A Soil Root Imaging Method and System Based on Bioimpedance Spectroscopy

Abstract

This application discloses a method and system for soil root imaging based on bioimpedance spectroscopy, relating to the field of soil root imaging. The method includes: acquiring bioimpedance spectroscopy measurement data of a target tree; the bioimpedance spectroscopy measurement data is obtained by repeated injection acquisition operations at at least three frequency points using an electrode array; constructing an inversion domain matched to the electrode array, and discretizing the inversion domain into a grid; introducing correlation constraints, electrode models, and boundary conditions into the inversion domain after grid discretization to obtain a forward model; performing imaging inversion based on the bioimpedance spectroscopy measurement data using the forward model to obtain a reconstructed image with soil electrical parameter distribution; identifying the root region of the target tree based on the reconstructed image, and determining the identified root region and its corresponding confidence level as the root imaging result. This application can achieve stable imaging and quantitative identification of root regions in a non-destructive and efficient manner in soil environments.

Description

Technical Field

[0001] This application relates to the field of soil root imaging, and in particular to a soil root imaging method and system based on bioimpedance spectroscopy. Background Technology

[0002] The root system is a crucial organ for fruit trees in absorbing water and fertilizer, resisting stress, and maintaining stable yields. The spatial distribution and changes in the root system often precede above-ground symptoms. In routine orchard management, root information is essential for assessing water stress, root decline, the extent of disease impact, and developing fertilization and remediation strategies. This is especially true in hilly orchards, where complex soil stratification, fluctuating moisture content, and limited operational conditions make it difficult for operators to objectively determine the location, extent, and priority of problem treatment without readily available and repeatable information on the underground root system.

[0003] Root system surveys and assessments typically employ methods such as excavation and observation, sampling and analysis, or reliance on empirical interpretation of aboveground data. While excavation and sampling methods are intuitive, they are destructive, time-consuming, and labor-intensive, making it difficult to conduct continuous retesting at the same location. Empirical interpretation methods are significantly affected by individual differences and environmental disturbances, making it difficult to establish stable and quantifiable data. Therefore, how to achieve stable imaging and quantitative identification of root systems in a non-destructive and efficient manner within soil environments has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a soil root imaging method and system based on bioimpedance spectroscopy, which can achieve stable imaging and quantitative identification of root areas in soil environment without damage and with high efficiency.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a soil root imaging method based on bioimpedance spectroscopy, comprising: Acquire bioimpedance spectrum measurement data of the target tree; the bioimpedance spectrum measurement data is obtained by repeated injection acquisition operation at at least three frequency points using an electrode array; the electrode array is an electrode array for electrical impedance tomography deployed in the soil environment where the target tree is located; the injection acquisition operation includes: applying an AC excitation current to the electrode array using a set injection mode, and measuring the voltage response under the AC excitation current; An inversion domain matching the electrode array is constructed, and the inversion domain is discretized into a grid. After the grid is discretized, correlation constraints, electrode models and boundary conditions are introduced into the inversion domain to obtain the forward model. Based on the bioimpedance spectroscopy measurement data, the forward model is used for imaging inversion to obtain a reconstructed image with the distribution of soil electrical parameters. The root system region of the target tree is identified based on the reconstructed image, and the identified root system region and the corresponding confidence level are determined as the root imaging result.

[0006] In one embodiment, an inversion domain matching the electrode array is constructed, and the inversion domain is discretized into a grid. Correlation constraints, electrode models, and boundary conditions are introduced into the inversion domain obtained after grid discretization to obtain a forward model, specifically including: The two-dimensional or three-dimensional region surrounding the root zone of the target tree is defined as the inversion domain, and the inversion domain is discretized by finite element mesh to obtain the mesh-discretized inversion domain. Soil is used as the background electrical parameter region, the root system is used as the local electrical parameter variation region, and the impedance parameter correlation constraint is established between different frequency points. Current injection boundary conditions and voltage measurement boundary conditions are set on the boundary of the inversion domain after grid discretization, and an electrode model including electrode contact impedance is introduced; the electrode model is used to characterize the current coupling, potential distribution and potential sampling relationship between the electrode and the soil medium. Based on the inversion domain after grid discretization, the associated constraints, the current injection boundary conditions, the voltage measurement boundary conditions, and the electrode model, the potential distribution is solved to obtain the forward model; the forward model is used to calculate the voltage response based on the distribution of electrical parameters.

