Edited by: Junfeng Gao, University of Lincoln, United Kingdom
Reviewed by: Lu Liu, Anhui Agricultural University, China; Han Tang, Northeast Agricultural University, China
*Correspondence: Zhijun Meng,
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To improve the mechanization level of rice planting, a new type of direct seeding device for rice was designed. The device's structural properties will be crucial in determining its seeding performance. Structure optimization in the current seed metering device design process focuses on a single or few indexes, resulting in improved individual performance but imbalanced overall performance. Therefore, a structure optimization method of the direct seeding device based on a multiindex orthogonal experiment was proposed in this study.
First, the DEMMBD coupling method observed the factors and levels that affected the performance overall. Second, a test platform based on the electric drive control model was constructed, and a multiindex orthogonal test was devised. Finally, the structural parameters of the seed metering devices were optimized based on matrix analysis.
From the results, the primary and secondary levels of significance of factors were just as follows: hole diameter > hole number > adjustment angle. The following are the optimal parameters found by optimization analysis: the diameter of the hole was 12 mm, the number of holes was 10, and the adjustment angle was 80°. Validation tests were carried out and analyzed based on the optimal structural parameter combination. The qualification rate of seeds per hole, empty hole rate, average seed number, coefficient of variation of seed number, average hole spacing, and the variance coefficient of hole spacing are 93.07%, 0%, 9.39,14.04%, 22.84 cm, and 9.14%, respectively.
In comparison to traditional design and structural parameter optimization methods for rice precision seed metering device, this study not just to provides an optimization scheme for improving the overall performance of rice precision seed metering device, but also serves as a technical reference for the development and design of new rice precision seed metering device.
Rice is the second largest food crop in the world, and about 50% of the world’s population lives on rice as a staple food (
The seedmetering device is an essential component for planting (
Scholars have studied rice direct seed metering devices, but most optimized the structure based on a single or a few indexes.
To conclude, the available study uses less than three indices to measure the performance of the seed metering device in the existing research (
The structural parameters of the seed metering devices will influence their working quality, which in turn will influence the emergence and yield of rice seeds (
The seed metering device designed in this study is shown in
Diagram of structure and working principle of seedmetering device.
The sowing rate adjustment mechanism should be adjusted to the theoretical sowing rate position before the seed metering device may function. Rice seeds complete the seedfilling process by virtue of gravity, seed pressure, and the rotation of the seed metering wheel. The seeds with an irregular posture that fall into the hole are cleaned by the brush and subsequently enter the retaining area, which is point E in
The seed metering device designed in this paper adopts the form of a gradual change in hole depth to realize sowing rate adjustment and adopts double tracks to realize the dynamic adjustment of the sowing rate. The adjustment method has the benefits of being easy to modify and having high accuracy. At the same time, the forced seeding mechanism effectively overcomes the issue of rice seed hole creation under card seed and multicast rates. Compared to existing rice direct seeding devices, this device has several advantages, including continuous adjustment of the sowing rate, a simple adjustment process, a more comprehensive adjustment range, and higher precision.
According to the results of related research (
Where
For the holetype rice direct seed metering device, the probability of two seeds lying flat and one seed lying sideways entering the hole is the highest when the seeds are filled. However, the increasing hole diameter resulted in a difference in seedfilling number in every layer (
This study conducted singlefactor experiments based on the cosimulation approach to examine the impact of the structure parameters of the seed metering device on the performance index and identify the particular value range of the structure parameters. The effect of hole diameter, number, and adjustment angle on seed separator performance was investigated, and the range of variables was selected for orthogonal testing.
The seed metering device’s single index test can only represent the main and secondary order of the structural characteristics, as well as the level’s importance (
Schedule of multiindex orthogonal test.
