Design and Testing of an Electrically Driven Precision Soybean Seeder Based an OGWO-Fuzzy PID Control Strategy

Abstract

In response to the challenges of reduced efficiency and compromised seeding accuracy in conventional soybean planters operating at high speeds, this research introduces a novel precision seeding system powered by an electric drive, aiming to enhance both operational reliability and sowing precision. The entire system is powered by the tractor’s 12 V battery and incorporates an OGWO-Fuzzy PID control strategy to regulate the seeding motor speed. To achieve faster and more accurate regulation of the seeding motor speed, this study employs a ternary phase-diagram-based strategy to optimize the weight allocation among the α, β, and δ wolves within the Grey Wolf Optimization (GWO) algorithm. Based on engineering requirements, the optimal weight ratio was determined to be 16:2:1. Simulation results indicate that the optimized OGWO-Fuzzy PID control strategy achieves a settling time of only 0.17 s with no overshoot. In bench tests, the OGWO-Fuzzy PID control strategy significantly outperformed both GWO-Fuzzy PID and Fuzzy PID in terms of seed-metering speed regulation time and accuracy. The average qualified seeding index reached 95.68%, demonstrating excellent seeding performance at medium-to-high operating speeds. This study provides a practical and technically robust approach to ensure seeding quality during medium–high-speed soybean planting

1. Introduction

As an important crop used worldwide for oil, food, and feed, soybeans are predominantly sown using ground-wheel-driven seeders in mechanized planting. Owing to challenges posed by terrain conditions and drivetrain limitations, the standard operating speed is generally confined to about 6 km/h. Attempts to increase the speed often result in a sharp decline in seeding quality. Therefore, enhancing operational speed without sacrificing seeding quality has emerged as a critical focus in the advancement of next-generation soybean seeders [1].

In recent years, numerous researchers have devoted themselves to the study of seeders and various stages of the seeding process, with a focus on achieving greater intelligence, precision, and efficiency [2,3,4,5,6,7]. Xing et al. [8] used EDEM software to simulate the relationship between seed flowability and the parameters of the seed-stirring device. The results indicated that increasing the diameter and thickness of the stirring granule device was beneficial for improving the seed pickup rate and reducing the missed seed rate. Zhao et al. [9] developed an electrically controlled precision seeding system for single-kernel maize planting, incorporating a Fuzzy PID control algorithm. Simulink simulations were conducted using MATLAB, which reduced the system response time by 0.23 s. The electronic control system met the agronomic requirements for precision seeding. Yu et al. [10] applied an improved Fuzzy PID algorithm, achieving an average error of ±1 rad/min in the actual motor speed and a coefficient of variation below 1%. This provided an effective technical reference for high-speed and precise peanut planting. Liu et al. designed a pneumatic missed-seed automatic compensation system based on speed synchronization for maize seeds. When the seeding speed ranged from 8 to 12 km/h, the effective single-kernel feeding rate exceeded 88%, significantly improving the operational performance of the seeder [11]. Researchers have sought to enhance seeding performance through multiple approaches, such as investigating the seed-filling mechanism, optimizing the planting depth, improving the seed-metering plate design, advancing electric-drive technologies, and refining control strategies [12,13,14,15,16,17]. However, most studies have focused on low-to-medium operating speeds, and seeding quality tends to decline rapidly when the speed exceeds 10 km/h [18,19].

The aim of this study is to design a low-voltage, highly reliable electric-driven seeding system that can maintain high seeding quality even under medium- and high-speed operating conditions. The electric precision seeding system for soybeans designed in this study is powered entirely by the 12 V tractor’s battery. A series of selection and verification tests were conducted to determine the optimal seed-metering motor and negative-pressure fan. Ultimately, a 12 V-rated motor coupled with a fan was selected, offering sufficient torque, appropriate rotational speed, and adequate negative pressure to meet the system’s performance requirements. This configuration enabled the construction of a reliable seed-metering system that operates without the need for a voltage-boosting module. In constructing the Simulink simulation model, through bench experiments, this study first quantified the actual correlation between the PWM duty cycle and the rotational speed of the seed-metering motor [20]. The data were then imported into MATLAB’s transfer function identification module for system identification. The collected data were subsequently imported into MATLAB’s system identification toolbox to derive the transfer function, which was then implemented in a Simulink simulation to ensure the accuracy and reliability of the simulation results [21].

In terms of control strategy selection, this study introduced the Grey Wolf Optimization (GWO) algorithm to optimize the parameters of the Fuzzy PID controller, enabling the seed-metering motor to achieve good performance even during initial startup [22]. To prevent the GWO algorithm from falling into local optima during the search for the global solution, a ternary plot approach was employed to determine the optimal weight distribution among the three wolf types—α, β, and δ. Through simulation analysis and comparison, it was concluded that when the weights of α, β, and δ were set to 16:2:1, the improved OGWO-Fuzzy PID control strategy achieved better performance in terms of regulation time, overshoot, and transient spikes caused by external disturbances. Both bench tests and simulation experiments demonstrated the excellent performance of the OGWO-Fuzzy PID control strategy, validating the effectiveness of the improvements made to the GWO algorithm in this study. This optimization approach contributed to enhanced soybean seeding quality.

