A microwave near-field imaging method, device and medium for corn ear water distribution

Abstract

The application provides a microwave near-field imaging method, device and medium for corn ear water distribution, which can solve the problem that the internal water distribution of a corn ear cannot be obtained layer by layer and accurately without damage in the related art. The method comprises the following steps: in a near-field region, a multi-frequency microwave signal is emitted to a corn ear to receive a scattering signal containing an evanescent wave component; the scattering signal is processed to obtain a pure scattering parameter reflecting electromagnetic characteristics of the corn ear; the pure scattering parameter is used to obtain a preliminary result of a dielectric constant distribution inside the corn ear; the preliminary result of the dielectric constant distribution is processed by using microwave signals with different polarization directions to extract layer-by-layer imaging features of the corn ear corresponding to the polarization directions; the layer-by-layer imaging features of the corn ear with different polarization directions are fused to obtain a final dielectric constant distribution capable of distinguishing a bract layer, a kernel layer and a cob layer; and the final dielectric constant distribution is mapped to a water spatial distribution of the corn ear.

Description

Technical Field

[0001] This application relates to the fields of non-destructive testing of agricultural products and microwave imaging technology, and in particular to a microwave near-field imaging method, device and medium for measuring the moisture distribution of corn ears. Background Technology

[0002] Maize is an important food crop worldwide, and monitoring the dynamic distribution of water during its growth and development is of great significance for physiological research during the grain-filling stage, maturity assessment, harvest prediction, and subsequent drying and storage. The maize ear has a complex structure, including different layers such as husks, kernels, and cob, and the water variation patterns of each layer are different and influence each other.

[0003] Existing detection methods mainly include: 1. Drying and weighing method: This is a destructive testing method, which is time-consuming, cannot achieve in-situ monitoring, and can only obtain the overall average moisture content, but cannot analyze the spatial distribution.

[0004] 2. Near-infrared spectroscopy: It has limited penetration depth and is mainly used to detect surface moisture. It is difficult to detect the moisture status of grains under the husks and the inner cob.

[0005] 3. Resistance / capacitance moisture meter: It is greatly affected by contact pressure and porosity, and it is usually a single-point measurement that cannot form an image.

[0006] Microwave imaging technology utilizes the differences in the propagation characteristics of electromagnetic waves in different media (such as dielectric constant and conductivity) to penetrate non-metallic materials and image their internal structures. However, for non-uniform, multi-layered organisms like corn ears, traditional far-field microwave imaging methods are limited by the Rayleigh diffraction limit, making it difficult to break through half a wavelength in resolution. This presents challenges such as low resolution, severe multipath effects, and high computational complexity in inversion. Therefore, there is an urgent need for a non-contact detection method that can overcome the diffraction limit, perform layered analysis, and accurately invert the moisture distribution of corn ears using near-field evanescent wave information. Summary of the Invention

[0007] This application provides a microwave near-field imaging method, device, and medium for measuring the moisture distribution in corn ears, which can solve the problem in related technologies that cannot obtain the moisture distribution inside corn ears in a non-destructive, layered, and accurate manner.

[0008] To achieve the above objectives, this application adopts the following technical solution: Firstly, a microwave near-field imaging method for measuring moisture distribution in maize ears is provided, including: In the near-field region, multi-frequency microwave signals are emitted toward the corn cob to receive scattered signals containing evanescent wave components; The scattered signal is processed to obtain pure scattering parameters that reflect the electromagnetic properties of the corn cob; The geometric profile of the corn cob is obtained by inversion using the pure scattering parameters located in the first frequency band. Using the geometric contour of the corn cob as a constraint, and the pure scattering parameters located in the second frequency band, a preliminary result of the dielectric constant distribution inside the corn cob is obtained by inversion; wherein, the lowest frequency in the second frequency band is greater than or equal to the highest frequency in the first frequency band. The preliminary results of the dielectric constant distribution are processed using microwave signals with different polarization directions to extract the layered imaging features of the corn ear with the corresponding polarization direction; wherein, the layered imaging features of the corn ear include the imaging features of the cob layer and longitudinal fiber structure, as well as the imaging features of the circumferential structure of the kernel layer and husk layer. By fusing the layered imaging features of the maize ears with different polarization directions, a final dielectric constant distribution that can distinguish the husk layer, kernel layer, and cob layer is obtained. The final dielectric constant distribution is mapped to the spatial distribution of moisture in a corn ear.

