Numerical Simulation of Dry and Wet Rice Seeds in an Air-Suction Seed Metering Device

Abstract

Rice direct seeding for bunch planting is a sustainable agricultural production method that reduces production costs, improves rice lodging resistance, and conserves irrigation water in the field. However, there are notable differences in seed treatment between direct seeding on dry land and in paddy fields, which can impact the seeding process’s accuracy. This study employs the numerical simulation methods of computational fluid dynamics (CFDs) and discrete element method (DEM) to examine the motion characteristics of dry and wet rice seeds in a fluid–solid coupled domain and their impact on seeding accuracy. The aim is to guide the optimization of the rice air-suction seed metering device. Rice seeds were divided into dry and wet groups, and their physical properties were measured. Discrete element models of rice seeds were constructed and calibrated using a polyhedral method. The results show that the static friction coefficient between the seed meter and the seed ranged from 0.902 to 0.950, and the thousand-grain weights ranged from 25.89 to 32.42 g, which were higher than those of the dry rice seed, which ranged from 0.774 to 0.839, and from 25.89 to 32.42 g. After calibration, the errors between the simulated dynamic stacking angles of HHZD, HYD, YLYD, HHZW, HYW, and YLYW and the physical–dynamic stacking angles were 0.12%, 0.13%, 0.75%, 0.62%, 0.08%, 0.75%, 0.59%, and 1.24%, respectively, which indicated that the discrete element model for rice was reliable. Additionally, a seeding accuracy test revealed that wet seeds of the same variety had higher missing and single indices, while dry seeds had higher triple and multiple indices. Furthermore, CFD-DEM simulations demonstrated that wet seeds’ normal and tangential forces were more significant than those on dry seeds during the seed-filling process. At 40 rpm, the normal and tangential forces during the seed-filling process of HYW are 37.69 × 10⁻³ N and 12.47 × 10⁻³ N, respectively, which are higher than those of HYD (25.18 × 10⁻³ N and 9.19 × 10⁻³ N). The action force of suctioned rice seeds was directly proportional to the missing and single indices. The primary factors contributing to the discrepancy in seeding accuracy between dry and wet rice are the thousand-grain weight, the static friction coefficient between the seed meter and the seed, and the action force exerted between the rice seeds. In addition, using a shaped hole structure and optimizing the seed chamber structure can reduce normal and tangential forces and improve seeding accuracy. This study provides a reference for the simulation of rice seed flow-solid coupling and optimization of air-suction seed metering devices.

1. Introduction

Rice is one of the world’s most important food crops, with planting methods primarily consisting of transplanting and direct seeding [1]. Transplanted rice allows more precise control of the distance between plants and optimizes the efficiency of light, water, and nutrient use. However, it should be noted that the planting cost of transplanting is much higher than direct seeding. Compared to transplanting, direct seeding can lower production costs by eliminating the need for seedling cultivation [2]. Mechanized precision direct seeding through bunch planting has been shown to improve rice root growth, enhance field ventilation and light transmission, and reduce water usage [3,4,5]. Bunch planting refers to the seeding method of concentrating multiple seeds into seed holes at a fixed row spacing and depth within the seeding rows. Air-suction seeding technology is employed in direct seeding to increase the precision of the seeding process, reduce seed consumption, and minimize damage rates [6]. Wet seeds are used for paddy field seeding, while dry seeds are used for dry land. Pre-germination of rice seeds has been observed to improve emergence rates and shorten emergence times [7]. Seed pre-germination is necessary for direct seeding in paddy fields. However, different treatments for rice seeds can affect the seeding accuracy of the air-suction seed metering device. The seeding accuracy of wet rice seed is lower than that of dry rice seed. Therefore, it is necessary to analyze the forces on dry and wet rice seeds in the fluid–solid coupling field to obtain the optimization guidelines for the air-suction seed metering device.

The seed metering device is a crucial component of the planter apparatus, directly influencing crop yield [8,9,10]. To improve seeding accuracy for various crops, researchers have optimized the key components of seed metering devices, such as the seed disc [11,12], seed tube [13], and band-type seeding device [14]. At the same time, various metering devices have been invented for different agronomic needs and operating environments, including field breeding [15], mulch planting [16], and no-tillage seeding [17]. With the development of machine learning, artificial neural networks have been used to predict the optimal operating parameters of seed metering devices [18,19]. However, the aforementioned studies on metering devices have focused on high-sphericity seeds such as corn, soybeans, cotton, and peanuts, not barred seeds such as rice.

In addition, previous studies have been dominated by dry seeds [20,21]. There is a big difference between the movement and force of dry and wet seeds in the seed metering device. The study of dry and wet seed movement and forces in seed metering devices will be an important guide for the optimization of air-suction seed metering devices for rice. Physical tests have limitations that make it challenging to analyze the forces and movements of seeds directly. Numerical simulation methods can be used to study the interaction mechanism between seeds and rows in detail and clarify seed movement rules [22,23]. The CFD-DEM coupling method is necessary to simulate the operation of the air-suction seed metering device [24,25]. CFD-DEM simulations are widely used in fields such as fluidized beds [26], pneumatic conveying [27], and fluid machinery [28].

Particle modeling methods primarily include the multi-sphere, ellipsoidal, and polyhedral models [29]. In other studies, maize seeds [30] and soybean seeds [31] were modeled using the multi-sphere modeling method and wheat seeds [32] were modeled using the ellipsoid modeling method. However, the multi-sphere and ellipsoid modeling methods have limitations for irregular particles, and the shape between the simulation model and the actual particles is large. The polyhedral modeling method uses multiple triangular facets to form a complex shape, reducing simulation errors for particles with intricate geometries [33,34]. Rice seeds have complex shapes, so the polyhedral modeling method is suitable for Rice seeds.

This study used the CFD-DEM numerical simulation method to examine the motion characteristics of dry and wet rice seeds in a fluid–solid coupled domain and their impact on seeding accuracy. First, the physical property parameters of rice seeds were measured, and the polyhedral method constructed discrete element models of six rice seeds. Seeding accuracy tests were conducted to analyze the effects of different rice seeds on the seeding accuracy. Finally, the CFD-DEM numerical simulation method was used to analyze the differences in forces in the fluid–solid coupling field of different rice seeds. The optimization guidelines for the air-suction rice seed metering device will be obtained based on the study results.