2. Materials and Methods

2.1. Overall System Introduction

The overall structure of the distributed electric-driven soybean seeding system designed in this study is shown in Figure 1. The overall seeding system primarily consists of three independently operating, non-interfering air-suction seeders, a human–machine interface (HIM) screen, a BDS (BeiDou Navigation Satellite System) signal receiver manufactured in Suzhou, China, and a main control box. In practical engineering applications, the rotational speed of the seed-metering disc must adjust in real time according to the machine’s travel speed. Therefore, a brushed DC motor with fast response characteristics was selected.

The BDS receiver is used to obtain the real-time location information of the machine. The user can set seeding parameters such as plant spacing and vehicle speed through the HMI screen. In this study, the seed-metering motor, negative-pressure fan, and the entire system were all powered by the tractor’s 12 V power supply, eliminating the need for a voltage-boosting module and resulting in a lighter and more reliable overall system.

2.1.1. Selection and Verification

To verify whether the selected seed-metering motor and negative-pressure fan in this study can meet the operational requirements of seeding, the negative-pressure sensor model selected for validation was DP-101 shown in Figure 2.

Tests showed that in the air-suction seeder selected for this study, soybean seeds can be properly picked up when the negative pressure reaches 1.6 kPa. The selected negative-pressure fan is capable of providing a maximum negative pressure of 2.7 kPa, which meets the operational requirements for seed pickup.

A TWR990A dynamic speed and torque sensor manufactured in Shanghai, China was used to verify the performance of the selected seed-metering motor. The torque sensor is shown in Figure 3.

According to measurements obtained using a torque wrench, the startup torque of the seed-metering device is 0.7 Nm. The dynamic torque and speed sensor showed that the selected seed-metering motor has a rated torque of 1.3 Nm and a rated speed of 98 r/min, further confirming that the selected motor is capable of ensuring proper seed pickup.

2.1.2. Circuit Design of Seeding System

The hardware system primarily consists of a power supply, signal acquisition module, speed detection module, motor drive module, control module, and interactive screen module. The single-unit drive circuit of electric- driven soybean seeder was shown in Figure 4.

As shown in the figure above, the main controller of the control system is the STM32F407VET6. This controller provides 15 communication interfaces, 140 interrupt-capable I/O ports, and 17 timers, fully meeting the requirements of the current study and future system development. The HMI screen is model AST035W02RGE. A power conversion module steps down the 12 V supply from the machine to 5 V to power the HMI screen. By inputting the desired plant spacing and simulated machine speed into the screen, the rotational speed of the seed-metering motor can be adjusted accordingly. The signal acquisition module selected was the YM-181Q, which offers a speed accuracy of 0.05 m/s. The control system uses the theoretical motor speed as the input, while the SCX-555 encoder provides real-time motor speed feedback to form a closed-loop control system for regulating the speed of the seed-metering motor [23]. The closed-loop control structure is shown in Figure 5.

2.2. Mathematical Relationship Between Tractor Speed and Seeder Revolution Speed

To investigate the relationship between the machine’s travel speed and the rotational speed of the seed-metering disc in practical engineering applications, it is necessary to establish a mathematical relationship for further study. The time interval $T$ (s) between two successive soybean seed drops is related to the metering device’s rotational speed $n$ (r/min) and the number of holes $m$ on the seed disc, as follows:

$$T={\displaystyle \frac{60}{nm}}$$

(1)

The relationship between soybean plant spacing $Z$ (mm) and vehicle speed $v$ (km/h) is given by

$$Z=TV={\displaystyle \frac{1.67\times {10}^{4}v}{nm}}$$

(2)

The selected soybean seed-metering disc contains 24 holes. The transmission ratio between the seed-metering disc and the seed-metering motor is 1:1. Substituting this into the equation yields

$$Z={\displaystyle \frac{695.8v}{n}}$$

(3)

2.3. Control Strategy Design of Electric-Drive Soybean Seeder

2.3.1. Fuzzy PID Controller Design

The fuzzy PID algorithm primarily consists of two components: a fuzzy controller and a PID controller. The working principle of the fuzzy controller is to input the error $e$ between the target value and the output value, along with the error rate $e_c$, and first perform fuzzification. This is followed by fuzzy inference, defuzzification of the inference result, and finally, the deblurring output is used to dynamically adjust the PID parameters.

$$K_p=k_p+\Delta k_p$$

(4)

$$K_i=k_i+\Delta k_i$$

(5)

$$K_d=k_d+\Delta k_d$$

(6)

In this study, the PID controller parameters are set as follows: $k_p$ = 2.8; $k_i$ = 0.5; $k_d$ = 0.15. The workflow of the fuzzy controller is as shown in Figure 6.

