# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 4 Test and Verification of Seed Metering Device
### Multi-Factor Test
From the Formula (23), the variation coefficient of seeding uniformity \(Y_{1}\) and the rotational speed of metering wheel \(x_{1}\), the diameter of the hole \(x_{2}\), and the depth of the hole \(x_{3}\) showed the quadratic function of opening upward, respectively. It showed an optimal parameter combination of the rotational speed of the metering wheel, the diameter of the hole, and the hole depth, which made the variation coefficient of seeding uniformity minimum. (2) The regression equation of the consistency coefficient \(Y_{2}\) of each row after excluding the insignificant indigenous term is:  
\[ \begin{array}{c}Y_{2}=0.79-0.26x_{1}+0.071x_{2}+0.16x_{3}+0.35x_{1}x_{2}-0.2 0x_{1}x_{3}\\ +0.16x_{2}x_{3}+0.43x_{1}^{2}+0.33x_{2}^{2}+0.16x_{3}^{2}\end{array}  \tag{24}\]  
Table 5 shows the results of the variance analysis. The consistency coefficient regression model of each row showed that the model was extremely significant (\(p<0.01\)), and the mismatch item was not significant (\(p\geq 0.05\)). The model was effective with determination coefficient \(R^{2}=0.991\); the calibration coefficient was 0.981, both were close to 1, and CV = 4.88%, Adeq precious = 82.66, indicating that the fitting equation was reliable. Among them, the rotational speed of the metering wheel, the depth of the hole, and the hole's diameter have extremely significant indigenous effects on the equation. The influence order of various factors on the coefficient of variation for consistency of seed quantity in each row \(Y_{2}\) of each row is the depth of the hole \(x_{3}\), the rotational speed of the metering wheel \(x_{1}\), and the diameter of the hole \(x_{2}\). The Formula (24) shows that the coefficient of variation for consistency of seed quantity in each row \(Y_{2}\) of each row is a quadratic function with the rotational speed of the metering wheel \(x_{1}\), the diameter of the hole \(x_{2}\), and the depth of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variance**} & \multicolumn{5}{c}{**Variation Coefficient of Seeding Uniformity \(Y_{1}\)/\%**} \\ \cline{2-6}  & **Sum of Square** & **Degree of Freedom** & **Mean Square** & **F Value** & **\(p\) Value** \\ \hline Model & 20.12 & 9 & 2.24 & 85.23 & \textless{}0.0001 ** \\ \(x_{1}\) & 0.47 & 1 & 0.47 & 17.75 & 0.048 ** \\ \(x_{2}\) & 0.27 & 1 & 0.27 & 10.16 & 0.0090 * \\ \(x_{3}\) & 0.07 & 1 & 0.07 & 2.68 & 0.0014 ** \\ \(x_{1>2}\) & 0.54 & 1 & 0.54 & 20.59 & 0.0012 ** \\ \(x_{1>3}\) & 0.15 & 1 & 0.15 & 5.80 & 0.0019 * \\ \(x_{2>3}\) & 0.25 & 1 & 0.25 & 9.34 & 0.0074 * \\ \(x_{1}^{2}\) & 9.58 & 1 & 9.58 & 36.497 & \textless{}0.0001 ** \\ \(x_{3}^{2}\) & 2.80 & 1 & 2.80 & 106.73 & \textless{}0.0001 * \\ \(x_{3}^{2}\) & 4.24 & 1 & 4.24 & 161.46 & \textless{}0.0001 ** \\ \hline Residual & 0.18 & 7 & 0.026 & & \\ Lack of Fit & 0.12 & 3 & 0.042 & 2.82 & 0.2953 \\ Pure Error & 0.059 & 4 & 0.015 & & \\ Cor Total & 20.31 & 16 & & & \\ \(R^{2}=0.9910;~{}R_{\text{adj}}^{2}=0.9793\); CV (Coefficient of Variation) = 3.12\% Adeq precious = 22.97 & & & \\ \hline \hline \end{tabular} Note. ** and \({}^{*}\) indicated significance at 0.01 and 0.05 levels, respectively.  
\end{table} 
Table 4: Variation coefficient of seeding uniformity analysis results. (1) Effect of interaction on variation coefficient of seeding uniformity  
As shown in Figure 12(a), when the hole depth is 7.5 mm, the interaction between the hole diameter \(x_{2}\) and the rotation speed \(x_{1}\) of the metering wheel affects the variation coefficient of seeding uniformity. When the rotation speed \(x_{1}\) is 90 r/min, the variation coefficient of seeding uniformity first decreases and then increases with the increase in hole diameter \(x_{2}\). When the diameter of hole \(x_{2}\) is 10 mm, the coefficient of variation of seed row uniformity decreases first and then increases with the increase in metering wheel speed \(x_{1}\). The slope of the response surface is large, so there is a significant correlation between them.