Technological Parameter Optimization of Double-Press Precision Depth-Control Seeding and Its Application in Rice Production

Abstract

Current rice cultivation relies on mechanical transplanting, which is costly and complex, and direct seeding, which suffers from poor quality and low efficiency. To address these issues, a double-press precision depth-control seeding method was developed in this study. Discrete element modeling (DEM) was employed to optimize key operational parameters—compaction force, soil covering cutter rotational speed, and penetration depth—using qualified seeding depth and missed seeding rates as performance metrics. Optimal results were achieved at a 60 kPa compaction force, a 300 rpm rotational speed, and a 7 cm penetration depth. A prototype seeder was manufactured and evaluated in three-year field trials against conventional dry direct seeders and mechanical transplanters. The double-press seeder demonstrated significantly superior performance compared to conventional direct seeding. It optimized the crop population structure by maintaining a high tiller number while increasing the productive tiller rate, resulting in stable annual yields exceeding 10.11 t·hm⁻². Although its yield was slightly lower than that of mechanical transplanting, the double-press seeder offers a compelling practical alternative due to its operational convenience and economic benefits.

1. Introduction

Rice is one of the most significant food crops for human sustenance, playing an irreplaceable role in global food security and social stability [1]. With rising labor costs and the advancement of agricultural modernization strategies, rice cultivation methods are gradually shifting from traditional manual water seeding and mechanical transplanting to efficient, labor-saving mechanical dry direct seeding. This technology, with its significant advantages in water conservation, labor reduction, cost efficiency, and planting efficiency, has become a key development direction in rice production [2,3,4,5]. However, conventional dry direct seeding often struggles to achieve precise seeding depth control and effective soil compaction under heavy clay or high-moisture conditions, leading to uneven seed coverage and inconsistent germination. Such instability in seeding quality directly affects seedling emergence and early growth, resulting in irregular emergence, weak seedlings, and susceptibility to drought or waterlogging stress [6,7,8], ultimately constraining yield and posing a bottleneck to achieving high and stable rice yields [9].

To address these challenges, researchers have explored various technological improvements. Wang J. [10] designed a compaction device for dry direct seeding to enhance seedbed compaction, demonstrating via DEM simulations that compaction intensities of 50–90 kPa promote better rice growth. Innovations in seeding mechanisms include Dai Y. et al.’s pneumatic centralized systems for high-speed operations [11], and Xing H. et al.’s adjustable metering devices designed to accommodate hybrid rice varieties [12]. In order to overcome the poor seeding quality associated with traditional ground-wheel-driven systems, intelligent electric control systems have been developed by He A. et al. [13]. Matin et al. [14] optimized a dual-compaction system for rotary seeders, which improved seed–soil contact. To enhance adaptability to specific environments, Zhao J. et al. [6] designed and tested a specialized planting unit for the dry direct seeding of rice in cold regions, focusing on improving sowing performance under challenging soil and climate conditions. Furthermore, to improve seedbed quality, Zhang C. et al. [15] utilized a sophisticated DEM-MFBD coupling simulation to design a compaction device that significantly improved seedbed uniformity and seedling emergence rates. However, while this approach enhanced post-seeding compaction, the structural complexity of its dual-roller system and its primary focus on shaping the final seedbed may not adequately address the critical need for precise depth control at the point of seed delivery [16], a key factor for uniform emergence.

While existing research confirmed the feasibility and advantages of direct seeding, effective and integrated solutions to the core challenges—unstable seeding depth, inadequate compaction, and uneven emergence—remained elusive. Current machinery designs also fail to cover all operational stages of rice direct seeding. To bridge this gap, in this study, we proposed a double-press precision depth-control seeding (DPDS) scheme, designed a DPDS machine, optimized seeding parameters via DEM simulations, and validated its applicability through field experiments.

