Design and Analysis of a High-Precision Dynamic Compensation System for Seed Dropping Position in Corn Sowing Operations

Abstract

In response to the problem of imprecise maintenance of plant spacing and row spacing during corn sowing operations in the middle of existing corn farms, this study designed a dynamic compensation system for corn seed dropping position. The test system includes a corn seeding machine operating unit, a seed dropping variable measurement and control system unit, a terminal control system, and a dynamic compensation system. The dynamic performance of the control system was tested experimentally. When undisturbed signals were loaded, regardless of whether the machine used the position compensation function or not, the coefficient of variation of longitudinal grain spacing was 2.6% ± 0.2%, and the longitudinal grain spacing was basically consistent. After adding disturbance signals, compared with not using the dynamic compensation function, the variation coefficient of seed spacing decreased by an average of 6.94% when using the dynamic compensation function. When using the dynamic compensation function, compared with not using dynamic compensation function, the variation coefficient of lateral grain spacing decreased by an average of 9.16%. Stability analysis of longitudinal and lateral grain spacing was conducted through bench tests, verifying the stability of the proposed dynamic compensation system for seed dropping position.

1. Introduction

The yield and quality of corn play a critical role in ensuring food security and promoting agricultural production [1,2,3]. The technology of precise positioning and sowing of corn involves the use of corn sowing machinery equipped with soil environment sensors, communication modules, control modules, and computer systems in accordance with modern agronomic requirements. The main purpose of this control method is to adjust plant spacing, row spacing, and seeding depth based on soil moisture and fertility factors in the agricultural production site. This is to quantitatively sow seeds in the ideal position within the soil layer. A multi-parameter control method for precise corn sowing can refer to mature cases of industrial control [4,5]. Precise positioning and sowing significantly reduces the amount of seed used and production costs, ensures the consistency of individual corn growth and development, improves the efficiency of field management, and provides technical support for attaining maximum corn yield potential, water and cost savings, and increased production efficiency [6].

The process of corn sowing is prone to being affected by factors such as unit quality, soil texture, field topography, and surface residues, which can lead to inconsistent sowing depth and poor grain spacing uniformity, thereby adversely affecting corn yield [7,8]. In order to achieve precise synchronization of the seed arranging speed of the planter and the forward movement of the planter, the Precision Planting Company in the US has developed a vDriveTM electric-drive seed arranging system. This system obtains the real-time operating speed of the planter using speed-measuring radar/GPS and adjusts the speed of the planter accordingly. Through controlling the amount of sowing, the uniformity of sowing is improved, changes in row spacing and plant spacing are reduced, and accurate positioning and sowing is achieved [9,10]. Additionally, the Exact Emerge series of air-suction seed extractors from the John Deere Company in the US has improved the traditional disc-shaped seed tray structure to increase the seed-filling power of the seeds and adapt to the high-speed seed discharge requirements during the sowing operation. To prevent seeds brought up during the rotation of the seed extractor from entering the seed guide chamber and causing replaying, the finger-clip seed extractor design of the Kinze company in the US has been optimized. The seed guide blades on the seed guide belt have also been optimized to enable different seeds to fall in the same position on the seed guide belt, thus improving planting accuracy [11,12]. Similarly, the British Stan-hay company has developed a high-precision belt-type precision seed arrangement device that replaces the traditional seed arrangement plate with a flexible seed arrangement belt to reduce the height of planting, reduce the slippage and bounce of seeds on the seed bed, and ultimately improve the quality of planting [13]. Lastly, Case IH Company in the US has designed an air-suction seed extractor with a flexible seed stirring finger wheel in the seed box that rotates synchronously with the seed tray. This, in turn, disturbs the seeds in the seed box, reduces the seed separation resistance during the seed filling process, and improves seed fluidity and seed filling performance [14].