[0009] Secondly, a microwave near-field imaging device for measuring moisture distribution in maize ears is provided, comprising: The transmitting module is used to transmit multi-frequency microwave signals to corn ears in the near-field region to receive scattered signals containing evanescent wave components; The signal processing module is used to process the scattered signal to obtain pure scattering parameters that reflect the electromagnetic properties of the corn cob; The first inversion module is used to invert the geometric contour of the corn cob using the pure scattering parameters located in the first frequency band. The second inversion module is used to invert the preliminary results of the dielectric constant distribution inside the corn cob by using the pure scattering parameters located in the second frequency band interval, constrained by the geometric contour of the corn cob; wherein the lowest frequency in the second frequency band interval is greater than or equal to the highest frequency in the first frequency band interval. An extraction module is used to process the preliminary results of the dielectric constant distribution using microwave signals with different polarization directions to extract the layered imaging features of the corn ear with the corresponding polarization direction; wherein, the layered imaging features of the corn ear include the imaging features of the cob layer and longitudinal fiber structure, as well as the imaging features of the circumferential structure of the kernel layer and husk layer. A fusion module is used to fuse the layered imaging features of the maize ear with different polarization directions to obtain the final dielectric constant distribution that can distinguish the husk layer, kernel layer and cob layer. The mapping module is used to map the final dielectric constant distribution to the spatial distribution of moisture in the corn ear.

[0010] Thirdly, an electronic device is provided, comprising: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0011] Fourthly, a microwave near-field imaging system for measuring moisture distribution in maize ears is provided, comprising: Multi-frequency signal transceiver subsystem: including microwave signal source; Imaging front-end subsystem: Several wideband directional antennas are used as transceiver units. The wideband directional antennas can be configured with different polarization directions to transmit microwave signals generated by microwave signal sources and receive scattered signals. The wideband directional antennas support polarization direction adjustment. Mechanical scanning subsystem: includes a precision rotary table and a lifting table, used to carry the corn ear to be tested or drive the antenna array to perform relative rotation and lifting movements; and has a radial distance adjustment mechanism to keep the distance between the antenna and the surface of the corn ear within the near-field radiation range in order to capture evanescent waves and achieve scanning.

[0012] The computing and control subsystem is used to control the multi-frequency signal transceiver subsystem to generate microwave signals, control the mechanical scanning subsystem to move the corn ears or antenna, control the imaging front-end subsystem to transmit microwave signals and receive scattered signals, and calculate the moisture distribution of the corn ears.

[0013] Fifthly, a computer program product is provided, comprising a computer program or instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect.

[0014] The beneficial effects of this invention are: 1. Non-contact, non-destructive testing: Internal moisture content can be detected without damaging the corn cob structure or removing the husks.

[0015] 2. Near-field super-resolution imaging: By measuring in the near-field region, the evanescent wave components that are almost completely attenuated in the far field are effectively captured. These components carry high-frequency detail information of the object, thus breaking through the diffraction limit of traditional imaging and significantly improving the spatial resolution of the fine structure inside the corn cob.

[0016] 3. Layered high-resolution imaging: Combining multi-frequency and multi-polarization technologies, the signal aliasing problem caused by the multi-layered structure of the corn ear is effectively solved, and layered resolution from the husk to the cob is achieved.

[0017] 4. The algorithm is robust: It integrates initial value estimation and nonlinear optimization, which not only ensures the inversion convergence speed, but also improves the spatial resolution and dielectric parameter accuracy of the imaging.

[0018] 5. Wideband Vivaldi antenna applications: Utilizing its good directivity and wide bandwidth, it balances the requirements of penetration depth and imaging resolution. Attached Figure Description

[0019] Figure 1 The diagram illustrates the steps of a microwave near-field imaging method for moisture distribution in maize ears.

[0020] Figure 2 The diagram illustrates the effect of multi-frequency microwave near-field frequency-segmented imaging of moisture content in corn ears.

[0021] Figure 3 A schematic diagram of a microwave near-field imaging device for measuring moisture distribution in a corn ear is shown.

[0022] Figure 4 A schematic diagram of a microwave near-field imaging system for measuring moisture distribution in a corn ear is shown.

[0023] Figure 5 The diagram illustrates a schematic representation of a computer device's program interface.

[0024] Figure 6 A block diagram of an electronic device is shown schematically.

[0025] Figure 7 A block diagram of a computer-readable medium is shown schematically. Detailed Implementation

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0027] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0028] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0030] It should be understood that although the terms first, second, third, etc., may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this application. As used herein, the term "and / or" includes all combinations of any and one or more of the listed applications.

[0031] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing this application, and therefore cannot be used to limit the scope of protection of this application.

[0032] The purpose of this invention is to provide a microwave near-field imaging method, apparatus, system, device, and medium for measuring moisture distribution in maize ears. It utilizes a broadband directional antenna to perform non-contact multi-frequency microwave scanning in the near-field region, effectively capturing evanescent wave components carrying structural information. First, the overall geometric structure of the maize ear is inverted using microwave signals in the lower frequency band. Then, fine imaging of the maize ear is achieved in the higher frequency band based on prior information about the geometric structure. By changing the antenna polarization, feature-enhanced imaging is performed on the kernel layer, husk layer, and cob layer of the maize ear. Multiple nonlinear optimization algorithms are used to reconstruct the dielectric constant distribution inside the maize ear, which is then mapped to the spatial distribution of moisture. This method overcomes the limitations of the far-field diffraction limit by utilizing microwave near-field detection, enabling accurate and non-destructive acquisition of moisture distribution information for each layer of the maize ear structure, solving the problem of existing technologies' inability to detect moisture in maize ears in situ and at different layers.