[0007] In one embodiment, based on the bioimpedance spectroscopy measurement data, the forward model is used for imaging inversion to obtain a reconstructed image with soil electrical parameter distribution, specifically including: The bioimpedance spectroscopy measurement data are preprocessed and calibrated to obtain calibration data; The calibration data is inverted using the forward model to obtain a reconstructed image with the distribution of soil electrical parameters.

[0008] In one embodiment, the bioimpedance spectroscopy measurement data is preprocessed and calibrated to obtain calibration data, specifically including: The bioimpedance spectroscopy measurement data are preprocessed to obtain preprocessed data; the preprocessing includes at least two of the following: noise reduction filtering, outlier removal, channel consistency verification, and frequency point normalization. The preprocessed data was calibrated using electrode contact impedance to obtain calibration data.

[0009] In one embodiment, the forward model is used to perform imaging inversion on the calibration data to obtain a reconstructed image with soil electrical parameter distribution, specifically including: The calibration data are jointly reconstructed using the iterative inversion method based on the forward model to obtain a reconstructed image with the distribution of soil electrical parameters. During the reconstruction process, the electrical parameters in the inversion domain are iteratively updated in the direction of decreasing objective function. The iteration stops and a reconstructed image showing the distribution of soil electrical parameters is output when at least one of a first and a second predefined condition is met. The first predefined condition is that the residual between the measured voltage response and the voltage response output by the forward model satisfies a preset residual threshold. The second predefined condition is that the increment of electrical parameters obtained from two consecutive iterations satisfies a preset parameter increment threshold. The objective function is determined based on residuals, spatial regularization terms, and frequency dimension smoothing constraints. The residual is used to characterize the difference between the measured voltage response and the voltage response output by the forward model.

[0010] In one embodiment, the root region of the target tree is identified based on the reconstructed image, and the identified root region and its corresponding confidence level are determined as the root imaging result, specifically including: The reconstructed image is subjected to threshold segmentation, connected component filtering, and morphological constraint processing to obtain the initial root region; The initial root region is then calibrated to obtain root imaging results.

[0011] In one embodiment, the electrode array is arranged in a ring around the rhizosphere region of the target tree, and the number of electrodes in the electrode array is 8 to 32.

[0012] In one embodiment, the electrode array includes an injection electrode pair and a measurement electrode pair; the two electrodes in the injection electrode pair are arranged adjacently or opposite to each other; an AC excitation current is applied to the injection electrode pair using a set injection mode, and the voltage response under the AC excitation current is measured using the measurement electrode pair.

[0013] In one embodiment, the set injection mode is at least one of adjacent injection mode and opposing injection mode.

[0014] Secondly, this application provides a soil root imaging system based on bioimpedance spectroscopy, including: an electrode array, a data acquisition unit, and a root imaging module; The electrode array is deployed in the soil environment where the target tree is located; the electrode array is used to repeatedly inject and acquire data at at least three frequency points to obtain bioimpedance spectrum measurement data of the target tree; the injection and acquisition operation includes: applying an AC excitation current to the electrode array using a set injection mode, and measuring the voltage response under the AC excitation current; The acquisition unit is used to acquire bioimpedance spectrum measurement data obtained by the electrode array and send the bioimpedance spectrum measurement data to the root imaging module; The root imaging module includes: The data acquisition unit is used to acquire bioimpedance spectrum measurement data of the target tree. The forward model building unit is used to construct an inversion domain that matches the electrode array, and to perform mesh discretization on the inversion domain. After mesh discretization, the inversion domain is introduced with correlation constraints, electrode models and boundary conditions to obtain the forward model. The imaging inversion unit is used to perform imaging inversion based on the bioimpedance spectrum measurement data and the forward model to obtain a reconstructed image with the distribution of soil electrical parameters. The root imaging unit is used to identify the root region of the target tree based on the reconstructed image, and to determine the identified root region and the corresponding confidence level as the root imaging result.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and system for soil root imaging based on bioimpedance spectroscopy. The method involves repeatedly injecting and acquiring bioimpedance spectral data of the target tree using an electrode array at at least three frequency points. An inversion domain matching the electrode array is constructed and then discretized into a grid. Correlation constraints, electrode models, and boundary conditions are introduced into the discretized inversion domain to obtain a forward model. Based on the bioimpedance spectroscopy measurement data, imaging inversion is performed using the forward model to obtain a reconstructed image with soil electrical parameter distribution, thereby enabling the identification of the root system region of the target tree and obtaining root imaging results. Compared with excavation observation and sampling analysis, this application can achieve root imaging non-destructively and efficiently. During the root imaging process, the introduction of bioimpedance spectroscopy electric field modeling and multi-frequency joint reconstruction mechanism enables stable differentiation of root regions under complex soil conditions. Therefore, it achieves stable imaging and quantitative identification of root regions in soil environments. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of a soil root imaging method based on bioimpedance spectroscopy provided in this application embodiment; Figure 2 A schematic diagram of the structure of a soil root imaging system based on bioimpedance spectroscopy provided in an embodiment of this application; Figure 3 A schematic diagram showing the layout of the soil root imaging system based on bioimpedance spectroscopy provided in this application embodiment in the rhizosphere region of fruit trees; Figure 4 A schematic diagram of the combination of injection electrode pairs and measurement electrode pairs of the electrode array provided in the embodiments of this application, as well as the cyclic scanning direction; Figure 5 A schematic diagram illustrating the module relationship between electric field modeling and forward computation of the soil root system provided in this application embodiment; Figure 6 A schematic diagram of the algorithm framework for multi-frequency impedance data processing and joint inversion reconstruction provided in the embodiments of this application; Figure 7 This is a schematic diagram of the processing chain for root region identification, confidence assessment, and result output provided in the embodiments of this application.

