A method for monitoring the spacing and depth of corn and soybean seeds in a strong vibration and dusty environment

Abstract

The application relates to the technical field of agricultural machinery automation monitoring, and particularly discloses a method for monitoring the seed spacing and depth of corn and soybean seeding in a strong-vibration and dusty environment. The method comprises the following steps: constructing a vibration and soundprint composite sensing array comprising an acceleration sensor and a pickup; identifying the characteristic signals of seed impact events by using an edge-end deep learning model, and combining the seed spacing data with the speed of travel; analyzing the vibration modal variation law of a furrow opener, constructing a multi-physical field coupling model to realize continuous inversion of the seeding depth; and implementing physical self-cleaning by using a micro-channel structure based on the Coanda effect, and dynamically compensating in signal processing. By using the above technical scheme, the method can avoid dust and vibration interference, realize accurate and synchronous monitoring of the seed spacing and depth, improve the robustness and operation efficiency of the monitoring system, and provide data support and decision basis for intelligent closed-loop control of the seeding operation.

Description

Technical Field

[0001] This invention belongs to the field of agricultural machinery automation monitoring technology, specifically relating to a method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions. Background Technology

[0002] With the continuous evolution of smart agriculture technology, automated monitoring of precision seeding operations has become a key link in improving crop yields and operational efficiency. Traditional agricultural machinery operation monitoring mainly relies on sensor technology to track the seeding process in real time, aiming to achieve precise control over the quality of agricultural machinery operations through digital analysis of operational parameters. In the precision seeding process of crops such as corn and soybeans, real-time acquisition of seeding status data is of significant reference value for assessing emergence rates and optimizing subsequent field management.

[0003] For corn and soybean planting, monitoring seed spacing and depth is a core indicator for evaluating planting operation quality. This technical direction requires the monitoring system to be able to calculate the seed metering frequency and furrow opening depth in real time, ensuring uniform seed distribution and consistent soil coverage on the seedbed. By dynamically matching the seeding position coordinates with the descent depth, the system can provide immediate feedback to the agricultural machinery driver or automatic control unit, adjusting the mechanical actuators according to soil conditions and operating speed to ensure a uniform environment for crop growth.

[0004] Currently, the industry mainly uses the following technical solutions for sowing quality monitoring. For example, photoelectric sensing-based monitoring schemes use infrared through-beam or reflective photoelectric elements arranged on both sides of the seed guide tube to count and calculate seed spacing by utilizing the beam-blocking effect of falling seeds. This type of scheme has high accuracy in laboratory environments, but in the strong vibration and dusty field conditions, the optical lens is easily covered by dust, causing beam attenuation or complete blockage. Simultaneously, mechanical vibration can easily cause misalignment of photoelectric elements or signal jitter, leading to a sharp increase in missed and false detection rates. Another type is ultrasonic sensing-based monitoring schemes, which achieve non-contact detection by emitting ultrasonic pulses and detecting seed echoes. However, ultrasonic sensors are easily interfered with by the complex sound field around the seeder (such as engine noise and mechanical collision noise from the seed metering device), and the dust generated by the furrow opener significantly attenuates ultrasonic energy, resulting in insufficient signal-to-noise ratio of the echo signal, making it difficult to reliably identify continuous seed falling events. In addition, there are mechanical contact schemes based on piezoelectric films or strain gauges, which trigger signals by sensing the weak pressure generated when seeds impact the seed guide tube wall. This scheme is highly dependent on the physical impact of seeds. After long-term use, the sensing elements are prone to fatigue or wear. It is also sensitive to changes in sowing speed. When the sowing speed increases, the impact force and angle of the seeds fluctuate drastically, which can easily lead to false triggering or missed triggering.

[0005] The aforementioned existing technologies generally face severe challenges in extreme working environments during corn and soybean planting monitoring, making it difficult to balance monitoring accuracy and system robustness. Traditional photoelectric or ultrasonic sensors are prone to physical shielding or signal attenuation under strong vibration and dusty field conditions, resulting in frequent missed and false detections. Existing monitoring methods often collect grain spacing and planting depth as independent physical quantities, lacking a spatiotemporal correlation mechanism, making it difficult to achieve synchronous mapping of the three-dimensional spatial position of a single seed. Traditional solutions typically treat mechanical vibration and dust disturbance as pure interference noise, passively suppressing them only by adding sealing or shock-absorbing structures. Existing solutions typically treat mechanical vibration and dust effects as noise sources and suppress them, lacking technical solutions to use changes in the working environment to assist in determining the planting status. This results in insufficient data reliability under harsh working conditions and high maintenance costs. Summary of the Invention

[0006] The purpose of this invention is to provide a method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions, which can solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions, comprising the following specific steps: Step 1: Construct a vibration and acoustic fingerprint composite sensor array. Distribute and embed microelectromechanical system accelerometers and high-sensitivity microphones at predetermined positions on the furrow opener, seed meterer, and soil covering and pressing wheel of the seeding unit. The frequency response range of the microphones is 20Hz to 20000Hz, and the signal-to-noise ratio is not less than 60dB, forming a collaborative sensing network of structural vibration and acoustic fingerprint. Step 2: Perform real-time monitoring of seed spacing. A high-sensitivity microphone and a microelectromechanical system accelerometer synchronously collect continuous vibration waveforms and acoustic features of seeds as they are discharged from the seed metering device, impact the seed guide tube wall, and fall into the seed bed. A deep learning model deployed at the edge is used to separate and identify the characteristic signals of single seed impact events from the background vibration. The time difference between two adjacent impact events is calculated, and the seed spacing data is obtained by combining the real-time travel speed of the seeder. Step 3: Perform dynamic monitoring of sowing depth. By analyzing the broadband vibration spectrum collected by the microelectromechanical system accelerometer on the furrow opener body, combined with the structural vibration mode change law caused by soil resistance and compaction degree when the furrow opener enters the soil, and integrating the vibration feedback generated by the compaction wheel during the soil covering and compaction process, a multi-physics field coupling model of vibration mode, soil resistance and furrowing depth is constructed to realize continuous inversion of sowing depth. Step 4: Implement dust disturbance inversion and self-cleaning treatment. Utilize the microfluidic structure based on the Coanda effect to form a high-speed adhering air film on the sensor's sensitive surface, continuously blowing away deposited dust and discharging it in a directional manner. The disturbance weight of dust concentration on acoustic and vibration signals is evaluated in real time through the auxiliary environmental monitoring unit, and dynamic compensation is performed in the signal processing logic. Step 5: Perform cross-modal data fusion and decision-making. At the seeder end, use edge computing nodes to process the data from the vibration acoustic pattern array. Upload the processed particle size, seeding depth, confidence level and abnormal events to the cloud or the cab terminal. The cloud is responsible for optimizing the vibration acoustic pattern recognition model and generating seeding strategies based on the historical data of the plot.