Based on expert knowledge, the error $e$, error change rate $e_c$, and output variables $\Delta k_p$, $\Delta k_i$, $\Delta k_d$ are each divided into seven fuzzy sets: [NB, NM, NS, ZO, PS, PM, PB], where P, Z, and N represent positive, zero, and negative, respectively, while B, M, and S represent big, medium, and small, respectively.: The division rules of $\Delta k_p$, $\Delta k_i$, $\Delta k_d$ were presented in

Table 1. $\Delta k_p$ Fuzzy rule.

$\mathit{E}$	$\mathit{E}\mathit{c}$
	NB	NM	NS	ZO	PS	PM	PB
NB	PB	PB	PM	PM	PM	PB	PB
NM	PB	PB	PM	PM	PM	PB	PB
NS	PM	PS	PS	ZO	PS	PM	PM
ZO	PM	PS	PS	ZO	PS	PM	PM
PS	PM	PM	PS	PS	PS	PM	PM
PM	PB	PB	PM	PM	PM	PM	PB
PB	PB	PB	PM	PM	PM	PB	PB

,

Table 2. $\Delta k_i$ Fuzzy rule.

$\mathit{E}$	$\mathit{E}\mathit{c}$
	NB	NM	NS	ZO	PS	PM	PB
NB	NS	NS	NM	NM	NM	NS	NS
NM	NS	NS	NM	NM	NM	NS	NS
NS	NM	NM	NS	NS	NS	NM	NM
ZO	NM	NM	NS	NS	NS	NM	NM
PS	NM	NM	NS	NS	NS	NM	NM
PM	NS	NS	NM	NM	NM	NS	NS
PB	NS	NS	NM	NM	NM	NS	NS

and

Table 3. $\Delta k_d$ Fuzzy rule.

$\mathit{E}$	$\mathit{E}\mathit{c}$
	NB	NM	NS	ZO	PS	PM	PB
NB	PB	PB	PM	PM	PM	PB	PB
NM	PB	PM	PS	PS	PS	PM	PB
NS	PM	PS	ZO	ZO	ZO	PS	PM
ZO	PM	PS	ZO	ZO	ZO	PS	PM
PS	PM	PS	ZO	ZO	ZO	PS	PM
PM	PB	PM	PS	PS	PM	PM	PB
PB	PB	PB	PM	PM	PM	PB	PB

, respectively.

For ease of engineering implementation, triangular membership functions are commonly used in practice, and the fuzzy inference process adopts the min–max inference method. The Figure 7 and Figure 8 below shows the corresponding configuration when the membership functions of both input variables $e$ and output variables $\Delta k_p$ are evenly divided into triangular shapes. The definitions and values of the input and output variables are shown in

Table 4. Fuzzy PID parameter settling.

Input/Output Variable	$\mathit{e}$	$\mathit{e}\mathit{c}$	$\mathbf{\Delta }{\mathit{k}}_{\mathit{p}}$	$\mathbf{\Delta }{\mathit{k}}_{\mathit{i}}$	$\mathbf{\Delta }{\mathit{k}}_{\mathit{d}}$
Linguistic variable	$E$	$Ec$	$\Delta k_p$	$\Delta k_i$	$\Delta k_{\mathrm{d}}$
Fuzzy domain	[−6 6]	[−6 6]	[−2 2]	[−0.5 0.5]	[−1 1]
Fuzzy subset	[NB NM NS ZO PS PM PB]

.

2.3.2. Introduction of GWO Algorithm

The Grey Wolf Optimizer (GWO) is a population-based metaheuristic algorithm that simulates the leadership hierarchy and hunting behavior of grey wolves in nature. The wolf population is typically divided into four hierarchical levels: α, β, δ, and ω, representing the best, second-best, third-best, and other potential solutions, respectively. The leader wolf α is responsible for making decisions related to encircling, hunting, and attacking prey. The subordinate wolf β assists the α wolf in managing the entire pack. The δ wolves typically consist of subgroups such as pups, sentinel wolves, elder wolves, and hunting wolves. The ω wolves represent the lowest-ranking members of the hierarchy, subordinate to the α, β, and δ wolves. The distances from the ω wolf to the α, β, and δ wolves are denoted as $\overrightarrow{D_{\alpha }}$, $\overrightarrow{D_{\beta }}$, and $\overrightarrow{D_{\delta }}$, respectively. The distances from the prey to the three wolf groups are denoted as $\overrightarrow{X_{\alpha }}$, $\overrightarrow{X_{\beta }}$, and $\overrightarrow{X_{\delta }}$. $\overrightarrow{X_1}$, $\overrightarrow{X_2}$, and $\overrightarrow{X_3}$ represent the positions that the ω wolf needs to adjust to under the influence of the α, β, and δ wolves, respectively. The hunting strategies of the α, β, and δ wolves in the pack are defined by Equations (7) and (8). The movement distance of the ω wolf is denoted as $\overrightarrow{X_{(t+1)}}$, which is jointly influenced by the α, β, and δ wolves and is calculated as their average, as shown in Equation (9). In the equations, $\overrightarrow{A}$ is the coefficient vector, and $\overrightarrow{C}$ is the random vector.