Arzu et al.’s research revealed that the diameter, length, and inclination angle of the seed discharge pipe have a substantial impact on the uniformity of actual seed discharge. Thus, it is essential to maintain consistency in these parameters to ensure each row’s seed discharge consistency [15,16]. Zhao Shuhong et al. designed a V-shaped groove dial-wheel seed guide component consisting of a V-shaped groove seed guide tube and a flexible sowing wheel [17]. They conducted a multi-factor orthogonal rotation experiment under virtual simulation conditions, obtaining the best structural parameters for the seed arrangement component. Liu Jianying used discrete element software to simulate and analyze the height of the seed discharge tube and found that it significantly impacts seed discharge performance [18]. The optimal seed discharge performance depended on the operating speed of the machine and the height of the seed discharge tube. Yatskul conducted a study on the influence of air velocity and material flow rate on distribution accuracy. The study also investigated the effects of outlet closure, different outlet pipe lengths, seal tightness of the distributing head, and angular position of the distributing head [19]. Ikbal applied a PID controller and model-based neural network PID control system for the control of unmanned agricultural vehicles [20]. Nielsen introduced the modification of an angle sensor at the pivot point of the drill plow, providing sensor feedback to the control system. The control system delivers a constant plow depth through an electric hydraulic actuator, thereby minimizing low-frequency variations in plow depth. Through using the developed sensor system and control system, it is possible to significantly reduce low-frequency variations in plow depth [21].

In general, research by scholars focuses on optimizing the original seed arrangement device structure and adjusting the seed arrangement tube to increase sowing depth consistency and grain spacing uniformity. However, in actual sowing operations, the seed arrangement tube’s outlet position varies significantly due to external factors, reducing the sowing monomer’s placement consistency, ultimately leading to reduced corn yield.

To address the issue of imprecise plant and row spacing during corn sowing in the middle of a field, this study proposes a dynamic compensation system for corn sowing positions. The experimental setup includes a corn sowing machine operating unit, a sowing variable measurement and control system unit, a terminal control system, and the dynamic compensation system. Stability analyses of the longitudinal and transverse grain spacing were conducted through bench testing to verify the proposed system’s stability.

2. Materials and Methods

2.1. Structure of the Corn Sowing Machine Operating Unit

To achieve adaptive regulation of sowing depth by electronic and hydraulic means, various pressure sensors and hydraulic cylinders need to be installed. Partial modifications were made to the sowing unit structure of the Zhong Nong Ji (CAAMS, Beijing, China) 2BJ-470B no-tillage precision corn seeder machine. The structure of a sowing unit is shown in Figure 1, which is mainly composed of a lower pressure sensor, depth-limiting wheel, depth-limiting block, disc opener, lower hydraulic cylinder, pressing wheel, spring, spring rod, and pressing hydraulic cylinder. Since axle shaft sensors have been used multiple times to detect the sowing unit’s downward pressure, they are reliable and adaptable. Therefore, they were selected as the lower pressure sensors.

The lower pressure sensor is mounted on the installation seat of the depth-limiting block, which has a hole and is hinged on the lower pressure sensor. An appropriate gap is left between the outer wall of the depth-limiting block and the inner wall of the installation seat. The depth-limiting wheel arm applies force to the depth-limiting block, which then exerts force on the lower pressure sensor. Consequently, the lower pressure sensor measures the force between the depth-limiting wheel arm and the depth-limiting block, i.e., the downward pressure.

During system operation, the terminal sets the target measurement value for the lower pressure sensor. The lower pressure sensor and hydraulic pressure sensor transmit the measured values to the PLC controller, which calculates the difference between the actual and target values and generates a new control signal based on the control algorithm. This control signal is then sent to the corresponding valves in the hydraulic valve group, where the main oil circuit solenoid reversing valve mainly adjusts the working state of the main oil circuit, i.e., whether it is supplied directly by the hydraulic pump or by the accumulator. Meanwhile, the solenoid reversing valve and proportional pressure reducing valve of the lower pressure circuit mainly adjust the movement and outlet pressure of the lower hydraulic cylinder, thereby regulating the motion of the four-bar linkage mechanism. When all measured values are within the set threshold range, it indicates that the sowing depth has met the requirements, and the control signal remains unchanged. Thus, this system achieves closed-loop control of sowing depth indirectly via controlling the downward pressure.