[0018] Attached reference numerals: Target tree body—1, Soil surface—2, Root system area—3, Electrode array—4, Acquisition unit—5, Attitude leveling mechanism—6, Cable—7, Injection electrode pair—8, Measurement electrode pair—9, Cyclic scanning direction—10. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Addressing the shortcomings of methods such as excavation observation, sampling analysis, or reliance on experience in interpreting aboveground structures, this application introduces Electrical Impedance Tomography (EIT). EIT can reconstruct the distribution of electrical parameters of the medium by injecting current through multiple electrodes and acquiring voltage responses, thus providing a non-destructive means for identifying underground structures. However, soil environments differ from relatively stable media such as the human body. Soil electrical properties vary significantly with moisture content, temperature, salinity, and compaction. Electrode contact states also tend to fluctuate with insertion depth and soil particle structure, leading to increased measurement noise and instability in inversion, which in turn affects the reliability and reproducibility of root identification. Particularly in the soil-root system, the impedance characteristics of roots and soil differ in the frequency dimension. Without introducing bioimpedance spectral modeling and multi-frequency joint reconstruction mechanisms tailored to this system, it is often difficult to stably distinguish root regions under complex soil conditions. Therefore, this application mainly addresses the following issues: First, the methods for acquiring root information are highly destructive or rely heavily on experience, making it difficult to achieve large-scale, repeatable, and objective assessments; second, significant fluctuations in the soil environment and changes in electrode contact impedance lead to high noise and insufficient stability in EIT measurement data, making imaging results susceptible to environmental and operational factors; third, the lack of bioimpedance spectrum modeling and multi-frequency data processing procedures for soil root systems makes it difficult to fully utilize frequency dimension features to improve root identification accuracy; and fourth, the lack of a unified accuracy calibration and engineered output method for imaging results hinders alignment with the orchard coordinate system and subsequent decision-making and retesting comparisons.

[0022] To address the aforementioned issues, this application proposes a bioimpedance spectroscopy imaging scheme for soil root systems. By standardizing the acquisition, modeling, data processing, and image reconstruction processes, it improves the stability and engineering usability of root imaging.

[0023] In one exemplary embodiment, such as Figure 1 As shown, a soil root imaging method based on bioimpedance spectroscopy is provided, including: Step 101: Obtain bioimpedance spectrum measurement data of the target tree; the bioimpedance spectrum measurement data is obtained by repeated injection acquisition operations at at least three frequency points using an electrode array.

[0024] The electrode array is an electrode array for electrical impedance tomography deployed in the soil environment where the target tree is located; the injection acquisition operation includes: applying an AC excitation current to the electrode array using a set injection mode, and measuring the voltage response under the AC excitation current.

[0025] Step 102: Construct an inversion domain that matches the electrode array, and perform mesh discretization on the inversion domain. After mesh discretization, introduce correlation constraints, electrode models and boundary conditions into the inversion domain to obtain a forward model.

[0026] Step 103: Based on the bioimpedance spectroscopy measurement data, the forward model is used to perform imaging inversion to obtain a reconstructed image with the distribution of soil electrical parameters.

