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Article

Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil

by
Yadong Li
1,2,
Caiyun Lu
1,2,*,
Hongwen Li
1,2,
Jin He
1,2,
Zhinan Wang
1,2 and
Chengkun Zhai
1,2
1
College of Engineering, China Agricultural University, Beijing 100083, China
2
Scientific Observing and Experiment Station of Arable Land Conservation (North Hebei), Ministry of Agricultural and Rural Affairs, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(14), 1540; https://doi.org/10.3390/agriculture15141540
Submission received: 24 June 2025 / Revised: 14 July 2025 / Accepted: 16 July 2025 / Published: 17 July 2025
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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Abstract

When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations and analyze propagation characteristics. The effects of the compression ratio (0/6/12%), excitation frequency (20/40/60 kHz), and amplitude (5/10/15 μm) on signal variation and attenuation were analyzed. The results show consistent trends: time/frequency domain signal intensity increased with a higher compression ratio and amplitude but decreased with frequency. Comparing ultrasonic signals at soil particles before and after the seed along the propagation path shows that the seed significantly absorbs and attenuates ultrasonic waves. Time domain intensity drops 93.99%, and first and residual wave frequency peaks decrease by 88.06% and 96.39%, respectively. Additionally, comparing ultrasonic propagation velocities in the double media of seed–soil and the single soil medium reveals that the velocity in the seed is significantly higher than that in the soil. At compression ratios of 0%, 6%, and 12%, the sound velocity in the seed is 990.47%, 562.72%, and 431.34% of that in the soil, respectively. These findings help distinguish seed presence and provide a basis for ultrasonic seed position monitoring after sowing.

1. Introduction

Ultrasonic testing is characterized by high precision, non-destructiveness, real-time performance, and low energy consumption [1], and it is widely applied in various fields, such as weld inspection [2], plate defect detection [3,4], material surface roughness testing [5], acoustic detection of pipeline blockages [6], acoustic testing of aviation components [7], and obstacle detection [8]. Meanwhile, ultrasonic technology has been extensively used in agricultural production, including cereal crop weed detection [9], fruit quality evaluation [10,11], maize row detection [12], and pest control [13]. To date, extensive research has been conducted on the application of ultrasonic waves in soil, such as water content detection [14,15,16], porosity testing [17,18], the effect of soil compaction on sound velocity [19], ultrasonic imaging analysis of geotechnical structure stability [20], and acoustic measurement of soil aggregate stability [21,22].
At present, although common seed metering detection methods [23,24,25] can predict the positions of seeds, they cannot accurately obtain the position information of seeds after soil covering, and manual detection methods after sowing [26,27] affect seed position. Ultrasonic testing methods are able to obtain the final position information of seeds, which is beneficial for more accurately acquiring sowing quality in agricultural production. Thus, ultrasonic technology has been increasingly applied to post-sowing seed detection in recent years. For example, Lu used non-destructive ultrasonic testing to detect missing seeding on farmland surfaces after sowing [28], and Huang conducted a series of studies on the propagation of acoustic waves in soil media [29]. However, the severe attenuation of ultrasonic waves in soil currently affects the judgment of seed characteristics, so current research mainly focuses on the propagation characteristics of ultrasonic waves in soil.
When detecting the positions of seeds after sowing, ultrasonic waves act under the double media of seed–soil. In the double media of seed–soil, the seed–soil composite is composed of a seed, minerals, organic matter, moisture, air, etc. Its complex microstructure leads to significant energy attenuation and signal scattering of ultrasonic waves during propagation. Moreover, the coupling mechanism between the seed and soil particles remains unclear, restricting the accuracy of ultrasonic detection [30]. Current research on ultrasonic propagation characteristics around the seed is limited because the seed is embedded in the soil. It is impossible to directly observe the acoustic wave propagation characteristics in and around the seed. Meanwhile, the variation laws of ultrasonic waves within the seed–soil composite are still unclear.
As the direct observation of acoustic wave propagation characteristics in and around the seed under seed–soil double media conditions is impossible via experiments, numerical simulation methods have been introduced for research. Numerical simulations include finite element and discrete element methods. The finite element method treats the soil medium as a whole, and despite considering parameters like overall soil elasticity and stiffness, it fails to reflect the characteristics of internal particles [31]. The discrete element method (DEM), taking particles as units, can fully characterize the influence of particle intrinsic parameters and contact parameters on acoustic wave propagation, providing an effective tool for revealing the propagation mechanism of ultrasonic waves in soil media [32]. Existing studies based on the Hertz–Mindlin (no-slip) model have successfully simulated the propagation mechanism of ultrasonic waves in pure soil, verifying the feasibility of using the discrete element method to study ultrasonic wave propagation in soil [33].
To summarize, the current monitoring of seed positions is mostly predictive research, and the propagation characteristics of ultrasonic waves in the area near the seeds cannot be monitored. To address the above issues, this study introduces the maize seed based on pure soil to further explore the acoustic wave propagation characteristics of the maize seed and surrounding regions under seed–soil double media conditions; the mind map is shown in Figure 1.
This study used ultrasonic waves with different compression ratios, excitation frequencies, and excitation amplitudes. Combined with three-factor three-level single-factor tests, the following scientific questions were analyzed:
(1)
The acoustic wave propagation characteristics of the maize seed under ultrasonic excitation;
(2)
The acoustic wave propagation characteristics of the surrounding region of the maize seed under ultrasonic excitation. This research provides references for ultrasonic detection technology for seed position information after sowing.

2. Materials and Methods

2.1. DEM Calculation Method

The discrete element method (DEM) enables the microscopic observation of acoustic wave signal propagation in seed–soil composites and allows the analysis of the dynamic and kinematic characteristics of maize seed and soil particles. This helps elucidate the causes and laws of waveform changes, attenuation, and energy dissipation under different ultrasonic signals. To control the influence of water content on ultrasonic signal propagation in soil, this study used air-dried soil as the research object. Under dry conditions, the bonding force between particles is small, so the Hertz–Mindlin (no-slip) model was selected. This model serves as the basic model of EDEM, primarily used for discrete particle simulation.
Seeding operation, one of the core links in agricultural production, has a close relationship with the quality of mechanical seeding and the seedling emergence quality of crops [34]. With the continuous improvement in the operation speed of modern seeding units, the requirements for seeding quality have become more stringent. Moreover, high-speed seeding operations have a more significant impact on seed position, making the evaluation of seeding quality increasingly important at present.
Based on a previous study [35], this paper constructed a three-dimensional (3D) ultrasonic testing model for the double media of seed–soil, as shown in Figure 2a. A maize seed was added to the acoustic wave propagation path, as depicted in Figure 2b,c. The main components and parameters of the model are introduced below. The height (h), length (l), and thickness (d) of the box were 130, 100, and 40 mm, respectively. The diameter of both the excitation and receiving transducers was 38 mm. The grid cell size was 2 mm, and the gravitational acceleration was 9.8 m/s2 [36]. The radius of soil particles was 1 mm. To investigate the acoustic wave propagation characteristics of the maize seed and its surrounding region in the double media of seed–soil under ultrasonic excitation, the maize seed was incorporated during soil particle generation, positioned between the excitation and receiving transducers. The file containing the generated particles was exported as the basic model, and subsequent motion analysis was designed using this model according to follow-up requirements. The box and pressure plate were made of steel plate, while the transducers were constructed from piezoelectric ceramic (pzt). The physical parameters of the materials are shown in Table 1, and the contact parameters between materials are listed in Table 2 [37,38].
During the simulation, the pressure plate moved downward from the top of the box to compress the seed–soil composite and prepare samples with different compression ratios. When the pressure plate reached the required compression ratio, it stopped moving. Before the ultrasonic excitation transducer operated, a 7 s quiescent simulation was continued to rebuild the equilibrium process of the composite. A continuous high-frequency sinusoidal motion was set in the y-axis direction to simulate the ultrasonic signal.