2. Materials and Methods

2.1. Measurement of Seed Physical Characteristics

2.1.1. Rice Seeds

The three types of rice, Huanghuazhan, Hanyou 73, and Yliangyou 900, are characterized by high yield, high quality, wide adaptability, and strong resistance. They are commonly used for direct seeding and have large differences in external dimensions. Hence, Huanghuazhan, Hanyou 73, and Yliangyou 900 were selected to measure the physical characteristics of the rice varieties to provide a basis for the numerical simulation of the air-suction seed metering device. Rice samples were divided into two groups, one with pre-germination treatment (wet seeds) and one without pre-germination treatment (dry seeds). The rice seeds were pre-germinated by thoroughly soaking the seeds in water, removing any impurities or empty husks floating on the surface of the water, and soaking the seeds for 24 h. The soaked seeds were drained and spread flat in a ventilated area for about 6 h to avoid the formation of agglomerates in wet rice seeds.

2.1.2. Size, Thousand-Grain Weight, and Density

The dimensions of the rice seeds were measured using a vernier caliper with an accuracy of 0.01 mm, and the sample size was 100. The thousand-grain weight was determined by counting the seeds on a seed counting board and weighing them on an electronic scale with a precision of 0.01 g. The average value was calculated from three measurements. The density of the rice was calculated using the formula in Equation (1), and the volume of the rice seed model was obtained using SolidWorks 2021

$$\rho ={\displaystyle \frac{m_t}{1000V_t}}$$

(1)

where ρ is density of rice, g/cm³; m_t is the thousand-grain weight of rice, g; and V_t is the volume of rice, cm³.

2.1.3. Contact Parameters

The contact parameters include the friction coefficient and collision recovery coefficient, in which the friction coefficient is divided into static and dynamic friction coefficients. Determining the contact parameters is important for studying the interaction between the seeds and the seed metering device. The inclinometer method can measure the static friction coefficient, and simulation tests will calibrate the dynamic friction coefficient. The static friction coefficient includes the static friction coefficient between the seed and the static friction coefficient between the seed and the seed metering device. Suppose the inclinometer method measures the static friction coefficient between the seed and the seed, a large error will occur in that case, so only the static friction coefficient between the seed and the seed metering device is measured. The test device of the static friction coefficient includes the inclined plane device and the electronic protractor. First, six rice seeds were glued to the same plastic plane to increase the number of seeds in each trial to minimize the measurement error. Then, a resin plate (the material used to make the seed metering device) was fixed on the inclinometer, and the rice seeds came into contact with the resin plate. Rotating the handle increased the inclination angle of the inclinometer until the seeds slid, and the inclinometer on the inclinometer could measure the inclination angle (as shown in Figure 1). While rotating the handle to tilt the inclinometer until the seed slides, the seed sliding process is played back in slow motion after the test is completed to determine the critical tilt angle for seed sliding. According to Equation (2) [35], the coefficient of static friction between the seed and the seed metering device (S_PG) can be obtained.

$$S_{PG}=\tan \theta$$

(2)

where S_PG is the coefficient of static friction between the seed and the metering device; θ is the angle as the seed slides, °.

The restitution coefficient is defined as the ratio of the relative velocity of the two objects after the collision to the absolute value of the relative velocity of the two objects before the collision. The collision between the seed and the seed metering device is inelastic, and the drop method is usually used to determine the restitution coefficient. Assuming that the air resistance is ignored, the seed starts free-fall movement from the initial height h₀ from the resin plate, the seed rebounds after collision with the resin plate, and the maximum height of the seed after rebound is measured h₁. The restitution coefficient of the collision between the seed and the seed metering device (R_PG) can be calculated according to Equation (3) [36]. The restitution coefficient measurement process is shown in Figure 2. Firstly, the resin flat plate is aligned with the scale of the coordinate paper by lifting the platform so that the seed is suctioned on the vacuum suction head. Then, the fan power is turned off, and due to the loss of suction force, the seed falls from the vacuum suction head and starts a free-fall motion, and the collision process is recorded at 960 frames per second.

$$R_{PG}=\sqrt{{\displaystyle \frac{h_1}{h_0}}}$$

(3)

2.1.4. Dynamic Stacking Angle

The dynamic stacking angle of rice seeds in the seed metering device must be determined to calibrate the parameters of different rice seed discrete element models. The experiment was conducted without vacuum pressure in the air-suction seed metering device, with only the disk rotating at 40 rpm (Figure 3). For each experiment, the volume of rice seeds used was 100 mL. The movement of the seeds in the metering device was recorded at 240 frames per second, and the dynamic stacking angles of the rice seeds were measured.

2.2. Parameter Calibration of the Discrete Elemental Model of Rice Seed

2.2.1. Modeling Methods

Rocky discrete element software was used to model the rice seeds. The modeling process of rice seed is shown in Figure 4, where the geometric model of rice seed is obtained by the inverse modeling technique, the geometric model is imported into the Rocky 2024 software, the triangular faceted data of the geometric model are extracted, and the discrete elemental model of rice seed is finally established. The normal force contact model for rice seeds is the Hysteretic linear spring model, while the tangential force contact model is the linear spring Coulomb limit model. The contact parameters for the discrete element models of the different wet and dry rice seeds vary, requiring calibration through dynamic stacking angle simulation experiments. The rotational speed of the simulation test should be consistent with the physical dynamic stacking angle test, which was set at 40 rpm. The time of the simulation test was 10 s to ensure that the stacking angle of the rice seeds was stabilized. The dynamic stacking angle stabilized when the simulation test was 7 s. Therefore, the dynamic stacking angles of 8 s, 9 s, and 10 s were chosen to be recorded as the results of each test (Figure 5). According to related studies [37,38,39], the intrinsic and contact parameters of the material are shown in

Table 1. Intrinsic parameters of the material.

Materials	Poisson’s Ratio	Young’s Modulus (MPa)	Density (g/cm3)
Rice	0.25	70	Test results
Metering device	0.4	2500	1.15

and

Table 2. Contact parameters of the material.

Parameters	Value
Dynamic friction coefficient between the seed and the metering device (DPG)	0.2~1.0
Dynamic friction coefficient between seeds and seeds (DPP)	0.2~1.0
Restitution coefficient between seeds and seeds (RPP)	0.2~0.6
Static friction coefficient between seeds and seeds (SPP)	0.4~1.6
Coefficient of static friction between the seed and the seed metering device (SPG)	Test results
Restitution coefficient of the collision between the seed and the seed metering device (RPG)	Test results

.