Experimental number  Factors  






1  1  1  1  1 
2  1  2  2  2 
3  1  3  3  3 
4  2  1  2  3 
5  2  2  3  1 
6  2  3  1  2 
7  3  1  3  2 
8  3  2  1  3 
9  3  3  2  1 
F_{a}, F_{b}, F_{c}, and F_{d} are different factors.
Optimization of structural parameters based on matrix analysis
The matrix analysis approach was utilized in this work to further evaluate and deal with the orthogonal test findings in order to thoroughly improve the structural parameters of the seed metering device under different indices. The method can solve the unreasonable configuration and selection of structural parameters of the seed metering devices under multiple indexes (
Secondly, we defined the factor layer matrix of the structural parameters of the seed metering device. When T_{i} equals
Finally, we defined the horizontal layer matrix of structural parameters of the seed metering device. The range of factor
To sum up, the weight matrix γ of defining the structural parameters of the seed metering device is the product of the above three matrices, so the weight matrix γ is shown in equation (5).
The specific steps for multiindex structure parameter optimization based on matrix analysis is shown in
Parameter optimization flowchart.
Longken 58 rice seeds from Northeast China were selected as experimental materials. The mean values of the long axis, short axis, thickness, 1000grain weight, moisture content, and sphericity of rice seeds were 6.98 mm, 3.29 mm, 2.35 mm, 24.84 g, 23.63%, and 54.14%, respectively. The rice seeds were washed and prepped before the experiment so that they could meet the direct planting criterion. According to the results of related research (
To ensure the accuracy of the test results and the simplicity of seeing the assessment index, the Discrete element method (DEM) and multibody dynamics (MBD) software were used to build a simulation platform in this paper (
The design processes are as follows, based on the DEMMBD coupling simulation test: (1) the number of fixed holes is determined to be 12, the adjustment angle is determined to be 80, and the hole diameters are changed to 10, 12, 14 and 16 mm, respectively, and the simulation analysis were carried out; (2) Take the result of higher qualified rate in the first step as the typical value of analyzing the diameter of the hole. Based on typical values of fixed hole diameters and adjustment angles, the variables for the number of holes are set to 8, 10, 12, and 14, respectively. (3) The higher qualified rate and lower coefficient of variation in the first and second steps are taken as typical values for analyzing fixed adjustment angles. Based on the typical values of the fixed hole diameter and the number of holes, the experiments were carried out by changing the adjustment angles to 50, 60, 70, and 80°, respectively. According to the relevant research, results of the pretest and rice varieties used in the test, when the diameter of the hole is 10, 12, 14, and 16, the number of seeds per hole is 5 ~ 7, 8 ~ 10, 15 ~ 17 and 22 ~ 24, respectively, which are regarded as qualified. The number of seeds per hole discharged by the seed metering device should be recorded, and every 250 holes should be divided into one group. Each group should be repeated three times, and the average value should be taken. The design of the test factor level is shown in
Levels of test factors.
Levels  Factors  

Diameter of hole/mm  Number of holes  Adjust the angle/°  
1  10  8  50 
2  12  10  60 
3  14  12  70 
4  16  14  80 
The 3D model of the rice direct seed metering device was constructed by Solidworks 3D software, and the assembly model after the simplified structure was imported into RecurDyn, as shown in
Model of the SeedMetering Device in RecurDyn. 1. Seedmetering device; 2. Simulated ground.
The discrete element model required for simulation is shown in
Discrete Element Model of Simulation.
HertzMindlin’s nonslip model was used as the particle contact model in this research. The shell of the seed metering device and forced seeding mechanism were ABS injection molded parts, and the material of the brush was plastic. According to the relevant research results (
Parameters of discrete element simulation.