[0027] Step 104: Identify the root system region of the target tree based on the reconstructed image, and determine the identified root system region and the corresponding confidence level as the root imaging result.

[0028] In another exemplary embodiment of this application, in step 101, the electrode array is arranged in a ring around the rhizosphere region of the target tree, and the number of electrodes in the electrode array is 8 to 32.

[0029] In another exemplary embodiment of this application, in step 101, the electrode array includes an injection electrode pair and a measurement electrode pair. The two electrodes in the injection electrode pair are arranged adjacently or oppositely. During injection acquisition, an AC excitation current is applied to the injection electrode pair using a set injection mode, and the voltage response under the AC excitation current is measured using the measurement electrode pair. The set injection mode is cyclically scanned to cover the measurement combinations of the electrode array. The set injection mode is at least one of an adjacent injection mode and an opposite injection mode.

[0030] In another exemplary embodiment of this application, in step 101, the amplitude of the AC excitation current remains constant at each frequency point, and the amplitude and phase information of the acquired voltage response are recorded.

[0031] In another exemplary embodiment of this application, step 102 specifically includes: The two-dimensional or three-dimensional region surrounding the root zone of the target tree is defined as the inversion domain, and the inversion domain is discretized using a finite element mesh to obtain the mesh-discrete inversion domain. Soil is considered as the background electrical parameter region, and the root system as the region of local electrical parameter variation. Impedance parameter correlation constraints are established between different frequency points. Current injection boundary conditions and voltage measurement boundary conditions are set on the boundary of the mesh-discrete inversion domain, and an electrode model incorporating electrode contact impedance is introduced. The electrode model is used to characterize the current coupling, potential distribution, and potential sampling relationship between the electrode and the soil medium. Based on the mesh-discrete inversion domain, the correlation constraints, the current injection boundary conditions, the voltage measurement boundary conditions, and the electrode model, the potential distribution is solved to obtain the forward model. The forward model is used to calculate the voltage response based on the electrical parameter distribution.

[0032] In another exemplary embodiment of this application, step 103 specifically includes: (1) The bioimpedance spectroscopy measurement data are preprocessed and calibrated to obtain calibration data.

[0033] Specifically, the bioimpedance spectroscopy measurement data is preprocessed to obtain preprocessed data; the preprocessing includes at least two of the following: noise reduction filtering, outlier removal, channel consistency verification, and frequency point normalization; the electrode contact impedance is estimated, and the preprocessed data is calibrated using the electrode contact impedance to obtain calibration data.

[0034] The contact impedance estimation process includes: before formal imaging inversion, applying a known AC excitation current to the preset injection electrode pairs in the electrode array, acquiring the voltage response of the corresponding measurement electrode pairs, and calculating the equivalent impedance of the measurement combination based on the voltage response and AC excitation current; estimating the equivalent contact impedance of each electrode based on the redundancy relationship and repeated measurement results between different measurement combinations, and using the equivalent contact impedance for subsequent measurement data calibration, abnormal channel elimination, or inversion weight adjustment.

[0035] Redundancy refers to the relationship between repeated injection and measurement of a ring of electrodes in the electrode array using different combinations, resulting in multiple EIT measurements. The same electrode will appear in multiple sets of data. If the electrode has poor contact, it will affect not only one set of data but multiple sets. This allows us to determine whether the contact impedance of the electrode is too high by observing whether these related data are abnormal simultaneously.

[0036] (2) The forward model is used to perform imaging inversion on the calibration data to obtain a reconstructed image with the distribution of soil electrical parameters.

[0037] Specifically, an iterative inversion method is used to jointly reconstruct the calibration data based on the forward model, obtaining a reconstructed image with soil electrical parameter distribution. During reconstruction, the electrical parameters in the inversion domain are iteratively updated in the direction of decreasing objective function. Iteration stops and the reconstructed image with soil electrical parameter distribution is output when at least one of a first and a second set condition is met. The first set condition is that the residual between the measured voltage response and the voltage response output by the forward model meets a preset residual threshold. The second set condition is that the increment of electrical parameters obtained from two consecutive iterations meets a preset parameter increment threshold. The objective function is determined based on residuals, spatial regularization terms, and frequency dimension smoothing constraints. The residuals characterize the difference between the measured voltage response and the voltage response output by the forward model. Spatial regularization terms and frequency dimension smoothing constraints are introduced into the objective function to improve reconstruction stability.