2.2.2. Experimental Design

Experimental factors include seed type and contact parameters (D_PG, D_PP, R_PP, and S_PP). All different rice seeds adopt the same modeling method and contact model. The differences among different rice seeds mainly lie in their intrinsic parameters, contact parameters, and seed shapes. Thus, the influence and significance of the contact parameters of different rice seeds on the dynamic stacking angle are consistent, and a preliminary test has also verified this result. We can reduce the number of tests by selecting only one type of rice seed to conduct a single-factor test to determine the contact parameters that significantly impact the dynamic stacking angle.

Firstly, a discrete element model was developed for HYW, and a single-factor experiment was conducted to identify the factors affecting the dynamic stacking angle. The fixed values of D_PG, D_PP, R_PP, and S_PP were set to 0.6, 0.6, 0.35, and 0.7, respectively. Fixed values are the level values of the remaining test factors for conducting a single-factor experiment of a particular factor. Then, based on the results of the one-factor experiment, two-factor experiment, and the optimization test, investigations were conducted to determine the optimal parameter combinations for the factors with significant effects.

2.3. Seeding Accuracy Test

The main structure of the air-suction seed metering device for rice is shown in Figure 6, which is mainly composed of the seed chamber, brush, seed protection ring, disc, seal ring, clearing hole device, and air chamber. Unlike other air-suction seed metering devices, this seed metering device contains double suction holes per set of holes. The seeding bench is shown in Figure 7 and includes the air-suction seed metering device for the rice, motor, DC fan, hall sensor, air pressure sensor, motor governor, fan governor, camera, and light.

The motor governor adjusts the rotational speed of the seed metering device with an accuracy of ±1 r/min, and the fan governor adjusts the vacuum pressure of the seed metering device with an accuracy of ±0.05 kPa. The Hall sensor and the air pressure sensor mainly measure the rotational speed and vacuum pressure of the seed metering device, which facilitates the precise adjustment of the rotational speed and the vacuum pressure. The working process of the seed metering device was filmed and recorded at a filming frame rate of 240 frames/second by the camera. Subsequently, the number of seeds suctioned by each set of suction holes of the seed metering device was counted manually based on the filmed video.

The motor and fan governor controlled the experiment’s vacuum pressure and rotational speed. In each experiment, the rice seed filling of 360 sets of suction holes was counted and repeated three times. The test indices included the missing index, single index, double index, triple index, and multiple index. The test indicators were calculated according to Equation (4).

$$\left\{\begin{array}{l}{Q}_0={\displaystyle \frac{N_0}{360}}\times 100\%\\ Q_1={\displaystyle \frac{N_1}{360}}\times 100\%\\ Q_2={\displaystyle \frac{N_2}{360}}\times 100\%\\ Q_3={\displaystyle \frac{N_3}{360}}\times 100\%\\ Q_{\ge 4}={\displaystyle \frac{N_{\ge 4}}{360}}\times 100\%\end{array}\right.$$

(4)

where Q₀, Q₁, Q₂, Q₃, and Q_≥4 are the missing index, single index, double index, triple index, and multiple index, respectively; N₀, N₁, N₂, N₃, and N_≥4 refer to the number of seeds that were 0, 1, 2, 3, and ≥4 on each set of suction holes at the time of testing, respectively.

2.4. CFD-DEM Two-Way Coupled Numerical Simulation

2.4.1. Theoretical Equations of CFD-DEM

Energy exchange is not considered in the numerical simulation of the air-suction seed metering device. The mass and momentum Equations of the fluid flow are solved using the finite volume method in Ansys Fluent 2024. The coupling between the particles and the fluid is achieved through the momentum exchange source term, which arises from the interaction between the particle phase and the fluid phase.

In the discrete cell method, the motion of the particles is tracked using the Lagrange approach, which is implemented by explicitly solving Euler’s first (Equation (5)) and second (Equation (6)) laws.

$${\mathrm{m}}_p{\displaystyle \frac{d{v}_p}{dt}}=F_c+F_{fp}+m_{p}g$$

(5)

$$I_p{\displaystyle \frac{d{\omega }_p}{dt}}=M_c+M_{fp}$$

(6)

where m_p is the mass of the particle, kg; v_p is the velocity of the particle, m/s; F_c is the contact force interacting between the particle and the particle and between the particle and the wall, N; g is the gravitational acceleration vector, m/s²; F_fp is the external force of the fluid acting on the particle, N; I_p is the moment of inertia of the particle, N·m; ω_p is the angular velocity vector, rad/s; M_c is the combined torque caused by the tangential force to make the particle rotate, N·m; and M_fp is the external torque caused by the gradient of the velocity of the fluid phase, N·m.

According to Equation (7), the external forces F_fp acting on the particles by the fluid mainly include the drag force F_D, the pressure gradient force F_∇p, the lift force F_L, the virtual mass force F_VM, and other forces due to the motion of the fluid F_others. According to the physical properties of the fluid, most of these forces can be neglected. Only the drag force and the pressure gradient force need to be considered, especially in the case of particles where the density of the particles is much higher than the density of the fluid.

$$F_{fp}=F_D+F_{\nabla p}+F_L+F_{VM}+F_{others}$$

(7)

The drag force F_D and the pressure gradient force F_∇p can be calculated according to Equations (8) and (9), respectively.

$$F_D={\displaystyle \frac{1}{2}}{C}_D{\rho }_{f}A{\left(u-v_p\right)}^2$$

(8)

where C_D is the drag force coefficient, ρ_f is the density of the fluid, g/cm³; A the projected area of the particle in the direction of the fluid, m²; and u is the velocity vector of the fluid, m/s.

$$F_{\nabla p}=-V_p\nabla p$$

(9)

where V_p is the volume of the particle, m³; ∇p is the local pressure gradient, Pa/m.

The fluid motion is described according to the Navier–Stokes equations, the mass conservation equation is Equation (10), and the momentum conservation Equation is Equation (11).

$${\displaystyle \frac{\partial }{\partial t}}\left({\alpha }_f{\rho }_f\right)+\nabla \left({\alpha }_f{\rho }_{f}u\right)=0$$

(10)

$${\displaystyle \frac{\partial }{\partial t}}\left({\alpha }_f{\rho }_{f}u\right)+\nabla \left({\alpha }_f{\rho }_{f}uu\right)=-{\alpha }_f\nabla p+\nabla \left({\alpha }_f\tau \right)+{\alpha }_f{\rho }_{f}g+{\mathrm{S}}_{fp}$$

(11)

where α_f is the volume fraction of the fluid, τ is the stress tensor of the fluid phase, Pa; and S_fp is the interphase momentum exchange source term, N∙s.