2. Materials and Methods

2.1. Design of the Double-Press Precision Depth-Control Seeding Machine

The double-press precision depth-control seeding machine (DPDS) (Yangzhou, China) is equipped with several essential components: a fertilization device, a dual-axis rotary tillage unit, a parallel four-bar linkage, a press grooving wheel, soil covering cutters, a rear press wheel, a furrow opener, a soil scraper, a seeding device, and a rear press wheel pressure adjustment mechanism (as shown in Figure 1). The dual-axis rotary tillage unit features two sets of offset upper and lower tillage blades. The upper set rotates forward, while the lower set rotates in reverse. This design allows for a tillage depth of up to 22 cm and facilitates the direct incorporation of straw from previous crops into the soil. The parallel four-bar linkage system ensures adaptive profiling, providing a consistent seeding height across various terrain types. The press grooving wheel compacts the tilled soil and is designed with 5 cm wide and 3 cm high protrusions that create seed grooves, accurately controlling the seeding depth to 3 cm. After sowing, the soil covering cutters perform shallow tillage to cover the seeds. This is followed by surface compaction from the rear press wheel, which enhances seed-to-soil contact and promotes germination. The furrow opener creates drainage channels in accordance with agronomic standards. Additionally, the rear press wheel pressure adjustment mechanism enables modulation of the compaction force, which can range from 40 to 80 kPa, by adjusting the spring preload to accommodate different soil conditions. This integrated design allows for rotary tillage, groove formation, seeding, soil covering, and compaction all in one operation, significantly enhancing overall efficiency. The complete working principle is shown in Figure 2.

2.2. Simulation Design

2.2.1. Simulation Model Development

In EDEM 2022 software, a multi-sphere model of rice seeds was constructed using basic particles for multi-sphere filling. The average dimensions (length, width, height) were 7.26 mm, 3.14 mm, and 2.36 mm, respectively, as shown in Figure 3. Particle density was set to 1.35 g∙cm⁻³, Poisson’s ratio to 0.42, and shear modulus to 5.1 × 10⁷ Pa [17,18].

A layered modeling approach was adopted to construct the soil particle model. The soil was divided into the plow layer (150 mm thick), plow pan (100 mm thick), and subsoil layer (100 mm thick). The soil particle radius was set to 10 mm, with a Poisson’s ratio of 0.3 and shear modulus of 1 × 10⁶ Pa. The Hertz–Mindlin bonding contact model was selected as the bonding model between soil particles. Based on soil property variations across layers, the particle densities were set to 1.18 g∙cm⁻³ for the plow layer, 1.32 g∙cm⁻³ for the plow pan, and 1.63 g∙cm⁻³ for the subsoil layer. Subsequently, a simulation region measuring 2750 mm × 2500 mm × 300 mm was defined. The modules for each soil layer were sequentially called to generate the particle bed. After finalizing the soil particle parameters, 393,933 soil particles were generated, as illustrated in Figure 4.

The material properties of the soil covering blade and compaction roller were set as follows: Poisson’s ratio of 0.3, density of 7.8 g∙cm⁻³, and shear modulus of 7.9 × 10¹⁰ Pa [19]. The contact parameters are detailed in

Table 1. Contact parameters for the tillage model.

Category	Coefficient of Restitution	Static Friction Coefficient
Plow layer–plow layer	0.35	0.29
Plow pan–plow pan	0.35	0.32
Subsoil–subsoil	0.40	0.25
Inter-layer	0.3	0.50
Rice–tillage components	0.3	0.3
Soil–rice	0.5	0.5
Soil–tillage components	0.6	0.6

.

In the simulation tests, a uniform travel speed of 3 km∙h⁻¹ was set for both the soil covering blades and the press roller. The soil covering component was configured with two motion types: linear travel motion and rotational motion. The simulation duration was fixed at 5 s, with the time step size set to a fixed 20% of the total step duration. The computational grid cell size was defined as three times the minimum particle radius. The simulation process is illustrated in Figure 5 and Figure 6.