[0038] In another exemplary embodiment of this application, step 104 specifically includes: performing threshold segmentation, connected component filtering, and morphological constraint processing on the reconstructed image to obtain an initial root region; performing accuracy calibration on the initial root region to obtain a root imaging result containing the root region and confidence level, and forming a standardized data file that can be used for retesting and comparison. The accuracy calibration includes at least one of image consistency assessment based on repeated measurements or spatial correction based on known reference points.

[0039] As an optional implementation, the initial root region can also be processed into a skeleton to output the root orientation or the root trunk path.

[0040] As an optional implementation, the root imaging results include: a raster file or vector file in the orchard operation coordinate system, and a confidence field corresponding to the root region.

[0041] The soil root imaging method based on bioimpedance spectroscopy described above provides a solution to the shortcomings of current root survey methods, such as strong destructiveness, time-consuming and labor-intensive nature, and difficulty in repeating measurements. It also addresses the problems of soil environmental electrical impedance tomography methods, such as measurement being easily affected by water content fluctuations and changes in electrode contact impedance, unstable inversion, insufficient consistency in root identification, and difficulty in engineering output of results. The method achieves stable imaging reconstruction, identification, and standardized output of soil root areas, and provides usable data for retesting and comparison in hilly orchards and other scenarios, as well as for subsequent precise operational decisions.

[0042] The soil root imaging method based on bioimpedance spectroscopy in the above embodiments can be summarized into the following four steps: Step 1, Impedance signal acquisition: An electrical impedance tomography (EIT) electrode array is deployed in the soil environment. An AC excitation current is applied to the injection electrode pairs in the electrode array according to a set injection mode, and the voltage response is collected at the measurement electrode pairs. Injection and acquisition are repeated at at least three frequency points to obtain bioimpedance spectroscopy measurement data; Step 2, Electric field modeling: A bioimpedance spectroscopy electric field model is established for the soil-root system. An inversion domain and grid matching the electrode array are constructed, boundary conditions are set, and a forward model is formed; Step 3, Data processing and image reconstruction: The bioimpedance spectroscopy measurement data is preprocessed to obtain calibration data, and the calibration data is imaged and inverted based on the forward model to obtain a reconstructed image of the distribution of soil electrical parameters; Step 4, Root identification and output: The root region is identified based on the reconstructed image, accuracy calibration is performed, and the root imaging results are output. The root imaging results include at least the root region and its confidence level.

[0043] The soil root imaging method based on bioimpedance spectroscopy described in the above embodiments has the following advantages: Because it employs multi-frequency bioimpedance spectroscopy acquisition, it can improve the differentiation between roots and soil by utilizing differences in frequency dimensions; because it establishes a forward model of the soil root system and performs inversion under model constraints, it can alleviate reconstruction drift caused by inversion instability; because it preprocesses and calibrates the measurement data, it can reduce errors introduced by soil environmental fluctuations and electrode contact changes, improving the consistency of repeated measurements; and because it outputs root regions and confidence levels and provides standardized data files, it can support subsequent risk assessment and engineering applications.

[0044] The soil root imaging method based on bioimpedance spectroscopy in the above embodiments performs soil root imaging in the soil environment based on bioimpedance spectroscopy impedance tomography. This method reconstructs the root distribution in the rhizosphere soil of fruit trees and performs root region identification, accuracy calibration and result output on the imaging results. This method can be used for root distribution identification, risk assessment and subsequent micro-creation decision-making in scenarios such as hilly orchards.

[0045] Based on the same inventive concept, this application also provides a soil root imaging system based on bioimpedance spectroscopy for implementing the aforementioned soil root imaging method based on bioimpedance spectroscopy. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the soil root imaging system based on bioimpedance spectroscopy provided below can be found in the limitations of the soil root imaging method based on bioimpedance spectroscopy described above, and will not be repeated here.

[0046] In one exemplary embodiment, such as Figure 2 and Figure 3 As shown, a soil root imaging system based on bioimpedance spectroscopy is provided, including: an electrode array 4, a data acquisition unit 5, and a root imaging module.

[0047] The electrode array 4 is deployed in the soil environment where the target tree 1 is located; the electrode array 4 is used to repeatedly perform injection acquisition operations at at least three frequency points to obtain bioimpedance spectrum measurement data of the target tree 1; the injection acquisition operation includes: applying an AC excitation current to the electrode array 4 using a set injection mode, and measuring the voltage response under the AC excitation current.