2.2. Test Design and Index Measurement

The propagation characteristics of ultrasonic waves in the medium are mainly related to three factors: compression ratio, excitation frequency, and excitation amplitude [29]. This study conducted three-factor three-level single-factor simulation tests. When investigating the variation of a certain factor, the intermediate level of other factors was used as the benchmark. Table 3 lists the levels of each factor. Clutter exists in the soil medium. When the ultrasonic intensity is low, the receiving transducer may fail to receive the ultrasonic signal and, at the same time, cannot easily distinguish the signal of the seed. Therefore, Figure 3 shows the time domain acoustic pressure signals of the excitation transducer at each level.
(1)
Composite compression ratio α.
The compression ratio was a key factor affecting the propagation of ultrasonic waves in the double media of seed–soil. The composite compression ratio was controlled by adjusting the downward movement distance h1 of the pressure plate, whose calculation formula is shown in Equation (1). Since the composite could not be compressed infinitely, the compression ratios in this study were set at 0%, 6%, and 12%.
α = h 1 / h
where α is the composite compression ratio; h1 is the compression height of the composite, mm; and h is the height of the composite, mm.
(2)
The excitation frequency f and excitation amplitude A1 of the ultrasonic transducer
The excitation transducer emits ultrasonic waves through the vibration of piezoelectric ceramics, while the excitation frequency f and excitation amplitude A1 affect the characteristics of the initial signal, thereby influencing the propagation process of ultrasonic waves. This study investigated the variations of f and A1 at different levels. The excitation transducer performs sinusoidal motion in the y-axis direction to simulate ultrasonic emission. The relationships among the phase angle, period, frequency, and amplitude of the excitation transducer are shown in Equations (2)–(4):
y = A 1 sin θ
θ = 2 π   f t
T = 1 / f
where y is the instantaneous displacement of the excitation transducer, μm; A1 is the maximum displacement of the excitation transducer, μm; θ is the phase angle, rad; f is the vibration frequency of the excitation transducer, Hz; t is the time, s; and T is the motion period of the excitation transducer, s.

2.3. Data Measurement and Processing

This study used the DEM model to obtain the movement and spatial trajectory changes of soil particles in and around the maize seed, thereby analyzing the acoustic wave propagation characteristics in this region. When extracting ultrasonic signals from the maize seed and surrounding soil particles, EDEM 2024 extracts the variation patterns of particle centroids.

2.3.1. Time Domain and Frequency Domain Signal of the Maize Seed Force

The forces acting on the maize seed and soil particles are the derivative of macroscopic sound pressure. That is to say, the force received by the maize seed can reflect the excitation process of ultrasonic waves at this position. Therefore, the force on the maize seed can reflect the characteristics of ultrasonic waves here to a certain extent. The time domain and frequency domain signals received by the maize seed under the ultrasonic excitation with a compression ratio of 6%, an excitation frequency of 40 kHz, and an excitation amplitude of 5 μm are shown in Figure 4. In the subsequent analysis, different factors and levels are described according to the nth level of the nth factor. For example, point A2 in Figure 4a represents point A under the conditions of a 6% compression ratio, an excitation frequency of 40 kHz, and an excitation amplitude of 10 μm.
To facilitate the research, the initial force F0 was subtracted from the force F1 acting on the maize seed, as shown in Equation (5).
F = F 1 − F 0
where F is the force acting on the maize seed, N; F1 is the force on the maize seed during ultrasonic influence, N; and F0 is the reference force, i.e., the force before ultrasonic emission, N.
Figure 4a shows the time domain signal of the maize seed under ultrasonic excitation. The maize seed starts to receive the ultrasonic signal from point A2, where the segment A2–B2 is the first-wave signal, B2–C2 is the after-wave signal, and C2–D2 is the invalid wave signal. The segment A2–C2 represents the continuous wave signal phase received by the maize seed. A quantitative analysis of the average peak-to-peak value H of the A–C segment under different levels can yield the overall fluctuation range and energy intensity of the ultrasonic signal within a certain period of time, as shown in Equation (6).
Figure 4b shows the frequency domain signal obtained by performing Discrete Fourier Transform (DFT) on the A2–C2 segment of the time domain signal of the maize seed. The dominant frequency and its peak value of the frequency domain signal are important indicators for studying the response of maize seed. Statistical and quantitative analyses are conducted on the dominant frequency of the first wave (Ff) and the dominant frequency of the after-wave (Fs) in the frequency domain signals under different levels, where the dominant frequency of the first wave (Ff) represents the first peak frequency and the dominant frequency of the after-wave (Fs) represents the second peak frequency.
H = ( P 1 − V 1 + P 2 − V 2 + P 3 − V 3 + P 4 − V 4 + P 5 − V 5 ) / 5
where H is the average peak-to-peak value, N; P1–P5 are wave peaks, N; and V1–V5 are wave valleys, N.

2.3.2. Time Domain and Frequency Domain Signals of Forces Acting on Soil Particles in Surrounding Area of the Maize Seed

Figure 5 shows soil particle 1 and soil particle 2 adjacent to the left and right sides of the maize seed along the ultrasonic propagation path, as well as soil particle 3, which does not pass through the maize seed. To ensure the accuracy of the test results, particle 1, particle 2, and particle 3 are located on the same horizontal plane. Since the laws under each factor and level are basically consistent, the signals received by soil particles in the surrounding area of the maize seed under the conditions of a 6% compression ratio, an excitation frequency of 40 kHz, and an excitation amplitude of 10 μm were analyzed. Figure 6a shows the time domain signal of particle 1 among the three soil particles. Particle 1 starts to be excited by ultrasonic waves from point A1. The segment A1–C1 represents the continuous wave signal phase received by particle 1. The average peak-to-peak value H of the A–C segment in the time domain signals of different particles was quantitatively analyzed, as shown in Equation (6).
The frequency domain signals obtained by performing Discrete Fourier Transform (DFT) on the A–C segments of the ultrasonic continuous wave signals received by the three soil particles are shown in Figure 6b for the frequency domain signal of particle 1. By studying the time domain and frequency domain variations of ultrasonic waves in the surrounding area of the maize seed, the influence of the maize seed on ultrasonic wave propagation can be analyzed.