2.2.2. Simulation Experiment Methodology

Based on previous optimization work on the grooving structure and seeding height control of the Double-Press Deep Seeder (DPDS) conducted by the research group [20,21], this study focuses exclusively on optimizing parameters related to seeding depth control, including the compaction force [10], the rotational speed [22], and the soil engagement depth of the soil covering cutters [23]. To determine optimal operational parameters, a response surface methodology (RSM) approach using Box–Behnken Design (BBD) was employed. A three-factor, three-level orthogonal combination simulation test was designed, with the factor levels detailed in

Table 2. Factor levels for simulation experiments.

Level	Test Factors
	Compaction Force (kPa)	Rotational Speed (rpm)	Soil Engagement Depth (cm)
1	40	250	5
2	60	300	7
3	80	350	9

.

Simulation tests employed the missed seeding rate and qualified seeding depth rate as evaluation indices to assess seeding performance. The missed seeding rate (M) represents the proportion of unseeded locations per unit area, calculated as

$$\begin{array}{c}M=\left({\displaystyle \frac{L}{S}}\right) \times 100\%\end{array}$$

(1)

where L is the number of missed seeds in a 50 cm × 50 cm area, and S is the total number of seeds sown in the same area.

The qualified seeding depth rate (N) represents the proportion of seeds within a 50 cm × 50 cm area that achieve a soil cover depth of 3 cm ± 0.5 cm, calculated as

$$\begin{array}{c}N=\left({\displaystyle \frac{A}{P}}\right) \times 100\%\end{array}$$

(2)

where A is seeds covered at 3 ± 0.5 cm depth, and P is the total number of seeds sown in a 50 cm × 50 cm area.

2.3. Field Experiment

2.3.1. Experimental Design

Field experiments were conducted from 2021 to 2024 at Qili Town, Jiangdu District, Yangzhou City, Jiangsu Province, China (119.5403° E, 32.4803° N). This experimental site lies within the Middle-Lower Yangtze River Plain, characterized by a typical northern subtropical humid monsoon climate, with mean annual temperature of 15 °C, annual precipitation of 1020 mm, and 2140 sunshine hours. The experimental field featured wheat as the previous season’s crop, with clay loam soil texture in the tillage layer. The key soil properties included clay content: 27%; sand content: <56%; pH: 8.35. The rice cultivar used throughout the trials was Nanjing 9108.

Three mechanized planting methods were set up for the experiment, with 3 repetitions for each treatment, as follows:

(1)

DPDS (S1): Integrated machine performing basal fertilization, dual-axis rotary tillage (20 cm depth), grooving, depth-controlled seeding (3 cm depth, 25 cm row spacing), soil covering (1.0–1.5 cm), and compaction. Seeding rate: 105 kg∙hm⁻²; compaction pressure: 60 kPa. Sown in early June annually; seedlings thinned to uniform density at three-leaf stage.

(2)

Conventional dry direct seeding (S2): Fertilization via broadcast spreader, stubble incorporation via reverse rotary tiller, seeding via 7.5 cm precision seeder (1 cm depth, 25 cm row spacing, 105 kg∙hm⁻²), followed by light compaction (60 kPa). Sowing and thinning timing matched S1.

(3)

Mechanical transplanting (S3): Nursery trays (60 cm × 30 cm) sown at 120 g/tray around May 25 annually. Transplanting specifications were set at 30 cm row spacing and 11 cm hill spacing, achieving a planting density of 303,000 hills per hectare with 4 seedlings retained per hill. On the 7th day after transplanting, seedling inspection and gap filling were conducted to ensure uniform seedling distribution.