2.2. Hardware Composition of Sowing Variable Measurement and Control System Unit

As shown in Figure 2, the sowing variable measurement and control system mainly consists of a drive motor, a chain wheel transmission mechanism, a control terminal, and a motor control unit. The system employs an IPCA-7010 industrial onboard computer, whose Atom motherboard based on the X86 architecture serves as the core. It integrates storage, communication, display, and input-output modules, and has good compatibility and scalability, making it easy to operate and maintain. It also meets well the various needs of multi-source data acquisition and storage, operation monitoring, information analysis, and decision-making in precision agriculture field operations. The terminal integrates a GPS positioning module, CAN bus module, and DTU unit. The GPS positioning module uses the UBlox-Neo M8 module, which supports dual-mode positioning of GPS and BeiDou systems and has a maximum positioning error of ≤2.5 m.

Traditional seeders adopt ground wheel transmission which, with the continuous increase of operating speed, causes vehicle slip to become one of the important factors affecting sowing quality. Direct-drive technology can effectively eliminate the adverse effects of mechanical transmission. The electrically driven seeding system mainly uses a DC motor to replace the mechanical transmission structure, directly driving the seeding disc, and obtains the operating speed of the seed drill through speed measuring devices such as speed radar or GPS. Based on the speed combined with the set sowing quantity information, the rotation speed of the seeding disc is adjusted in real time to achieve a rational match between sowing quantity and operating speed. Adopting an electrically driven seeding structure not only suits high-speed sowing operations well, but also further realizes independent start and stop control of each seeding unit.

A BG45 × 15SI integrated DC motor is used as the driving source for the seeding axis and fertilization axis. The motor integrates the drive circuit inside and uses a DC analog voltage signal to dynamically adjust the motor speed, with a signal range of 0–10 V. The motor output power is 52.5 W, working voltage is DC12 V, maximum speed is 3080 rpm, and it is equipped with a PLG52 planetary reducer with a reduction ratio of 50. After the control terminal makes a decision, the control instructions are sent to the seeding and fertilizing driving motors through the CAN bus, and the seeding and fertilization axes are driven to rotate.

The motor control unit adopts K8516 CAN bus analog output module, with the main technical parameters as follows: 4 channels of output signals, 12-bit DA resolution, output signal range of 0–10 V, and DC9–24 V power supply voltage. It communicates with the control terminal through the CAN bus interface.

2.3. Software Design for Terminal Control System

The software design of the terminal control system needs to achieve functions such as real-time monitoring of the seeder’s operating status, adjusting the seeding disc speed, receiving control commands, providing independent start-stop control of seeding units, performing fault diagnosis and handling, etc., and also support user-customized interface and operation shortcut keys. During the development process, object-oriented programming should be adopted with modular design to reduce coupling and improve code maintainability and extensibility. Strict testing and acceptance should be performed to ensure software correctness and practicality, to ensure the stable operation of the system. The IEI Ikarp vehicle-mounted computer is used as the control terminal, and Labwindows CVI2012 software environment is used for the development of the control terminal software, with Windows7 operating system as the system software platform. The interface is shown in Figure 3. The software program adopts modular and structured design principles, mainly including a data acquisition module, a data storage and display module, and a parameter configuration module, mainly realizing functions such as collecting and displaying seeder operating status information, real-time acquisition of GPS positioning information, seeding and fertilization variable control, and real-time storage and processing of system data.

2.3.1. Data Acquisition and Control

Data acquisition and control is a key component of the corn planter variable measurement and control system software, which mainly completes the collection of various data information such as seeding quality, seed-fertilizer box level, GPS information, etc. The communication methods between external devices and the interactive terminal of the system mainly include RS232 serial communication and CAN bus communication. Seeding quality information and vehicle traveling speed information are communicated with the interactive terminal via the CAN bus. Based on the loaded prescription information, current GPS information, and implement parameters, the measurement and control system determines the desired seeding and fertilization speed and sends it to the control unit via the CAN bus to control the seeding and fertilizing motors to operate at the desired speed.

2.3.2. Data Storage and Display

The monitoring system acquisition software displays GPS location information, seeding depth, and vehicle operating parameters (traveling speed, seed-fertilizer box level status, etc.) on the interface, and the system software also saves all data for subsequent analysis and processing.

2.3.3. Parameter Setting

Parameter setting mainly completes the configuration of seeding and fertilization working parameters and implement parameter settings to ensure normal equipment operation. The target downforce can be set, and the actual measured value is displayed through the control terminal. The set target value is sent to the PLC controller via the CAN bus interface. The main interface of the designed monitoring system is shown in Figure 3.