2.3.3. Displacement of the Maize Seed and Soil Particles in Its Surrounding Area

When subjected to ultrasonic excitation, the maize seed and soil particles exhibit certain displacement. This displacement variation not only reflects the movement process of the seed and soil particles but also interferes with the excitation signal, thereby influencing the accurate observation and analysis of the maize seed characteristics.
To study the displacement changes of maize seed and soil particles in its surrounding area, quantitative analysis was conducted on the maximum displacement X of soil particles, as shown in Equation (7). This analysis enables the determination of the vibration amplitude of different soil particles under ultrasonic excitation, thereby facilitating the analysis of acoustic wave propagation characteristics in the vicinity of maize seed.
X = X max − X min
where X is the peak-to-peak displacement, mm; Xmax is the maximum displacement in the y-axis direction, mm; and Xmin is the minimum displacement in the y-axis direction, mm.

2.3.4. Propagation Velocity of Acoustic Wave in Maize Seed c

The ultrasonic propagation time Δt can be obtained from the force curves of the left and right soil particles of the maize seed. By extracting the time when the particles reach the first wave peak or wave valley, as well as the distance difference between the two particles, the propagation velocity c of the ultrasonic waves in the maize seed is thereby calculated, as shown in Equation (8):
c = b / Δ t
where c is the propagation velocity, m/s; b is the distance between the left and right soil particles of the maize seed, m; and Δt is the propagation time of ultrasonic waves in the maize seed, s.

3. Results and Discussion

3.1. Validation of the Reliability of the EDEM Simulation Model

To validate the effectiveness of the model, an ultrasonic test bench was built for verification, as shown in Figure 7. The experimental instruments include a ZBL-U5200 non-metallic ultrasonic detector, an RGM-4005 universal material testing machine, and a self-made acoustic wave testing device for seed–soil composites. The ZBL-U5200 non-metallic ultrasonic detector is manufactured by Beijing Zhibolian Technology Co., Ltd, Beijing, China, with an acoustic time reading accuracy of 0.025 μs and a sampling interval of 0.05 μs. The RGM-4005 universal material testing machine is produced by Reger Instruments Co., Ltd., Shenzhen, China, with a maximum load of 5 kN and a relative error of test force ≤1%. The self-made acoustic wave testing device for seed–soil composites includes a box body, a cover plate, excitation and receiving transducers, etc.
During the experiment, soil particles with a diameter of 1–2 mm were placed in an oven and dried until their mass became constant. The soil, a sandy loam, was selected from the experimental field of the Agricultural Science Observation and Experiment Station for Cultivated Land Conservation in Northern Hebei, Ministry of Agriculture, Zhuozhou City, Hebei Province, China. The maize seed and soil were loaded into the self-made acoustic wave testing device for seed–soil composite. The seed–soil composite was compressed to a fixed compression ratio using a universal material testing machine. The ultrasonic detector was used to transmit and receive acoustic wave signals, collect the first-wave signal of the receiving transducer, and calculate the wave velocity. The test results are shown in Table 4. The results indicate that the velocity error is less than 8.13%, the simulation results basically coincide with the experimental results, and the error is within an acceptable range, verifying the reliability of the model.

3.2. Time Domain and Frequency Domain Signal of the Maize Seed Force

3.2.1. Time Domain Signal of the Maize Seed Force

Figure 8 shows the time domain curves of force acting on the maize seed under ultrasonic excitation with different parameters.
(1)
Compression ratio (α)
Figure 8a shows that when the maize seed is excited by ultrasonic waves under different compression ratios, the magnitude of the signals received by the maize seed varies. As the compression ratio increases, the average peak-to-peak value H gradually increases. When the compression ratios are 6% and 12%, their average peak-to-peak values H are 198% and 768.09% of that at a compression ratio of 0%. This may be because a high compression ratio brings the seed–soil composite particles closer together, strengthens particle constraints, and improves ultrasonic signal transmission efficiency; in contrast, a low compression ratio increases particle gaps, weakens inter-particle cohesion, and intensifies ultrasonic attenuation [39,40]. Meanwhile, changes in the compression ratio lead to different vibration times of the maize seed. As shown in Figure 9, the time required for ultrasonic waves to propagate to the maize seed at a 0% compression ratio (point A1) is 242.86% and 309.09% of that at compression ratios of 6% (point A2) and 12% (point A3), respectively. This is because the propagation velocity of ultrasonic waves is related to the elastic modulus and density of the medium. When the seed–soil composite is compressed, its elastic modulus and density increase, altering the propagation velocity of ultrasonic waves.
(2)
Excitation frequency (f)
Figure 8b shows that when other factors remain constant, the average peak-to-peak value H gradually increases with the increase in excitation frequency. The average peak-to-peak value H of ultrasonic waves at an excitation frequency of 20 kHz is 1396.65% and 1269.04% of that at excitation frequencies of 40 kHz and 60 kHz, respectively. The reason for this is that as the excitation frequency increases, ultrasonic wave attenuation during transmission becomes more significant, but when the excitation frequency reaches a certain level, the attenuation of ultrasonic waves decreases instead. This is related to the main frequency distribution of ultrasonic waves. When ultrasonic waves of different frequencies propagate in the same medium, the acoustic attenuation coefficient first increases and then decreases, which is consistent with the research results of Huang [33]. Meanwhile, for low-frequency ultrasonic waves, due to their long period (segment A1–C1), the recovery speed of the seed–soil composite is higher than the movement speed of the excitation transducer, causing the excitation transducer to act on the seed–soil composite again before returning to the origin, thus resulting in better penetration of low-frequency ultrasonic waves [41]. In contrast, for high-frequency ultrasonic waves, due to their short period (segments A2–C2 and A3–C3), the recovery speed of the seed–soil composite is lower than the movement speed of the excitation transducer, leading to the excitation transducer being unable to act on the seed–soil composite again when returning to the origin, so high-frequency ultrasonic waves suffer more severe attenuation [42].
(3)
Excitation amplitude (A1)
Figure 8c shows that when the excitation amplitude increases, the force acting on the maize seed becomes greater. When the excitation amplitudes are 10 μm and 15 μm, the average peak-to-peak values H are 199.65% and 298.73% of that at an excitation amplitude of 5 μm, respectively. This is because a larger excitation amplitude leads to a greater displacement generated by the excitation transducer, resulting in a higher compression of the seed–soil composite and more energy applied to the composite [29]. By comparing with Figure 3, it is found that the A–C segments in Figure 8a–c all exhibit inconsistencies with the sound pressure waveform changes of the excitation transducer. The reason for this is that during the process of the maize seed receiving ultrasonic signals, it is affected by the reflected waves from the receiving transducer, forming an interference region near the maize seed, as shown in Figure 10. Therefore, the received signals are inconsistent with the changes in the excitation transducer.