Parameters  Materials  

Rice seeds  ABS  Brush  
Poisson’s ratio  0.3  0.34  0.4 
Shear modulus/Pa  1.82×10^{8}  3×10^{9}  1×10^{8} 
Density/(kg.cm^{3})  1239  1250  1150 
Recovery coefficient(With rice seed particles)  0.30  0.32  0.45 
Static friction coefficient(With rice seed particles)  0.56  0.46  0.61 
Coefficient of rolling friction(With rice seed particles)  0.01  0.01  0.02 
In the simulation, the EDEM particle plant was set to generate 5000 seeds at a rate of 10000 seeds/s, and the total time to generate seed particles was set to 0.5 s. To maintain simulation continuity, a fixed time step of 1×10^{6} s was established, which is about 25% of the Rayleith time step. To increase simulation efficiency, the entire simulation duration was set to 11s, with the rice seed generation time being less than 0.5 s.
In the bench experiment, the motor drive was utilized to precisely adjust the working speed, simulating field sowing (
Where v is the running speed of the planter in km/h; T is the working time in s; R_{s} is the rotational speed of the seed metering device in r/min; R_{m} is the motor speed in r/min; n_{s} is the number of molded holes; d is the hole spacing in m; i is motor deceleration group.
Since the model is constructed at the same time, Equation (7) may be found by sorting the formula (6).
According to equation (7), when the rotational and working speeds are constant, the hole spacing is only related to the number of holes. In other words, when the number of holes is 8, 10, 12 and 14, the theoretical hole spacing is 27.78, 22.22, 18.52, and 15.87 cm, respectively.
In this paper, the sowing test platform was set up as shown in
Test device and schematic diagram.
The optical fiber sensor was installed under the seed tube to detect the time between seeds in each hole. A fiber amplifier was used to amplify the sensor’s signal and convert the optical signal into the electrical signal. The data acquisition system was used to collect the sensor’s output data, transmit the data to the computer terminal through the communication interface, and then obtain the corresponding evaluation index after postprocessing. The sensor detection principle is shown in
As the sensor collects excessive data in each group of tests, the data processing procedure will be briefly described by considering the original data of one hole distance collected by the sensor, as shown in
Original sensor data (One hole spacing).
Number  Record time  Level state  Intermediate time  Time between holes  Hole spacing/m 

1  18:32:33.911 (t_{1})  Low  18:32:33.928 (T_{1})  0.204  0.227 
2  18:32:33.944 (t_{2})  High  
3  18:32:34.121 (t_{3})  Low  18:32:34.132 (T_{2})  
4  18:32:34.143 (t_{4})  High 
The influence of hole diameter, number, and adjusting angle on the performance of the seed metering device was evaluated by a single factor in section 2.2.1, and the analysis results may determine the value range of factors for the orthogonal test. The following are the particular research and analysis.
Different hole diameters lead to a different number of seeds filled in each layer, affecting the seed metering device’s performance index. The analysis of specific influence degrees is as follows. According to the results of single factor analysis, the hole diameter range can be accurately determined, and the factor level can be determined for the orthogonal test. The influence of different hole diameters on the performance of the seed metering device is shown in
Effect of different hole diameters on the seedmetering device performance.
As shown in
When the seed metering device’s rotational speed is constant, the performance of the seed metering device can be improved by changing the number of holes. The specific influence of the number of holes on the performance of the seed metering device is as follows. From the analysis results, we can determine the factor level value of the orthogonal test, and the influence of different hole numbers on the performance of the seed metering device is shown in
Influence of different holes number on the seedmetering device performance.