[0033] According to a first specific embodiment of the present invention, the present invention provides a microwave near-field imaging system for the moisture distribution of maize ears, such as... Figure 4 As shown, the microwave near-field imaging system includes the following four subsystems: Multi-frequency signal transceiver subsystem: This subsystem is responsible for generating high-frequency excitation signals and acquiring response signals, and is the core signal processing unit of the imaging system. This subsystem can employ a vector network analyzer, a microwave signal source, and a receiver assembly, supporting wideband frequency sweeping and possessing multi-channel synchronous transmission and reception capabilities. The microwave signal source is programmable; the receiver features high dynamic range and low noise characteristics, enabling the extraction of multiple parameters such as amplitude and phase, providing a high-precision scattering parameter matrix for subsequent imaging.

[0034] Imaging Front-End Subsystem: This subsystem consists of an integrated transceiver antenna array. The array elements employ high-gain, wide-bandwidth Vivaldi end-fire antennas, possessing excellent time-domain characteristics. The antenna elements are arranged in a reconfigurable manner, supporting polarization mode switching. The array layout can be configured according to imaging requirements, operating through multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) modes to achieve microwave illumination and scattering signal capture from corn ears under multi-angle and multi-polarization conditions.

[0035] Mechanical Scanning Subsystem: This system provides precise spatial positioning and scanning motion control, including a high-precision rotary table and an electric lifting platform for carrying the corn cob or driving the antenna array to achieve relative motion; and an integrated radial distance adjustment mechanism that dynamically adjusts the distance between the antenna and the sample surface during scanning to ensure the antenna is in the near-field radiation zone and effectively captures the evanescent wave component carrying the sample's dielectric information. All motion units are fed back through encoder closed-loop feedback and are strictly synchronized with the signal acquisition system.

[0036] The computing and control subsystem is the core of the entire imaging system's central control and data processing. It is primarily used for controlling signal transmission, mechanical movement, and synchronous acquisition of multi-channel data. Equipped with computer equipment for data processing and imaging, it integrates various custom imaging algorithms, enabling rapid reconstruction of massive amounts of data, obtaining dielectric distribution maps of the internal structure of corn ears, and retrieving the moisture content and distribution of the kernels.

[0037] The entire system achieves fully automated coordination from signal generation, spatial scanning, data acquisition to image inversion. The computer control subsystem, as the core of the system, first sends commands to the multi-frequency transceiver subsystem to configure and generate microwave signals in a specific frequency band. Simultaneously, it precisely controls the mechanical scanning subsystem to move the corn ear or antenna. The signal is transmitted to the imaging front-end subsystem for close-range scanning of the corn ear. Its end-firing characteristics and broadband performance ensure efficient excitation and reception of near-field microwave signals containing information about the corn ear's interior. The received scattered signals are analyzed by the transceiver subsystem, and the data is transmitted back to the computer control subsystem. There, the imaging algorithm processes and reconstructs the data, ultimately generating a high-resolution image of the corn ear's moisture distribution.

[0038] According to a second specific embodiment of the present invention, such as Figure 1 As shown, the present invention provides a microwave near-field imaging method for moisture distribution in maize ears, comprising: Step S11: In the near-field region, a multi-frequency microwave signal is emitted toward the corn cob to receive a scattered signal containing an evanescent wave component.

[0039] Among them, evanescent waves refer to the electromagnetic field formed at the interface when microwaves are incident from a medium with a high dielectric parameter to a medium with a low dielectric parameter and undergo total reflection. The evanescent waves propagate along the interface and their amplitude decays exponentially. They carry high-frequency spatial information of the object at the subwavelength scale. By capturing and converting fine high-frequency spatial information into measurable signals, high-resolution imaging that breaks through the diffraction limit and is sensitive to dielectric properties can be achieved.

[0040] Furthermore, prior to step S11, the following steps may also be included: Step S101: Based on the transceiver system including a wideband directional antenna array, an open field microwave imaging detection environment is constructed.

[0041] Specifically, the broadband directional antenna array adopts a Vivaldi antenna, a log-periodic antenna, or a horn antenna, preferably a Vivaldi antenna with end-fire characteristics that has both good directivity and stable radiation characteristics.

[0042] Step S102: Perform field calibration using a standard simulant with known dielectric properties in a field environment.

[0043] Secondly, the real-time calibration in the field specifically involves: using a standard simulant with known dielectric properties to perform real-time calibration in a field environment, calculating and compensating for systematic error terms to eliminate interference from environmental noise, temperature drift, and cable deformation; dielectric properties refer to the polarization capability and energy storage and loss properties under the action of an electric field, usually expressed by dielectric constant and dielectric loss.