A unified fertilization scheme was applied to all treatments. A total nitrogen (N) application rate of 270 kg∙hm⁻² was implemented through a single basal application of blended fertilizers, comprising fast-release urea, 40-day controlled-release nitrogen fertilizer, and 100-day controlled-release nitrogen fertilizer mixed at a 5:1:4 ratio. Phosphorus fertilizer (P₂O₅) and potassium fertilizer (K₂O) were applied at rates of 135 kg∙hm⁻² and 240 kg∙hm⁻², respectively. These were incorporated into the soil via broadcast spreader as a single basal application before seeding (S1, S2) or transplanting (S3). For S1 and S2, soil moisture was maintained post-sowing for emergence, followed by 3–5 cm shallow water after the three-leaf stage. For S3, 2–3 cm shallow water was maintained post-transplanting. All treatments received intermittent irrigation from jointing to heading, and alternate wet–dry irrigation during grain filling. Pest control followed local recommendations.

2.3.2. Measurement Indicators and Methods

To comprehensively evaluate and compare the agronomic performance of the Double-Press Deep Seeder (DPDS) against conventional methods, a series of key indicators were systematically measured throughout the 2021–2024 growing season. The following parameters were assessed:

(1)

Seeding performance indicators

To compare the seeding performance between the DPDS (Double-Press Deep Seeder) and conventional dry direct-seeding methods, key performance indicators (seeding uniformity, qualified seeding depth rate, and missed seeding rate) were measured on the sowing date during the 2021 growing season. Following operation completion, three seeding rows were randomly selected. From each row, ten consecutive 10 cm long segments were sampled [24], with all aforementioned performance indicators measured within each segment. The calculation methods for qualified seeding depth rate and missed seeding rate followed the same procedures as previously described, while seeding uniformity was calculated as follows:

$$\left\{\begin{array}{c}\overline{X}={\displaystyle \frac{1}{n}}\sum X\\ S=\sqrt{{\displaystyle \frac{1}{n}}\sum {\left(X-\overline{X}\right)}^2}\\ U=\left(1-{\displaystyle \frac{S}{\overline{X}}}\right)\times 100\%\end{array}\right.$$

(3)

where X is the number of seeds per segment, $\overline{X}$ is the average number of seeds per segment, n is the number of measured segments, S is the standard deviation, and U is sowing uniformity.

(2)

Yield and components

Pre-harvest sampling involved randomly selecting five points per plot. For dry direct-seeding treatments (S1/S2), effective panicle counts were recorded within a continuous 2 m row section at each point [25], with each sample comprising 20 consecutive plants. For machine-transplanted rice (S3), effective panicles were counted from 10 consecutive hills per point, with each sample consisting of one hill of plants. Filled grains were identified using the water flotation method, followed by the quantification of grain number per panicle and seed-setting rate. Representative sampling points were selected based on average panicle values. Thousand-grain weight was determined by weighing 1000 randomly selected grains (including unfilled grains) in three replicates (Sartorius BSA224S-CW electronic balance, Germany) (weighing error ≤ 0.05 g), with the final thousand-grain weight calculated as the mean value. During the maturity stage, each plot yielded 10 m², which was converted into actual yield after drying, and the final grain yield was adjusted to 14% moisture content.

(3)

Stem and Tiller Dynamics and Spike Rate

Five representative observation points were identified in each community. S1 and S2 treatments identified one stem tiller observation point for a continuous 1 m double row [26], while S3 treatment identified 10 consecutive holes as one stem tiller observation point. The number of tillers at the jointing stage, heading stage, and maturity stage, respectively, are recorded. The formula for calculating the percentage of productive tillers is as follows:

$$\begin{array}{c}Percentage of productive tillers(\%)={\displaystyle \frac{\mathit{Effective} \mathit{panicles} \mathit{at} \mathit{maturity}}{\mathit{Tillers} \mathit{at} \mathit{jointing}}} \times 100\%\end{array}$$

(4)

Tables were prepared using Microsoft Excel 2017 for Windows. DPS V7.05 was used to analyze the data, and means were tested using the least significant difference at p = 0.05 (LSD0.05).

3. Results and Discussion

3.1. Simulation Results for Operational Parameter Optimization

Based on the response surface methodology (RSM) model, simulation experiments were conducted to calculate the qualified seeding depth rate and missed seeding rate, with the results presented in

Table 3. Simulation experimental design and results for soil covering and compaction.