2.4.2. Fluid Domain Meshing

The fluid domain of the air-suction seed metering device primarily consists of a seed chamber, suction holes, and an air chamber. First, Ansys Space Claim defines the fluid domain boundary conditions, such as inlet, outlet, wall, and interface. When performing mesh generation for the fluid domain using Fluent Meshing, local refinement is applied to the seed chamber and the intersection surfaces of the gas suction and suction holes to improve the convergence of the calculations. A polyhedral mesh is used for body meshing, which reduces the number of meshes by over 30% compared to a tetrahedral mesh. In order to minimize the effect of the number of meshes on the accuracy of the simulation, six sets of meshes were meshed for the fluid domain to verify the mesh independence. The test results are shown in Figure 8, and the mesh IV was determined based on the test results. The fluid domain mesh IV is shown in Figure 9. The cells, faces, and nodes of mesh are 844,353, 5,823,820 and 4,477,278. Maximum skewness values of the suction hole, gas chamber, and seed chamber are 0.23, 0.25, and 0.21, respectively. To verify the simulation accuracy, we measured the vacuum pressure values inside the simulated and experimental chambers and the results are shown in Figure 10. The simulation results are in good agreement with the experimental data as a whole, verifying the reliability of the model. The drag law is set to Haider and Levenspiel law.

Ansys Fluent is used to solve the fluid domain. For the computational fluid dynamics (CFD) analysis of the air-suction seed metering device, transient analysis is selected. The SST k-ω model is more suitable for fluid domains with rotating motions [40], and the fluid domain of this study belongs to the rotating fluid domain, so the turbulence model is set to the SST k-ω. During the operation of the seed meter, the suction hole rotates, causing the fluid domain of the suction hole to undergo a circular motion. This motion is simulated using the Mesh Motion method, with the angular velocity of the circumferential motion set accordingly. For boundary conditions, the vacuum pressure at the outlet is set to 2.6 kPa. It is because there is a large difference between wet and dry rice seeds at a vacuum pressure of 2.6 kPa. Two interface pairs are created for data exchange: one between the suction hole and the seed chamber and another between the suction hole and the gas chamber. The time step for the numerical simulation in the fluid domain is set to 0.0005 s. After 35 ~ 42 iteration steps, the residual values satisfy the convergence criteria, so the maximum number of iteration steps is 50.

2.4.3. Data Processing

The physical property parameters (density, Poisson’s ratio, and Young’s modulus) and contact parameters (static friction coefficient, dynamic friction coefficient, and restitution coefficient) of different rice seeds were set according to the calibrated experimental results. During the simulation, the disk’s rotational speed was kept consistent with that of the suction hole fluid domain. After completing the simulation, cubes were added to each suction hole. Their dimensions were adjusted to ensure that the seeds adsorbed on the suction holes could be selected Figure 11. The normal, tangential, drag, and pressure gradient forces acting on the seeds adsorbed on the suction holes were extracted. The data were divided into the seed-filling process and the seed-carrying process.

3. Results

3.1. Seed Physical Characteristics

The experimental samples are shown in Figure 12, which includes the following: dry seeds of Huanghuazhan (HHZD), dry seeds of Hanyou 73 (HYD), dry seeds of Yliangyou 900 (YLYD), wet seeds of Huanghuazhan (HHZW), wet seeds of Hanyou 73 (HYW), and wet seeds of Yliangyou 900 (YLYW). Three-dimensional models of the rice seeds were created using inverse modeling technology, which was then used to develop a discrete element model of the seeds.

The results of the rice size measurements are presented in Figure 13a. The rice’s length, width, and thickness follow an approximately normal distribution, and there are significant differences in the three-dimensional dimensions of the different rice varieties. The rice seeds are ranked from highest to lowest in width in the order of HYW, HYD, YLYW, YLYD, HHZW, and HHZD. The length, width, and thickness of wet rice seeds of the same variety are greater than those of dry rice seeds due to the swelling that occurs when the seeds absorb water.

The measurements of thousand-grain weight and density are shown in Figure 13b. The thousand-grain weight of wet rice seed was 20.77~25.43 g, higher than that of dry rice seed, 25.89~32.42 g. The seeds are ranked from highest to lowest by thousand-grain weight in the order of HYW, YLYW, HHZW, HYD, HHZD, and YLYD. The S_PG of 0.902~0.950 for wet rice was higher than that of 0.774~0.839 for dry rice (shown in Figure 13c). It is because the moisture on the surface of the rice seed increases the coefficient of static friction. There was no significant difference between dry rice seeds and rice seeds for R_PG (shown in Figure 13d). The dynamic stacking angle is used as an optimization objective for the discrete element model. There was also no significant difference between the dynamic stacking angles of dry and wet seeds (shown in Figure 13e).

3.2. Discrete Elemental Model of Rice Seed

The results are shown in Figure 14. As the values of R_PP and S_PP increased, there was no significant change in the dynamic stacking angle (DSA) of HYW. The effects of R_PP and S_PP on the DSA were insignificant (p > 0.05). In subsequent experiments, the RPP and SPP for the discrete element models of all the rice seeds were set to 0.35 and 0.7, respectively. As the values of D_PG and D_PP increased, the DSA of HYW tended to rise, and the differences between the various levels of DSA were significant (p < 0.05). In the subsequent experiments, the levels of D_PG and D_PP were set to range from 0.2 to 0.6, respectively.

The results of the two-factor experiment are shown in Figure 15. For different rice seeds, there is a positive correlation between the DSA and D_PG. The DSA for a D_PP of 0.2 is significantly smaller than for the other values, and there is no clear trend of an increase in the DSA with increasing D_PP when the value of D_PG is fixed. All DSA, except for the D_PP of 0.2, intersect with the reference line of the target optimization value. It is necessary to determine the D_PP value before determining the optimization interval for D_PG. When determining the fixed D_PP value, preference should be given to DSA curves near to or those that intersect the experimental levels and exhibit smoother changes.

Table 3. Dynamic stacking angle optimization experiment results.