[0008] Preferably, in step 1, the distributed embedded microelectromechanical system (MEMS) accelerometer is installed on a feature node within the seed unit that readily generates a structural response. The high-sensitivity microphone is encapsulated in a protective housing with acoustic transparency characteristics, the housing being made of a polymer material with a high elastic modulus. The sensor array is electrically connected to the edge processing unit via shielded cables or a short-range wireless transmission module. The sensor arrangement at each monitoring point has been verified through structural dynamics simulation to ensure the acquisition of the strongest characteristic signals of seed impact and structural modal changes.

[0009] Preferably, in step 2, the seed impact event identification process includes three stages: signal preprocessing, feature extraction, and pattern recognition. In the signal preprocessing stage, the system uses an adaptive filter to eliminate low-frequency noise interference from the tractor engine and ground undulations. In the feature extraction stage, the system extracts composite features of the original signal in the time domain, frequency domain, and time-frequency domain, including short-time energy, zero-crossing rate, Mel-frequency cepstral coefficients, and wavelet packet decomposition coefficients. In the pattern recognition stage, a pre-trained convolutional neural network is used to classify the feature vectors, accurately determining the instantaneous moment of seed impact on the seed delivery tube wall.

[0010] Preferably, in step 2, the final calculation of the seed spacing incorporates the real-time travel speed of the seeder. This real-time travel speed is provided by a Global Navigation Satellite System module or an encoder mounted on the seeder's ground wheels. The system performs an arithmetic product of the time interval between the impact points of two adjacent seeds and the current travel speed to determine the physical arrangement distance of the seeds. To improve accuracy, the system performs a sliding window averaging on multiple continuously collected seed spacing data sets to eliminate measurement fluctuations caused by random factors.

[0011] Preferably, in step 3, the construction of the multiphysics coupling model is based on the stress state analysis of the trencher structure at different soil penetration depths. When the trencher penetrates deeper into the soil, the lateral friction and positive resistance it experiences change the overall stiffness matrix of the structure, causing a shift in its natural frequency. A microelectromechanical system (MEMS) accelerometer monitors this frequency shift in real time. The system pre-stores a mapping table of frequency shifts and soil penetration depths for different soil types, and obtains the initial seeding depth value through real-time table lookup and linear interpolation calculation.

[0012] Preferably, in step 3, the system uses the infrasound and vibration signals generated by the compaction wheel during the soil compaction process to perform closed-loop correction on the initial sowing depth value. Since there is a non-linear correlation between the downward pressure of the compaction wheel on the soil layer and the seed burial depth, the specific frequency vibration generated by the compaction wheel in contact with the ground can reflect the degree of soil compaction. By analyzing the amplitude and phase characteristics of the feedback signal from the compaction wheel, the system can identify whether the trenching depth has reached the preset target and issue adjustment commands to the hydraulic downward pressure actuator of the trencher.

[0013] Preferably, in step 4, the microchannel structure based on the Coanda effect guides the natural airflow or compressed air generated by the auxiliary air pump during the seeder's operation to form a continuous laminar air film near the microphone's sound guide hole and the accelerometer mounting base. This air film utilizes the fluid's adhesion properties to forcibly alter the trajectory of dust particles, preventing them from depositing in the sensor's sensitive area. The negative pressure zone generated at the microchannel outlet can simultaneously draw out and discharge suspended dust from the monitoring chamber outside the monitoring area, achieving dynamic self-cleaning at the physical level.

[0014] Preferably, in step 4, the signal compensation mechanism acquires dust concentration data in real time through an auxiliary environmental monitoring unit. When the dust concentration exceeds a preset threshold, the attenuation coefficient of the sound wave in the air medium will increase accordingly, and the damping ratio of the structural vibration will also drift due to the influence of the attached dust mass. The system calculates compensation for the collected acoustic signature amplitude and vibration frequency by calling a preset environmental transfer function. The compensation logic ensures the consistency of identification results under different dust load environments by adjusting the gain coefficient and filter cutoff frequency in the signal processing algorithm.

[0015] Preferably, in step 5, the edge computing node performs high-frequency data sampling and feature comparison, achieving synchronous monitoring of multiple rows of sown units through specific parallel processing logic. The edge processing unit only sends the compressed and structured quality monitoring results, such as average grain spacing, standard deviation of sowing depth, and alarms for missed sowing events, to the cloud server. The cloud server utilizes big data analytics, combining geographical information of the current work area, soil fertility data, and historical yield data, to predict the impact of current sowing quality on future seedling emergence rate using a long short-term memory neural network model.

[0016] Preferably, in step 5, the cloud-generated sowing strategy includes control commands to the seeder's actuators. When the particle spacing deviation of a specific row of seeded units is detected to continuously exceed a preset range, the cloud system sends a control signal to the seeder's control bus, automatically adjusting the speed of the drive motor of the seed metering device for that row. When a trend of fluctuation in sowing depth is detected, the system adjusts the counterweight of the furrow opener or the downward pressure compensation device in conjunction with the operation. All operational data is recorded in real time in the cloud database, forming a complete digital map of sowing operations for subsequent plant protection and harvesting stages.

[0017] Preferably, the method further includes health monitoring of the sensor array's operating status. The system periodically sends self-test pulse signals of a specific frequency to the microelectromechanical system (MEMS) accelerometer. By detecting whether the excitation response waveform returned by the sensor conforms to standard modal characteristics, it determines whether the sensor is loose, damaged, or completely blocked by foreign objects. Once an anomaly is detected, the edge processing unit immediately generates a fault diagnosis report and feeds it back to the operating terminal.

[0018] Preferably, the method employs dual verification logic during signal processing. For each seed sowing event, the system requires that the peak characteristics of the vibration signal and the specific frequency envelope of the acoustic signature signal meet a preset synchronization tolerance on the time axis. If only a single-dimensional feature is detected, the system marks it as a suspected interference signal and removes it. Only when the signal features of both dimensions appear simultaneously within a preset time window and the matching degree is higher than a preset threshold is it determined to be a valid seed sowing event.