3.2.2. Frequency Domain Signal of the Maize Seed Force

Figure 11 shows the frequency-domain curves of force acting on the maize seed under ultrasonic excitation with different parameters.
(1)
Compression ratio (α)
As shown in Figure 11a, as the compression ratio increases, the dominant frequency Ff of the first wave gradually shifts to the right. When the compression ratios are 6% and 12%, the dominant frequency Ff of the first wave expands to 333.89% and 458.90% of that at a compression ratio of 0%, respectively. This is because the dominant frequency Ff of the first wave is related to the natural frequency of the soil. As the compression ratio of the composite increases, the natural frequency of soil particles increases, and the maize seed is affected by the vibration of surrounding soil particles [43]. When the compression ratios are 6% and 12%, the peak values of the dominant frequency Ff of the first wave are 252.11% and 513.09% of that at a compression ratio of 0%. A possible reason is that a higher compression ratio makes the particles closer together, resulting in better ultrasonic wave transmission, while lower compression ratios lead to larger pores between particles, causing more severe acoustic wave attenuation [44]. The dominant frequency Fs of the after-wave remains basically unchanged, but its peak value gradually increases. When the compression ratios are 6% and 12%, the peak values of the dominant frequency Fs of the after-wave are 165.82% and 727.19% of that at a compression ratio of 0%, respectively. The dominant frequency Fs of the after-wave is related to the excitation frequency of ultrasonic waves (the excitation frequency in (a) is 40 kHz). Compared with ultrasonic pulse waves, continuous ultrasonic waves have more significant energy at specific frequencies. The data show that an increase in the compression ratio affects the transmission of ultrasonic waves, thereby influencing the dominant frequency and its peak value.
(2)
Excitation frequency (f)
Figure 11b shows that as the excitation frequency increases, the peak values of the dominant frequency Ff of the first wave and the dominant frequency Fs of the afterwave gradually decrease. When the excitation frequencies are 40 kHz and 60 kHz, the peak values of the dominant frequency Ff of the first wave are 9.56% and 8.38% of that at an excitation frequency of 20 kHz, while the peak values of the dominant frequency Fs of the after-wave are 100.86% and 18.51%, respectively. At 20 kHz, the peak value is far greater than those at other frequencies. The reason for this phenomenon is that low-frequency ultrasonic waves have strong penetration ability, resulting in less energy attenuation during propagation; meanwhile, it is related to the natural frequency of soil particles near the maize seed. When the ultrasonic frequency is close to the natural frequency of soil particles, resonance occurs, leading to a larger peak value of the dominant frequency Ff of the first wave at 20 kHz. The peak values of the dominant frequency Fs of the after-wave at 20 kHz and 40 kHz are close, which is due to the harmonic effect of ultrasonic waves: 20 kHz ultrasonic waves can superimpose to form 40 kHz ultrasonic waves. However, at 60 kHz, the peak value of the dominant frequency Fs of the after-wave is lower because the higher excitation frequency of ultrasonic waves causes more severe attenuation.
(3)
Excitation amplitude (A1)
Figure 11c shows that as the amplitude increases, the peak values of the dominant frequency Ff of the first wave and the dominant frequency Fs of the after-wave gradually increase. When the excitation amplitudes are 10 μm and 15 μm, the peak values of the dominant frequency Ff of the first wave are 189.03% and 270.65% of that at an excitation amplitude of 5 μm, while the peak values of the dominant frequency Fs of the after-wave are 203.55% and 305.15%, respectively. The dominant frequency Fs of the after-wave is generated by the continuous after-wave signal (segment B–C in Figure 4a) and is related to the excitation frequency of the excitation transducer. Under the premise of the same multiple increase in excitation amplitude, the growth rate of the peak value of the dominant frequency Fs of the after-wave is significantly higher than that of the dominant frequency Ff of the first wave, indicating that when the excitation amplitude changes, the propagation effect of the dominant frequency Fs of the after-wave is better. This shows that the energy transmission effect of continuous ultrasonic waves is better, which is consistent with the research results of Kim [45].
When other conditions remain unchanged, by observing different compression ratios, excitation frequencies, and excitation amplitudes, it can be concluded that the signal amplitude received by the maize seed increases with the increase in excitation amplitude and compression ratio but decreases with the increase in frequency. However, greater energy transmission may be better, and further research is required to verify this.

3.3. Time Domain and Frequency Domain Signals of Forces Acting on Soil Particles in Surrounding Area of the Maize Seed

As shown in Figure 12a, comparing the forces acting on three soil particles along the ultrasonic propagation path reveals that the average peak-to-peak value H of particle 2 decreases by 93.99% compared to particle 1 and by 44.72% compared to particle 3. This phenomenon occurs because when ultrasonic waves propagate through the maize seed, the vibration of the maize seed and friction with surrounding soil particles consume part of the energy, leading to a certain degree of attenuation after the ultrasonic waves pass through the maize seed. This is consistent with the research results of [46]. The onset time of vibration for particle 2 is 35.52% earlier than that for particle 3. The reason for this is that soil particles and maize seed are different media, and the propagation velocity of ultrasonic waves varies in different media. The maize seed has a higher elastic modulus, making the propagation velocity faster where maize seeds exist along the acoustic path, thus advancing the time when particle 2 receives the ultrasonic signal. In Figure 12a, particle 2 directly exhibits a wave trough instead of a wave peak at the initial vibration, not following the same trajectory variation pattern as other particles. A possible reason for this may be that particle 2 is located behind the maize seed along the acoustic propagation path, and the vibration of the maize seed affects the vibration pattern of particle 2.
As shown in Figure 12b, during the propagation of ultrasonic waves, maize seeds cause absorptive attenuation of ultrasonic signals. The peak values of the dominant frequency Ff of the first wave and the dominant frequency Fs of the after-wave for particle 2 are reduced to 11.94% and 3.61% of those for particle 1, respectively. Meanwhile, the peak values of the dominant frequency Ff of the first wave and the dominant frequency Fs of the after-wave for particle 3 are 186.84% and 239.43% of those for particle 2. The reason for this phenomenon is that during the propagation of ultrasonic waves, maize seeds consume part of the ultrasonic energy for their own vibration and friction with surrounding soil particles. Compared with particle 3, which has no maize seed along the propagation path, ultrasonic waves suffer less attenuation during transmission.
Through comparative analysis, it can be seen that the presence of the maize seed significantly alters the propagation characteristics of ultrasonic waves during transmission. Along the propagation path, the maize seed absorbs part of the ultrasonic energy, leading to acoustic wave attenuation. Meanwhile, due to its higher elastic modulus than soil particles, the ultrasonic wave propagates faster in the seed, causing particle 2 to receive the ultrasonic signal and vibrate earlier than particle 3. The abnormal response of particle 2 originates from its adjacency to the maize seed: the vibration of the maize seed affects the vibration of particle 2, causing its initial vibration pattern to deviate from that of other particles. The maize seed exhibits a “filtering effect” on ultrasonic waves: its own vibration and friction with soil particles consume energy, not only reducing the overall amplitude of particle 2 but also causing the peak value of the after-wave dominant frequency Fs to disappear in the spectrogram due to the preferential attenuation of the high-frequency component Fs.

