## 4 Conclusions  
In this paper, an efficient parameter optimization method based on BPNN and the MOPSO algorithm was proposed to improve the efficiency of the parameter optimization for a pneumatic drum seed metering device. The main research conclusions are presented as follows:  
1. For six types of seeds (cabbage, carrot, radish, onion, Chinese cabbage, and sesame seeds), the range of the suction hole diameter was determined to be from 0.6 to 1.0 mm through the testing and analysis of the seed adsorption process. The suitable range of vacuum pressure for each seed type was determined by single-factor experiments: 6 to 10 kPa for Chinese cabbage, carrot, sesame, and onion seeds; 8 to 12 kPa for cabbage seeds; and 10 to 14 kPa for radish seeds. The seeding performance experiments were carried out, and the dataset used for network training and testing was obtained. 2. For the BPNN predictive model, when the number of the hidden-layer neurons and the combination of the activation functions of the hidden layer and output layer are eight and logsig/purelin, respectively, the training set \(\overline{\text{R}^{2}}\) (0.9641) and test set \(\overline{\text{R}^{2}}\) (0.9290) are at a high level, and the values of the error indices (\(\overline{\text{RMSE}}\) and \(\overline{\text{MAE}}\)) are basically within 2%, which are better than those obtained under other network parameters, showing good network performance. Thus, the number of the hidden-layer neurons and the combination of the activation functions were determined to be eight and logsig/purelin, respectively. Under the above values of network parameters, the BPNN with the optimal performance was obtained through training and testing. The \(\text{R}^{2}\) values of two outputs for the training set are both above 0.96; the \(\text{R}^{2}\) values of two outputs for the test set are both above 0.92; and the values of the error indices (RMSE and MAE) are basically within 2%, demonstrating the excellent fitting performance and strong generalization ability of the optimal BPNN predictive model. 3. In the validation of optimization capability, the values of \(QI\) optimized by the BPNN-MOPSO algorithm are higher than the optimal experimental ones for cabbage, carrot, radish, onion, Chinese cabbage, and sesame seeds, and the increases are 1.55%, 1.43%, 0.59%, 1.87%, 0.22%, and 2.53%, respectively. 4. Using the BPNN-MOPSO algorithm to carry out the efficient parameter optimization, the optimal device parameters and seeding performance for three untested seed types (tomato, pepper, and bok choi seeds) were sought. For tomato seeds, with a hole diameter of 0.75 mm, vacuum pressure of 5.6 kPa, and drum rotational speed of 13.7 rpm, the \(MI\), \(RI\), and \(QI\) were 11.85%, 2.66%, and 85.50%, respectively. For pepper seeds, with a hole diameter of 1.0 mm, vacuum pressure of 10.4 kPa, and drum rotational speed of 18.0 rpm, the \(MI\), \(RI\), and \(QI\) were 11.54%, 2.94%, and 85.52%, respectively. For bok choi seeds, with a hole diameter of 0.67 mm, vacuum pressure of 8.6 kPa, and drum rotational speed of 14.0 rpm, the \(MI\), \(RI\), and \(QI\) were 10.72%, 4.41%, and 84.87%, respectively. The bench experiments verified the optimization results. The results show that the absolute errors between the predicted and experimental values were all less than 3%. Compared with commercial seeders, the pneumatic drum seed metering device in this study has a lower machining accuracy and was designed for a relatively large number of seed types. Thus, the seeding qualified index of the device (80% < \(QI\) < 90%) is lower than that of commercial seeders (\(QI\) \(\geq\) 90%). In subsequent research, we will further improve and optimize the structure of the device to enhance its seeding performance. Conceptualization, Y.P. and Y.Y. ; methodology, Y.P. and Y.Y. ; software, Y.P. ; validation, Y.P., J.Z., and Y.W. ; formal analysis, Y.P. ; investigation, Y.P., J.Z., W.Q., and Q.W. ; data curation, Y.P. ; writing--original draft preparation, Y.P. ; writing--review and editing, Y.P. and Y.Y.