[0019] Preferably, the method also has a specific database of identification parameters preset for the different physical characteristics of corn and soybeans. Because corn and soybean seeds differ in quality, hardness, and shape, the acoustic spectrum distribution and vibration energy intensity generated when they impact the seed guide tube are different. Users select the current crop type through the operating terminal, and the system automatically loads the corresponding feature vector template and deep learning classification weights to ensure high accuracy in monitoring different crops.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves superior robustness in extreme environments with strong vibration and high dust levels. By shifting the core sensing mechanism from traditional photoelectric or ultrasonic detection to a combined vibration and acoustic sensing approach, the interference of dust obscuring and strong light environments on monitoring accuracy is avoided at the physical principle level. This invention transforms mechanical vibration, which was originally a source of interference, into a signal source carrying dynamic seed information, and defines dust as a basis for environmental compensation instead of a pollutant. This allows the system to maintain stable and reliable monitoring performance even under extreme field conditions where traditional sensors commonly fail. Compared to existing technologies, the system's anti-interference capability is significantly improved, the mean time between failures (MTBF) is extended, and the frequency of field maintenance is reduced.

[0021] 2. This invention achieves inherent unity and high synchronization across monitoring dimensions. Utilizing a single vibrational acoustic physical field coupling solution logic, this invention can acquire two key operational parameters: seed spacing and sowing depth. This deep integration at the physical layer eliminates spatiotemporal mapping errors caused by differences in installation location and asynchronous sampling times between sensors based on different physical principles, achieving precise correlation between the spatial coordinates of each seed's landing point and its implantation depth. This high-fidelity data stream provides accurate foundational support for subsequent agronomic analysis, solving the problem of fragmented and difficult-to-integrate data in traditional monitoring.

[0022] 3. This invention boasts advantages in system architecture simplicity and low-cost deployment. Compared to expensive optical focusing systems or high-power ultrasonic drive circuits, micro-electromechanical system (MEMS) sensors offer advantages such as extremely low cost, minimal power consumption, and tiny size. Due to the sensor's strong shock resistance, the system eliminates the need for complex shock-absorbing structures, simplifying the mechanical design of the seeding unit. This streamlined hardware architecture enables large-scale, low-cost deployment of this monitoring solution on large, high-speed multi-row seeders, enhancing the accessibility of precision agricultural equipment.

[0023] 4. This invention provides a reliable technological foundation for intelligent closed-loop control of sowing operations. By acquiring high-confidence particle size and depth data in real time, the seeder can leap from passive digital monitoring to proactive intelligent adaptation. The real-time feedback data provided by the system can drive the dynamic adjustment of the seed metering motor speed and the real-time compensation of the furrow opener's downward pressure, realizing real-time online control of sowing quality. This marks a shift in sowing operations from traditional post-event statistical evaluation to real-time precise intervention during the process, which has significant strategic importance for ensuring the consistency of crop growth and increasing yield per unit area.

[0024] 5. The edge-cloud collaboration mechanism of this invention improves the operational efficiency of agricultural IoT. By completing feature extraction and real-time processing of massive amounts of raw data at the edge, it alleviates the wireless communication bandwidth pressure at agricultural machinery operation sites. The big data analysis and model iteration capabilities of the cloud enable the monitoring system to have the characteristics of continuous learning and self-optimization, and can dynamically generate optimal monitoring parameters and sowing strategies for different plots and different soil conditions. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall technical solution architecture according to the present invention; Figure 2 This is a schematic diagram of the real-time monitoring data flow of seeding spacing based on the edge deep learning model according to the present invention; Figure 3 This is a flowchart illustrating the dynamic monitoring of seeding depth based on a multi-physics coupling model according to the present invention. Figure 4 This is a flowchart illustrating the logic flow of microchannel self-cleaning and dust disturbance compensation based on the Coanda effect in this invention. Figure 5 This is a schematic diagram illustrating cross-modal data fusion and intelligent decision-making interaction between edge computing nodes and cloud servers according to the present invention; Figure 6 This is a flowchart of the dual verification logic based on vibration characteristics and acoustic frequency envelope synchronization tolerance in this invention. Detailed Implementation

[0026] Example 1: Please refer to the appendix Figure 1 To be continued Figure 6 During the monitoring of corn and soybean sowing quality in a strong vibration and dusty environment, the system achieves accurate inversion of sowing particle spacing and depth by deeply coupling structural dynamics and acoustic sensing technology.

[0027] In step 1, a vibration-acoustic fingerprint composite sensor array is constructed. Micro-electromechanical system (MEMS) accelerometers and high-sensitivity microphones are distributed and embedded at predetermined locations on the furrow opener, seed metering device, and soil-covering and pressing wheel of the seeding unit, forming a collaborative sensing network of structural vibration and acoustic fingerprint. The distributed embedded MEMS accelerometers are installed on characteristic nodes in the seeding unit that readily generate structural responses. These nodes include the back side of the furrow opener's soil-entry front edge, the vibration-sensitive ribs of the seed metering device housing, and the fixed end of the pressing wheel bearing seat. The high-sensitivity microphone is encapsulated in a protective housing with acoustic transparency characteristics. The protective housing is made of a polymer material with a high elastic modulus, such as polycarbonate or polyetheretherketone (PEEK) with a Young's modulus between 2000 MPa and 3500 MPa, to ensure low energy attenuation of sound waves when penetrating the housing. The sensor array is electrically connected to the edge processing unit via a multi-core shielded cable with a metal shielding layer or a short-range wireless transmission module based on the 2.4 GHz band. The sensor layout scheme for each monitoring point has been verified by structural dynamics simulation. By establishing a finite element model of the seeding unit, the first 10 modes of vibration in the frequency band from 0Hz to 5000Hz are calculated. The location with the larger mode displacement is selected as the sensor installation point to ensure that the strongest characteristic signals of seed impact and structural modal changes are collected.

[0028] The target monitoring frequency band is determined based on the seed impact characteristics and the vibration characteristics of the seed metering device. Specifically, the target monitoring frequency band is set to 10Hz to 5000Hz, which covers the high-frequency acoustic and vibration signals generated by the seed impact on the seed guide tube and the low-frequency modal response generated by the furrow opener under soil excitation. The set displacement threshold is determined based on relative amplitude. Specifically, the node positions of the top 20% of the relative displacement amplitudes in each vibration mode or those with relative amplitudes greater than 70% of the maximum displacement amplitude are selected as candidate installation points to ensure that the sensor collects the characteristic signal with the strongest structural response. The relative displacement amplitude refers to the dimensionless ratio of the displacement components of each node under the corresponding vibration mode after normalization, in order to eliminate the influence of the mesh density of the finite element model on the absolute displacement value.