Test No.	Factor			N (%)	M (%)
	A (kPa)	B (rpm)	C (cm)
1	60	250	9	88.72	5.14
2	80	300	9	82.29	2.57
3	60	300	7	94.81	1.29
4	40	300	9	88.72	5.14
5	80	300	5	82.29	7.71
6	60	300	7	95.15	0
7	60	300	7	94.81	1.29
8	40	350	7	86.15	7.71
9	40	250	7	88.72	9.00
10	80	250	7	88.72	5.14
11	60	300	7	94.81	1.29
12	60	300	7	94.81	1.29
13	80	350	7	84.87	5.14
14	40	300	5	84.87	9.00
15	60	350	9	81.01	2.57
16	60	250	5	84.87	7.71
17	60	350	5	82.29	7.71

. In the table, A, B, and C represent compaction force, rotational speed of the soil covering cutters, and soil engagement depth of the soil covering cutters, respectively. The analysis demonstrated that both regression models for the qualified seeding depth rate (N) and missed seeding rate (M) exhibit p < 0.0001 (highly significant), while the lack-of-fit term showed p > 0.05 (not significant), indicating excellent model fit.

Multiple regression fitting and variance analysis were performed on the test data using Design Expert 12 software. The results are shown in

Table 4. Analysis of variance of simulation results.

Source	N				M
	Mean Square	Mean Square Sum	F	p	Mean Square	Mean Square Sum	F	p
Model	500.69	55.63	36.59	<0.0001	149.10	16.57	45.13	<0.0001
A	25.03	25.03	16.46	0.0048	13.24	13.24	36.05	0.0005
B	20.67	20.67	13.6	0.0078	1.86	1.87	5.07	0.0590
C	10.1	10.1	6.64	0.0366	34.90	34.90	95.08	<0.0001
AB	3.72	3.72	2.45	0.1615	0.42	0.42	1.13	0.3224
AC	1.65	1.65	1.09	0.3320	0.41	0.41	1.12	0.3259
BC	0.43	0.43	0.28	0.6144	1.65	1.65	4.50	0.0716
A2	53.43	53.43	35.14	0.0006	38.38	38.38	104.54	<0.0001
B2	112.53	112.53	74.01	<0.0001	30.62	30.62	83.40	<0.0001
C2	231.64	231.64	152.34	<0.0001	17.76	17.77	48.39	0.0002
Residual	10.64	1.52			2.57	0.37
Anomalistic term	8.67	2.89	5.86	0.0602	1.24	0.41	1.24	0.4054
Pure error	1.97	0.5			1.33	0.33

Note: p < 0.01 highly significant; 0.01 < p < 0.05 significant; p > 0.05 not significant.

. From the perspective of the overall significance of the model, the p values of N and M were less than 0.01, and the impact was extremely significant, which can effectively explain the changes of their indicators. In terms of the influence of various factors, A had a significant influence on N and M. B had a significant effect on N, but had no significant effect on M. C had a significant effect on N and a very significant effect on M. The interaction of AB, AC, and BC had no significant effect on N or M. A² B² and C² had a very significant impact on N and M, indicating that there may be a nonlinear relationship between the compaction force, rotational speed, and soil engagement depth and the missed seeding rate and qualified seeding depth rate. Regression analysis was conducted on the data in the table, and the quadratic polynomial regression model for N and M was obtained as follows:

N = 95.2 − 1.77A − 1.61B − 1.12C − 0.97AB + 0.65AC + 0.33BC − 3.56A² − 5.17B² − 37.42C²

(5)

M = 1.03 − 1.29A − 0.49B − 2.09C + 0.32AB − 0.32AC − 0.65BC + 3.02A² + 2.7B² + 2.05C²

(6)