3.4. Displacement of the Maize Seed and Soil Particles in Its Surrounding Area

3.4.1. Displacement of the Maize Seed

Figure 13 shows the spatial trajectory and displacement time domain signal of the maize seed under ultrasonic excitation. As can be seen from Figure 13a, the maize seed moves from the initial position (0, 0, 0) and reaches (0.5247 × 10−5 mm, −10.301 × 10−5 mm, −10.639 × 10−5 mm) after a certain time. In the trajectory diagram, the maize seed does not return to their initial position, whereas in a continuous medium, each particle eventually returns to its initial position when sound waves propagate. A possible reason is that the maize seed and soil particles are discrete particles rather than a whole. The displacement of the maize seed under sound waves consumes part of the energy, and the friction between the maize seed and the surrounding soil particles also consumes part of the energy, leading to the conversion of acoustic energy. This is consistent with Brandão’s view that frictional force plays an important role in particle dynamics [47].
To study the displacement characteristics of the maize seed in each direction within a 3D space, the displacement-time functions of the maize seed in the x, y, and z directions were extracted, as shown in Figure 13b. Under ultrasonic excitation, in the directions perpendicular to the acoustic wave propagation (x and z directions), the maize seed generates displacement due to the force exerted by nearby soil particles. This occurs because soil particles are spherical, and when acoustic waves propagate, soil particles can transmit force in all directions, enhancing diffuse attenuation. Martin R et al. also demonstrated the lateral attenuation of acoustic waves through experiments [48].
Figure 13b shows that the maize seed generates significant displacement in the acoustic wave propagation direction (y-axis direction), so this paper focuses on analyzing the displacement curve of the maize seed in the y direction. As can be seen from Figure 13b, the maize seed does not vibrate near y = 0, indicating that the vibration center of the maize seed deviates from the initial position. When acoustic waves pass through a continuous medium, particles vibrate back and forth around the initial position and finally return to the initial position. However, in discrete media such as soil, particles move in the direction of acoustic wave propagation because there is no fixed force between particles to drive them back to the initial position after the action of acoustic waves. Therefore, the maize seed does not return to the initial position after being acted upon by acoustic waves.

3.4.2. Displacement of Soil Particles in the Surrounding Area of the Maize Seed

As the particles exhibit significant displacement in the acoustic wave propagation direction (y-axis direction), subsequent analysis focuses on the displacement of the three soil particles in the y-axis direction. Figure 14 shows the displacement-time curves of the three soil particles under ultrasonic excitation. In Figure 14, particles 1 to 3 generate different displacements when excited by ultrasonic waves. The peak-to-peak displacement X1 of particle 1 is 278.96% and 223.32% of that of particle 2 and particle 3, respectively, while the maximum displacement X2 of particle 2 is 80.05% of that of particle 3. Analysis of the peak-to-peak displacement X of the particles indicates that acoustic wave attenuation occurs in the region near the maize seed, which may be related to the presence of the maize seed. Compared with the displacements of soil particles in Huang [33], the displacements of soil particles in this study are significantly smaller. The reason is that the maize seed was added in this study, and there is a clear distinction in the medium between the two studies. The presence of the maize seed in this study resulted in smaller displacements of soil particles.
In Figure 14, the curve trajectories of particle 2 and particle 3 differ significantly. When particles 2 and 3 receive acoustic signals, particle 2 vibrates more slowly in the y-direction than particle 3, while particle 3 vibrates more vigorously. Although the subsequent trajectories are basically consistent, particle 3 has many minute vibrations compared to particle 2. This may be because the movement of the maize seed itself consumes part of the acoustic wave energy, causing the adjacent particle 2 to be forced to be disturbed by the vibration of the maize seed, showing a pattern basically consistent with the vibration of the maize seed in the y-direction, as shown in Figure 13b. Since there is no maize seed in the acoustic propagation path of particle 3, and only soil particles exist in the propagation path, particle 3 still has some vibrations. Therefore, the presence of the maize seed significantly alters the signal of particle 2, indicating that the maize seed has an absorptive attenuation effect on acoustic waves, leading to acoustic wave signal attenuation.

3.5. Propagation Velocity of Acoustic Wave in the Maize Seed

It can be seen from Figure 12a that the initiation times of vibration for particle 2 and particle 3 are different, so the propagation velocity of ultrasonic waves in the maize seed can be calculated. The propagation velocity of acoustic waves under various parameters is calculated according to Equation (8), as shown in Figure 15.
As shown in Figure 15a, as the compression ratio of the seed–soil composite increases from 0% to 6% and 12%, the propagation velocity of ultrasonic waves in maize seed increases by 29.53% and 49.88%, respectively. The reason for this phenomenon is that as the compression ratio of the composite increases, the maize seed is compressed, its internal structure becomes gradually denser, and thus its elastic modulus and density change. According to acoustic principles, an increase in density and elastic modulus leads to an acceleration of ultrasonic propagation velocity. In Figure 15b,c, it can be seen that the propagation velocity of ultrasonic waves remains constant with changes in excitation frequency and excitation amplitude. This is because the propagation velocity of ultrasonic waves in the same medium is constant; that is, the wave velocity is determined by the properties of the medium, including the elasticity and density of the medium. In a homogeneous medium, the propagation velocity is a constant value, independent of the frequency and amplitude of the ultrasonic waves.
By comparing with the propagation velocity of acoustic waves in soil by Huang [43], it is found that at compression ratios of 0%, 6%, and 12%, the propagation velocity of ultrasonic waves in maize seed is 990.47%, 562.72%, and 431.34% of that in soil, respectively. Although the acoustic wave propagation velocity in the maize seed is faster, increasing the compression ratio does not widen the velocity gap between the maize seed and the soil. Therefore, the increase in the compression ratio may affect the judgment of maize seed signals.
The above experiments were conducted under relatively ideal conditions, without considering the influences of factors such as soil type, moisture content, or stones. In the future, we not only need to address these related issues but also conduct experiments targeting different soil environments and establish a database so that the research can ultimately be applied to various environments.