$$\left\{\begin{array}{l}\overrightarrow{D_{\alpha }}=\left|\overrightarrow{C_1}•\overrightarrow{X_{\alpha }}-\overrightarrow{X}(t)\right|\\ \overrightarrow{D_{\beta }}=\left|\overrightarrow{C_2}•\overrightarrow{X_{\beta }}-\overrightarrow{X}(t)\right|\\ \overrightarrow{D_{\delta }}=\left|\overrightarrow{C_3}•\overrightarrow{X_{\delta }}-\overrightarrow{X}(t)\right|\end{array}\right.$$

(7)

$$\left\{\begin{array}{l}\overrightarrow{X_1}=\overrightarrow{X_{\alpha }}-\overrightarrow{A_1}•\overrightarrow{D_{\alpha }}\\ \overrightarrow{X_2}=\overrightarrow{X_{\beta }}-\overrightarrow{A_2}•\overrightarrow{D_{\beta }}\\ \overrightarrow{X_3}=\overrightarrow{X_{\delta }}-\overrightarrow{A_3}•\overrightarrow{D_{\delta }}\end{array}\right.$$

(8)

$${\overrightarrow{X}}_{(t+1)}={\displaystyle \frac{\overrightarrow{X_1}+\overrightarrow{X_2}+\overrightarrow{X_3}}{3}}$$

(9)

2.4. GWO-Fuzzy PID Controller Design

Quantization factors and scaling factors are critical parameters of the Fuzzy PID controller. They not only determine the input–output relationship of the controller but also affect the system’s stability and response speed. In this study, the GWO algorithm is employed to identify the optimal quantization factors ($E$, $E_c$) and scaling factors ($\Delta k_p$, $\Delta k_i$, $\Delta k_d$) for the Fuzzy PID controller. A total of five parameters are optimized, with 50 population individuals initialized as five-dimensional vectors. The maximum number of iterations P is set to 50.

The Integrated Time and Absolute Error (ITAE) is used as the fitness function for the GWO-Fuzzy PID algorithm. ITAE reflects both the duration of the system’s transient response and the magnitude of the deviation. It is denoted as $J_{(p)}$, and its calculation formula is as follows:

$$J_{(P)}=E_{ITAE}={\displaystyle {\int }_0^{t}t\left|e(t)\right|dt}$$

(10)

To improve the optimization efficiency, an early termination mechanism is introduced in this study. A threshold $\epsilon$ is defined such that if the absolute difference in the fitness function value remains below this threshold for five consecutive iterations, the optimization process is considered to have converged with no significant improvement, and the iteration is terminated early.

$$\left|J_{(P)}-J_{(P-1)}\right|<\epsilon$$

(11)

During each iteration, the ITAE of each individual in the population is calculated to comprehensively evaluate the system’s static and dynamic performance, including response speed, accuracy, and stability. The best, second-best, third-best, and other potential solutions are then identified. A new population is generated, and the process is repeated until the maximum number of iterations is reached. Finally, the optimal solution is output. The flowchart of the algorithm is shown in the Figure 9 below.

2.5. Optimization of Grey Wolf Algorithm Based on Ternary Plot Approach

During the iterative process of the GWO algorithm, the grey wolf individuals gradually converge toward the positions of the α, β, and δ wolves. This leads to a clustering tendency within the population, making it prone to falling into local optima. In practical engineering applications, the seed-metering motor must quickly and accurately adjust its speed according to the machine’s travel velocity. If the algorithm remains trapped in a suboptimal solution after several iterations, it may result in poor optimization performance, leading to increased missed seeding rates and reduced seeding accuracy, ultimately compromising the seeding quality. To prevent the GWO algorithm from falling into local optima and consequently reducing its global search capability, this study introduces an improved search strategy. Based on the application of the GWO-Fuzzy PID controller for seed-metering motor control, enhancements are made to the algorithm’s search logic.

When searching for the optimal solution, the GWO algorithm assigns equal weights (1:1:1) to the α, β, and δ wolves, as shown in Equation (5). As a result, the grey wolf individuals converge evenly around these three positions. During local search, the equal weighting intensifies the combined guiding effect of the three leaders, thereby exacerbating the focus on local regions and increasing the risk of premature convergence.

