Small Vein (ea)**} \\ \hline  & \multicolumn{3}{c}{**Healthy**} & \multicolumn{6}{c}{**Infected**} \\ \hline
**Number** & **Avg.** & **Min.** & **Max.** & **StdDev.** & **Number** & **Avg.** & **Min.** & **Max.** & **StdDev.** \\ \hline
1 & 10 & 10 & 10 & 0 & 1 & 18 & 17 & 19 & 1 \\
2 & 9 & 8 & 9 & 1 & 2 & 12 & 12 & 12 & 0 \\
3 & 11 & 10 & 12 & 1 & 3 & 14 & 13 & 14 & 1 \\
4 & 10 & 9 & 10 & 1 & 4 & 18 & 17 & 18 & 1 \\
5 & 8 & 8 & 9 & 1 & 5 & 18 & 17 & 19 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerically enumerated average, minimum, and maximum number of small veins compared between healthy and infected specimens, with standard deviations. Figure 7: 2D-OCT image based thickness and mid-vein angular comparison between healthy and abnormal rice leaf specimens. (**a,c**) Healthy leaf specimen. (**b,d**) Abnormal specimen. \(\Delta t\): thickness between lower epidermis cells and lower mid-vein of both healthy and abnormal specimens. MV: mid-vein; LEC: lower epidermis cells. Since Xan-grading bacterial leaf blight is one of the most serious biotic stressors of rice, the earlier the disease occurs, the higher the yield loss. Along with growth of susceptible varieties, yield loss due to bacterial blight can be increased by up to 70%. Since degree of thickness reduction and the change in the angle of leaf surface, which leads to leaf rolling of rice, are important plant phenotypic data for quantitative trait loci (QTL) analysis of plant architecture and associated genes, we determined the pathological features in the interior of the rice leaf by comparing the physiological structural changes based morphological assessments before and after inoculation of the leaf surface. Thus, the most probable morphological changes occur due to mutations can be identified at a more prior incubation stage within a short duration by employing the proposed OCT-image based analysis procedure. According to the literature, the described rice leaf abnormalities have been identified through visual inspection, leaf surface based leaf rolling index measurements, leaf external thickness, and destructive sectioning assessments [52; 56; 57; 58]. Leaf thickness is measured from the traversed distance across the leaf blade mesophyll, and values from more than twelve locations along the mesophyll layer of each segment are \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Thickness (\(\mu\)m)**} \\ \hline  & \multicolumn{3}{c}{**Healthy**} & \multicolumn{8}{c}{**Infected**} \\ \hline
**Number** & **Avg.** & **Min.** & **Max.** & **StdDev.** & **Number** & **Avg.** & **Min.** & **Max.** & **StdDev.** \\ \hline
1 & 597.1 & 586.9 & 602.7 & 5.6 & 1 & 211.5 & 198.7 & 219.3 & 7.2 \\
2 & 667.6 & 663.4 & 668.9 & 2.1 & 2 & 126.9 & 117.8 & 138.1 & 6.8 \\
3 & 427.2 & 419.5 & 434.8 & 5.1 & 3 & 176.1 & 166.5 & 181.5 & 5.3 \\
4 & 641.6 & 635.8 & 647.2 & 4.8 & 4 & 111.4 & 102 & 126.8 & 9.6 \\
5 & 798.8 & 795.7 & 801 & 2.5 & 5 & 153.8 & 144.5 & 159.6 & 6.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerically evaluated average, minimum, and maximum leaf layer thickness compared between healthy and infected specimens with standard deviations.