4. Conclusions

In this study, the discrete element method was used to investigate the acoustic wave propagation characteristics of maize seed and the surrounding area in the double media of seed–soil under different influencing factors. The research focused on the time domain and frequency domain characteristics of the maize seed and surrounding soil particles under various parameters, as well as the propagation velocity of ultrasonic waves in the maize seed, to explain the influence of the maize seed on ultrasonic wave propagation in the soil medium.
The discrete element method can effectively visualize the propagation process of ultrasonic waves, enabling analysis of the time domain and frequency domain variation laws of any particle within the seed–soil composite during ultrasonic propagation. This method clarifies the propagation rules of acoustic waves in maize seed and adjacent soil particles.
During the propagation of ultrasonic waves, maize seeds cause absorptive attenuation of the ultrasonic signal, resulting in weakened energy in subsequent transmission. Such a result is helpful for distinguishing whether maize seeds exist in the soil. A comparison of ultrasonic signals at soil particles before and after the maize seed shows that after passing through the maize seed, the intensity of the time domain signal decreases by 93.99%, while the main frequency peaks of the first wave and residual wave in the frequency domain signal are reduced by 88.06% and 96.39%, respectively.
The propagation speed of ultrasonic waves in maize seeds is significantly higher than that in soil. At compression ratios of 0%, 6%, and 12%, the sound velocity in maize seeds is 990.47%, 562.72%, and 431.34% of that in soil, respectively. However, the composition of actual soil is more complex. This study, which focuses on the propagation process of ultrasonic waves in the seed–soil double medium, was conducted under relatively ideal conditions and did not consider interfering factors such as soil type, moisture content, organic matter content, stones, or plant roots. Additionally, this research did not investigate seed types with different shapes and sizes. These factors may affect the experimental results and require further study in the future.
In future research, we will not only consider the impact of complex environmental factors on the signal propagation process but also establish a database to adjust for different environments, enabling the detection to be applicable under various conditions. Additionally, we need to process the signals received by the receiving transducer and adopt methods such as filtering algorithms to determine whether maize seeds exist in the soil. This work will provide theoretical support for the development of detection instruments under the seed–soil double medium ultrasonic system.