The results show that with the increase of the number of holes, the qualification rate of seeds per hole y_{1} first increases and then decreases, the coefficient of variation of seed number y_{4} first decreases and then increases, the coefficient of variation of hole spacing y_{6} first decreases and then increases, the average seeds per hole y_{3} and the average hole spacing y_{5} gradually decrease, and the empty hole rate y_{2} is zero. When the number of holes was 10, the qualification rate of seeds per hole was 94.11%, and the coefficient of variation of seed number was 17.95%. When the number of holes was 12, the qualification rate of seeds per hole was 88.23%, slightly lower than that when the number of holes was 10 (94.11%). However, the coefficient of variation of seed number (16.21%) is better than that when the number of holes was 10. According to section 3.1.1, the number of holes with the higher qualification rate and lower coefficient of variation of seed number was regarded as the typical value of subsequent singlefactor analysis. Therefore, in the subsequent singlefactor test, the number of holes was determined to be 12. When the number of holes was 8, the qualification rate of seeds per hole was 82.35% (the lowest). For precision direct seeding technology of rice, the qualification rate of seeds per hole can directly reflect whether the seed metering device meets the requirements of precision direct seeding. Therefore, the number of holes 10, 12, and 14 was selected as the orthogonal test factor level in the subsequent test.
Different adjustment angles indicate different prefilling seed areas. Too small prefilling seed areas can affect the filling seeds of the seed metering device, and then affect the sowing performance of the seed metering device. The specific effects are explained as follows. The influence of different adjustment angles on the performance of the seed metering device is shown in
Influence of different adjustment angles on the seedmetering device performance.
Through the analysis of
According to Section 2.2, it is necessary to carry out the orthogonal test based on the singlefactor test results after completing it. The orthogonal test results of structural parameter optimization of the seed metering device are shown in
Results of orthogonal test.
Number  Structural parameters  Evaluation index  

A  B  C  D  y_{1}/(%)  y_{2}/(%)  y_{3}/(grain)  y_{4}/(%)  y_{5}/(cm)  y_{6}/(%)  
1  1 (12)  1 (10)  1 (60°)  1  80.73±1.24  0.00±0.00  8.95±0.56  18.88±2.32  21.63±0.13  15.79±2.37 
2  1 (12)  2 (12)  2 (70°)  2  81.41±1.12  0.13±0.00  8.97±0.71  17.47±2.01  18.23±0.17  12.37±1.97 
3  1 (12)  3 (14)  3 (80°)  3  85.61±1.36  0.00±0.00  9.18±0.62  14.84±2.67  15.87±0.15  12.98±2.08 
4  2 (14)  2 (12)  3 (80°)  1  94.47±1.78  0.00±0.00  16.09±0.87  14.93±2.07  18.67±0.23  10.62±1.86 
5  2 (14)  3 (14)  1 (60°)  2  85.07±1.23  0.37±0.00  15.82±1.04  23.12±1.98  16.21±0.14  17.23±2.46 
6  2 (14)  1 (10)  2 (70°)  3  91.27±1.38  0.00±0.00  15.91±0.82  17.91±1.56  21.98±0.47  8.28±2.12 
7  3 (16)  3 (14)  2 (70°)  1  82.73±1.35  0.00±0.00  22.16±1.33  23.81±1.98  15.99±0.36  17.01±2.52 
8  3 (16)  1 (10)  3 (80°)  2  84.73±1.42  0.00±0.00  22.23±1.51  19.35±1.56  21.55±0.24  10.04±2.22 
9  3 (16)  2 (12)  1 (60°)  3  80.07±1.47  0.00±0.00  22.12±1.63  28.14±2.78  18.44±0.36  22.74±2.55 
The range analysis of the orthogonal test results of the seedmetering device is shown in
Range analysis of single index.
Similarly, the optimum level combinations of the empty hole rate, coefficient of variation of seed number, and hole spacing were A_{2}B_{1}C_{3}, A_{1}B_{1}C_{3,} and A_{2}B_{1}C_{3}, respectively. The average seeds per hole and hole spacing should be selected under the premise of a small coefficient of variation. The optimal level combination of average seeds per hole is A_{1}B_{1}C_{3}, and the optimal level combination of average hole spacing is A_{2}B_{1}C_{3}.
The variance analysis of the orthogonal test results of the seed metering device is shown in
Variance analysis of single index.
The matrix analysis method was used to optimize the structure parameters of the seed metering device, which solves the unreasonable problem of multifactor optimization under multiindex. According to the theoretical analysis in section 2.3.3, the weight matrix of each evaluation index of the seed metering device can be calculated as shown in equations (8)–(13).