Indicator	Rice Seed
	HHZD	HHZW	HYD	HYW	YLYD	YLYW
DPP	0.40	0.60	0.40	0.50	0.50	0.40
DPG	0.32	0.38	0.42	0.26	0.36	0.32
DSA (°)	36.17 ± 0.34	35.38 ± 0.68	34.56 ± 0.40	32.33 ± 0.32	32.83 ± 0.19	31.41 ± 0.59
DPG	0.34	0.40	0.44	0.28	0.38	0.34
DSA (°)	36.31 ± 0.53	36.23 ± 0.51	35.30 ± 0.37	34.61 ± 0.74	33.64 ± 0.17	32.64 ± 0.81
DPG	0.36	0.42	0.46	0.30	0.40	0.36
DSA (°)	36.83 ± 0.19	37.3 ± 0.53	35.97±	35.13 ± 0.85	33.72 ± 0.96	33.58 ± 0.76
DPG	0.38	0.44	0.48	0.32	0.42	0.38
DSA (°)	37.31 ± 0.18	39.41 ± 0.28	36.41±	35.77 ± 0.51	34.91 ± 0.63	34.18 ± 0.97

shows the results of the DSA optimization experiments, which determined that the D_PP and D_PG for HHZD are 0.4 and 0.32, for HHZW are 0.6 and 0.40, for HYD are 0.4 and 0.44, for HYW are 0.5 and 0.28, for YLYD are 0.5 and 0.38, and for YLYW are 0.4 and 0.36. The DSA of calibrated rice seed is shown in Figure 16. After calibration, the errors between the simulated dynamic stacking angles of HHZD, HYD, YLYD, HHZW, HYW, and YLYW and the physical–dynamic stacking angles were 0.12%, 0.13%, 0.75%, 0.62%, 0.08%, 0.75%, 0.59%, and 1.24%, respectively, which indicated that the discrete metamodels for rice were accurate.

3.3. Seeding Accuracy

Figure 17 illustrates the accuracy of the air-suction seed metering device at a speed of 20 rpm. At vacuum pressures ranging from 2.4 kPa to 3.6 kPa, the missing index of wet rice seeds was higher than that of dry rice seeds. It may be attributed to the fact that the surface of wet rice seed contains moisture, which reduces seed mobility, requiring greater suction force to detach the seed. The single index of wet rice seeds was higher than that of dry rice seeds and tended to decrease as vacuum pressure increased. When the vacuum pressure varied, no significant difference was observed in the double index between dry and wet rice seeds.

The double index for HHZD was also considerably smaller than for the other rice seeds. When the vacuum pressure exceeded 2.2 kPa, the triple index for dry rice seeds was higher than that of wet rice seeds. Dry rice seeds exhibited a higher multiple index than wet rice seeds, and the multiple index of HHZD was higher than those of YLYD and HYD. The missing and single indices of dry seeds followed this ascending order: HHZD, YLYD, and HYD. The thousand-grain weight of HYD was greater than that of HHZD and YLYD, which explains why the missing index for HYD was lower than for HHZD and YLYD. Although the thousand-grain weight of HHZD was higher than YLYDs, the S_PG of HHZD was greater than YLYDs, which may provide more friction in the seed-filling process.

At a rotational speed of 40 rpm, the missing index of wet rice seeds was significantly higher than that of dry rice seeds, with HYW exhibiting the highest missing index (Figure 16). When the vacuum pressure ranged from 2.0 kPa to 3.4 kPa, the single index of wet rice seeds was also higher than that of dry rice seeds. HYW exhibited the highest single index, while HHZD demonstrated the lowest. No significant difference was observed between the double index of dry and wet rice seeds. HHZD and YLYD exhibited the highest triple and multiple indexes, while HYW exhibited the lowest. These findings are illustrated in Figure 18. Similarly, the missing index of dry seeds and the single index of HHZD, YLYD, and HYD were in ascending order. When the vacuum pressure ranged from 2.0 kPa to 2.6 kPa, the missing index of YLYW was more extensive than HHZW’s. However, when the vacuum pressure ranged from 2.8 kPa to 3.6 kPa, the missing index of HHZW was more significant than that of YLYW.

Regarding the physical characteristics of the rice, the difference in the static friction coefficient between YLYW and HHZW was less pronounced, and the thousand-grain weight of YLYW (27.18 g) was higher than that of HHZW (25.89 g), resulting in a greater missing index for YLYW compared to HHZW. It may be because when the vacuum pressure is between 2.0 kPa and 2.6 kPa, the thousand-grain weight is the primary factor affecting seed-filling performance, so the missing index of YLYW is greater than that of HHZW. However, when the vacuum pressure is between 2.8 kPa and 3.6 kPa, the action force between the rice seeds of HHZW may be greater than that between the rice seeds of YLYW, or the drag force or pressure gradient force on YLYW may be greater than that on HHZW. Therefore, the missing index of HHZW is greater than that of YLYW.

At a rotational speed of 60 rpm, HYW exhibited the highest missing index and single index. In contrast, HHZD and YLYD demonstrated lower missing and single index values than the other rice seeds (Figure 19). As with the results at 20 and 40 rpm, no significant difference was observed in the double index between the dry and wet rice seeds. The triple and multiple indexes were higher for HHZD and YLYD than for the other rice seeds. Conversely, the triple and multiple indices were lower for HYW than for the other rice seeds. Unlike the observations at 20 and 40 rpm, dry seeds’ missing index and single index increased in the order of YLYD, HHZD, and HYD. This phenomenon may be attributed to the inter-seed force being higher for HHZD than for YLYD at 60 rpm. The higher missing index observed in HHZW than YLYW may be due to two potential factors: either the interaction force between rice seeds in HHZW is greater than that in YLYW or the drag or pressure gradient force of YLYW is higher than HHZW. At a rotational speed of 60 rpm, the discrepancy between the missing indices of HYD and YLYW was minimal, with even the missing index of HYD exceeding that of YLYW. It can be attributed to the fact that the higher rotational velocity enhances the mobility of the seeds, thereby reducing the influence of thousand-grain weight on the missing index of HYD and YLYW. Consequently, the discrepancy between the two missing indices at 60 rpm was not pronounced.

To further analyze the relationships between length, width, thickness, thousand-grain weight, S_PG, R_PG, rotational speed, negative pressure, and the missing index, single index, double index, triple index, and multiple index Pearson correlation analysis was performed. The results are shown in Figure 20. The missing index was positively correlated with length, width, thickness, thousand-grain weight, S_PG, and rotational speed, while it was negatively correlated with negative vacuum pressure. The single index showed significant positive correlations with length, width, thickness, thousand-grain weight, S_PG, and rotational speed and a significant negative correlation with negative vacuum pressure. The double index was significantly and positively correlated with width and R_PG. The triple index was significantly negatively correlated with length, width, thickness, thousand-grain weight, S_PG, and rotational speed but positively correlated with negative vacuum pressure. The multiple index was significantly negatively correlated with length, width, thickness, thousand-grain weight, S_PG, and rotational speed, while it was positively correlated with negative vacuum pressure. The correlation analysis revealed that width, thickness, thousand-grain weight, and S_PG had the highest correlation coefficients.