Author Contributions

Conceptualization, Y.L. and C.L.; methodology, Y.L. and C.L.; software, Y.L., Z.W., and C.Z.; investigation, Y.L.; data curation, Z.W. and C.Z.; writing—review and editing, Y.L.; supervision, H.L., J.H. and C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 32401710, the funder is Caiyun Lu.), the National Key Research and Development Program of China (Grant No. 2023YFD1500401, the funder is Caiyun Lu.), the Xing’an League Local Level Science and Technology Cooperation Project of Inner Mongolia Autonomous Region (The funder is Caiyun Lu.), and the 2115 Talent Development Program of China Agricultural University (2115, the funder is Hongwen Li.).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data are contained within this article. The original contributions presented in this study are included in this article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Sun, B.; Zhang, C.; Liu, S.; Xu, Z.; Zhao, Z. Ultrasonic Non-Destructive Testing on Fast-Charging Lithium-Ion Battery’s Capacity Fading. J. Electrochem. Soc. 2024, 171, 030517. [Google Scholar] [CrossRef]
  2. Yuan, Z.; Gao, X.; Yang, K.; Peng, J.; Luo, L. Performance Enhancement of Ultrasonic Weld Defect Detection Network Based on Generative Data. J. Nondestruct. Eval. 2024, 43, 102. [Google Scholar] [CrossRef]
  3. Hayashi, T.; Nakao, S. Defect imaging for plate-like structures using diffuse acoustic wave generated by modulated laser. AIP Conf. Proc. 2019, 2102, 050003. [Google Scholar] [CrossRef]
  4. Lin, W.S.; Wu, J.Z. Non-Destructive Testing of Wood Defects Based on Ultrasonic Technology. Appl. Mech. Mater. 2013, 2668, 1124–1128. [Google Scholar] [CrossRef]
  5. Sukmana, D.D.; Ihara, I. Surface Roughness Characterization through the Use of Diffuse Component of Scattered Air-Coupled Ultrasound. Jpn. J. Appl. Phys. 2006, 45, 4534–4540. [Google Scholar] [CrossRef]
  6. Dragonetti, R.; Romano, R.A.; Giulio, E.D. Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping. Appl. Sci. 2025, 15, 1373. [Google Scholar] [CrossRef]
  7. Kenta, M.; Takumi, I.; Ren, K.; Kazuhisa, O. Proposal of detecting method for small delamination in heat seal of flexible packages using aerial ultrasonic wave. Proc. Dyn. Des. Conf. 2017, 2017, 524. [Google Scholar] [CrossRef]
  8. Rosli, E.; Husaini, Y. Design and Development of Smart Medicine Box. IOP Conf. Ser. Mater. Sci. Eng. 2018, 341, 012004. [Google Scholar] [CrossRef]
  9. Andújar, D.; Weis, M.; Gerhards, R. An Ultrasonic System for Weed Detection in Cereal Crops. Sensors 2012, 12, 17343–17357. [Google Scholar] [CrossRef]
  10. Marek, D.; Krzysztof, P.; Krzysztof, K. The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques. Sensors 2023, 23, 1787. [Google Scholar] [CrossRef]
  11. Yildiz, F.; Özdemir, A.T.; Uluışık, S. Evaluation Performance of Ultrasonic Testing on Fruit Quality Determination. J. Food Qual. 2019, 2019, 6810865. [Google Scholar] [CrossRef]
  12. Austin, S.; Chetan, B.; Hasib, M.; Daniel, F.; Brian, M.; Ajay, S. Design of a Reconfigurable Crop Scouting Vehicle for Row Crop Navigation: A Proof-of-Concept Study. Sensors 2022, 22, 6203. [Google Scholar]
  13. Nik Abdull Halim, N.M.H.; Che Dom, N.; Mat Seleei, N.M.A.S.; Mohd Jamili, A.F.; Precha, N. Radiation effects on dengue vectors: A systematic review. DYSONA-Appl. Sci. 2025, 6, 160–171. [Google Scholar] [CrossRef]
  14. Adamo, F.; Andria, G.; Attivissimo, F.; Giaquinto, N. An Acoustic Method for Soil Moisture Measurement. IEEE Trans. Instrum. Meas. 2004, 53, 891–898. [Google Scholar] [CrossRef]
  15. Gorthi, S.; Chakraborty, S.; Li, B.; Weindorf, D.C. A field-portable acoustic sensing device to measure soil moisture. Comput. Electron. Agric. 2020, 174, 105517. [Google Scholar] [CrossRef]
  16. Sharma, R.K.; Gupta, A.K. Continuous wave acoustic method for determination of moisture content in agricultural soil. Comput. Electron. Agric. 2010, 73, 105–111. [Google Scholar] [CrossRef]
  17. Bradley, S.; Ghimire, C. Design of an Ultrasound Sensing System for Estimation of the Porosity of Agricultural Soils. Sensors 2024, 24, 2266. [Google Scholar] [CrossRef]
  18. Salem, H.S. Poisson’s ratio and the porosity of surface soils and shallow sediments, determined from seismic compressional and shear wave velocities. Géotechnique 2000, 50, 461–463. [Google Scholar] [CrossRef]
  19. Lu, Z.; Hickey, C.J.; Sabatier, J.M. Effects of Compaction on the Acoustic Velocity in Soils. Soil Sci. Soc. Am. J. 2004, 68, 7–16. [Google Scholar] [CrossRef]
  20. Xianjian, Z.; Bingrui, C.; Huan, S.; Zhimin, M.; Shuangyuan, C. The stability analysis of weak structural planes based on multi-frequency ultrasonic imaging characteristics during rock and soil physical model tests. Environ. Earth Sci. 2021, 80, 775. [Google Scholar] [CrossRef]
  21. Schomakers, J.; Mentler, A.; Degischer, N.; Blum, W.E.H.; Mayer, H. Measurement of soil aggregate stability using low intesity ultrasonic vibration. Span. J. Soil Sci. 2011, 1, 8–19. [Google Scholar]
  22. Rawlins, B.G.; Wragg, J.; Lark, R.M. Application of a novel method for soil aggregate stability measurement by laser granulometry with sonication. Eur. J. Soil Sci. 2013, 64, 92–103. [Google Scholar] [CrossRef]
  23. Tang, H.; Xu, C.; Wang, Z.; Wang, Q.; Wang, J. Optimized Design, Monitoring System Development and Experiment for a Long-Belt Finger-Clip Precision Corn Seed Metering Device. Front. Plant Sci. 2022, 13, 814747. [Google Scholar] [CrossRef] [PubMed]
  24. Zhai, C.; Lu, C.; Li, H.; He, J.; Wang, Q.; Chang, F.; Bi, J.; Wu, Z. A precise maize seeding parameter monitoring system at the end of seed tube: Improving monitoring accuracy using near-infrared diffusion emission-diffuse reflectance (NIRDE-DR). Comput. Electron. Agric. 2024, 227, 109626. [Google Scholar] [CrossRef]
  25. Zhang, J.; Hou, Y.; Ji, W.; Zheng, P.; Yan, S.; Hou, S.; Cai, C. Evaluation of a Real-Time Monitoring and Management System of Soybean Precision Seed Metering Devices. Agronomy 2023, 13, 541. [Google Scholar] [CrossRef]
  26. Cui, J.; Zheng, H.; Zeng, Z.; Yang, Y.; Ma, R.; Tian, Y.; Tan, J.; Feng, X.; Qi, L. Real-time missing seedling counting in paddy fields based on lightweight network and tracking-by-detection algorithm. Comput. Electron. Agric. 2023, 212, 108045. [Google Scholar] [CrossRef]
  27. Zhang, C.; Xie, X.; Zheng, Z.; Wu, X.; Wang, W.; Chen, L. A Plant Unit Relates to Missing Seeding Detection and Reseeding for Maize Precision Seeding. Agriculture 2022, 12, 1634. [Google Scholar] [CrossRef]
  28. Lu, C.; Li, H.; He, J.; Wang, Q.; Wang, C.; Liu, J. A Preliminary Study of Seeding Absence Detection Method for Drills on the Soil Surface of Cropland Based on Ultrasonic Wave without Soil Disturbance. J. Sens. 2019, 2019, 7434197. [Google Scholar] [CrossRef]
  29. Huang, S.; Lu, C.; Li, H.; He, J.; Wang, Q.; Yuan, P.; Gao, Z.; Wang, Y. Transmission rules of ultrasonic at the contact interface between soil medium in farmland and ultrasonic excitation transducer. Comput. Electron. Agric. 2021, 190, 106477. [Google Scholar] [CrossRef]
  30. Zhou, H.; Chen, Y.; Sadek, M.A. Modelling of soil–seed contact using the Discrete Element Method (DEM). Biosyst. Eng. 2014, 121, 56–66. [Google Scholar] [CrossRef]
  31. Li, M.; Xu, S.; Yang, Y.; Guo, L.; Tong, J. A 3D simulation model of corn stubble cutting using finite element method. Soil Tillage Res. 2017, 166, 43–51. [Google Scholar] [CrossRef]
  32. Lei, X.; Wu, W.; Deng, X.; Li, T.; Liu, H.; Guo, J.; Li, J.; Zhu, P.; Yang, K. Determination of Material and Interaction Properties of Granular Fertilizer Particles Using DEM Simulation and Bench Testing. Agriculture 2023, 13, 1704. [Google Scholar] [CrossRef]
  33. Huang, S.; Lu, C.; Li, H.; He, J.; Wang, Q.; Cao, X.; Gao, Z.; Wang, Z.; Lin, H. Research on acoustic wave attenuation from the perspective of soil particle movement using the discrete element method. Comput. Electron. Agric. 2023, 207, 107747. [Google Scholar] [CrossRef]
  34. Cay, A.; Kocabiyik, H.; May, S. Development of an electro-mechanic control system for seed-metering unit of single seed corn planters Part II: Field performance. Comput. Electron. Agric. 2018, 145, 11–17. [Google Scholar] [CrossRef]
  35. Wang, Z.; Lu, C.; Li, H.; Wang, C.; Wang, L.; Yang, H. Propagation Laws of Ultrasonic Continuous Signals at the Transmitting Transducer–Soil Interface. Agriculture 2024, 14, 1470. [Google Scholar] [CrossRef]
  36. Huang, S.; Lu, C.; Li, H.; He, J.; Wang, Q.; Yuan, P.; Xu, J.; Jiang, S.; He, D. Calibration of Acoustic-Soil Discrete Element Model and Analysis of Influencing Factors on Accuracy. Remote Sens. 2023, 15, 943. [Google Scholar] [CrossRef]