The index of the qualification rate of seeds per hole should be maximized to ensure the best overall performance of the seed metering device. In contrast, the empty hole rate, the coefficients of variation of the seed number, and the hole spacing should be as low as possible. Combined with the structural parameters and experimental conditions, the calculated theoretical seeds per hole and hole spacing all meet the agronomic requirements. Therefore, the average seeds per hole and the hole spacing should be the parameters when the corresponding coefficient of variation is the smallest. That is, the average seeds per hole and the hole spacing should be the smallest. In summary, if the total weight matrix of multiindex evaluation is the average of a single index matrix, then there is a total weight matrix, as shown in Equation (14).
We can observe from the total weight matrix that A_{1} is the highest weight of factor A, indicating that the first level of hole diameter in the structural parameter of the seed metering device has the most effect on the multiindex test results. Similarly, the first level in factor B has the most significant influence on the results of the multiindex test, and the third level in factor C has the most significant influence on the results of the multiindex test. Therefore, the optimal factor level combination of structural parameters of the seedmetering device is A_{1}B_{1}C_{3}. The hole diameter, number of holes, and adjustment angle was 12 mm, 10, and 80°, respectively. The primary and secondary order of structural parameter effect on the complete assessment index is A > B > C.
The verification experiment occurred under identical test conditions using the ideal combination of structural characteristics, the experiment was repeated three times, as well as the average value was taken. The experimental results are shown in
Results of optimal combination test. y_{1} is the qualification rate of seeds per hole; y_{2} is the empty hole rate; y_{3} is the average seeds per hole; y_{4} is the coefficient of variation of seed number; y_{5} is the average hole spacing; y_{6} is the coefficient of variation of the hole spacing.
The connection between the evaluation index and each component was analyzed to determine the variables that impact the performance index of the seed metering device. To begin, the singlefactor test serves to establish the value range of each structural parameter. The orthogonal test would then be run to determine the best combination of structural parameters. Finally, the validity of the modified structural parameters was tested, and the seed metering mechanism was fully evaluated. Following is a discussion of the main test results of a comprehensive analysis:
1) The diameter of the hole affects the performance of the seed metering device (
2) As shown in the range analysis (
3) As shown by the analysis of variance (
4) Although if the qualification rate of seeds per hole is lower than that of the fourth group, the coefficient of variation of seed number and hole spacing is better, according to the findings of the verification test. The performance of a seed metering device is often determined by multiple indexes, and a single index does not always indicate superior performance. Thus, this also demonstrates the necessity and significance of this research.
5) The maximum number of seeds per hole under the optimal parameter combination was 11, signifying that the seed metering mechanism described in this research can plant 11 seeds with a sphericity of 54.14% per hole. According to the agronomic requirements of rice direct seeding, the seed metering device designed in this study can achieve an accurate adjustment of 37~92 kg/hm^{2}, and the adjustment range is superior to the rice precision seed metering device now on the market.
6) This experiment cannot accurately represent the adaptability of the seed metering device, thus many other types of spherical rice seeds will be chosen to explore adaptation further forward. Moreover, the coupling simulation method utilized in this experiment may expedite the development cycle for seed metering device research. Simultaneously, the multifactor and multiindex optimization approach that this study presents will be helpful to future researchers studying other relevant parameter optimization.
7) The existing research methods mainly have three evaluation indexes, while this study selects six evaluation indexes. The more evaluation indexes, the more complex the optimization process becomes. The results obtained using this research method are superior to the existing relevant studies. This reflects the reliability of this research and highlights the necessity and importance of this research. The research methods and ideas are not limited to the rice direct seeding device, but can also be applied to optimizing parameters of other devices. Using this method has a significant effect on improving device performance and can improve intelligent plant protection.