Based on the response surface plots in Figure 7 and Figure 8, it can be concluded that interactions exist among the three factors A, B, and C. Moderate compaction force (60 kPa), rotational speed of the soil covering cutters (300 rpm), and soil engagement depth of the soil covering cutters (7 cm) significantly improved the qualified seeding depth rate and reduced the missed seeding rate. This is because moderate compaction force ensures sufficient and stable seed–soil contact, providing favorable conditions for seed germination. Simultaneously, moderate rotational speed and soil engagement depth of the soil covering cutters facilitate uniform soil covering and seeding, thereby optimizing seed distribution and growth conditions. However, when compaction force, rotational speed, and soil engagement depth of the soil covering cutters were either too high or too low, issues such as excessive soil compaction, uneven seed distribution, or unsuitable covering depth arise. These problems lead to a decreased qualified seeding depth rate and an increased missed seeding rate.

3.2. Field Experiment Results

3.2.1. Seeding Performance Comparison

Based on field test data (

Table 5. Seeding performance measurement results.

Performance Indicator (Mean)	DPDS Machine	Conventional Dry Seeding
Sowing uniformity (%)	85.01 ± 0.25	79.04 ± 0.21
Qualified seeding depth (%)	94.24 ± 0. 18	85.32 ± 0.20
Missed seeding rate (%)	2.31 ± 0.13	4.56 ± 0.17

), the DPDS demonstrated superior performance across all key sowing metrics compared to the conventional dry direct seeding machine, achieving 85.01% sowing uniformity—approximately 6 percentage points higher than the 79.04% of conventional approaches. In terms of qualified seeding depth rate, DPDS achieved a rate of 94.24%, significantly higher than the 85.32% observed with traditional methods. Most notably, DPDS’s missed seeding rate was reduced to 2.31%, representing a reduction of nearly 50% compared to the 4.56% rate in traditional dry direct seeding.

3.2.2. Rice Yield and Components Under Different Planting Methods

It can be seen from

Table 6. Rice yield and components under different planting methods.

Year	Method	Panicle Number (×104 hm−2)	No. of Grains per Panicle	Seed-Setting Rate (%)	1000-Grain Weight (g)	Yield (t·hm−2)
2022	S1	396.18 ± 0.26 b	113.81 ± 0.15 b	91.80 ± 0.11 b	25.15 ± 0.11 b	10.02 ± 0.26 b
	S2	402.39 ± 0.18 a	103.95 ± 0.23 c	89.80 ± 0.21 c	24.85 ± 0.31 b	9.25 ± 0.39 c
	S3	361.15 ± 0.34 c	122.60 ± 0.28 a	93.38 ± 0.28 a	25.30 ± 0.23 a	10.35 ± 0.21 a
2023	S1	400.00 ± 0.35 b	114.10 ± 0.11 b	92.05 ± 0.09 b	25.19 ± 0.27 b	10.43 ± 0.35 b
	S2	415.17 ± 0.12 a	101.77± 0.16 c	90.06 ± 0.16 c	25.15 ± 0.29 b	9.57 ± 0.47 c
	S3	363.17 ± 0.16 c	127.41 ± 0.35 a	93.27 ± 0.32 a	25.41 ± 0.37 a	10.96 ± 0.31 a
2024	S1	401.40 ± 0.09 b	109.80 ± 0.29 b	92.80 ± 0.45 b	24.95 ± 0.28 b	10.25 ± 0.25 b
	S2	410.44 ± 0.25 a	101.50 ± 0.31 c	89.11 ± 0.26 c	25.05 ± 0.19 b	9.39 ± 0.20 c
	S3	361.55 ± 0.27 c	123.22± 0.09 a	94.00 ± 0.24 a	25.15 ± 0.13 a	10.65 ± 0.25 a
Av.	S1	397.82	112.57	92.22	25.10	10.11
	S2	408.15	102.41	89.99	25.02	9.33
	S3	363.29	124.08	93.55	25.29	10.79

Note: Different lowercase letters within a column for the same year indicate significant differences at p < 0.05.

