A garden soil quality detection system and method based on multi-source data

Abstract

The application relates to the technical field of soil detection, in particular to a garden soil quality detection system and method based on multi-source data, which comprises the following modules: a data acquisition module: implanting multiple groups of bioimpedance sensors in to-be-detected garden soil, and acquiring bioimpedance spectrum data collected by the bioimpedance sensors; a data analysis module: determining the metabolic activity distribution of microbial communities in the soil based on the bioimpedance spectrum data; a morphological change monitoring module: planting indicator plants in the soil, and monitoring morphological change responses of the indicator plants in a growth cycle; and a soil activity evaluation module: generating a comprehensive soil biological activity evaluation report based on the proxy activity distribution and the morphological change responses. The application establishes an evaluation system from microbial metabolic activity to plant growth state through bioimpedance measurement and plant growth response monitoring, and can directly reflect the metabolic activity of microbial communities.

Description

Technical Field

[0001] This invention relates to the field of soil testing technology, specifically to a garden soil quality testing system and method based on multi-source data. Background Technology

[0002] With the acceleration of urbanization and the advancement of ecological environment construction, the impact of garden soil quality on plant growth and ecosystem health has received increasing attention. Traditional soil testing methods mainly rely on chemical analysis to assess soil quality by measuring indicators such as nutrient content and pH value. These methods have obvious limitations. Chemical analysis methods cannot effectively reflect the life activity status of microbial communities in the soil, while microbial activity is an important indicator of soil ecosystem health. Secondly, traditional methods require collecting soil samples and sending them to the laboratory for analysis, which is a cumbersome and destructive process.

[0003] Existing soil bioactivity detection technologies mainly include microbial culture methods and analysis using soil sensors. Microbial culture methods assess the activity of soil microorganisms by culturing them, but this method is time-consuming and can only cultivate a portion of culturable microorganisms. Soil sensors detect basic parameters such as soil conductivity and moisture through electrochemical methods, thereby directly relating them to the activity of soil microorganisms, but most of these sensors have the problem of detecting only a single parameter. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a garden soil quality testing system and method based on multi-source data.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A garden soil quality detection system based on multi-source data, comprising: Data acquisition module: Multiple sets of bioimpedance sensors are implanted in the garden soil to be tested to acquire bioimpedance spectrum data collected by the bioimpedance sensors; Data analysis module: Based on the bioimpedance spectrum data, determine the distribution of metabolic activity of microbial communities in the soil; Morphological change monitoring module: Indicator plants are planted in the soil, and the morphological change response of the indicator plants during their growth cycle is monitored; Soil activity assessment module: Based on the distribution and morphological change response of the proxies, a comprehensive assessment report of soil biological activity is generated.

[0006] In a preferred embodiment, the data acquisition module implants multiple sets of bioimpedance sensors into different depth layers of the garden soil to be tested. The bioimpedance sensors are composed of excitation electrodes and detection electrodes, and their deployment depth needs to cover the main biological activity areas of the soil profile. After the bioimpedance sensor is deployed, an AC excitation signal is applied to the excitation electrode through an impedance analyzer. When the AC excitation signal passes through the soil medium, the microbial cells in the soil will undergo a polarization reaction due to the dielectric properties, causing the response signal received by the detection electrode to change relative to the AC excitation signal. The system synchronously collects the amplitude ratio and phase difference between the response signal and the excitation signal at different frequencies. The system sets the frequency scanning range and step interval to generate a frequency sequence containing several discrete frequency points. When the AC excitation signal outputs a sinusoidal electrical signal of a specific frequency, the data acquisition unit is started synchronously, and the excitation signal acquisition channel and the response signal acquisition channel are captured simultaneously. The two signals are synchronously acquired at the same sampling rate to ensure that waveform data of the complete signal cycle is acquired.