To address this issue, this study introduces a ternary plot method to determine the optimal weight distribution of the α, β, and δ wolf populations. The corresponding weight proportions of α, β, and δ are denoted as ${\omega }_{\alpha }$ ${\omega }_{\beta }$, and ${\omega }_{\delta }$, respectively. Assuming that the combined weight equals 10, the relationship among ${\omega }_{\alpha }$ ${\omega }_{\beta }$, and ${\omega }_{\delta }$ can be expressed as shown in Equation (12):

$${\omega }_{\alpha }+{\omega }_{\beta }+{\omega }_{\delta }=10$$

(12)

Equation (5) can therefore be rewritten as follows:

$${\overrightarrow{X}}_{(t+1)}={\omega }_{\alpha }\overrightarrow{X_{\alpha }}+{\omega }_{\beta }\overrightarrow{X_{\beta }}+{\omega }_{\delta }\overrightarrow{X_{\delta }}$$

(13)

A ternary plot is introduced to partition the weight proportions among the three wolves. The three axes, each separated by an angle of 60°, red, green and blue were represent the respective proportions of the α, β, and δ wolves. Any point within the triangular region corresponds to a feasible combination of weight ratios. By drawing lines parallel to the three axes through a given point, the intersections with the axes determine the weight distribution of the α, β, and δ wolves at that point. The ternary plot Weight division range distribution was shown in Figure 10.

In the GWO-Fuzzy PID controller, the role of the GWO algorithm is to output the position of the α wolf, which corresponds to the optimal parameters of the Fuzzy PID controller. In practical engineering applications, only the optimal solution is used to adjust the seed-metering motor speed, meaning that only the optimal solution impacts the system. Therefore, when defining the range of weight values, the greater weight proportion between the β and δ wolves is constrained not to exceed that of the α wolf, thereby increasing the weight proportion of the α wolf. The specific weight allocation ranges are shown in the

Table 5. Weight division range.

Grey Wolf Type	Weight Value Range	Grey Wolf Level
$\alpha$	[3.33, 10]	Optimal solution
$\beta$	[0, 5]	Suboptimal solution
$\delta$	[0, 5]	The third most optimal solution

below.

The selected weight ratio points were substituted into the algorithm for analysis. In the simulation experiments, the target speed of the seed-metering motor was set to 60 r/min. After the speed stabilized, a disturbance was introduced to simulate the machine’s vibration during operation. Three parameters—overshoot, settling time, and disturbance-induced transient jump—were used as evaluation criteria for determining the optimal weight ratio.

2.6. Bench Test Verification and Comparison

This study conducted both a seed-metering disc speed-following test and a soybean seeding spacing accuracy test based on the algorithms before and after improvement [24,25,26]. The test bench was constructed using No. 45 steel as the main frame. An STM32F407VET6 controller manufactured in Calamba, Philippines was used to send PWM signals and receive rotational speed feedback from the encoder. The motor control module was connected to the power supply and amplified the pulse signals from the STM32 controller to control the rotational speed of the seed-metering motor. The experimental seeds used were Zhonghuang 13 soybean seeds, with a thousand-kernel weight of 250 g and a moisture content of 12%. The tests were conducted at the Institute of Agricultural Machinery Engineering Design, Hubei University of Technology. The test bench is shown in Figure 11.

3. Results

3.1. Optimization Results of Optimal Weight Ratio of Ternary Plot Approach

The selected weight ratio points were substituted into the algorithm for analysis. In the simulation experiments, the target speed of the seed-metering motor was set to 60 r/min. After the speed stabilized, a disturbance was introduced to simulate the machine’s vibration during operation.

Three parameters—overshoot, settling time, and disturbance-induced transient jump—were used as evaluation criteria for determining the optimal weight ratio. Based on the simulation results, the three-dimensional ternary plots corresponding to the three different evaluation criteria were shown in Figure 12.

As shown in the three-dimensional ternary plots, when the weight proportions of the ${\omega }_{\alpha }$ ${\omega }_{\beta }$, and ${\omega }_{\delta }$ wolves are set to 1:1:1, the overshoot, settling time, and disturbance-induced transient jump are relatively high. As the weight distribution among the wolf groups changes, the overshoot exhibits significant variation. When the proportions of ${\omega }_{\beta }$ and ${\omega }_{\delta }$ wolves are relatively high, the overshoot tends to be larger. As the proportion of α wolves increases, the overshoot shows a decreasing trend. However, when the α wolf proportion becomes excessively high, the overshoot begins to rise again. Settling time and transient spikes caused by disturbances exhibit similar variation trends. When the proportions of ${\omega }_{\alpha }$ ${\omega }_{\beta }$, and ${\omega }_{\delta }$ wolves are equal (1:1:1), both metrics show relatively poor performance. As the proportion of α wolves increases, the regulation time and disturbance-induced transients gradually decrease. However, when the α wolf weight becomes excessively high, the upward trend in these metrics is less pronounced compared to the overshoot behavior. This indicates that increasing the weight assigned to the optimal solution during the search process can improve the optimization performance, but an excessively dominant α weight may negatively affect the quality of the solution. According to simulation-based inference, the GWO-Fuzzy PID algorithm achieves the strongest search capability when the weight ratio of ${\omega }_{\alpha }$ ${\omega }_{\beta }$, and ${\omega }_{\delta }$ wolves is 8.26:1.18:0.55. For practical implementation, the total weight is normalized to 19 in this study, and the ratio is simplified to 16:2:1. Consequently, Equation (14) is formulated as follows:

$${\overrightarrow{X}}_{(t+1)}={\displaystyle \frac{16\overrightarrow{X_{\alpha }}+2\overrightarrow{X_{\beta }}+\overrightarrow{X_{\delta }}}{19}}$$

(14)

3.2. Establishment of Simulink Simulation Model

In this study, a Fuzzy PID-based seed-metering motor control system was constructed, as shown in Figure 9. The simulation environment included an Intel(R) Core i5-10200H CPU @ 2.40 GHz, 16 GB RAM, a 64-bit Windows 10 operating system, and MATLAB 2023b/Simulink as the simulation platform. The Simulink system model primarily consisted of a performance evaluation module, a fuzzy inference module, a PID controller module, and a DC seed-metering motor model. The GWO algorithm was used to optimize the inputs and outputs of the fuzzy inference module. The Simulink simulation system is shown in Figure 13.

The DC motor used in the system was directly coupled to the seed-metering unit via a shaft coupling. To determine the transfer function of the motor during the seeding process, the motor speed data collected by the encoder was imported into MATLAB for system identification. The resulting transfer function achieved a 92.8% fitting accuracy, which meets the requirements of this study. The final transfer function of the seed-metering motor control system is given as Equation (15):

$$G(s)={\displaystyle \frac{9.44s+0.11}{s^2+0.8309s+0.1121}}$$

(15)

3.3. Algorithm Step Response Comparison

In this study, five parameters of the Fuzzy PID controller were optimized using the Grey Wolf Optimizer (GWO). To better meet the practical requirements of soybean seeding, the search logic of the GWO algorithm was improved based on ternary plot analysis, yielding an optimal α:β:δ weight ratio of 16:2:1, referred to as the Optimized Grey Wolf Optimizer (OGWO). A comparative simulation was conducted among four control strategies: OGWO-Fuzzy PID, GWO-Fuzzy PID, Fuzzy PID, and conventional PID. The comparison of step response simulation results and the fitness values before and after algorithm improvement are shown in Figure 14 and Figure 15, respectively.

During seeding operations, the working speed of the seed-metering device must be adjusted in real time according to the machine’s travel speed, while external disturbances such as vibration and uneven terrain are inevitable. To simulate actual operating conditions, the Simulink simulation was set to run for a total duration of 2 s. The simulation takes time t as the independent variable, with the theoretical speed as the system input and the step response as the system output. At 1 s, white noise with an amplitude of 0.1 is introduced to evaluate the response speed and anti-disturbance capability of the four control strategies.

As shown in Figure 14 and

Table 6. Performance data of 4 control strategies.

Control Strategy	Settling Time (s)	Overshoot (%)	Steady-State Error (r)	Interference Fluctuation Peak (r/min)	Fitness
PID	0.72	26.67	0.051	64.85
Fuzzy PID	0.64	12.58	0.038	64.12
GWO-Fuzzy PID	0.39	7.17	0.021	61.34	2.4657
OGWO-Fuzzy PID	0.17	0	0.009	60.18	0.8153

, the OGWO-Fuzzy PID-based electric soybean seeding system demonstrates clear advantages over the other three control strategies in terms of overshoot, settling time, steady-state error, and disturbance-induced peak fluctuation. The settling time is only 0.17 s, and the steady-state error is only 0.009 r. The GWO algorithm reached the optimal solution at the 42nd iteration, with a fitness value of 2.4657, while the OGWO algorithm achieved the optimal solution at the 31st iteration, with a fitness value of 0.8153. The GWO-Fuzzy PID algorithm required more iterations and resulted in a higher fitness value, indicating inferior control performance compared to the OGWO-Fuzzy PID approach. Therefore, the improvement of the GWO algorithm in this study is effective, and the selection of OGWO-Fuzzy PID as the control strategy for the seed-metering motor is well justified.