[0048] The acquisition unit 5 is used to acquire the bioimpedance spectrum measurement data obtained by the electrode array 4 and send the bioimpedance spectrum measurement data to the root imaging module.

[0049] The root imaging module includes: The data acquisition unit is used to acquire bioimpedance spectrum measurement data of the target tree 1.

[0050] The forward model building unit is used to construct an inversion domain that matches the electrode array 4, and to perform mesh discretization on the inversion domain. After mesh discretization, the inversion domain is introduced with associated constraints, electrode models and boundary conditions to obtain the forward model.

[0051] The imaging inversion unit is used to perform imaging inversion based on the bioimpedance spectroscopy measurement data and the forward model to obtain a reconstructed image with the distribution of soil electrical parameters.

[0052] The root imaging unit is used to identify the root region 3 of the target tree 1 based on the reconstructed image, and to determine the identified root region 3 and the corresponding confidence level as the root imaging result.

[0053] As an optional implementation, when acquiring data in complex terrain such as slopes, acquisition unit 5 performs signal acquisition and imaging calculations after its attitude stabilizes, in order to reduce the impact of attitude changes on measurement consistency. For example... Figure 3 As shown, the soil root imaging system based on bioimpedance spectroscopy further includes an attitude leveling mechanism 6. When collecting data in hilly terrain, the attitude leveling mechanism 6 adjusts the acquisition unit 5 to a preset attitude before execution, thereby reducing the impact of attitude changes on electrode contact and the consistency of measurement data.

[0054] The following provides a more specific implementation method, which combines a soil root imaging system based on bioimpedance spectroscopy to introduce the process of soil root imaging method based on bioimpedance spectroscopy in practical application.

[0055] This embodiment uses electrical impedance tomography (EIT) as the measurement framework. It acquires the impedance spectrum response of the soil root system through multi-frequency AC excitation. Based on electric field modeling and forward computation, data calibration and joint inversion reconstruction are performed to obtain the distribution of root-related electrical parameters. Then, the reconstruction results are used for root region identification and confidence assessment. Finally, a root imaging result file that can be directly used for orchard management is output. Figures 3 to 7 The diagram illustrates the correspondence between system layout, measurement combination, modeling calculation, reconstruction algorithm, and output format.

[0056] See Figure 3 and Figure 4 Within the rhizosphere of the target tree (1), a test area is selected, and an electrode array (4) is evenly distributed around the soil surface (2). The number of electrodes, N, ranges from 8 to 32; in this embodiment, N=16. The insertion depth (d) of each electrode into the soil is 30mm-80mm; in this embodiment, d=50mm. The electrode array (4) is connected to the acquisition unit (5) via a cable (7).

[0057] The acquisition unit 5 applies an AC excitation current to the injection electrode pair 8 according to a preset injection mode, and acquires the voltage response using the measurement electrode pair 9. The injection mode employs at least one of adjacent injection and opposing injection, and covers all measurement combinations in the cyclic scanning direction 10.

[0058] The quantization parameters are set as follows: the effective value of the excitation current I0 is 0.5mA-2.0mA, and 1.0mA is used in this embodiment. The number of frequency points is no less than 3, and the frequency point set F = {5kHz, 20kHz, 100kHz} is selected in this embodiment. The sampling duration T of each injection-measurement combination is 50mA-200ms, and 100ms is used in this embodiment. The number of repeated acquisitions R for each combination is 2-5, and 3 is used in this embodiment, and the average value of the repeated results is taken. The amplitude and phase information of the voltage response are recorded to form a multi-frequency measurement matrix.

[0059] like Figure 5 As shown, the inversion domain is defined as a two-dimensional or three-dimensional region surrounding the root zone of the target tree. The inversion domain is discretized using a finite element mesh, and electrode models and boundary conditions are introduced at the boundaries. Soil is used as the background medium, and the root region as the local parameter variation region. Correlation constraints on impedance spectrum parameters are established in the frequency dimension. A forward solver is used to calculate the model voltage under given electrical parameters, and the model voltage and sensitivity matrices are constructed for subsequent iterative inversion.

[0060] Quantization parameter examples: The feature size h of the mesh cell is 10mm-30mm, and 20mm is used in this embodiment. The initial contact impedance Zc0 is 10Ω-200Ω, and 50Ω is used in this embodiment. The inversion parameters include at least one of conductivity or equivalent dielectric parameter.