3.4. CFD-DEM Simulation

The pressure gradient force of different rice seeds in the air-suction seed meter at a speed of 40 rpm is shown in Figure 21. The forces exerted on the seed in the flow field primarily comprise the drag force and the pressure gradient force, as illustrated in

Table 4. Drag force and pressure gradient force on rice seeds in filling and carrying.

Process	Location	Seed	Drag Force (×10−3N)			Pressure Gradient Force (×10−3N)			Sum (×10−3N)
			Rotational Speed (rpm)
			20	40	60	20	40	60	20	40	60
Filling	Inside	HHZD	4.19	3.80	3.83	8.75	9.19	8.76	12.94	12.99	12.59
		HHZW	3.53	3.00	2.82	8.97	8.43	8.13	12.5	11.43	10.95
		HYD	2.48	2.25	2.42	9.61	8.67	9.64	12.09	10.92	12.06
		HYW	2.52	2.41	2.37	10.03	9.70	9.37	12.55	12.11	11.74
		YLYD	3.37	2.92	2.97	7.57	6.74	6.70	10.94	9.66	9.67
		YLYW	2.64	2.42	2.37	10.72	9.75	9.86	13.36	12.17	12.23
	Outside	HHZD	3.78	3.70	3.80	8.74	7.89	7.83	12.52	11.59	11.63
		HHZW	3.40	2.88	2.72	7.96	8.62	6.72	11.36	11.5	9.44
		HYD	2.47	2.39	2.25	9.63	9.57	8.98	12.1	11.96	11.23
		HYW	2.51	2.23	2.17	10.16	8.72	8.77	12.67	10.95	10.94
		YLYD	3.26	2.82	2.78	7.25	6.59	6.81	10.51	9.41	9.59
		YLYW	2.57	2.21	2.24	10.48	8.93	9.33	13.05	11.14	11.57
Carrying	Inside	HHZD	2.96	3.24	3.02	3.44	6.22	4.89	6.4	9.46	7.91
		HHZW	2.54	2.32	2.46	5.85	6.09	6.27	8.39	8.41	8.73
		HYD	1.91	1.87	1.77	8.14	7.92	7.45	10.05	9.79	9.22
		HYW	1.96	1.98	1.80	8.46	8.60	7.75	10.42	10.58	9.55
		YLYD	2.51	2.42	2.24	4.03	3.78	4.72	6.54	6.2	6.96
		YLYW	1.88	1.86	1.79	7.94	7.83	7.55	9.82	9.69	9.34
	Outside	HHZD	2.98	3.40	2.97	3.73	6.83	4.22	6.71	10.23	7.19
		HHZW	2.54	2.23	2.45	5.44	6.50	5.28	7.98	8.73	7.73
		HYD	1.88	1.90	1.77	7.95	8.11	7.61	9.83	10.01	9.38
		HYW	1.96	1.96	1.86	8.49	8.48	8.10	10.45	10.44	9.96
		YLYD	2.46	2.38	2.21	3.64	4.03	4.58	6.1	6.41	6.79
		YLYW	1.82	1.82	1.81	7.63	7.64	7.64	9.45	9.46	9.45

. The analysis of variance for drag force and pressure gradient force is shown in

Table 5. Analysis of variance for drag force and pressure gradient force.

Process	Location	Indicators	Factors	df	F	p
Filling	Inside	Drag force	Seed	5	72.16	<0.01
			Rotational speed	2	13.76	<0.01
		Pressure gradient force	Seed	5	29.16	<0.01
			Rotational speed	2	4.54	0.04
		Sum	Seed	5	17.43	<0.01
			Rotational speed	2	8.87	<0.01
	Outside	Drag force	Seed	5	58.94	<0.01
			Rotational speed	2	12.23	<0.01
		Pressure gradient force	Seed	5	13.29	<0.01
			Rotational speed	2	5.58	0.02
		Sum	Seed	5	7.13	<0.01
			Rotational speed	2	9.35	<0.01
Carrying	Inside	Drag force	Seed	5	75.01	<0.01
			Rotational speed	2	2.49	0.13
		Pressure gradient force	Seed	5	19.41	<0.01
			Rotational speed	2	0.64	0.55
		Sum	Seed	5	10.09	<0.01
			Rotational speed	2	0.62	0.56
	Outside	Drag force	Seed	5	41.05	<0.01
			Rotational speed	2	1.09	0.37
		Pressure gradient force	Seed	5	19.04	<0.01
			Rotational speed	2	2.26	0.16
		Sum	Seed	5	10.19	<0.01
			Rotational speed	2	2.14	0.17

. The analysis reveals that the drag force acting on rice seeds is less in magnitude than the pressure gradient force; this trend is consistent with the findings of He et al. [41]. The discrepancy is observed between the drag force and the pressure gradient force on rice seeds during the filling and carrying processes. Specifically, the drag force and pressure gradient force during filling are found to be greater than those during carrying. This disparity can be attributed to the displacement of the seeds from the vacuum pressure port during the carrying process, resulting in a decline in drag force and pressure gradient force with an increase in carrying time. In the filling process, the difference in drag force and pressure gradient force between the inside and outside suction holes is mainly related to the seed layer height. When the seed layer height is greater, the energy loss of the airflow reaching this seed layer height is greater. During the seed-carrying process, for the same variety of rice, the difference in drag force and pressure gradient force between the inside and outside suction holes is minimal.

The drag force and pressure gradient force vary for different rice seeds, which is due to differences in the windward projected area, volume, drag coefficient, flow field velocity, and pressure gradient of rice. For dry and wet rice seeds, the drag force of dry rice seeds is, in most cases, smaller than that of wet rice seeds. This is mainly because the porosity of wet rice seeds is lower, which results in greater loss of airflow velocity. There is no significant difference in the pressure gradient force of dry and wet rice seeds. However, the difference between the sum of the drag force and the pressure gradient force of different rice seeds is small. This shows that the drag force and pressure gradient force are not the main factors contributing to the difference in filling performance.

If two rice seeds come into contact with each other, the force perpendicular to the contact surface is the normal force. The force in the direction of the tangent of the contact surface is called the tangential force, which mainly affects the relative sliding and rotation of the rice seeds. Excessive normal and tangential forces can cause suctioned seeds to detach from the suction hole, resulting in an elevated missing index. Smaller normal and tangential forces contribute to the reduction in the missing index. The normal and tangential forces acting on the seeds during filling and carrying are shown in

Table 6. Normal and tangential forces on rice seeds in filling and carrying.