[0044] Furthermore, before transmitting multi-frequency microwave signals to the corn ears, the target corn ears are removed from the field measurement location, and a scattering parameter matrix signal containing empty background information is collected for subsequent background de-embedding processing.

[0045] In one specific embodiment, the corn cob is held on the plant or fixed on a rotating platform, placing it in the near-field radiation region of the antenna array; a Vivaldi antenna array with an operating frequency coverage of 1–6 GHz is vertically arranged; each Vivaldi antenna in the array is evenly distributed on a circle of radius R to form an omnidirectional measurement array; the distance between the broadband directional antenna and the surface of the corn cob is adjusted to a near-field range of 3 cm–10 cm; the antenna array is controlled to transmit multi-frequency microwave signals to receive microwave signals containing evanescent wave components after being scattered by the corn cob and convert them into a matrix containing scattering parameter information for storage.

[0046] After receiving the scattered signal containing the evanescent wave component, step S12 is executed.

[0047] Step S12: Process the scattering signal to obtain pure scattering parameters that reflect the electromagnetic properties of the corn cob.

[0048] Specifically, the processing of the scattered signal includes: differentiating the scattered signal containing the evanescent wave component after being scattered by the corn cob with the scattered parameter matrix signal containing the empty background information, so as to remove system and environmental clutter and obtain pure scattered parameters that reflect the electromagnetic properties of the corn cob.

[0049] like Figure 4 As shown, the rotary table in the mechanical scanning subsystem performs one transmission and reception cycle every certain angle of rotation, acquiring data in the low-frequency and high-frequency bands respectively, and simultaneously obtaining two sets of scattered data from the antenna in the V-polarization and H-polarization directions. Antenna position r m Given in polar coordinates: (1) Where, θ m R is the position of the antenna in polar coordinates; m is the radius of the circle around which the antenna is located; N is the antenna number; and m is the number of antennas.

[0050] In one embodiment, after transmitting multi-frequency microwave signals to a corn cob and receiving the scattered signal containing evanescent wave components, the corn cob is removed, the antenna array is kept in the near-field radiation region, and the antenna array is controlled to transmit multi-frequency microwave signals to obtain a scattering parameter matrix containing no-load background information. Two N×N complex scattering parameter matrices are obtained through measurement: (1) S bg When there is no target, only the scattering parameter matrix under the background condition is retained, that is, the scattering parameter matrix containing the background information of the empty target is retained. (2) S obj : The scattering parameter matrix when the target is present, i.e., the scattering signal containing the evanescent wave component.

[0051] Then, background de-embedding processing is performed using scattering parameters containing both the empty background signal and the corn ear microwave signal to obtain pure scattering parameters. Background de-embedding processing is performed on the acquired scattering signal containing evanescent wave components; that is, the difference between the scattering parameter matrix containing the acquired scattering signal with evanescent wave components after scattering by the corn ear and the empty background signal is calculated to remove system and environmental clutter, ultimately extracting pure scattering parameters reflecting the electromagnetic properties of the corn ear itself. The pure scattering parameters S are then obtained. scat The calculation is shown in the following formula: S scat= S obj -S bg (2) Where S scat The element (r,t) represents the scattered field transmitted by the t-th antenna and received by the r-th antenna.

[0052] After obtaining the pure scattering parameters that reflect the electromagnetic properties of the corn cob, step S13 is executed.

[0053] Step S13: Using the pure scattering parameters located in the first frequency band, the geometric contour of the corn cob is inverted to obtain the geometric contour.

[0054] Step S14: Using the geometric contour of the corn cob as a constraint, and the pure scattering parameters located in the second frequency band, the preliminary results of the dielectric constant distribution inside the corn cob are obtained by inversion.

[0055] Wherein, the lowest frequency of the second frequency band is greater than or equal to the highest frequency of the first frequency band.

[0056] Steps S13 and S14 involve frequency-band imaging of the corn ear. Combining the characteristics of different frequency bands, the scattering parameters after background de-embedding are used for imaging. Specifically, in the low-frequency range of the first frequency band, the scatterer contour of the corn ear is qualitatively reconstructed using a linear sampling method or a back-projection algorithm to determine the support set of the imaging region. In the high-frequency range of the second frequency band, the support set is used as a priori geometric constraint, and a nonlinear optimization algorithm is used to iteratively solve the complex permittivity within the support set to overcome the nonlinear scattering problem caused by high contrast.