2.4. Dynamic Compensation System Design

The dynamic compensation system mainly consists of a six-degree-of-freedom platform, a seeding unit, a dynamic compensation mechanism, a dual-axis inclination sensor, and an industrial control computer. The industrial control computer loads excitation signals to the six-degree-of-freedom platform through RS232 to simulate the pose changes of the seeding unit during field operations. The dual-axis inclination sensor collects the inclination data of the seeding unit and transmits it to the industrial control computer via the CAN bus for analysis. The dynamic compensation mechanism is installed on the seeding unit, and the dynamic compensation is completed via a servo electric push rod and a linear displacement sensor. The compensation range of the system is −5~5 cm, and the compensation speed is 800 mm/s.

When the six-degree-of-freedom motion platform receives the loaded simulation signal through RS232, it simulates the field working state according to the pre-set waveform. The seeding tube falls from its ideal seeding position, and the dual-axis inclination sensor detects the changes of the x and y-axis angles of the seeding unit, which are transmitted to the control system via the CAN bus. The control system uses fuzzy PID control to drive the servo electric push rod to compensate for the deviation of the seeding tube. The displacement sensor detects whether the seeding tube has reached the predetermined position until the dynamic compensation of the seeding position is completed.

The seeding position compensation system is mainly realized using two servo electric push rods for offset compensation. The servo electric push rod is driven by a servo motor (Panasonic, model MSMF0121V2M, rated speed 3000 r/min, rated power 100 W). The dynamic equation and the electrical equation that the whole system satisfies during the movement are:

$$\left\{\begin{array}{l}U=E+I{R}_1+L_1\frac{\mathrm{d}I}{\mathrm{d}t}=K_1\frac{\mathrm{d}{\theta }_1}{\mathrm{d}t}+I{R}_1+L_1\frac{\mathrm{d}I}{\mathrm{d}t}\\ T=K_{2}I\\ T=T_1+T_2+T_3+T_4\\ T_1=J_1\frac{\mathrm{d}\omega }{\mathrm{d}t}\\ T_2=b_1\omega =K\left({\theta }_2-{\theta }_1\right)\\ T_3=J_2\frac{\mathrm{d}\omega }{\mathrm{d}t}\\ T_4=b_2\omega =K\left({\theta }_3-{\theta }_2\right)\\ Z={\theta }_{3}R\end{array}\right.$$

(1)

where U is the armature voltage, V; I is the armature current, A; L₁ is the armature inductance, H; R₁ is the armature resistance, Ω; Z is the displacement of the pushrod, cm; J₁ is the rotor inertia of the motor, kg·m²; J₂ is the rotational inertia of the screw, kg·m²; θ₁ is the angle of rotation of the motor, rad; θ₂ is the angle of rotation of the motor driving the rotor shaft, rad; θ₃ is the angle of rotation of the screw, rad; T₄ is the output torque of the screw, N·m; T is the electromagnetic torque, N·m; T₁ is the inertia torque of the motor, N·m; T₂ is the friction torque of the motor, N·m; T₃ is the inertia torque of the load, N·m; T₄ is the frictional torque of the load, N·m; b₁ is the coefficient of viscous friction of the motor, N·m·s/rad; b₂ is the coefficient of viscous friction of the load, N·m·s/rad; E is the motor counter-electromotive force, V; K₂ is the motor torque coefficient, N·m/A; K₁ is the motor counter-electromotive force coefficient, V·s/rad; K is the stiffness coefficient of the drive shaft, N·m/rad; ω is the motor angular velocity, rad/s; R is the unit angle telescopic displacement of the actuator, mm/rad. When $\theta ={\theta }_1={\theta }_2$ and $J=J_1+J_2$, then the transfer function from the armature voltage to the output displacement is:

$$\frac{Z(s)}{U(s)}=\frac{K_{2}R}{(L_{1}s+R_1)Js+K_2{K}_1}$$

(2)

The relevant parameters of the motor used in this test are: R₁ = 1.2, R = 1, L₁ = 0.001, K₂ = 0.6, K₁ = 0.029, and J = 0.01. Substituting into Equation (2), the transfer function of the motor is:

$$G(s)=\frac{Z(s)}{U(s)}=\frac{60\text{,}000}{s^2+1200s+1740}$$

(3)

