## 3 Results and Discussion
### Verification of Optimization Capability of BPNN-MOPSO  
Before carrying out efficient parameter optimization, the optimization capability of the BPNN-MOPSO algorithm needs to be verified. We optimized the device parameters for the six types of seeds used in the seeding performance experiments (cabbage, carrot, radish, onion, Chinese cabbage, and sesame seeds) using the BPNN-MOPSO algorithm. Then, the  
Figure 11: Comparison of the experimental and BPNN-predicted values for the missing index (_RI_).  
Figure 12: Comparison of the experimental and BPNN-predicted values for the reseeding index (_RI_).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Connection Weight**} & \multicolumn{3}{c}{**Hidden-**} & \multicolumn{3}{c}{**Output-**} \\ \multicolumn{1}{c}{\multirow{-2}{*}{**Connection Weight Between Input and Hidden Layers**}} & \multicolumn{3}{c}{**Output Layers**} & \multicolumn{3}{c}{**Hidden-**} & \multicolumn{3}{c}{**Layer**} \\ \multicolumn{1}{c}{} & & & & \multicolumn{3}{c}{**(Transposition)**} & \multicolumn{3}{c}{**Bias**} & \multicolumn{3}{c}{**Bias**} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{3}{c}{**W\({}_{2}\)**} & \multicolumn{3}{c}{**b\({}_{\mathbf{n}}\)**} & \multicolumn{3}{c}{**b\({}_{\mathbf{o}}\)**} \\ \hline \(-2.7320\) & \(-1.3529\) & \(-0.6447\) & \(0.5044\) & \(0.7261\) & \(3.9120\) & \(-0.9372\) & \(-2.1289\) & \(-0.5536\) & \(6.0500\) & \(3.1567\) \\ \(2.2852\) & \(2.0053\) & \(-1.2572\) & \(0.5124\) & \(3.9060\) & \(0.9832\) & \(0.1191\) & \(0.5815\) & \(-1.7001\) & \(-4.0843\) & \(-2.1084\) \\ \(-2.3290\) & \(0.2466\) & \(0.3522\) & \(0.8968\) & \(4.4255\) & \(0.5757\) & \(-0.0804\) & \(-1.4565\) & \(0.3460\) & \(6.4274\) \\ \(-1.6748\) & \(-0.3326\) & \(0.8352\) & \(0.3386\) & \(0.3735\) & \(2.1485\) & \(-0.5065\) & \(-0.2870\) & \(1.3906\) & \(0.0285\) \\ \(0.4245\) & \(0.2209\) & \(4.0421\) & \(-0.1324\) & \(-0.7695\) & \(-2.7666\) & \(-1.7444\) & \(0.0490\) & \(-0.2990\) & \(-3.8582\) \\ \(-0.1538\) & \(-1.4328\) & \(1.3190\) & \(-2.7081\) & \(-0.5418\) & \(-1.2597\) & \(-0.0582\) & \(0.2649\) & \(-0.5468\) \\ \(0.5534\) & \(1.7438\) & \(-0.7404\) & \(1.0796\) & \(2.8059\) & \(1.2724\) & \(0.0536\) & \(-0.8076\) & \(2.3069\) & \(-2.5635\) \\ \(1.8411\) & \(-1.7465\) & \(-3.2982\) & \(2.4162\) & \(-0.7691\) & \(-1.9419\) & \(0.2147\) & \(-0.0975\) & \(1.2290\) & \(3.1703\) \\ \hline \hline \end{tabular}
\end{table}
Table 19: Weights and biases of the optimal performance BPNN.  
optimized values of \(QI\) were compared with the optimal experimental ones (as shown in Tables 6-11) to validate whether the algorithm is able to improve the seeding performance by its optimization. The verification results are shown in Figure 13 and Table 20.  
Table 20 and Figure 13 show that the optimized values of \(QI\) are higher than the optimal experimental ones, and the increases are 1.55%, 1.43%, 0.59%, 1.87%, 0.22%, and 2.53% for cabbage, carrot, radish, onion, Chinese cabbage, and sesame seeds, respectively. Therefore, the algorithm's capability to optimize the seeding performance of the seed metering device was verified, laying a foundation for the implementation of efficient parameter optimization.