[0029] In the aforementioned construction process, a triaxial accelerometer was selected as the microelectromechanical system accelerometer, with a range set to ±16g and a sampling frequency set to 10000Hz, to capture the weak high-frequency impact signal generated by seed collisions. The high-sensitivity microphone has a wide frequency response range of 20Hz to 20000Hz and a signal-to-noise ratio of no less than 60 dB. To enhance the stability of the sensing network, each sensor node integrates signal conditioning circuitry, including a charge amplifier and a 16-bit analog-to-digital converter, to convert analog signals into digital signals for transmission at the near end, thereby reducing electromagnetic interference caused by long-distance transmission.

[0030] Step 2 involves real-time monitoring of seed spacing. A high-sensitivity microphone and a microelectromechanical system (MEMS) accelerometer synchronously collect continuous vibration waveforms and acoustic signatures of seeds as they exit the seed metering device, impact the seed guide tube wall, and fall into the seed bed. The seed impact event identification process includes three stages: signal preprocessing, feature extraction, and pattern recognition. In the signal preprocessing stage, the system uses an adaptive digital filter with a specific cutoff frequency to eliminate low-frequency noise interference from tractor engine rotation and ground undulations. The low-pass cutoff frequency of this filter is typically set at 50Hz. In the feature extraction stage, edge computing nodes extract composite features of the original signal in the time domain, frequency domain, and time-frequency domain for each frame of sampled data. These features include short-time average energy, zero-crossing rate, Mel-frequency cepstral coefficients, and node energy distribution coefficients obtained based on four-layer wavelet packet decomposition. These feature vectors constitute a high-dimensional space describing the seed impact event. In the pattern recognition stage, a pre-trained convolutional neural network deployed at the edge performs real-time classification of the feature vectors. This convolutional neural network contains three convolutional layers and two fully connected layers. It uses a non-linear activation function to process the complex relationships between features and accurately determine the instantaneous moment when the seed hits the seed tube wall. The recognition accuracy at this moment is within 1 millisecond.

[0031] Furthermore, the final calculation of seed spacing incorporates the real-time travel speed of the seeder. This real-time travel speed is provided by a Global Navigation Satellite System module with high-precision positioning capabilities or by an encoder mounted on the seeder's ground wheels, with the encoder's line count set to 1024 pulses per revolution. The system calculates the physical spacing of the seeds by arithmetically multiplying the time interval between the impact points of two adjacent seeds with the current travel speed. To improve accuracy, the system performs sliding window averaging on multiple continuously collected seed spacing data sets. The sliding window length is set to 5 to 10 seed events to eliminate measurement fluctuations caused by localized soil loosening or instantaneous mechanical vibration. The system employs dual-check logic during signal processing. For each seed sowing event, the system requires that the peak characteristics of the vibration signal captured by the accelerometer and the specific frequency envelope of the acoustic signature signal captured by the microphone meet a preset synchronization tolerance on the time axis, typically set to 5 milliseconds. If only a single-dimensional feature is detected, the system marks it as a suspected interference signal and removes it. Only when signal features of two dimensions appear simultaneously within a preset time window and the matching degree is higher than a preset threshold is it determined to be a valid seed sowing event. The system has a specific recognition parameter library preset for the different physical characteristics of corn and soybeans. Users select the current crop type through the operation terminal, and the system automatically loads the corresponding feature vector template and deep learning classification weights. Because corn seeds are heavier and harder, their impact sound patterns have concentrated energy in the 3000Hz to 5000Hz frequency band, while soybean seeds have relatively lower characteristic frequencies and more dispersed energy distribution. The system accurately distinguishes between them based on these differences.

[0032] Step 3 involves dynamic monitoring of sowing depth by analyzing the broadband vibration spectrum collected by the microelectromechanical system (MEMS) accelerometer on the furrow opener body. The construction of the multiphysics coupling model is based on the stress state analysis of the furrow opener structure at different penetration depths. As the furrow opener penetrates deeper into the soil, the lateral friction and forward resistance it experiences alter the overall stiffness matrix of the furrow opener structure. The soil envelope area of ​​the furrow opener increases with depth, equivalent to adding distributed nonlinear spring constraints to the structure, causing the natural frequency of the structure to shift towards higher frequencies. The microelectromechanical system accelerometer monitors this frequency shift in real time. The system pre-stores a multidimensional mapping table of frequency shifts and penetration depths for different soil types such as black soil, clay, and sandy soil. During monitoring, the system obtains the initial sowing depth value through real-time table lookup and linear interpolation calculations.

[0033] To further correct measurement errors, the system utilizes the infrasound and vibration signals generated by the compaction wheel during soil covering to perform closed-loop correction on the initial sowing depth value. Since there is a non-linear correlation between the downward pressure of the compaction wheel on the soil layer and the seed burial depth, the specific frequency vibrations generated by the compaction wheel in contact with the ground, specifically the low-frequency components between 5Hz and 15Hz, can reflect the degree of soil compaction and the amount of wheel sinking into the soil. By analyzing the amplitude and phase characteristics of the feedback signal from the compaction wheel, the system can identify whether the current furrowing depth has reached the preset target depth. If the calculated deviation exceeds 1 cm, the system will issue an adjustment command to the hydraulic pressure actuator of the furrow opener, compensating for the depth error by changing the extension and retraction length of the hydraulic cylinder, ensuring the consistency of seed implantation depth.

[0034] Step 4 involves dust disturbance inversion and self-cleaning. In dusty, high-vibration environments, to ensure the continuous effectiveness of the sensor, this invention utilizes a microchannel structure based on the Coanda effect. This structure is designed at the front end of the sensor mounting position, guiding the natural relative airflow generated during high-speed operation of the seeder, or compressed air generated by a small auxiliary air pump, into the microchannel. Inside the microchannel, as the airflow passes through the wall-attachment section with a specific curvature, it is affected by the fluid wall-attachment effect, forming a continuous laminar air film near the microphone guide hole and the accelerometer mounting base. The thickness of this air film is maintained between 0.5 mm and 1.5 mm, and the flow velocity is set between 10 m / s and 25 m / s. This air film utilizes the fluid's wall-attachment properties to forcibly change the trajectory of dust particles, causing them to deviate from the sensor's sensitive area under inertia, preventing them from depositing on the surface. The local negative pressure zone at the outlet of the microchannel, caused by the high flow rate, can produce a vacuum-like effect, which can draw out and discharge the suspended dust in the monitoring chamber to the outside of the monitoring area, thus achieving dynamic self-cleaning at the physical level.