[0057] Further, step S13 includes: using the pure scattering parameters located in the first frequency band interval, employing a linear sampling method or a back projection algorithm to invert and obtain the geometric contour of the corn cob; step S14 includes: using the pure scattering parameters located in the second frequency band interval, employing a nonlinear optimization algorithm to invert and obtain preliminary results of the dielectric constant distribution inside the corn cob within the geometric contour constraints. Wherein, the first frequency band interval, i.e., the low-frequency band, can be 1GHz-3GHz, the second frequency band interval, i.e., the high-frequency band, can be 4GHz-6GHz, and the nonlinear optimization algorithm includes at least one of truncated singular value decomposition (TSVD), confocal imaging (CI), or contrastive source inversion (CSI) algorithms.

[0058] In one specific embodiment, within an imaging square region determined according to the antenna coordinates and the size of the corn cob, it is discretized into N. x ×N y Mesh. For two-dimensional scalar approximation, the Hankel function is used as the Green's function: (3) Where j is the imaginary unit, Let r be the zeroth-order Hankel function of the first kind, r' be the field point position, r' be the source point position, and the operating frequency be . f The corresponding free space wavenumber k0 is 2π f / c c is 3×10 8 m / s.

[0059] Utilizing low-frequency data in the 1-3 GHz band, which has a longer wavelength and better penetration, a linear sampling method is used for rapid qualitative inversion within the imaging region. This yields a low-resolution but accurately contoured equivalent scatterer region resembling a corn cob, which is then binarized and used as a geometric constraint mask for subsequent inversions. Based on this regional constraint, the signal is switched to a high-frequency signal in the 4-6 GHz band. Leveraging its high resolution, nonlinear optimization algorithms, such as contrast source inversion, are used for fine inversion within the defined contour region. The detailed distribution of the internal complex permittivity is iteratively solved, effectively overcoming the nonlinear challenges posed by strong scatterers. The specific imaging results are shown below. Figure 2 As shown in the figure, the imaging results of three nonlinear optimization algorithms—Comparative Source Inversion (CSI), Confocal Imaging (CI), and Truncated Singular Value Decomposition (TSVD)—are presented. The imaging color gradation transitions from cool to warm tones. The deeper the color, the higher the inversion dielectric constant value of the region, quantitatively indicating a higher moisture content in the medium at that location. Conversely, cool-toned regions correspond to low dielectric properties and low moisture content. The specific steps of the nonlinear optimization algorithms are described in detail below: (1) Confocal Imaging Algorithm (CI): Based on the principle of two-way wave path superposition, the measurement signals of each transmit / receive antenna pair are backpropagated to each pixel in the imaging area and coherently superimposed. Its physical essence is linear diffraction superposition, suitable for rapid imaging under weak scattering conditions. Imaging value I(r) p At pixel r p The calculation is as follows: (4) Where △A is the pixel area, * denotes complex conjugate operation, and r t Indicates the location of the transmitting antenna, r r Indicates the location of the receiving antenna, r p These are the pixels in the imaging result, i.e., the pixels in the inverted water image.

[0060] (2) Contrast Source Inversion Algorithm (CSI): Based on the Lippmann-Schwinger integral equation, the inverse scattering problem is transformed into a joint optimization problem of the contrast source w(r) and the contrast χ(r). The total field u(r) satisfies: (5) Among them, u inc (r) represents the incident field; r is the location of the field point; r' represents the location of the source point, located in the imaging region.V Internal. Define the comparison source. Then we obtain the data equation and the state equation: (6) (7) Among them, u scat (r) represents the scattered field; For measuring the boundary or scanning the circumference of the antenna; V Let represent the imaging region. The solution is obtained by minimizing the following cost function: (8) Where s represents the incident field generated by the s-th transmitting antenna when the target is illuminated by the s-th transmitting antenna as the excitation source, and the corresponding scattered field is collected by all receiving antennas; Let be the scattered field under the s-th illumination. Let be the total field under the s-th irradiation. For the integral operator under the s-th illumination, This is the regularization parameter. An alternating optimization method is used to iteratively update the comparison source and contrast to find the optimal solution.

[0061] (3) Truncation Singular Value Decomposition Algorithm (TSVD): Linearizes the inverse scattering problem and establishes the system matrix A∈C M×P (M=(N-1),P=N) x ×N y ): B = Ax + e (9) Where: b is the measurement vector, derived from the scattered field S scat Arranged as follows, x is the contrast distribution to be determined, and the elements of A are... , where e represents measurement noise. Perform economical singular value decomposition (SVD) on A: (10) Among them, the singular values ​​satisfy σ1≥σ1≥…≥σ min(M,P) ≥0, u i It is an eigenvector of AA* and U is composed of column vectors u i Composition, v i V is an eigenvector of A*A and V is composed of column vectors v i Composition: V* is the conjugate transpose of V, and Σ is the singular value diagonal matrix. The TSVD solution takes the first k singular values: (11) Then, the singular value spectrum is normalized, the point of maximum curvature is found, and the optimal solution is found through iterative improvement using residual feedback. (12) Where μ is the relaxation factor.

[0062] Based on the above three nonlinear optimization algorithms, the preliminary dielectric constant distribution is obtained by inverting the scattering parameter information under different frequency bands.