To verify the effectiveness of the proposed fuzzy PID control method, a white noise random signal was used as the road input, and its expression is shown in Equation (4):

$$u(t)=K_p\left[e(t)+\frac{1}{T_i}{\displaystyle {\int }_0^{t}e(t)\mathrm{d}t}+T_d\frac{\mathrm{d}e(t)}{\mathrm{d}t}\right]$$

(4)

where $e(t)$ represents the control deviation; $K_P$ is the proportional coefficient; $T_i$ is the integral time constant; $T_d$ is the derivative time constant. Through using fuzzy control rules to modify the PID parameters, correction coefficients were obtained through continuous testing: $K_P=35$, $T_i=3$, $T_d=15$.

To verify the effectiveness of the proposed adaptive fuzzy PID control method, a C-class road (with a speed of 20 m/s) was selected, and a white noise random signal was used as the road input, with its expression shown in Equation (5).

$$\mathsf{\Phi }(t)=2\pi n_0\sqrt{G_q(n_0)v}{\displaystyle \int w(t)\mathrm{d}t}$$

(5)

where $n_0$ is the reference spatial frequency, which is set to 0.1 m⁻¹; $G_q{n}_0$I s the road roughness coefficient; and the vehicle’s speed is v = 20 m/s, with $w(t)$ being white noise with a mean of 0.

In the control model, sinusoidal and square wave signals are input separately to analyze the tracking characteristics of the fuzzy PID control system for DC servo motors, as shown in Figure 4. It can be seen from the figure that the control accuracy of the seeding position dynamic compensation system of the fuzzy PID controller is 99.5%, with a response speed of 17.2 mm/s, and the system is stable and meets the design requirements. The output-input relationship of the motor Simulink model can be represented using this transfer function and will be used as the object for subsequent fuzzy PID controller calibration. Based on the determined transfer function, a fuzzy PID controller is designed and a servo motor fuzzy PID simulation model is established, as shown in Figure 4.

2.5. Seeding Spacing Test Method

To analyze the stability of the dynamic compensation system for seeding position, stability tests were conducted on longitudinal and transverse grain spacing. For this experiment, the maize seed variety used was Zhengdan 958, and the seed spacing was set at 30 cm. The conveyor speed was set to 1.67 m/s to maintain consistent flow, and the time interval between the falling of two seeds was no less than 400 ms. The seeding spacing test method involved using a measuring tape to determine the distance between the centers of two adjacent seeds, both longitudinally and transversely. The collected data were then analyzed, with the mean value, standard deviation, and coefficient of variation calculated to quantify the stability of the seeding spacing. This process was repeated multiple times to obtain sufficient data for analysis. Additionally, the falling interval time of the two seeds is

$$t=\frac{d}{v}$$

(6)

where t is the falling interval time of 2 seeds, s; d is seeding grain distance, m; v is the running speed of conveyor belt, m/s. According to Formula (6), the falling interval time of 2 seeds is 0.18 s, and the seed metering device rotation speed is

$$r=\frac{60}{nt}$$

(7)

where r is the rotation speed of the seed metering device, r/min; n is the seed holes of the seed metering device; t is the time between the falling of 2 seeds, s.

According to Formula (7), the speed of the seed metering device is 30 r/min. In field conditions, corn planters experience vibration due to uneven ground during the seeding process. To simulate such conditions, researchers conducted experiments using a six-degree-of-freedom platform and applied three different disturbance signals: a sine wave disturbance signal with an amplitude of 10 mm, a random disturbance signal with an amplitude of 6.2 mm, and a combination of random disturbance signal and sine wave disturbance signal with an amplitude of 15.7 mm.

To prevent corn seeds from bouncing, the conveyor belt was sprayed with high-viscosity hydraulic oil before starting the conveyor and seed metering device. For each experiment, the disturbance signals were applied, and the setup was repeated three times.

In the experiment, as illustrated in Figure 5a, eleven consecutive corn seeds were selected after each sowing test, and the distance between adjacent seeds was measured to obtain ten measured values of corn seeding distance. Four sets of longitudinal grain distance deviation data were obtained under different conditions: no disturbance signal, random disturbance signal, sine wave disturbance signal, and random disturbance signal plus sine wave disturbance signal.

