[17] measured the thousand-grain weight, projected area, sphericity, kernel density, and geometric mean diameter of seven types of seeds, and conducted performance experiments. A regression model was established using genetic programming, with the seed properties and the operational parameters of the device as inputs and the uniformity of seed spacing as output. The coefficient of determination (R\({}^{2}\)) of the model was 0.938, indicating that the model described the influence of these factors on the uniformity of seed spacing well. [PERSON] and [PERSON] et al. both used physical properties of seeds as model inputs, establishing seeding performance models using machine learning methods with strong learning ability to handle the extremely intricate nonlinear problem for which the patterns are exceedingly difficult to obtain--the differences in the device's seeding performance between different types of seeds. Inspired by their research, we take the concept of utilizing physical properties of seeds to represent seeds and application of machine learning methods as the key to improvement of parameter optimization efficiency. To be specific, we aim to employ machine learning methods to establish a seeding performance predictive model with both seed physical properties and parameters of the device as inputs. Thus, when physical properties of seeds in the model inputs are fixed as those of a specific seed, the seeding performance for the seed can be predicted by inputting parameters of the device into the model. Machine learning methods, including artificial neural networks (ANNs), random forest (RF), adaptive boosting (AdaBoost), support vector regression (SVR), and extreme learning machine (ELM), have been used for parameter optimization modeling in the field of agriculture. In the studies [16; 18; 19] of seed metering devices, ANNs were used to establish seeding performance predictive models, which showed excellent predictive accuracy. The high learning and information-processing abilities of ANNs make them more suitable for complex nonlinear modeling [20], which leads to the decision of using ANN to establish a seeding performance predictive model in this study. The optimal parameters must be searched from the established model by specific methods to achieve parameter optimization. Moreover, multi-objective methods are needed for problems with multiple objectives. [PERSON] [21] used the multi-objective particle swarm optimization (MOPSO) algorithm to optimize the qualified index and seed spacing variation index of a seed metering device; [PERSON] [22] used the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the residual stress and Vickers hardness of the WC-10 Co-4 Cr coating. Meta-heuristic search algorithms, such as MOPSO and NSGA-II, are widely used in multi-objective optimization. Compared to other multi-objective optimization algorithms, the MOPSO algorithm has fewer adjustment parameters, faster convergence speed, and uniform Pareto optimal frontier distribution [21], which is suitable for further parameter optimization study. In this paper, nine types of vegetable seeds and a pneumatic drum seed metering device were taken as the research objects, and the research on the efficient parameter optimization of the device was carried out using the BPNN and MOPSO algorithms. This paper consists of three parts:  
1. A discussion of the dataset required for ANN training and testing through seeding performance experiments;
2. An explanation of using the backpropagation neural network (BPNN) to establish a predictive model of seeding performance from the input physical properties of seeds (geometric mean diameter, sphericity, thousand-grain weight, and kernel density), operational parameters (vacuum pressure and drum rotational speed), and structural parameters (suction hole diameter), producing seeding performance indices (missing index and reseeding index);
3.