[0061] See Figure 5 The multi-frequency measurement matrix is ​​preprocessed, including at least denoising filtering and outlier removal, and channel consistency verification and frequency normalization are performed. Then, contact impedance estimation and multi-frequency calibration are performed to obtain calibration data for inversion. Simultaneously, based on the calibration data and the forward model, joint inversion reconstruction is performed. A spatial regularization term and frequency smoothing constraint are introduced into the objective function to alleviate ill-posedness and improve the stability of multi-frequency reconstruction. Iterative updates and convergence criteria are used to control the stopping of iterations. Finally, an image of electrical parameters is output.

[0062] Examples of quantization parameters are as follows: Spatial regularization weight λ ranges from 0.01 to 1.0, and is set to 0.10 in this embodiment. Frequency smoothing weight μ ranges from 0.01 to 1.0, and is set to 0.20 in this embodiment. The relative residual threshold δ is set to 1 × 10⁻⁶. -3 -1×10 -2 In this embodiment, 5×10 -3 The maximum number of iterations K is set to 10-50; in this embodiment, it is set to 25. For example... Figure 6 and Figure 7 In the control test, the inversion parameters were verified using the point measurement results of an external conductivity reference instrument. After adopting a multi-frequency calibration and joint inversion strategy, the relative error of key electrical parameters was controlled within 5%. The confidence field obtained through the consistency of repeated measurements can be used to distinguish between high-reliability areas and areas requiring retesting, thereby reducing the operational risks caused by misjudgment in a single measurement.

[0063] This application comprises four steps: impedance signal acquisition, electric field modeling, data processing and image reconstruction, root identification, and result output. By deploying an electrical impedance tomography (EMT) electrode array, applying alternating current according to a preset injection mode, and acquiring voltage responses at multiple frequency points, bioimpedance spectrum data is obtained. Then, a forward model of the soil root electric field is established, and the data is denoised, outlier removed, and contact impedance calibrated. Imaging inversion is then performed to obtain an image reconstructed from electrical parameters. Finally, based on the reconstructed image, the root region is identified and its accuracy is calibrated, outputting imaging results containing the root region and its confidence level. This application achieves stable imaging and quantitative identification of root regions in a soil environment.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for imaging soil roots based on bioimpedance spectroscopy, characterized in that, include: Acquire bioimpedance spectroscopy data of the target tree; the bioimpedance spectroscopy data is obtained by repeated injection and acquisition operations at at least three frequency points using an electrode array; The electrode array is an electrode array for electrical impedance tomography deployed in the soil environment where the target tree is located. The injection acquisition operation includes: applying an AC excitation current to the electrode array using a set injection mode, and measuring the voltage response under the AC excitation current; An inversion domain matching the electrode array is constructed, and the inversion domain is discretized into a grid. After the grid is discretized, the inversion domain is introduced with correlation constraints, electrode model and boundary conditions to obtain the forward model. Based on the bioimpedance spectroscopy measurement data, the forward model is used for imaging inversion to obtain a reconstructed image with the distribution of soil electrical parameters. The root system region of the target tree is identified based on the reconstructed image, and the identified root system region and the corresponding confidence level are determined as the root imaging result.

2. The soil root imaging method based on bioimpedance spectroscopy according to claim 1, characterized in that, An inversion domain matching the electrode array is constructed, and the inversion domain is discretized into a grid. Correlation constraints, electrode models, and boundary conditions are introduced into the inversion domain obtained after grid discretization to obtain a forward model, specifically including: The two-dimensional or three-dimensional region surrounding the root zone of the target tree is defined as the inversion domain, and the inversion domain is discretized by finite element mesh to obtain the mesh-discretized inversion domain. Soil is used as the background electrical parameter region, the root system is used as the local electrical parameter variation region, and the impedance parameter correlation constraint is established between different frequency points. Current injection boundary conditions and voltage measurement boundary conditions are set on the boundary of the inversion domain after grid discretization, and an electrode model including electrode contact impedance is introduced; the electrode model is used to characterize the current coupling, potential distribution and potential sampling relationship between the electrode and the soil medium. Based on the inversion domain after grid discretization, the associated constraints, the current injection boundary conditions, the voltage measurement boundary conditions, and the electrode model, the potential distribution is solved to obtain the forward model; the forward model is used to calculate the voltage response based on the distribution of electrical parameters.