Aiming at the problem of an unreasonable configuration of structural parameters optimization method of rice precision direct seeding metering device, a structural optimization method of rice direct seeding apparatus based on the multiindex orthogonal experiment was proposed in this paper. Firstly, the influencing factors and levels of the overall performance of the rice direct seeding device were assessed using the coupling simulation analysis method. Second, a test platform was constructed using the electric drive control model, and orthogonal tests based on numerous indices were created. Finally, a multiindex comprehensive optimization theoretical model based on the matrix analysis method was established, through which the optimal structural parameters of the seed metering device were determined, and the accuracy and qualitativeness of the optimization method of the seed metering device structure based on the multiindex orthogonal test was verified. The following are the main conclusions of this study.
1) On the basis of the multiindex orthogonal test, a thorough optimization method for the structural parameters of the seed metering device was proposed. Experiments have confirmed the suggested method. The method combines theory with cosimulation, designs the test platform using the electric drive exact control theory model, and performs the test while avoiding precise control of the speed of the seeding device in the real operation by depending on the mechanical drive.
2) The singlefactor simulation test demonstrates that the seed metering device construction parameters have an effect on the performance evaluation index, and the orthogonal test parameter range was identified. From the results of a multiindex orthogonal test, the hole diameter has the most effect on the complete assessment index, while the adjustment angle has the lowest impact.
3) Following the multiindex comprehensive optimization method, the optimal factor level combination of the seed metering device’s structural parameters was A_{1}B_{1}C_{3}. In other words, the hole’s diameter was 12 mm, the number of holes was 10, and the adjustment angle was 80°. The verification tests showed that the qualification rate of seeds per hole, empty hole rate, average seed number, coefficient of variation of seed number, average hole spacing, and variance coefficient of hole spacing are 93.07%, 0%, 9.39,14.04%, 22.84 cm, and 9.14%, respectively, under the optimal structural parameters. These results have confirmed the accuracy of the proposed method.
4) The existing research on rice direct seeding metering devices shows that the seed qualification rate per hole is less than 90%, the empty hole rate is over 3%, and the coefficient of variation of hole spacing is over 10%. The optimization method used in these studies typically focuses on no more than three evaluation indicators (
5) Most of the seed metering devices currently available have 8 holes, while the device studied in this research has 10 holes. The results indicate that when seeding with the same hole spacing, the seed metering device with 10 holes has a lower rotational speed. The lower the rotational speed, the smaller the impact on the device, and the more stable its performance.
6) At present, a comprehensive optimization method of structural parameters based on a multiindex orthogonal test has been provided, which has been validated in a rice precision direct seeding metering system, and its application must be confirmed. The test method needs further improvement. For a more exact analysis, it is also important to enhance the test conditions on the bench, a challenge for future study. This approach enhances research in the area of detailed optimization of the structural parameters of a precision rice metering device under multiple indexes. The concepts and findings of this study provide techniques and references for optimizing the parameters of a precision rice seed metering device.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Conceptualization, HaL, LL, and BY. Methodology, HaL, CW, and GW. Validation, HaL, ZM, and BY. Formal analysis, HaL, LL, and BY. Investigation, HuL, ZM, XA, and BY. Resources, LL, HaL, and GW. Writing—original draft preparation, HaL, CW, and BY. Writing—review and editing, HaL, and BY. Supervision, HuL, XA, and ZM. Funding acquisition, XA, ZM, and BY. All authors contributed to the article and approved the submitted version.
This research was funded by the National Key Research and Development Program of China (2021ZD0110902), Youth Research Fund project of Beijing Academy of Agriculture and Forestry Sciences (QNJJ202222), and the Key Research and Development Program of Shandong Province (2022CXGC010608).
The authors gratefully acknowledge the funding’s support. We would like to thank “Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences”, “School of Electrical and Information, Northeast Agricultural University” and “State Key Laboratory of Intelligent Agricultural Power Equipment”.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.