that through the three-year field planting test of rice, the change law of rice yield among planting methods is consistent, and the three-year yield is S3 > S1 > S2 from high to low. Compared with S1 and S2, the yield of S3 increased by 3.3–5.1% and 11.9–14.5%, respectively, and the difference was significant. In terms of yield components, there was a significant difference in the amount of spikelet (Panicle number × No. of grains per panicle × Seed-setting rate) between planting methods. The amount of spikelet in S3 increased by −0.1–2.7% and 10.0–13.4% compared with S1 and S2, respectively. The number of effective panicles in S2 was the highest, which was significantly increased by 1.6–3.8% and 11.4–14.3% compared with S1 and S3, respectively. The number of grains per panicle was the highest in S3, the lowest in S2, and the middle in S1. The difference among treatments was significant. The number of grains per panicle in S3 was 18.0–25.2% and 7.7–12.2% higher than that in S2 and S1, respectively; the overall seed-setting rate showed that S3 was the highest, S2 was the lowest, and S1 was between the two. Among them, S3 was significantly higher than S2 by 3.6–5.5%. The effect of sowing methods on 1000 grain weight was significant, and the data trend was the highest in S3, which increased by 0.4–1.8% and 0.6–0.9% compared with S2 and S1, respectively.

3.2.3. Tiller Dynamics and Productive Tiller Rate

It can be seen from

Table 7. Tiller dynamics and productive tiller rate under different planting methods.

Year	Method	No. of Stems and Tillers/(×104 hm−2)			Percentage of Productive Tillers (%)
		Jointing	Heading	Maturity
2022	S1	513.25 ± 0.31 b	413 ± 0.26 b	371 ± 0.37 b	77.74 ± 0.23 b
	S2	535.74 ± 0.17 a	452 ± 0.13 a	385 ± 0.23 a	74.86 ± 0.26 c
	S3	455.71 ± 0.33 c	361 ± 0.41 c	355 ± 0.19 c	78.94 ± 0.21 a
2023	S1	553.63 ± 0.25 b	416.04 ± 0.15 b	397.58 ± 0.27 b	72.58 ± 0.25 b
	S2	601.36 ± 0.14 a	432.69 ± 0.23 a	409.24 ± 0.24 a	69.04 ± 0.21 c
	S3	469.96 ± 0.32 c	378.69 ± 0.10 c	357.43 ± 0.12 c	77.28 ± 0.19 a
2024	S1	551.42 ± 0.43 b	409.14 ± 0.29 b	386.11 ± 0.15 b	73.05 ± 0.28 b
	S2	587.71 ± 0.38 a	435.09 ± 0.17 a	399.28 ± 0.08 a	69.59 ± 0.26 c
	S3	463.31 ± 0.17 c	375.69 ± 0.25 c	354.28 ± 0.17 c	77.70 ± 0.25 a
Av.	S1	539.43	412.73	384.90	74.46
	S2	574.94	439.93	397.84	71.16
	S3	462.99	371.79	355.57	77.97

Note: Different lowercase letters within a column for the same year indicate significant differences at p < 0.05.

that in the field planting test of rice in these three years, the number of tillers in the jointing stage, heading stage, and maturity stage of rice growth was S2 > S1 > S3, and the difference between S2 and S1 and S3 was significant, but in terms of panicle rate, it was S3 > S1 > S2, and the difference between S3 and S1 and S2 was significant, with an increase of 1.30–8.26% and 4.30–13.47%, respectively. The results showed that S3 had the best performance in controlling ineffective tillers and improving panicle rate. Although the number of stems and tillers in S1 decreased to a certain extent compared with S2, the percentage of productive tillers in S1 increased by 2.88–3.54% compared with S2.