[0035] Step 4 also includes a signal compensation mechanism, which uses an auxiliary environmental monitoring unit to acquire dust concentration data in real time. This unit consists of an infrared scattering dust sensor that monitors the mass concentration of suspended particulate matter in the air in real time. When the dust concentration exceeds a preset threshold, the attenuation coefficient of the sound wave in the air medium will increase accordingly, and the sound wave amplitude calculated by the system will be smaller due to dust damping. The damping ratio of structural vibration will also drift due to the additional influence of the mass of dust adhering to the surface. The system calculates compensation for the collected acoustic signature amplitude and vibration frequency by calling a preset environmental transfer function. The compensation logic ensures the physical consistency of the identification results under different dust load environments by dynamically adjusting the analog front-end gain coefficient and the cutoff frequency of the digital filter in the signal processing algorithm.

[0036] Step 5 involves cross-modal data fusion and decision-making. The edge computing nodes deployed at the seeder employ embedded processors with a main frequency of at least 800MHz and support for single-precision floating-point operations. They perform high-frequency data sampling and complex feature comparisons, using multi-threaded parallel processing logic to achieve synchronous monitoring of all seeding units on the seeder. The edge processing unit transforms massive amounts of raw sampled waveforms into structured quality monitoring data packets, including average grain spacing, standard deviation of seeding depth, timestamps and geographic coordinates of missed seeding events, and sensor system confidence assessment values. This data is uploaded to a cloud server via a mobile communication network or transmitted to a smart terminal in the driver's cab.

[0037] The cloud server utilizes big data analytics, combining geographic information system data of the current work area, historical soil fertility data, and yield measurement maps from previous years, to predict the impact of current sowing quality on future emergence rate and final yield using a long short-term memory neural network model. The cloud-generated sowing strategy includes precise control commands for the seeder's actuators. When the grain spacing deviation of a particular row of seeds consistently exceeds the preset range, the cloud system sends a control signal to the seeder's control bus, automatically adjusting the drive motor speed of the seed metering device for that row via the CAN protocol to achieve real-time grain spacing correction. When the sowing depth shows a trend of fluctuation due to changes in soil hardness at the field edge, the system adjusts the counterweight of the furrow opener or the hydraulic pressure compensation device accordingly. All operational data is recorded in real-time in a distributed cloud database, forming a digital map of sowing operations with high spatiotemporal resolution, providing underlying data support for subsequent precision spraying, precision fertilization, and mechanized harvesting.

[0038] This embodiment also includes intelligent health monitoring of the sensor array's operating status. At predetermined time intervals, the system sends a self-test pulse signal of a specific frequency to the self-test capacitor built into the microelectromechanical system (MEMS) accelerometer. By detecting whether the excitation response waveform returned by the sensor conforms to its factory-calibrated standard modal characteristics, it determines whether the sensor has experienced abnormalities such as loose fixing bolts, mechanical structural damage, or complete blockage of the sensitive surface by foreign objects. Once the edge processing unit detects that the autocorrelation coefficient of the self-test waveform is below 0.8, it immediately generates a fault diagnosis report and prominently reminds the driver to perform maintenance on the operating terminal interface.

[0039] In the specific implementation of signal processing, the dual verification logic ensures high accuracy under complex interference backgrounds. The system extracts the Mel-frequency cepstral coefficients of the acoustic signal and inputs them into a random forest classifier. It also extracts the peak factor and kurtosis index of the vibration signal and inputs them into another classifier. The system only confirms a seed drop when both classifiers simultaneously determine seed impact, and the time difference between the two signal peaks is less than the set time window width. This heterogeneous redundancy method based on physical mechanisms enhances the system's ability to identify random noise such as impacts from stones in the field and collisions from mechanical parts.

[0040] Example 2: Based on Example 1, this example provides an optimized implementation scheme for high-speed seeding conditions. When the seeding speed is increased to more than 12 kilometers per hour, the background noise energy of mechanical vibration will increase, and the frequency range will cover the main frequency band of seed impact.

[0041] In step 2, to address the signal recognition problem under high background noise, the system introduces adaptive noise cancellation technology. An additional reference accelerometer is installed at a frame location on the seeding unit that is unaffected by seed impact, specifically for acquiring pure mechanical background vibrations. The edge processing unit utilizes a minimum mean square error algorithm, taking the signal from the reference sensor as input, and iteratively updates the filter weights to subtract coherent mechanical background noise components from the mixed signal from the main sensor, thereby improving the signal-to-noise ratio of the seed impact signal.

[0042] In step 3, to address the issue of large vibrations in the furrow opener during high-speed operation, a vertical displacement compensation term was added to the multiphysics coupling model. A laser displacement gauge mounted on the frame monitors the instantaneous height change of each individual unit relative to the ground, and this height change is input as a compensation parameter into the sowing depth inversion model. When calculating the furrow opener's penetration depth, the system weights and fuses the depth values ​​derived from vibration mode inversion with the relative displacement measured by the laser displacement gauge. The weighting coefficients are dynamically adjusted according to the sowing speed: at lower speeds, the weight of the vibration mode characteristics is set to 0.8, and the displacement gauge weight is 0.2; at higher speeds, the vibration mode is subject to increased nonlinear disturbances, its weight decreases, and the weights of the displacement gauge and the press wheel feedback are increased.

[0043] In step 4, regarding dust treatment, this embodiment employs a pulse-type cleaning strategy. The auxiliary air pump no longer provides continuous air supply but instead supplies air on demand based on real-time readings from the dust sensor. When the dust concentration reaches 50 milligrams per cubic meter, the air pump activates pulse mode, generating five high-pressure airflow pulses per second, each lasting 100 milliseconds. This method not only removes sticky dust but also significantly reduces system power consumption. Simultaneously, temperature and humidity correction terms are introduced into the signal compensation algorithm. Since the speed of sound varies with temperature, and the absorption characteristics of dust are enhanced in high humidity environments, the system reads environmental sensor data and normalizes the voiceprint features according to the following formula: Let the measured sound wave propagation time be... (Unit: seconds), ambient temperature is (Unit: °C), relative humidity is (Value range 0 to 1), then the compensated propagation time The calculation is as follows:

[0044] in, The standard reference temperature is 20℃. The humidity influence coefficient, calibrated experimentally, ranges from 0.001 to 0.005. The system will calculate the compensated propagation time. It replaces the original measurement value and is used to determine the time difference in particle distance calculation, thereby eliminating the interference of ambient temperature and humidity on acoustic ranging.