3.4. Bench Test Results and Comparison

3.4.1. Seed-Metering Device Speed Response Accuracy Test and Comparison

To verify the control accuracy and response time of the improved OGWO-Fuzzy PID control strategy for the seed-metering motor, this study employed the TWR90A torque–speed sensor (Figure 3) to conduct rotational speed response tests, evaluating the speed accuracy and response time of the seed-metering disc. During the experiment, the soybean plant spacing was set to 150 mm. The response time and peak rotational speed of the seed-metering disc reaching the theoretical speed were recorded as the tractor accelerated from a standstill to 5 km/h and from 5 km/h to various higher speeds. The results were compared with those of the GWO-Fuzzy PID and Fuzzy PID control strategies. The settling time $T$ (s) to reach the target speed and the peak speed $R_{\max }$ (r/min) during the regulation process were recorded for each trial. Each set of experiments was conducted three times, and the average value was taken. The experimental results are shown in Figure 16 and

Table 7. Test data of three control strategies.

Tractor Speed Change (km/h)		0 to 5		5 to 7		5 to 9		5 to 11		5 to 13		5 to 15
		$\mathit{T}$	${\mathit{R}}_{\mathbf{max}}$	$\mathit{T}$	${\mathit{R}}_{\mathbf{max}}$	$\mathit{T}$	${\mathit{R}}_{\mathbf{max}}$	$\mathit{T}$	${\mathit{R}}_{\mathbf{max}}$	$\mathit{T}$	${\mathit{R}}_{\mathbf{max}}$	$\mathit{T}$	${\mathit{R}}_{\mathbf{max}}$
Control Strategy	OGWO-Fuzzy PID	0.18	23.2	0.13	32.5	0.19	41.9	0.23	51.2	0.27	60.7	0.35	70.1
	GWO-Fuzzy PID	0.23	23.7	0.17	33.2	0.25	45.7	0.32	51.7	0.43	61.3	0.56	71.5
	Fuzzy PID	0.32	24.5	0.21	34.1	0.33	43.2	0.39	53.6	0.51	63.2	0.67	73.8

.

As shown clearly in the experimental results, under various tractor speed change conditions in the seed-metering disc speed-following tests, the OGWO-Fuzzy PID control strategy consistently demonstrated shorter regulation times and smaller deviations between the peak rotational speed and the theoretical speed.

The tractor speed increased from 0 to 5 km/h, and the rotational speed settling time of the soybean seed-metering device under the OGWO-Fuzzy PID control strategy was only 0.18 s, representing a 43.8% improvement compared to the Fuzzy PID. Moreover, the peak speed exhibited virtually no deviation from the theoretical speed. As the variation in tractor speed increased, both the settling time and peak r speed showed corresponding increases across all three control strategies. However, even when the tractor speed increased from 5 km/h to 15 km/h, the OGWO-Fuzzy PID maintained a settling time of just 0.35 s—only 62.5% and 52.2% of the times required by the GWO-Fuzzy PID and Fuzzy PID, respectively. Throughout the experiments, its peak rotational speed remained the closest to the theoretical value among the three strategies.

3.4.2. Soybean Planting Spacing Accuracy Test and Comparison

To further verify the superiority of the improved algorithm, this study conducted soybean planting spacing accuracy tests based on both the pre- and post-improvement algorithms. The experimental setup is shown in Figure 10. During the tests, 90 consecutive plant spacing data points were collected from the conveyor belt after the electric control seed-metering system stabilized. Each set of experiments was repeated three times, and the average value was taken. The data collection site for plant spacing was shown in Figure 17

For the seeding spacing accuracy test, experiments were conducted within a target plant spacing range of 130–290 mm and an operational speed range of 8–14 km/h, based on actual field requirements for soybean planting. According to theTest Methods for Precision Seeders, when the target plant spacing is set, a spacing greater than 1.5 Z is considered to be a missed seeding, while a spacing less than 0.5 Z is considered to be a replay seeding. The qualification index, missed seeding index, replay seeding index, and plant spacing coefficient of variation were selected as evaluation metrics for comparing the seed-metering performance of the two algorithms.

The experimental results are shown in

Table 8. Comparison of seeding performance indicators between the two algorithms before and after improvement.

Control Strategy	Working Speed/ (km/h)	Qualification Index/%			Replay Index/%			Missed Seeding Index/%			Coefficient of Variation/%
		130 mm	210 mm	290 mm	130 mm	210 mm	290 mm	130 mm	210 mm	290 mm	130 mm	210 mm	290 mm
GWO-Fuzzy PID	8	90.46	92.97	95.41	4.51	5.19	3.27	5.03	1.84	1.32	12.48	10.75	7.24
	10	89.17	90.31	93.76	6.78	7.33	3.76	4.05	2.36	2.48	13.61	11.29	9.08
	12	87.61	88.45	90.43	5.71	6.08	5.13	6.68	5.47	4.44	15.73	13.06	10.34
	14	86.33	87.26	88.35	5.34	5.12	6.57	8.33	7.62	5.08	16.37	14.27	11.68
OGWO-Fuzzy PID	8	94.84	97.71	98.52	3.24	1.16	1.27	1.92	1.13	0.21	5.61	4.79	3.24
	10	93.53	96.19	97.27	2.65	2.46	2.14	3.82	1.35	0.59	6.33	5.27	4.63
	12	93.17	96.56	96.34	2.71	2.23	3.29	4.12	1.21	0.37	7.25	6.29	6.04
	14	92.81	94.89	96.27	2.37	2.49	2.16	4.82	2.62	1.57	7.96	6.73	6.56

. At the same plant spacing, as the working speed increases, the qualification index gradually decreases, while the missing sowing index and variation coefficient increase to varying degrees. When the working speed is constant, as the plant spacing increases, the qualification index increases, the overlap index gradually rises, and the missing sowing index and variation coefficient decrease to varying extents.