3. The soil root imaging method based on bioimpedance spectroscopy according to claim 1, characterized in that, Based on the bioimpedance spectroscopy measurement data, the forward model is used for imaging inversion to obtain a reconstructed image with soil electrical parameter distribution, specifically including: The bioimpedance spectroscopy measurement data are preprocessed and calibrated to obtain calibration data; The calibration data is inverted using the forward model to obtain a reconstructed image with the distribution of soil electrical parameters.

4. The soil root imaging method based on bioimpedance spectroscopy according to claim 3, characterized in that, The bioimpedance spectroscopy measurement data are preprocessed and calibrated to obtain calibration data, specifically including: The bioimpedance spectroscopy measurement data are preprocessed to obtain preprocessed data; the preprocessing includes at least two of the following: noise reduction filtering, outlier removal, channel consistency verification, and frequency point normalization. The preprocessed data was calibrated using electrode contact impedance to obtain calibration data.

5. The soil root imaging method based on bioimpedance spectroscopy according to claim 3, characterized in that, The forward model is used to perform imaging inversion on the calibration data to obtain a reconstructed image with soil electrical parameter distribution, specifically including: The calibration data are jointly reconstructed using the iterative inversion method based on the forward model to obtain a reconstructed image with the distribution of soil electrical parameters. During the reconstruction process, the electrical parameters in the inversion domain are iteratively updated in the direction of decreasing objective function. The iteration stops and a reconstructed image showing the distribution of soil electrical parameters is output when at least one of a first and a second predefined condition is met. The first predefined condition is that the residual between the measured voltage response and the voltage response output by the forward model satisfies a preset residual threshold. The second predefined condition is that the increment of electrical parameters obtained from two consecutive iterations satisfies a preset parameter increment threshold. The objective function is determined based on residuals, spatial regularization terms, and frequency dimension smoothing constraints. The residual is used to characterize the difference between the measured voltage response and the voltage response output by the forward model.

6. The soil root imaging method based on bioimpedance spectroscopy according to claim 1, characterized in that, Based on the reconstructed image, the root system region of the target tree is identified, and the identified root system region and its corresponding confidence level are determined as the root imaging result, specifically including: The reconstructed image is subjected to threshold segmentation, connected component filtering, and morphological constraint processing to obtain the initial root region; The initial root region is then calibrated to obtain root imaging results.

7. The soil root imaging method based on bioimpedance spectroscopy according to claim 1, characterized in that, The electrode array is arranged in a ring around the rhizosphere region of the target tree, and the number of electrodes in the electrode array is 8 to 32.

8. The soil root imaging method based on bioimpedance spectroscopy according to claim 1, characterized in that, The electrode array includes an injection electrode pair and a measurement electrode pair; the two electrodes in the injection electrode pair are arranged adjacently or opposite to each other; an AC excitation current is applied to the injection electrode pair using a set injection mode, and the voltage response under the AC excitation current is measured using the measurement electrode pair.

9. The soil root imaging method based on bioimpedance spectroscopy according to claim 1, characterized in that, The injection mode is set to at least one of adjacent injection mode and opposing injection mode.

10. A soil root imaging system based on bioimpedance spectroscopy, characterized in that, include: Electrode array, acquisition unit, and root imaging module; The electrode array is deployed in the soil environment where the target tree is located; The electrode array is used to repeatedly inject and acquire data at at least three frequency points to obtain bioimpedance spectrum measurement data of the target tree; the injection and acquisition operation includes: applying an AC excitation current to the electrode array using a set injection mode, and measuring the voltage response under the AC excitation current; The acquisition unit is used to acquire bioimpedance spectrum measurement data obtained by the electrode array and send the bioimpedance spectrum measurement data to the root imaging module; The root imaging module includes: The data acquisition unit is used to acquire bioimpedance spectrum measurement data of the target tree. The forward model building unit is used to construct an inversion domain that matches the electrode array, and to perform mesh discretization on the inversion domain. After mesh discretization, the inversion domain is introduced with correlation constraints, electrode models and boundary conditions to obtain the forward model. The imaging inversion unit is used to perform imaging inversion based on the bioimpedance spectrum measurement data and the forward model to obtain a reconstructed image with the distribution of soil electrical parameters. The root imaging unit is used to identify the root region of the target tree based on the reconstructed image, and to determine the identified root region and the corresponding confidence level as the root imaging result.

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