[0045] For sound wave amplitude attenuation, the following compensation formula is used:

[0046] in, To measure the amplitude of the voiceprint, To compensate for the amplitude, and The experimental calibration coefficient (typical value: =0.2, =0.002). This compensation ensures that the sensitivity of impact event recognition remains consistent under different temperature and humidity conditions.

[0047] In the decision-making closed loop of step 5, this embodiment adds decision logic based on the expected seedling uniformity. The edge computing node not only calculates the current grain spacing but also calculates the coefficient of variation of the grain spacing distribution in real time. When the coefficient of variation exceeds a preset 10%, the system determines that the sowing quality has declined. Even if the average grain spacing is still within the allowable range, it will send a deceleration suggestion command to the tractor's automatic navigation system via the cloud, reducing the vehicle speed to achieve higher sowing uniformity. This shift from monitoring a single parameter to monitoring the quality of the entire population distribution further improves the operational level of smart agriculture.

[0048] Example 3: Based on Example 1 or 2, this example describes in detail an adaptive calibration method for large-area plot heterogeneity. Because soil texture and moisture content vary between different plots, and even between different areas of the same plot, this affects the mapping relationship between vibration modes and seeding depth in step 3.

[0049] In the specific implementation of step 3, the system introduces an online parameter identification mechanism. The first 50 meters of the seeder's operation in a new plot are designated as a self-calibration phase. During this phase, the drive mechanism controls the furrow opener to travel a distance at three preset depths (e.g., 3 cm, 5 cm, and 7 cm), and sensors record the vibration mode frequencies at these three known depths. Using these sampling points and the principle of least squares, the system fits a frequency-depth mapping curve for the specific plot within a textually described logical framework. This process is equivalent to updating the parameters of the multiphysics coupling model in real time, enabling it to automatically adapt to the spatial heterogeneity of soil hardness.

[0050] In the sensor array construction of step 1, this embodiment employs a redundant design. Two high-sensitivity microphones, arranged at a 90-degree angle, are installed at the critical seed tube outlet. This spatial diversity monitoring method addresses the acoustic shadowing problem caused by unilateral impacts. Edge computing nodes perform maximum ratio merging on the acoustic signature signals from the two channels, extracting and fusing the feature segments with stronger signal energy and higher signal-to-noise ratio from both channels. In this way, even with severe wear on the inner wall of the seed tube and distortion of the impact sound, the system can still maintain extremely high recognition confidence.

[0051] In step 4, the dust disturbance inversion mechanism is further upgraded to dual-spectrum dust detection. The auxiliary environmental monitoring unit includes sensors for both infrared and green light bands. By comparing the scattering intensity of dust on different wavelengths of light, the particle size distribution of the dust (such as the ratio of PM2.5 to PM10) is identified. The system dynamically adjusts the inlet pressure of the Coanda microchannel according to the size of the dust particles. For fine particles, a higher air film velocity is used to enhance the deflection effect; for larger particles, a mode with increased negative pressure suction is used to prevent large dust particles from falling into the sensor's sound-transmitting hole due to gravity.

[0052] Within the edge-cloud collaborative framework described in step 5, this embodiment implements a federated learning model update mechanism. Multiple seeder systems distributed across different regions upload locally verified, locally validated typical abnormal signal feature extraction codes (rather than raw waveform data) collected under different operating conditions to the cloud. The cloud server aggregates this heterogeneous data and updates the weights of the deep learning classifier using a global optimization algorithm. The updated model parameters are then distributed wirelessly to all network-connected seeder edge computing nodes. This mechanism enables the monitoring system to continuously learn new types of interference (such as abnormal vibration characteristics caused by a specific type of weed entanglement), achieving a clustered improvement in the system's intelligence level.

[0053] For health monitoring of the sensor array, this embodiment adds a cross-correlation-based diagnostic method. The edge processing unit calculates the cross-correlation coefficient of signals collected by two adjacent sensors (such as the furrow opener accelerometer and the press wheel accelerometer) within the same seeding unit in real time. Under normal operating conditions, since the two are mechanically connected, their vibration signals have a certain degree of coherence. If the cross-correlation coefficient suddenly drops to an extremely low level, it is determined that one of the sensors may have mechanically detached. The system automatically retrieves the self-test pulse response at that moment for secondary comparison, ultimately confirming the fault type and locating the specific sensor number.

[0054] In the dual verification logic of the signal processing stage, this embodiment introduces fuzzy logic judgment. When the matching degree between the vibration signal and the acoustic signature signal is in the critical region, the system no longer performs a simple binary selection and elimination, but instead calculates a comprehensive probability score by combining the real-time downforce data of the current seeder and the soil hardness index. If the score is higher than 0.65, it is considered a valid seed implantation. This fuzzy judgment strategy reduces the signal false alarm rate under extremely complex geological conditions.

[0055] For the identification parameter library of corn and soybeans, this embodiment adds an automatic identification function based on a clustering algorithm. When the user forgets to select the crop type, the system performs real-time cluster analysis on the collected impact soundprint features within the first 10 seconds after sowing begins. Since the feature vectors of corn and soybeans have spatial separation, the system can automatically lock the current crop type by calculating the Euclidean distance between the signal vector to be tested and two preset cluster centers (corn center and soybean center) and load the corresponding monitoring weights, thus achieving intelligent and user-friendly operation.

[0056] Example 4: This example focuses on describing the hardware implementation and system integration details based on the method of the present invention, so as to demonstrate its engineering feasibility on large high-speed agricultural machinery.

[0057] The entire monitoring system is built on the local area network bus of the seeder's main controller. Each seeder unit acts as an independent intelligent node, integrating a dedicated data acquisition and processing module. This module contains a multi-channel, high-precision 24-bit Σ-Δ analog-to-digital converter circuit, capable of acquiring the microphone's audio stream at an instantaneous sampling rate of up to 100kHz to ensure no transient acoustic details are missed. A microelectromechanical system (MEMS) accelerometer is connected to this module via an I2C digital interface, with a data refresh rate of 1600Hz.

[0058] In terms of physical installation, the sensor assembly on the trencher is embedded in a specially made precision-cast steel housing, which is rigidly connected to the trencher body by high-strength bolts. To reduce mechanically transmitted noise, a 0.2 mm thick Teflon film is used as a gasket between the sensor base and the housing. This film ensures the transmission of high-frequency vibrations while isolating low-frequency, large-amplitude mechanical impacts.