The average seeding qualification rate under the OGWO-Fuzzy PID control strategy reached 95.68%, representing an improvement of 5.63 percentage points compared to the GWO-Fuzzy PID strategy. At a working speed of 8 km/h and a plant spacing of 290 mm, the qualification index under OGWO-Fuzzy PID was as high as 98.5%, with a replay index of 1.27% and a missed seeding index of only 0.21%. Even at a high working speed of 14 km/h with a plant spacing of 130 mm, the qualification index remained at 92.81%, 6.48 percentage points higher than that of the GWO-Fuzzy PID strategy. The average replay and missed seeding index under OGWO-Fuzzy PID were 2.35% and 1.98%, respectively—reductions of 3.05 and 2.58 percentage points compared to GWO-Fuzzy PID, respectively. The average plant spacing coefficient of variation under the OGWO-Fuzzy PID control strategy was 5.89%, representing a reduction of 6.27 percentage points compared to the GWO-Fuzzy PID strategy. The OGWO-Fuzzy PID control strategy significantly improves seeding quality compared to the GWO-Fuzzy PID strategy.

The OGWO-Fuzzy PID-based electric-driven soybean seeding system developed in this study demonstrated excellent seeding performance across all test conditions, providing strong data support and technical assurance for the development of high-quality, medium–high-speed electric soybean seeders.

4. Discussion

The improved OGWO-Fuzzy PID control strategy proposed in this study demonstrates a shorter response time compared with the GWO-Fuzzy PID and Fuzzy PID strategies. In the simulation model based on the motor transfer function identified in this study, the improved OGWO-Fuzzy PID achieved a regulation time of only 0.17 s. It also demonstrated excellent performance in terms of overshoot and steady-state error, highlighting its superior control capability under dynamic conditions. These advantages were further validated through experimental results [27,28,29]. This indicates that in the GWO algorithm, when the α, β, and δ wolves converge with equal weighting, the optimal solution is not always achieved. By increasing the weight proportion of the α wolf to a certain extent, the GWO algorithm is able to identify better solutions during iteration. A similar approach has been applied in the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) frameworks.

The experimental results in this study show that the control performance of the OGWO-Fuzzy PID strategy is consistent with the simulation results. It demonstrates superior performance in terms of settling time and response accuracy. The differences observed between the bench and simulation results may be attributed to unstable disturbances in the transmission of the seed-metering motor and the variable friction between the seed-metering disc and seeds.

The low-voltage electric-driven seeding system developed in this study can be applied as an energy-saving electronic control system in unmanned seeders. In addition, the proposed optimization approach, which increases the weight proportion of the α wolf for the optimal solution, along with the visualization of the optimization process using a ternary plot, can provide insights for the optimization of other algorithms. Moreover, the seeding accuracy results obtained using the OGWO-Fuzzy PID control strategy can serve as valuable data support for research on other electric-driven seed-metering systems.

Although the electric-driven soybean seed-metering system based on the OGWO-Fuzzy PID control strategy demonstrated high seeding quality in bench tests, the actual field environment is more complex [30,31,32]. Future research could be carried out in the following directions: (i) consider the effects of mechanical vibrations during field operation to further improve the anti-disturbance capability of the seed-metering motor; (ii) conduct simulation analysis of the negative pressure required by the seed-metering disc, and optimize the power of the negative-pressure fan while ensuring proper seed pickup; (iii) explore independent control of multiple seed-metering motors to enable more flexible and adjustable seeding patterns, thereby further promoting the implementation of precision seeding.

5. Conclusions

This study designed an electric-driven soybean seeding system powered by the machine’s 12 V supply. The system was built around an STM32 microcontroller as the central processing unit (CPU), with a DC motor serving as the actuator, an encoder providing feedback, and integration with a human–machine interface (HMI) screen. This study introduced the Grey Wolf Optimizer (GWO) and further optimized it using a ternary plot approach. An OGWO-Fuzzy PID control strategy is proposed, featuring short regulation time, low speed error, and reliable seeding quality. High-quality seeding operations were achieved at medium-to-high operating speeds (10–14 km/h), with an average seeding qualification rate exceeding 95%, providing strong data support for field trials of electric-driven soybean seeders and offering technical assurance for the development of high-speed precision soybean seeding machines.