Process	Location	Seed	Normal Force (×10−3N)			Tangential Force (×10−3N)
			Rotational Speed (rpm)
			20	40	60	20	40	60
Filling	Inside	HHZD	22.18	21.74	20.51	6.60	6.41	6.32
		HHZW	24.31	24.17	23.33	8.60	9.24	9.28
		HYD	28.67	25.18	27.86	9.93	9.19	10.15
		HYW	36.02	37.69	30.36	11.91	12.47	10.03
		YLYD	22.12	18.40	17.27	7.76	6.67	6.45
		YLYW	30.29	26.96	22.56	9.67	9.14	7.77
	Outside	HHZD	29.19	24.44	22.10	8.62	7.57	6.78
		HHZW	32.27	27.23	27.43	11.93	10.45	11.64
		HYD	37.68	35.11	31.37	13.29	12.50	11.59
		HYW	46.96	51.24	41.43	15.73	17.79	13.70
		YLYD	26.02	23.91	20.40	9.19	11.45	7.68
		YLYW	39.04	41.55	25.80	12.65	14.22	8.89
Carrying	Inside	HHZD	5.81	9.00	6.43	1.52	2.01	1.54
		HHZW	7.59	7.64	8.22	2.10	2.10	2.35
		HYD	9.86	9.53	8.82	2.89	2.69	2.65
		HYW	10.21	10.31	9.06	2.66	2.48	2.49
		YLYD	5.82	5.16	5.90	1.58	1.49	1.67
		YLYW	9.66	9.36	8.96	2.21	2.31	2.34
	Outside	HHZD	7.06	9.15	5.77	1.83	1.89	1.51
		HHZW	7.24	8.08	6.31	2.10	2.10	1.91
		HYD	9.76	9.70	12.25	2.71	2.94	3.92
		HYW	10.26	10.14	9.40	2.56	2.65	2.43
		YLYD	5.21	5.05	5.55	1.40	1.97	1.58
		YLYW	9.28	9.08	9.03	2.21	2.46	2.24

; analysis of variance is shown in

Table 7. Analysis of variance for normal and tangential force.

Process	Location	Indicators	Factors	df	F	p
Filling	Inside	Normal force	Seed	5	21.03	<0.01
			Rotational speed	2	4.73	0.04
		Tangential force	Seed	5	19.43	<0.01
			Rotational speed	2	1.67	0.24
	Outside	Normal force	Seed	5	29.16	<0.01
			Rotational speed	2	21.88	<0.01
		Tangential force	Seed	5	13.23	<0.01
			Rotational speed	2	5.13	0.03
Carrying	Inside	Normal force	Seed	5	11.60	<0.01
			Rotational speed	2	0.78	0.48
		Tangential force	Seed	5	23.29	<0.01
			Rotational speed	2	0.02	0.98
	Outside	Normal force	Seed	5	10.40	<0.01
			Rotational speed	2	0.35	0.71
		Tangential force	Seed	5	9.43	<0.01
			Rotational speed	2	0.59	0.57

. There are significant differences in the normal and tangential forces acting on dry and wet rice seeds. During the filling process, wet rice seeds of the same variety experience higher normal and tangential forces than dry rice seeds. For example, at 40 rpm, the normal and tangential forces of HYW are 37.69 × 10⁻³ N and 12.47 × 10⁻³ N, respectively, which are higher than those of HYD (25.18 × 10⁻³ N and 9.19 × 10⁻³ N). During the filling process, the rice seeds in the outside suction hole experience higher normal and tangential forces than those in the inside pores. This is because the seed layer height of the outside suction hole is higher during filling, which results in the suctioned seeds being subjected to the action of more rice seeds.

The normal force and tangential force experienced by the seed during carrying are generated by the contact between the suction hole and the seed. In order to more intuitively analyze the normal and tangential forces acting on the seeds in the seed chamber, the action force between the rice seeds is used as an evaluation index. The action force between rice seeds is the difference between the force applied to the suctioned seeds during the seed-filling process and the force experienced during the seed-carrying process. The forces of different rice varieties on suctioned seeds are shown in

Table 8. Normal and tangential action force on rice seeds.

Process	Seed	Normal Action Force (×10−3N)			Tangential Action Force (×10−3N)
		Rotational Speed (rpm)
		20	40	60	20	40	60
Inside	HHZD	16.37	12.74	14.08	5.08	4.53	4.78
	HHZW	16.72	16.53	15.11	6.49	7.14	6.93
	HYD	18.81	15.65	19.05	7.04	6.5	7.51
	HYW	25.81	27.38	21.30	9.25	9.99	7.54
	YLYD	16.30	13.24	11.38	6.18	5.18	4.77
	YLYW	20.63	17.6	13.61	7.45	6.83	5.43
Outside	HHZD	22.13	15.29	16.32	6.79	5.68	5.27
	HHZW	25.03	19.15	21.13	9.83	8.35	9.73
	HYD	27.92	25.41	19.13	10.57	9.56	7.67
	HYW	36.71	41.1	32.03	13.17	15.14	11.26
	YLYD	20.81	18.86	14.85	7.79	9.48	6.10
	YLYW	29.76	32.47	16.77	10.44	11.76	6.66

; analysis of variance is shown in

Table 9. Analysis of variance for normal and tangential action force.

Location	Indicators	Factors	df	F	p
Inside	Drag force	Seed	5	12.46	<0.01
		Rotational speed	2	4.39	0.04
	Tangential force	Seed	5	11.50	<0.01
		Rotational speed	2	1.67	0.24
Outside	Drag force	Seed	5	11.52	<0.01
		Rotational speed	2	6.44	0.02
	Tangential force	Seed	5	10.56	<0.01
		Rotational speed	2	5.41	0.03

. As shown in Table 8, the normal action force between rice seeds is greater than the tangential action force.

When the rotation speed is 20 rpm, the tangential action forces of dry rice seeds are in descending order: HYD, YLYD, and HHZD. The normal forces of dry rice seeds at 20 rpm are in descending order: HYD, HHZD, and YLYD, and the difference in normal force between HHZD and YLYD is small. At a rotational speed of 40 rpm, the normal and tangential action forces between dry rice seeds followed the order HYD, YLYD, and HHZD, in descending order, which aligns with the descending order of the missing index and single index of dry seeds. Similarly, at a rotational speed of 60 rpm, the normal action forces between dry rice seeds followed the same order: HYD, HHZD, and YLYD, consistent with the missing index and single index rankings of dry seeds. Although the order of magnitude of the tangential action forces at 60 rpm did not fully align with the missing index and single index rankings, the discrepancy of tangential action forces between YLYD and HHZD was minimal.