  37. Ucgul, M.; Saunders, C.; Fielke, J.M. Discrete element modelling of tillage forces and soil movement of a one-third scale mouldboard plough. Biosyst. Eng. 2017, 155, 44–54. [Google Scholar] [CrossRef]
  38. Ucgul, M.; Fielke, J.M.; Saunders, C. 3D DEM tillage simulation: Validation of a hysteretic spring (plastic) contact model for a sweep tool operating in a cohesionless soil. Soil Tillage Res. 2014, 144, 220–227. [Google Scholar] [CrossRef]
  39. Derbane, S.; Mansouri, M.; Messast, S. Micromechanical Modeling for Analysis of Shear Wave Propagation in Granular Material. Rom. J. Transp. Infrastruct. 2024, 13, 1–19. [Google Scholar] [CrossRef]
  40. Zhao, K.; Wang, Q.; Chen, Q.; Zhuang, H.; Chen, G. Simplified effective stress simulation of shear wave propagation in saturated granular soils. Géotech. Lett. 2020, 11, 1–22. [Google Scholar] [CrossRef]
  41. Xiao, D.G.; Zhang, Z.; Zhu, Y.L.; Zhang, J.Q.; Xu, C.G.; Ma, X. The Frequency Dependence of Ultrasonic Attenuation in Water. In Proceedings of the 2016 IEEE Far East NDT New Technology & Application Forum, Nanchang, China, 22–24 June 2016; pp. 113–116. [Google Scholar]
  42. Crescenti, R.A.; Bamber, J.C.; Partridge, M.; Bush, N.L.; Webb, S. Characterization of the ultrasonic attenuation coefficient and its frequency dependence in a polymer gel dosimeter. Phys. Med. Biol. 2007, 52, 6747–6759. [Google Scholar] [CrossRef]
  43. Huang, S.; Lu, C.; Li, H.; He, J.; Wang, Q.; Gao, Z.; Yuan, P.; Li, Y. The attenuation mechanism and regular of the acoustic wave on propagation path in farmland soil. Comput. Electron. Agric. 2022, 199, 107138. [Google Scholar] [CrossRef]
  44. Naas, M.; Sebaa, N.; Fellah, Z.; Fellah, M.; Lauriks, W.; Depollier, C. Excess ultrasonic attenuation due to inhomogeneities in porous media. J. Acoust. Soc. Am. 2008, 123, 3639. [Google Scholar] [CrossRef]
  45. Jinhyuk, K.; Jungwoo, L. Acoustic and Thermal Characterization of Therapeutic Ultrasonic Langevin Transducers under Continuous- and Pulsed Wave Excitations. Sensors 2022, 22, 9006. [Google Scholar] [CrossRef]
  46. Zaccherini, R.; Palermo, A.; Marzani, A.; Colombi, A.; Dertimanis, V.K.; Chatzi, E.N. Geometric and material attenuation of surface acoustic modes in granular media. Geophys. J. Int. 2022, 230, 288–297. [Google Scholar] [CrossRef]
  47. Brandão, R.J.; Calheiros, C.J.P.; Machado, M.V.C.; Lima, R.M.; Duarte, C.R.; Barrozo, M.A.S. A systematic evaluation of DEM input parameters measurement and calibration. Powder Technol. 2024, 448, 120157. [Google Scholar] [CrossRef]
  48. Martin, R.; István, V.; Thomas, B.; Michael, S.; Susanne, K.; Guqi, Y.; Edgar, S.; Clemens, G. Determining longitudinal and transverse elastic wave attenuation from zero-group-velocity Lamb waves in a pair of plates. J. Acoust. Soc. Am. 2023, 153, 2090. [Google Scholar]
Agriculture 15 01540 g001
Figure 1. Mind map about this study.
Figure 1. Mind map about this study.
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Figure 2. DEM model: (a) 3D model; (b) position of maize seed; (c) model of maize seed.
Figure 2. DEM model: (a) 3D model; (b) position of maize seed; (c) model of maize seed.
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Figure 3. Acoustic pressure time domain signals of excitation transducer. (a) Compression ratio; (b) excitation frequency; (c) excitation amplitude.
Figure 3. Acoustic pressure time domain signals of excitation transducer. (a) Compression ratio; (b) excitation frequency; (c) excitation amplitude.
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Figure 4. Fourier transform of maize seed force signal. (a) Time domain signal; (b) frequency domain signal.
Figure 4. Fourier transform of maize seed force signal. (a) Time domain signal; (b) frequency domain signal.
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Figure 5. Slice view and particles distribution (6%-40 kHz-10 μm).
Figure 5. Slice view and particles distribution (6%-40 kHz-10 μm).
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Figure 6. Fourier transform of particle 1 force signal. (a) Time domain signal; (b) frequency domain signal.
Figure 6. Fourier transform of particle 1 force signal. (a) Time domain signal; (b) frequency domain signal.
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Figure 7. Ultrasonic test bench for seed–soil composite.
Figure 7. Ultrasonic test bench for seed–soil composite.
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Figure 8. Time domain signal of maize seed force. (a) Compression ratios; (b) excitation frequencies; (c) excitation amplitudes.
Figure 8. Time domain signal of maize seed force. (a) Compression ratios; (b) excitation frequencies; (c) excitation amplitudes.
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Figure 9. Acoustic velocity under different compression ratios.
Figure 9. Acoustic velocity under different compression ratios.
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Figure 10. Acoustic wave interference region (6%-40 kHz-10 μm).
Figure 10. Acoustic wave interference region (6%-40 kHz-10 μm).
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Figure 11. Frequency domain signal of the maize seed force. (a) Compression ratios; (b) excitation frequencies; (c) excitation amplitudes.
Figure 11. Frequency domain signal of the maize seed force. (a) Compression ratios; (b) excitation frequencies; (c) excitation amplitudes.
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Figure 12. Time domain and frequency domain curves of soil particle forces (6%-40 kHz-10 μm). (a) Time domain curve; (b) frequency domain curve.
Figure 12. Time domain and frequency domain curves of soil particle forces (6%-40 kHz-10 μm). (a) Time domain curve; (b) frequency domain curve.
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Figure 13. Spatial trajectory and displacement time domain signal of maize seed. (a) Spatial trajectory; (b) displacement time domain signal.
Figure 13. Spatial trajectory and displacement time domain signal of maize seed. (a) Spatial trajectory; (b) displacement time domain signal.
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Figure 14. Displacement–time curves of three soil particles in y direction (6%-40 kHz-10 μm).
Figure 14. Displacement–time curves of three soil particles in y direction (6%-40 kHz-10 μm).
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Figure 15. Acoustic velocity under different parameters. (a) Compression ratios; (b) excitation frequencies; (c) excitation amplitudes.
Figure 15. Acoustic velocity under different parameters. (a) Compression ratios; (b) excitation frequencies; (c) excitation amplitudes.
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Table 1. Physical parameters of materials.
Table 1. Physical parameters of materials.
ProjectPoisson’s RatioShear Modulus (Pa)
Soil particle0.31 × 109
Maize seed0.41.37 × 108
Steel0.257.9 × 1010
pzt0.327.5 × 1010
Table 2. Contact parameters among different materials.
Table 2. Contact parameters among different materials.
ProjectCoefficient of RestitutionCoefficient of Static Friction
soil–soil0.60.5
soil–stee0.50.5
soil–pzt0.50.4
soil–maize seed0.160.1
Table 3. Factors and levels.
Table 3. Factors and levels.
LevelsCompression Ratio α/%Excitation Frequency f/kHz
1020
2640
31260
Table 4. Simulation and experimental results verification.
Table 4. Simulation and experimental results verification.
ProjectSimulation ResultsExperimental ResultsError (%)
Acoustic velocity in maize seed (m/s)1948.911802.438.13
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Li, Y.; Lu, C.; Li, H.; He, J.; Wang, Z.; Zhai, C. Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil. Agriculture 2025, 15, 1540. https://doi.org/10.3390/agriculture15141540

AMA Style

Li Y, Lu C, Li H, He J, Wang Z, Zhai C. Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil. Agriculture. 2025; 15(14):1540. https://doi.org/10.3390/agriculture15141540

Chicago/Turabian Style

Li, Yadong, Caiyun Lu, Hongwen Li, Jin He, Zhinan Wang, and Chengkun Zhai. 2025. "Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil" Agriculture 15, no. 14: 1540. https://doi.org/10.3390/agriculture15141540

APA Style

Li, Y., Lu, C., Li, H., He, J., Wang, Z., & Zhai, C. (2025). Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil. Agriculture, 15(14), 1540. https://doi.org/10.3390/agriculture15141540

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