[0063] After obtaining the preliminary results of the dielectric constant distribution inside the corn cob through inversion, step S15 is executed.

[0064] Step S15: The preliminary results of the dielectric constant distribution are processed using microwave signals with different polarization directions to extract the layered imaging features of the corn ear with the corresponding polarization direction.

[0065] The layered imaging features of the corn ear include imaging features of the cob layer and longitudinal fiber structure, as well as imaging features of the circumferential structure of the kernel layer and husk layer.

[0066] Step S16: Fuse the layered imaging features of the corn ear with different polarization directions to obtain the final dielectric constant distribution that can distinguish the husk layer, kernel layer and cob layer.

[0067] Step S15 performs layered imaging. Step S16 fuses the imaging results of different polarization directions and uses tensor imaging or weighted fusion to eliminate signal crosstalk between layers. After combining the dielectric constant distribution obtained by the above operation with the signals of different polarization directions, the kernel layer, husk layer and cob layer of the corn ear are extracted for layered feature extraction and the final dielectric constant distribution is obtained by inversion.

[0068] For the cob layer, V-polarization data is mainly used for inversion. This is because the vascular bundles inside the cob are arranged axially and have obvious dielectric anisotropy, which produces stronger scattering of electric fields parallel to the maize axis. Based on a nonlinear optimization algorithm, the dielectric constant of the central region is mainly inverted. For the grain and husk layers, H-polarization data and high-frequency signals are mainly used. Because H-polarization is less affected by the longitudinal fiber structure and has a shorter high-frequency wavelength, it is more sensitive to scattering of small grains. A nonlinear optimization algorithm is used to invert the entire region, and the cob dielectric constant obtained in the previous step is used as background prior information to improve the imaging accuracy of the outer structure.

[0069] Based on the above analysis of the differences between components of maize ears under different polarization modes, in order to improve imaging contrast and structural resolution, the sensitivity of parallel polarization to the cob structure and the effective penetration of vertical polarization to the kernel layer are utilized. The dielectric constant distribution obtained after the above operation is combined with the signals from different polarization directions. Layered feature extraction is performed on the kernel layer, husk layer and cob layer of maize ears. The imaging results of the two polarization modes are fused, and tensor imaging or weighted fusion is used to eliminate signal crosstalk between layers, and the final dielectric constant distribution is obtained by inversion.

[0070] Step S17: Map the final dielectric constant distribution to the spatial distribution of moisture in the corn ear.

[0071] Based on the broadband dielectric constant distribution obtained by inversion, an intuitive moisture distribution map is generated and a pseudo-color image is produced, thus completing the complete analysis and visualization of three-dimensional information on the internal structure and moisture content of corn ears from microwave signals.

[0072] Using the above-described specific implementation method, a broadband directional antenna is used to perform non-contact multi-frequency microwave scanning in the near-field region, effectively capturing evanescent wave components carrying structural information of the object. First, the overall geometric structure of the corn ear is inverted using microwave signals in the lower frequency band, and then fine imaging of the corn ear is achieved based on prior information about the geometric structure in the higher frequency band. By changing the antenna polarization, feature enhancement imaging is performed on the kernel layer, husk layer, and cob layer of the corn ear. Using various nonlinear optimization algorithms, the dielectric constant distribution inside the corn ear is reconstructed, and then mapped to the spatial distribution of moisture. In other words, this application uses microwave near-field detection to overcome the limitation of the far-field diffraction limit, and can accurately and non-destructively obtain the moisture distribution information of each layer of the corn ear structure, solving the problem that existing technologies cannot detect corn ear moisture in situ and layer by layer.

[0073] According to a third specific embodiment of the present invention, such as Figure 3 As shown, the present invention provides a microwave near-field imaging device 100 for measuring the moisture distribution of corn ears, comprising: The transmitting module 110 is used to transmit multi-frequency microwave signals to corn ears in the near-field region to receive scattered signals containing evanescent wave components. The signal processing module 120 is used to process the scattered signal to obtain pure scattering parameters that reflect the electromagnetic properties of the corn cob; The first inversion module 130 is used to invert the geometric contour of the corn cob using the pure scattering parameters located in the first frequency band. The second inversion module 140 is used to invert the preliminary result of the dielectric constant distribution inside the corn ear by using the pure scattering parameters located in the second frequency band interval, constrained by the geometric contour of the corn ear; wherein the lowest frequency in the second frequency band interval is greater than or equal to the highest frequency in the first frequency band interval. The extraction module 150 is used to process the preliminary results of the dielectric constant distribution using microwave signals with different polarization directions to extract the layered imaging features of the corn ear with the corresponding polarization direction; wherein, the layered imaging features of the corn ear include the imaging features of the cob layer and longitudinal fiber structure, as well as the imaging features of the circumferential structure of the kernel layer and husk layer. The fusion module 160 is used to fuse the layered imaging features of the corn ear with different polarization directions to obtain the final dielectric constant distribution that can distinguish the husk layer, kernel layer and cob layer. The mapping module 170 is used to map the final dielectric constant distribution to the spatial distribution of moisture in the corn ear.