[0007] After data acquisition is completed, the acquired excitation signal waveform and response signal waveform are converted from time-domain waveform data to frequency-domain spectrum data through fast Fourier transform. Frequency points are located on the frequency-domain spectrum data obtained after fast Fourier transform. By comparing the magnitude of each frequency point, the frequency point closest to the preset excitation frequency is determined. The preset excitation frequency is each frequency point in the frequency sequence contained in the AC excitation signal. Within the neighborhood of the frequency point, the amplitude spectrum is locally refined using a parabolic interpolation algorithm to determine the accurate location of the actual fundamental frequency. The specific calculation formula for the parabolic interpolation algorithm is as follows: ; in, This indicates the precise location of the actual fundamental frequency. Indicates the initial frequency point position. Indicates frequency resolution. , , This represents the amplitude at three adjacent frequency points. At a given fundamental frequency point, the corresponding spectral line position is located in the frequency domain spectrum data obtained by the Fast Fourier Transform. The real and imaginary part values ​​at this spectral line are read from the frequency domain spectrum data of the excitation signal and the frequency domain spectrum data of the response signal, respectively, and combined to form the corresponding original complex spectral value. The complex spectral value contains the amplitude and phase information of the corresponding signal. The extracted complex spectral value is normalized to obtain the standardized fundamental component data. Based on the frequency domain data obtained by transformation, the amplitude ratio of the response signal to the excitation signal at the excitation frequency is calculated to obtain the amplitude ratio data at that frequency point. The specific calculation formula for the amplitude ratio is as follows: ; in, Indicates the amplitude ratio, The modulus of the complex spectrum of the normalized response signal is represented by... This represents the modulus of the complex spectrum of the normalized excitation signal. This represents the real part of the complex spectrum of the normalized response signal. This represents the imaginary part of the complex spectrum of the normalized response signal. This represents the real part of the complex spectrum value of the normalized excitation signal. The imaginary part of the normalized complex spectrum value of the excitation signal is represented by the phase difference between the fundamental components of the two signals, and the phase difference data at this frequency point is obtained by calculating the phase difference data. The specific formula for calculating the phase difference is as follows: ; in, Indicates phase difference, This represents the phase angle of the response signal. This represents the phase angle of the excitation signal. Represents the arctangent function. This represents the ratio of the imaginary part to the real part of the complex spectrum of the response signal. This represents the ratio of the imaginary part to the real part of the complex spectrum value of the excitation signal; The amplitude ratio and phase difference data calculated at each frequency point are temporarily stored, and it is determined whether the measurement of all frequency points in the frequency sequence has been completed. If not, the signal acquisition and processing steps are automatically switched to the next frequency point and repeated until the entire frequency sequence is traversed. The amplitude ratio and phase difference data corresponding to all frequency points are integrated in frequency order to form complete bioimpedance spectrum data. The collected bioimpedance spectrum data were preprocessed, including removing background interference caused by non-biological conductors in the soil and compensating for measurement errors caused by electrode polarization effects. The bioimpedance spectrum data obtained after preprocessing retained electrical characteristic information related to soil microbial activity.