3.3. Discussion

DPDS can significantly enhance the sowing quality of dry direct-seeded rice, thereby increasing rice yield. In this experiment, the three planting methods ranked by yield from high to low were S3 > S1 > S2, with average yields of 10.79 t·hm⁻², 10.11 t·hm⁻², and 9.33 t·hm⁻², respectively. Notably, S1 increased the yield by approximately 8.4% compared to S2, which is likely attributed to DPDS’s double-press precision depth-control seeding technology. This technology enhances soil compaction and moisture content, promoting seed water absorption, germination, and nutrient uptake [27]. Regarding yield components, while the S1 treatment showed slightly fewer productive panicles per unit area than S2, it demonstrated significant improvements in grains per panicle (7.96–12.14% increase), seed-setting rate (3.59–4.91% increase), and total spikelet number per unit area (11.34–14.72% increase) relative to S2. These results indicate that DPDS optimizes population structure by reducing ineffective tillering losses and enhancing photosynthetic efficiency, consistent with our research group’s earlier findings [28].

This study reveals distinct patterns in the number of stems and tillers and percentage of productive tillers across different rice cultivation methods during major growth stages. The number of stems and tillers at jointing, heading, and maturity stages consistently followed the order S2 > S1 > S3. Conversely, the productive tiller percentage ranked S3 > S1 > S2, with S1 showing a significant 2.88–3.54% increase over S2. This demonstrates DPDS’s effectiveness in suppressing ineffective tillers [29]. Although initial tiller counts were slightly lower in DPDS, its uniform seeding and depth control reduced tiller attrition, achieving an average productive tiller percentage of 74.46%—approaching S3’s 77.97%. Existing research indicates that dry direct-seeded rice typically exhibits higher tiller density per unit area than transplanted rice, but a lower productive tiller percentage, often due to nutrient competition and water stress inducing ineffective tillering [30]. Additionally, shortened growth periods in dry direct seeding reduce grains per panicle and seed-setting rates [31]. DPDS mitigates these limitations by optimizing early growth conditions, narrowing the yield gap.

While mechanical transplanting (S3) still yielded slightly more, a result often attributed to the head start that nursery-grown seedlings receive [31,32,33]. The DPDS method presents a more practical and economically attractive alternative. Its ability to nearly match the yield of transplanted rice is significant because it simultaneously eliminates the labor, water, and time-intensive stages of nursery management and transplanting [34]. This combination of high yield potential and operational efficiency positions DPDS as a compelling option for modernizing rice cultivation. However, the broader applicability of the DPDS technology requires further investigation. The machine’s performance should be validated across a wider range of soil types and under heavy straw residue conditions, a known challenge for precision seeders [23]. Furthermore, the higher initial investment for the more complex DPDS machine warrants a thorough economic analysis to assess its viability for farmers at different scales. Future work should focus on integrating the seeder with precision agriculture technologies, such as variable-rate controls [16], to further enhance efficiency. Quantifying the system’s environmental benefits, including water savings and reduced greenhouse gas emissions, will also be crucial for establishing DPDS as a robust and sustainable solution for future rice farming systems.

4. Conclusions

This study tackled the ongoing issue of inadequate seeding depth in the traditional dry direct seeding of rice by introducing a double-press precision depth-control seeding method. Using discrete element method simulations, key operational parameters such as compaction force, rotational speed, and soil engagement depth of soil covering cutters were optimized. These were evaluated based on the seeding depth rate and missed seeding rate. Response surface analysis identified an optimal combination of 60 kPa, 300 rpm, and 7 cm that maximized depth accuracy while minimizing seed omission. Field experiments demonstrated that the double-press depth-control seeding (DPDS) method significantly outperformed conventional dry direct seeders in terms of sowing performance. Over three years of comparative experiments, DPDS also showed improved results compared to conventional dry direct seeding methods and achieved yields comparable to machine-transplanted rice, averaging over 10.11 t·hm⁻². This was accomplished by optimizing the population structure, which maintained a high number of tillers and increased the percentage of productive tillers by 2.88% to 3.54%. Although DPDS yielded 6.3% less than transplanted rice, it offers substantial practical advantages, such as increased operational efficiency and cost savings in seedling production, land preparation, and fertilization. These factors position DPDS as a viable and sustainable solution for dry-seeded rice systems.