[0074] According to a fourth embodiment of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. Internally, it stores a software program that executes the aforementioned imaging algorithm, and the program interface is as follows: Figure 5 As shown, the interface presents the inversion results of moisture distribution in corn ears. The color gradation transitions from cool to warm tones; the deeper the red, the higher the inversion dielectric constant value, quantitatively indicating a higher moisture content at that location. Conversely, cool-toned areas correspond to low dielectric properties and low moisture content. The program module includes: a data reading module, a calibration module, an inversion calculation module integrating TSVD, CSI, and CI algorithm libraries, an image post-processing module, and a user interface. The processor executes as follows... Figure 1 The steps are shown in the figure.

[0075] According to a fifth specific embodiment of the present invention, the present invention provides an electronic device, such as... Figure 6 As shown, Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0076] The following reference Figure 6 To describe an electronic device 200 according to this embodiment of the present application. Figure 6 The electronic device 200 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 6 As shown, the electronic device 200 is presented in the form of a general-purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including storage unit 220 and processing unit 210), a display unit 240, etc.

[0078] The storage unit stores program code that can be executed by the processing unit 210, causing the processing unit 210 to perform the steps described in this specification according to various exemplary embodiments of this application. For example, the processing unit 210 can perform actions such as... Figure 1 The steps are shown in the figure.

[0079] The storage unit 220 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 2201 and / or a cache storage unit 2202, and may further include a read-only memory unit (ROM) 2203.

[0080] The storage unit 220 may also include a program / utility 2204 having a set (at least one) program module 2205, such program module 2205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0081] Bus 230 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0082] Electronic device 200 can also communicate with one or more external devices 200' (e.g., keyboard, pointing device, Bluetooth device, etc.), enabling users to communicate with devices that interact with electronic device 200, and / or any device that allows electronic device 200 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 250. Furthermore, electronic device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of electronic device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0083] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware.

[0084] Therefore, according to a sixth specific embodiment of the present invention, the present invention provides a computer-readable medium. For example... Figure 7 As shown, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the above-described method according to the embodiments of the present invention.

[0085] The software product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0086] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0087] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0088] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the functions of the first embodiment.

[0089] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0090] Through the description of the above embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions of the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of the present invention.

[0091] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A microwave near-field imaging method for moisture distribution in maize ears, characterized in that, include: In the near-field region, multi-frequency microwave signals are emitted toward the corn cob in order to receive the scattered signals containing evanescent wave components; The scattered signal is processed to obtain pure scattering parameters that reflect the electromagnetic properties of the corn cob; Using the pure scattering parameters located in the first frequency band, the geometric contour of the corn cob is obtained by inversion using a linear sampling method or a back projection algorithm; Using the geometric contour of the corn cob as a constraint, and utilizing the pure scattering parameters located in the second frequency band, a nonlinear optimization algorithm is used to invert the preliminary results of the dielectric constant distribution inside the corn cob within the geometric contour constraint; wherein, the lowest frequency in the second frequency band is greater than or equal to the highest frequency in the first frequency band. The preliminary results of the dielectric constant distribution are processed using microwave signals with different polarization directions to extract the layered imaging features of the corn ear with the corresponding polarization direction; wherein, the layered imaging features of the corn ear include the imaging features of the cob layer and longitudinal fiber structure, as well as the imaging features of the circumferential structure of the kernel layer and husk layer. By fusing the layered imaging features of the maize ears with different polarization directions, a final dielectric constant distribution that can distinguish the husk layer, kernel layer, and cob layer is obtained. The final dielectric constant distribution is mapped to the spatial distribution of moisture in the corn ear; The step of processing the preliminary results of the dielectric constant distribution using microwave signals with different polarization directions to extract the layered imaging features of the corn ear corresponding to the polarization direction includes: The strong scattering response of the longitudinal vascular bundles inside the ear axis is excited by polarized waves with an electric field direction parallel to the axis of the ear, so as to obtain the imaging characteristics of the ear axis layer and longitudinal fiber structure. By utilizing polarized waves with electric fields perpendicular to the corn ear axis to reduce the depolarization effect of longitudinal fibers, imaging features of the circumferential structure of the kernel layer and husk layer can be obtained. The step of using the pure scattering parameters located in the first frequency band interval to invert the geometric contour of the corn cob using a linear sampling method or a back projection algorithm includes: using the pure scattering parameters located in the first frequency band interval to invert the equivalent scattering volume region of the corn cob using a linear sampling method or a back projection algorithm, and binarizing the equivalent scattering volume region into a geometric constraint mask. The method of using the geometric contour of the corn cob as a constraint and employing the pure scattering parameters located in the second frequency band to invert within the geometric contour constraint of the corn cob to obtain preliminary results of the dielectric constant distribution inside the corn cob includes: within the equivalent scatterer region defined by the geometric constraint mask, using the V-polarization pure scattering parameters and H-polarization pure scattering parameters located in the second frequency band, respectively, performing fine inversion using a nonlinear optimization algorithm to obtain preliminary results of the dielectric constant distribution inside the corn cob in the corresponding polarization direction; the first frequency band is 1GHz-3GHz, and the second frequency band is 4GHz-6GHz.