[0059] The Coanda effect-based microfluidic structure is precision-formed using additive manufacturing technology, with a channel width of only 2 millimeters and an internal surface roughness of 0.8 micrometers. The microfluidic inlet is connected to the seeder's built-in compressed air tank, and the intake pressure is precisely controlled by an electromagnetic proportional valve, with a pressure adjustment range between 0.1 MPa and 0.5 MPa. The system dynamically adjusts the proportional valve opening based on the signal quality feedback from the edge computing nodes to maintain laminar flow on the sensor surface.

[0060] The edge computing node employs a heterogeneous computing architecture, comprising a general-purpose processor core for logic control and data communication, and a dedicated neural network accelerator core for performing convolution operations. This architecture reduces power consumption when processing single-seed collision recognition, adapting to the limited power supply environment of the seeder.

[0061] The cloud-based decision-making platform is built on a distributed cloud architecture, enabling real-time data access from hundreds or even thousands of seeders. The platform interface displays the operational status of each seeder in the form of a digital twin. When a decrease in the seeding depth stability of a particular machine is detected, the platform automatically retrieves historical rainfall and soil moisture data for the area, analyzes whether excessively sticky soil is causing soil sticking to the furrow opener, and provides precise maintenance recommendations.

[0062] This system also integrates a seeder wear prediction function based on acoustic fingerprinting. By tracking the periodic acoustic signature characteristics generated during the seeder's rotation over a long period, it uses trend analysis algorithms to monitor abnormal energy increases in specific frequency bands. For example, when the seeder bearing experiences fatigue spalling, it will generate periodic impact characteristics at a specific frequency. After detecting such characteristics, the system will send a warning message to the user's mobile application, reminding them to replace vulnerable parts promptly after the planting season to avoid operational interruptions due to mechanical failure.

[0063] In the arithmetic logic of seed spacing calculation, the system also considers the influence of the seed guide tube curvature on the seed flight trajectory. By introducing a geometric correction factor into the model, which is determined by the installation angle and length of the seed guide tube, the product of time difference and linear velocity is fine-tuned to minimize the deviation in the flight time of the seed from impact with the tube wall to falling into the seed bed, thus limiting the absolute error of seed spacing monitoring to an engineering-acceptable range.

[0064] In summary, this invention constructs a complete, closed-loop, and highly robust seeding quality monitoring system through deep fusion of multiple sensors at the physical layer, efficient edge computing, and cloud-based big data decision-making. It no longer passively defends against harsh environments but actively transforms environmental characteristics, converting strong vibrations and dust into useful information flow. This enables precise control over the seeding status of each seed under extreme conditions, providing solid technical support for the implementation of smart agriculture.

[0065] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions, characterized in that, The method includes the following steps: Step 1: Construct a vibration and acoustic fingerprint composite sensor array. Accelerometers and microphones are distributed and embedded at predetermined positions of the furrow opener, seed meterer, and soil covering and pressing wheel of the seeding unit to form a collaborative sensing network of structural vibration and acoustic fingerprint. Step 2: Perform real-time monitoring of seed spacing. The continuous vibration waveform and acoustic features of seed impact events are collected synchronously by a microphone and an accelerometer. The deep learning model deployed at the edge is used to identify the feature signals of single seed impact events from the background vibration, determine the moment of single seed impact, and obtain the seed spacing data by combining the real-time travel speed of the seeder. Step 3: Perform dynamic monitoring of sowing depth, analyze the broadband vibration spectrum collected by the accelerometer on the furrow opener body, combine the structural vibration mode change law caused by soil resistance and compaction degree when the furrow opener enters the soil, and integrate the vibration feedback generated by the compaction wheel during the soil covering and compaction process to construct a multi-physics coupling model to realize the continuous inversion of sowing depth. Step 4: Implement dust disturbance inversion and self-cleaning treatment. Utilize the microchannel structure based on the Coanda effect to form a high-speed adhering air film on the sensor's sensitive surface, continuously blowing away deposited dust. Based on the dust concentration assessed in real time by the auxiliary environmental monitoring unit, dynamically compensate the acoustic and vibration signals in the signal processing logic.

2. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 1, characterized in that, The process of constructing the vibration and acoustic texture composite sensor array in step 1 includes: installing the acceleration sensor on a feature node in the seed unit that is easy to generate a structural response. The feature node includes the back side of the front edge of the furrow opener, the vibration-sensitive rib of the seed metering device housing, and the fixed end of the press wheel bearing seat. The microphone is encapsulated in a protective housing with acoustic transparency characteristics, and the protective housing is made of a polymer material with a preset elastic modulus. The sensor layout scheme of each monitoring point was verified by structural dynamics simulation. By establishing a finite element model of the seeding unit, its multiple vibration modes in the target monitoring frequency band were calculated, and the position where the relative amplitude of the vibration mode displacement is greater than the set displacement threshold was selected as the sensor installation point. Signal conditioning circuitry is integrated at the sensor node to convert analog signals into digital signals for transmission near the sensor node via charge amplifiers and analog-to-digital converters.

3. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 2, characterized in that, The process of identifying single seed impact events in step 2 includes the following stages: signal preprocessing stage, in which low-frequency noise interference from tractor engine rotation and ground undulation is eliminated by configuring an adaptive digital filter with a cutoff frequency; In the feature extraction stage, edge computing nodes are used to extract composite features of the original signal in the time domain, frequency domain, and time-frequency domain from the sampled data. The composite features include short-time average energy, zero-crossing rate, Mel frequency cepstral coefficients, and node energy distribution coefficients obtained based on multi-layer wavelet packet decomposition. In the pattern recognition stage, a pre-trained convolutional neural network deployed at the edge is used to classify the feature vector composed of the composite features in real time. A nonlinear activation function is used to process the logical relationship between the features to determine the instantaneous moment when the seed hits the seed tube wall.

4. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 3, characterized in that, The process of obtaining the grain spacing data in step 2 also includes: acquiring the real-time travel speed provided by the global navigation satellite system module or the encoder installed on the seeder wheel in real time; The physical distance between the two adjacent seeds is obtained by performing an arithmetic product of the time interval between the impact points of the two adjacent seeds and the real-time travel speed. A sliding window averaging process is performed on multiple sets of continuously collected particle size data. By setting a preset length of sliding window, measurement fluctuations caused by local soil loosening or instantaneous mechanical vibration are eliminated. Mechanical background vibration is collected using a reference accelerometer installed at the position of the seeding unit frame, and the coherent mechanical background noise component is subtracted from the mixed signal of the main sensor using a minimum mean square error algorithm.

5. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 4, characterized in that, The process of real-time monitoring of seed spacing in step 2 also includes the following verification and adaptation logic: implementing dual verification logic, requiring that the peak characteristics of the vibration signal captured by the accelerometer and the frequency envelope of the acoustic signal captured by the microphone meet the preset synchronization tolerance on the time axis. If the signal features of both dimensions appear simultaneously within the preset time window and the matching degree is higher than the preset threshold, it is determined to be a valid seed planting event; otherwise, it is marked as an interference signal and removed. Load the recognition parameter library and automatically load the corresponding feature vector templates and deep learning classification weights based on the different physical characteristics of corn and soybeans and the type of crop selected by the user. Automatic identification is performed using clustering algorithms. Within a preset start time after sowing begins, the Euclidean distance between the signal vector to be tested and the preset cluster centers of different crops is calculated, and the current crop type is automatically locked.

6. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 5, characterized in that, The process of constructing the multiphysics coupling model in step 3 includes: analyzing the stress state of the trencher structure at different soil penetration depths, and converting the constraint change caused by the soil's envelope area on the trencher as the depth increases into an equivalent distribution of nonlinear spring constraints on the structure, which causes the natural frequency of the structure to shift towards higher frequencies. The frequency offset of the inherent frequency is monitored in real time using an accelerometer, and a pre-stored multidimensional mapping table of frequency offset and soil penetration depth for different soil types is invoked. The initial seeding depth value is obtained by real-time table lookup and linear interpolation calculation; The instantaneous height change of the individual seedlings relative to the ground is monitored by a laser displacement meter installed on the frame. The height change is used as a compensation parameter and combined with a weighting coefficient dynamically adjusted according to the sowing speed. The initial sowing depth value is then weighted and fused with the relative displacement measured by the laser displacement meter.

7. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 6, characterized in that, The process of performing dynamic monitoring of seeding depth in step 3 also includes a closed-loop correction step: The initial sowing depth value was corrected by using the infrasound and vibration signals generated by the compaction wheel during the soil compaction process. The amplitude intensity and phase characteristics of the signal components in the feedback signal of the compaction wheel within the preset low frequency range were analyzed. Based on the nonlinear correlation between the pressure exerted by the compaction wheel on the soil layer and the seed burial depth, it is determined whether the current trenching depth has reached the preset target depth. When the calculated depth deviation value exceeds the preset deviation threshold, an adjustment command is sent to the hydraulic pressure actuator of the trencher to compensate for the depth error by changing the extension and retraction length of the hydraulic cylinder. An online parameter identification mechanism is introduced. During the self-calibration phase of entering a new plot of land, the trencher is controlled to run at multiple preset depths, and the vibration mode frequencies at known depths are recorded to fit the frequency-depth mapping curve of the plot.

8. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 7, characterized in that, The self-cleaning process in step 4 includes: guiding the natural relative airflow generated during the operation of the seeder or the compressed air generated by the auxiliary air pump into the microchannel structure; When the airflow passes through the curved wall section inside the microchannel, it is affected by the fluid wall adhesion effect and a laminar air film is formed near the microphone guide hole and the accelerometer mounting base. By utilizing the wall-adhesion characteristics of the laminar air film, the movement trajectory of dust particles is forcibly altered, preventing dust from depositing in the sensor's sensitive area. The suction effect is generated by the local negative pressure zone at the outlet of the microchannel due to the increased flow velocity, which discharges the suspended dust in the monitoring chamber to the outside of the monitoring area. A pulse cleaning strategy is adopted, which controls the auxiliary air pump to start the pulse mode based on the real-time reading of the dust sensor, generating high-pressure airflow pulses with a preset frequency and preset duration.

9. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 8, characterized in that, The dust disturbance inversion process in step 4 includes: real-time monitoring of the mass concentration of suspended particulate matter in the air using an infrared scattering dust sensor in the auxiliary environmental monitoring unit; When the dust concentration exceeds the preset concentration threshold, the preset environmental transfer function is invoked based on the change in the attenuation coefficient of sound waves in the air medium and the drift caused by the influence of the mass of attached dust on the structural vibration damping ratio. The environmental transfer function is used to characterize the mapping relationship between the real-time dust concentration value collected by the infrared scattering dust sensor and the acoustic amplitude compensation coefficient and vibration frequency drift compensation amount. The function is pre-constructed based on experimental calibration, and its input parameters include dust concentration, while the output parameters include the analog front-end gain adjustment value and the cutoff frequency offset of the digital filter. The amplitude and vibration frequency of the collected voiceprints are compensated and calculated. The physical consistency of the recognition results is maintained by dynamically adjusting the analog front-end gain coefficient and the cutoff frequency of the digital filter in the signal processing algorithm. The infrared scattering dust sensor has a dual-spectrum detection function. By comparing the scattering intensity of dust on different wavelengths of light, it identifies the particle size distribution of dust and dynamically adjusts the air intake pressure of the microchannel according to the particle size.

10. The method for monitoring the seed spacing and depth of corn and soybeans under strong vibration and dusty conditions according to claim 9, characterized in that, The method also includes the following edge-cloud collaboration and decision-making steps: the edge computing node converts the massive amount of raw sampled waveforms into structured quality monitoring data packets, which include average particle spacing, seeding depth standard deviation, timestamps of missed seeding events, and geographic coordinates. Cross-modal data fusion and decision-making are performed. At the seeder end, edge computing nodes are used to process data from the vibration acoustic pattern array. The processed particle size, sowing depth, confidence level and abnormal events are uploaded to the cloud. The cloud optimizes the vibration acoustic pattern recognition model and generates sowing strategies based on the historical data of the plot. The cloud server combines geographic information system data of the current work area, historical soil fertility data, and historical yield maps to use a long short-term memory neural network model to predict the impact of current sowing quality on future seedling emergence rate. When the grain spacing deviation is detected to continuously exceed the preset ratio range, the cloud system sends a control signal to the control bus of the seeder and automatically adjusts the speed of the drive motor of the corresponding row seed metering device. Implement health monitoring of the sensor array, periodically send self-test pulse signals to the accelerometer, detect whether the excitation response waveform returned by the sensor conforms to the standard modal characteristics, and calculate the autocorrelation coefficient of the self-test waveform; Implement a seed meter wear prediction based on acoustic fingerprints. By tracking the periodic acoustic fingerprint characteristics generated during the rotation of the seed meter over a long period of time, use trend analysis algorithms to monitor abnormal growth of frequency band energy and generate early warning information.

Images