## 4 Discussion  
To further validate the results, the results were compared with other methods, namely, the Hamming distance measure (Figure 3), the Euclidean distance measure (Figure 4), and the fuzzy simple additional weighting (fuzzy SAW) method [19] (Figure 5). It was shown that the Hamming distance method and fuzzy SAW ranked the top five results identically to our proposed method (\(E8\succ E4\succ E19\succ E12\succ E22\)). On the other hand, the Euclidean distance measure ranked them slightly differently (\(E8\succ E4\succ E19\succ E22\succ E12\)). However, experiments E12 and E22 led to poor results, whereby E12 had 6% dispersion outside the 150 mm diameter, while the seed depth in experiment E22 exceeded the standard threshold of 5-10 mm, which would cause the seeds to rot. Therefore, in terms of practical application techniques, the top three results were E8, E4, and E19. The specifications and outputs of these options are shown in Table 4.  
Figure 2: Values of CC using newly proposed similarity measure.  
Figure 4: Values of CC using Euclidean distance.  
Figure 5: Values of CC using fuzzy SAW method.  
Figure 3: Values of CC using Hamming distance.  
As can be seen in Table 4, when comparing seeding depth, the top four choices all met the agronomic requirements, whereas option 5 exceeded the optimal range. The seeding area precision of option 5 was highest; however, the seed depth in this case could lead to rotting paddy sprouts, thereby decreasing the germination rate. Therefore, option 5 is not recommended. On the other hand, option 1 presented the best seeding area precision at a diameter of 50 mm among the options with appropriate seeding depth.  
The distance between the injection port and soil surface is also an influential factor. Too great a distance would lead to the seeding accuracy being easily affected by wind during sowing, whereas too small a distance would affect the working mode of the machine in the case of uneven fields.  
The above analysis validates the selection order identified using Algorithm 2. The chosen weight vectors were varied to further confirm the above conclusion.  
The correlation of each variable with the CC decision variable was also determined, corresponding to the above weight vectors, to determine their significance (i.e., hstqi denotes the correlation coefficient corresponding to weight wi (i = 1, 2, 3, 4, 5, and 6)) (See Table 5). This allows determining the factors contributing to product quality. It can be seen that, if the weight of the variable \"seeding depth\" is lower than 50%, the correlation coefficient of this variable becomes significantly worse; otherwise, its correlation coefficient exceeds 90% (see Figure 6 and Table 6). This is completely consistent with the actual requirements of the sowing machine applied in agriculture. The correlation coefficients of C1 and C3 were higher than those of C2, indicating their greater influence on the final result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sections** & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline Air-assisted velocity (m/s) & 5 & 6 & 6 & 4 & 4 \\ Distant between injection port and soil surface (mm) & 150 & 200 & 200 & 100 & 100 \\ Diameter of injection port (mm) & 15 & 12 & 15 & 18 & 15 \\ Seeding area precision, diameter 50 mm (\%) & 79 & 75 & 73 & 72 & 89 \\ Seeding area precision, diameter 100 mm (\%) & 21 & 23 & 22 & 22 & 11 \\ Seeding area precision, diameter 150 mm (\%) & 0 & 2 & 5 & 6 & 0 \\ Seeding depth (mm) & 6 & 6 & 5 & 7 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selection results with respect to optimal seeding accuracy.  
Figure 6: Correlation coefficients between variables and the CC decision variable.