When the rotational speed is 20 rpm, the normal force between the rice seeds of wet rice seeds is in descending order: HYW, YLYW, and HHZW. At 40 rpm, the normal action forces followed the order HYW, YLYW, and HHZW in descending order, consistent with the missing index and single index rankings of wet rice seeds. At a rotational speed of 60 rpm, the normal and tangential action forces on wet rice seeds followed the order HYW, HHZW, and YLYW, consistent with the missing index and single index rankings.

4. Discussion

Using the discrete element method to simulate agricultural material particles helps researchers study the movement of particles in agricultural machinery and thus improves agricultural machinery [42]. Therefore, it is crucial that the discrete element model accurately represents the morphological characteristics of the particles. In this study, we used the polyhedral method to establish a discrete element model of rice seeds, and the experimental results also proved the superiority of the method. The polyhedral method can be used to model more non-spherical particles to reduce the simulation error. Compared with the discrete elemental model of rice seed using the multi-sphere method [43], the rice seed modeled with the polyhedral method better reflects the morphological characteristics of the actual rice seed, which helps to reduce the error of simulation.

The seeding accuracy test results showed that the air-suction seed metering device for rice could maintain a low missing index under suitable vacuum pressure. However, there were significant differences in the missing index, single index, triple index, and multiple index between dry and wet rice seeds of the same variety. Wet rice seeds exhibited higher missing and single indices, while dry rice seeds had higher triple and multiple indices. This results in higher vacuum pressures for the air-suction seed metering device when sowing wet rice seeds, which consumes more energy. No significant difference was observed in the double index between dry and wet rice seeds. The primary factors contributing to these differences were the physical characteristics, such as thousand-grain weight and S_PG. For the differences in the missing index, single index, triple index, and multiple index among different varieties of seeds under the same treatment, the key factors were not only thousand-grain weight and S_PG but also potential variations in the drag force, pressure gradient force, and action force on the seeds during seed filling.

The CFD-DEM simulation results showed that rice seeds are suctioned mainly by suction forces (drag force and pressure gradient force) and friction force. When the normal and tangential action forces on rice seeds exceed a critical level, the rice seeds will detach from the suction holes, resulting in an increase in the missing index and single index. To minimize this effect, the friction force can be increased by increasing the contact area between the suction hole and the rice through the shaped hole structure, and the normal and tangential action forces can be reduced by decreasing the contact area between the rice seed on the suction hole and the rest of seeds in the seed chamber. Of course, it is also possible to reduce the number of rice seeds by optimizing the structure of the seed chamber, thereby reducing the normal and tangential forces. The above improvements will improve the seeding accuracy of the air-suction seed metering device for wet and dry rice seeds. This will reduce the vacuum pressure for sowing wet rice seeds and reduce energy consumption.

Although the contact parameters for rice seeds were calibrated, they may not fully represent the actual physical properties of the seeds. Future studies will focus on determining additional contact parameters through non-simulation experiments. Meanwhile, in future research, we should improve the parameters of the simulation model by experimentally measuring the suction force of the rice seed to reduce the error in the simulation.

5. Conclusions

This study employed experimental analysis and CFD-DEM numerical simulation methods to investigate the primary factors contributing to the differences in seeding accuracy between dry and wet rice seeds. Three rice seeds were categorized into dry and wet (subjected to pre-germination treatment). The physical property parameters of the seeds were measured, and the contact parameters of the seed discrete element model were calibrated. Differences in seeding accuracy among the rice seeds were analyzed, followed by CFD-DEM numerical simulations. The following conclusions were drawn from the study:

(1)

Rice seeds subjected to pre-germination treatment exhibited greater triaxial dimensions, thousand-grain weight, density, and coefficient of static friction between the seeds and the seed discharger than dry rice seeds. The effects of R_PP and S_PP on the dynamic stacking angle were found to be insignificant (p > 0.05), whereas the effects of D_PG and D_PP on the dynamic stacking angle were significant (p < 0.05). The difference between the calibrated seeds’ simulated and physical dynamic stacking angles was minimal, indicating that the polyhedral method is reliable for constructing the discrete elements of rice seeds;

(2)

For the same variety of seeds, the missing index and single index of wet rice seeds were higher than those of dry rice seeds, while the triple index and multiple index of dry rice seeds were higher than those of wet rice seeds. No significant difference was observed in the double index between dry and wet rice seeds. Additionally, there were differences in the missing index, single index, triple index, and multiple index for the same treatment across different rice seed varieties;

(3)

For the same variety of rice seeds, the normal and tangential forces on wet rice seeds during seed filling were higher than those on dry rice seeds. Additionally, for the same treatment, the greater the action force between the seeds, the higher the missing index and single index. The differences in seeding accuracy between dry and wet rice seeds of the same variety were primarily attributed to the thousand-grain weight, the coefficient of static friction between the air-suction seed metering device and the seed, and the action force between the seeds.

These findings are instructive for improving the optimization of the air-suction seed metering device for rice. In our future research, we will optimize the shaped hole structures and the seed chamber to improve the seeding accuracy and reduce the energy consumption of the seed metering device. From the theoretical point of view, applying the CFD-DEM simulation method not only verifies the validity of the study of non-spherical particles in the fluid–solid coupled field but also enriches the existing research methods in agricultural engineering.

However, this study also has some limitations. The study mainly focused on a limited number of rice seed varieties with relatively controlled environmental conditions during the experiment. In actual agricultural production, a wide range of rice seed varieties are used, the seeding environment is more complex, and factors such as soil type, temperature, and humidity may also affect the seeding accuracy. Future research could expand the range of seed varieties and conduct more in-depth field tests under different environmental conditions to further validate and refine the findings. In addition, exploring the interactions between seeds and different types of soil during sowing will also provide a more comprehensive understanding of the mechanisms of seeding accuracy.

The results of this study provide insights into the optimization of the rice air-suction seed metering device, which can be achieved by refining the device structure to reduce the action force between the seeds and the suction system, thereby improving seeding accuracy. The CFD-DEM simulation method used in this study offers valuable guidance for simulating non-spherical particles in fluid–solid coupled fields.