Additionally, Figure 5b shows that a laser transmitter was used to emit a reference line along the first and 11th seeds of each test, and the distance from the second seed to the reference line was measured continuously for 10 seeds. Three sets of transverse grain distance data were obtained under different disturbance signals: random disturbance signal, sine wave disturbance signal, and random disturbance signal plus sine wave disturbance signal.

3. Results and Discussion

3.1. Analysis of Longitudinal Grain Spacing Stability Test Results

The results and analysis of the longitudinal grain spacing stability test are presented in Figure 6. When no disturbance signal is applied, regardless of whether the machine utilizes the position compensation function, the coefficient of variation of the longitudinal grain spacing remains consistent at 2.6% ± 0.2%, suggesting that the longitudinal grain spacing is relatively stable.

However, when a disturbance signal is applied, there is a noticeable increase in the coefficient of variation of the grain distance, and the longitudinal grain distance divergence also increases. It was found that the use of the dynamic compensation function significantly reduces the variation coefficient of seed spacing by an average of 6.94% compared to when it is not utilized, which meets the requirement of JB/T 10293-2013 “Technical Conditions of Single Seed (Precision) Seeder” that states the variation coefficient of seed spacing should be less than or equal to 30%. Furthermore, the seed spacing falls within the range of 0.5 to 1.5 times the theoretical grain spacing, meeting the requirements of the NY/T 1628-2008 “working quality of no-tillage corn planter” and ensuring good sowing plant spacing.

3.2. Stability Test Results and Analysis of Sowing Transverse Grain Spacing

Figure 7 depicts that as the disturbance signal increases, the lateral sowing grain distance tends to increase along with an increase in dispersion degree regardless of whether the machine uses the position compensation function. However, when the dynamic compensation function is implemented, the coefficient of variation of the sowing transverse grain spacing is reduced by an average of 9.16% compared to when it is not used. Under the three working conditions, NY/T 1628-2008 “Operation Quality of Corn No-Tillage Planter” requires that the maximum deviation of the row spacing within the same sowing range should not exceed 4 cm, meaning that the transverse grain spacing of sowing seeds should all fall within a range of 2 cm, while the row spacing deviation will not exceed 4 cm when more than two rows are used. The use of the dynamic compensation function satisfies these requirements.

During testing, the servo electric push rod’s range was limited to 100 mm, resulting in a large overall structure with installation limitations. However, in real-world field environments, the offset of the seed discharge pipe outlet is often less than 100 mm. Thus, reducing the measuring range of the servo electric push rod can result in a smaller overall structure that can be installed on seeding machines to achieve multi-row sowing.

While adding a device to the existing sowing machine will inevitably increase costs, it can maintain optimal plant and row spacing, thereby providing better growth conditions for corn seeds and increasing production. In further research and planning, a balance between economic and social benefits and cost should be sought.

4. Conclusions

When uncorrupted signals are loaded, the coefficient of variation of the longitudinal grain spacing is 2.6% ± 0.2%, indicating essentially uniform longitudinal grain spacing, regardless of whether the machine uses a position compensation function or not. However, when interference signals are added, the coefficient of variation in grain spacing increases, along with deviations in longitudinal grain spacing, whether the machine uses position compensation or not. With the dynamic compensation function, the variation coefficient in seeding distance decreases by an average of 6.94% as compared to not using it, after adding interference signals.

As the strength of the interference signal increases, the lateral grain spacing will increase, and its dispersion will also increase, regardless of whether the machine uses a position compensation function or not. When the dynamic compensation function is used, the coefficient of variation of lateral grain spacing decreases by an average of 9.16% compared to not using it. Through bench testing, the stability of the longitudinal and lateral planting distances was analyzed, thereby verifying the stability of the proposed planting position dynamic compensation system.

Overall, the successfully designed and implemented dynamic compensation system allows for real-time adjustment of planting positions based on feedback from the sowing variable measurement and control system. This system improves the precision and uniformity of individual plant placement and row spacing during corn sowing operations, thereby promoting optimal crop growth and yield. In summary, this study presents an effective solution to the issue of inconsistent plant and row spacing during corn sowing in the middle of a field. In the future, adding a set of equipment to existing seeding machines will certainly increase costs when applying this seeding technology. Therefore, in further research and planning, it is necessary to find a balance point among economic benefits, social benefits, and costs.