2. The method according to claim 1, characterized in that, The method further includes: An open field microwave imaging detection environment is constructed based on a transceiver system that includes a wideband directional antenna array; Field calibration was performed using standard simulants with known dielectric properties in a field environment.

3. The method according to claim 2, characterized in that, The aforementioned transceiver system, based on a wideband directional antenna array, constructs an open field microwave imaging detection environment, including: A wideband directional antenna array with an operating frequency coverage of 1–6 GHz is vertically arranged; Each broadband directional antenna in the array is evenly distributed on a circle of radius R to form an omnidirectional measurement array; Adjust the distance between the broadband directional antenna and the surface of the corn cob to a near-field range of 3cm–10cm.

4. The method according to claim 3, characterized in that, The process of processing the scattered signal to obtain pure scattering parameters reflecting the electromagnetic properties of the corn cob includes: The scattering signal containing the evanescent wave component is differentiated from the scattering parameter matrix signal containing the empty background information to obtain the pure scattering parameters that reflect the electromagnetic properties of the corn cob. The scattering parameter matrix signal containing the empty background information is obtained by removing the corn cob, keeping the broadband directional antenna array in the near-field radiation region, and controlling the broadband directional antenna array to transmit multi-frequency microwave signals to obtain the scattering parameter matrix signal containing the empty background information.

5. The method according to claim 3, characterized in that, The nonlinear optimization algorithm includes at least one of truncated singular value decomposition (TSVD), confocal imaging (CI), or contrast source inversion (CSI) algorithms.

6. The method according to claim 3, characterized in that, The layered imaging features of the maize ears with different polarization directions are fused, including: Using tensor imaging or weighted fusion of imaging features between different layers of the corn ear, the final dielectric constant distribution that can distinguish the husk layer, kernel layer and cob layer is obtained.

7. A microwave near-field imaging device for measuring moisture distribution in corn ears, characterized in that, include: The transmitting module is used to transmit multi-frequency microwave signals to corn ears in the near-field region to receive scattered signals containing evanescent wave components; The signal processing module is used to process the scattered signal to obtain pure scattering parameters that reflect the electromagnetic properties of the corn cob; The first inversion module is used to invert the geometric contour of the corn cob using the pure scattering parameters located in the first frequency band interval, employing a linear sampling method or a back projection algorithm. The second inversion module is used to invert the preliminary result of the dielectric constant distribution inside the corn ear within the geometric contour constraint of the corn ear by using the pure scattering parameters located in the second frequency band interval and employing a nonlinear optimization algorithm; wherein the lowest frequency of the second frequency band interval is greater than or equal to the highest frequency of the first frequency band interval. An extraction module is used to process the preliminary results of the dielectric constant distribution using microwave signals with different polarization directions to extract the layered imaging features of the corn ear with the corresponding polarization direction; wherein, the layered imaging features of the corn ear include the imaging features of the cob layer and longitudinal fiber structure, as well as the imaging features of the circumferential structure of the kernel layer and husk layer. A fusion module is used to fuse the layered imaging features of the maize ear with different polarization directions to obtain the final dielectric constant distribution that can distinguish the husk layer, kernel layer and cob layer. A mapping module is used to map the final dielectric constant distribution to the spatial distribution of moisture in a corn ear; The extraction module is further configured to: use polarized waves with electric field direction parallel to the corn ear axis to excite the strong scattering response of the longitudinal vascular bundles inside the ear axis to obtain imaging features of the ear axis layer and longitudinal fiber structure; and use polarized waves with electric field direction perpendicular to the corn ear axis to reduce the depolarization effect of the longitudinal fibers to obtain imaging features of the circumferential structure of the grain layer and husk layer. The first inversion module is further configured to: use the pure scattering parameters located in the first frequency band interval to invert the equivalent scattering body region of the corn ear using a linear sampling method or a back projection algorithm, and binarize the equivalent scattering body region into a geometric constraint mask; The second inversion module is further configured to: within the equivalent scatterer region defined by the geometric constraint mask, use the V-polarized pure scattering parameters and H-polarized pure scattering parameters located in the second frequency band interval to perform fine inversion using a nonlinear optimization algorithm to obtain preliminary results of the dielectric constant distribution inside the corn cob in the corresponding polarization direction; the first frequency band interval is 1GHz-3GHz, and the second frequency band interval is 4GHz-6GHz.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-6.

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