[0008] In a preferred embodiment, the data analysis module decomposes the bioimpedance spectrum data into multiple frequency bands, dividing the complete impedance spectrum into a first frequency range and a second frequency range, wherein the first frequency range corresponds to the low-frequency response dominated by the microbial cell membrane polarization effect, and the second frequency range mainly corresponds to the high-frequency response dominated by the intracellular liquid ion conduction effect. After completing the frequency interval division, all frequency points and their corresponding phase angle raw data of the first frequency interval and the second frequency interval are obtained respectively. The validity of the phase angle dataset of each frequency interval is verified. Statistical feature calculation is performed on the phase angle dataset of each frequency interval that has passed the verification. The arithmetic mean of all phase angles in the phase angle dataset is calculated as the feature value representing the central trend. The dispersion of all phase angles in the phase angle dataset relative to its mean is calculated as the variance feature value. All phase angles in the phase angle dataset are sorted according to their numerical values, and the phase angles located at the preset specific quantile values ​​are extracted as distribution feature values. The mean, variance and specific quantile values ​​calculated for each frequency interval are standardized and combined to generate the corresponding impedance phase angle feature components. The impedance phase angle feature components are input into a pre-defined microbial metabolic activity correlation model. The input impedance phase angle feature components are weighted and calculated. Based on the historical correlation strength between each phase angle feature and microbial metabolic activity, a weight coefficient is assigned to each feature component. Features with a higher correlation to metabolic activity are assigned a larger weight coefficient. The model then linearly combines the weighted feature components to calculate the sum of the weighted features. The specific calculation formula is as follows: ; Where Z represents the weighted feature composite value, This represents the weighting coefficient of the i-th phase angle feature. Let represent the i-th standardized impedance phase angle characteristic component, and n represent the total number of phase angle characteristics. The weighted sum of characteristics is input into the regression analysis function for nonlinear transformation. Based on the transformation result, the calculated value of metabolic activity intensity corresponding to the measurement points at different soil depths is output. In a preferred embodiment, the morphological change monitoring module plants specific indicator plants in the soil area where bioimpedance detection has been completed. In the early stage of planting, the initial state of the indicator plants is holographically scanned by a three-dimensional scanning device to obtain the baseline three-dimensional data of their root system structure and leaf morphology, and to establish an initial spatial model of plant growth. During the growth cycle of the indicator plant, a three-dimensional scanning device is periodically activated at set time intervals to monitor the indicator plant throughout its entire life cycle. During the scanning process, multi-angle image acquisition and point cloud reconstruction technology are used to obtain root spatial distribution data and leaf surface morphology data of the indicator plant at the current time point. The root spatial distribution includes root length density, root and stem distribution, and root topology parameters. The leaf surface morphology data includes leaf area, leaf tilt angle, and leaf three-dimensional curvature characteristics. All acquired data are stored in the plant growth database and associated with corresponding timestamp information. Based on root system point cloud data acquired by a 3D scanning device over two consecutive acquisition cycles, a point cloud registration algorithm was used to unify the root system point cloud data at the two time points into the same coordinate system. The 3D spatial system enclosed by the root system point cloud data at the two time points was calculated separately. The convex hull of the point cloud data was calculated, and the volume was obtained by triple integration of the convex hull region. The 3D space was divided into a regular voxel grid, and the number of voxels occupied by the point cloud was counted and multiplied by the volume of a single voxel to obtain the total volume. The length data of the root system principal axis and branches were obtained through a root system skeleton extraction algorithm. The length of each edge in the skeleton was obtained by calculating the Euclidean distance between adjacent skeleton points. The path length from the root node to any node was obtained by accumulating the lengths of all edges on the path. The path lengths from all root nodes to leaf nodes were compared, and the longest path was selected as the root system principal axis. For each branch, the length of the branch was obtained by accumulating the lengths of all edges on the branch. The total root length was obtained by accumulating the lengths of all edges in the skeleton. The maximum extension length change of the root system in 3D space was calculated. The specific calculation formula is as follows: ; in, This indicates the maximum change in extension length. Indicates the first data collection cycle Root principal axis length at time Indicates the second data collection cycle The root system main axis length at time is calculated. The change in volume and the change in length are divided by the time interval between adjacent collection cycles to obtain the root system volume expansion rate and length extension rate per unit time. The root system volume expansion rate and length extension rate are weighted and fused to form a comprehensive root system expansion rate. A 3D scanning device equipped with a multispectral imaging system was used to acquire multispectral data of leaves in the visible and near-infrared bands. The focus was on acquiring optical information in the first characteristic band, which is sensitive to chlorophyll content, and the second characteristic band, which is sensitive to carotenoid content. Continuously acquired vegetation index time-series data were analyzed, and vegetation index sequences of specified leaves at multiple consecutive sampling time points were extracted. After arranging the sequences chronologically, outlier detection and removal were performed. The data sequences after outlier detection and removal were then smoothed using a moving average. The rate of change sequence of vegetation indices at adjacent time points was calculated. The specific calculation formula is as follows: ; in, This indicates the rate of change of vegetation indices over a time interval. Indicates a point in time Smoothed vegetation index Indicates the time interval between adjacent samplings. Indicates a point in time After smoothing the vegetation index, the mean, variance, and linear regression slope trend feature parameters of the rate of change sequence were extracted. The vegetation index change trend was evaluated based on the trend feature parameters. If the change trend showed a continuous decline and the rate of decline exceeded the preset threshold, it was determined to be a decrease in pigment deposition. If it showed a continuous increase and the rate of increase exceeded the preset threshold, it was determined to be an increase in pigment deposition. The evaluation results were quantified as the change in leaf pigment deposition. The change in leaf pigment deposition characterizes the photosynthetic efficiency and nutritional status of plants. The calculated root expansion rate and leaf pigment deposition changes were standardized and integrated into a comprehensive morphological change response dataset, which records the adaptive responses of indicator plants to changes in the soil environment throughout their growth cycle.

[0009] In a preferred embodiment, the soil activity assessment module performs coordinate registration and data association between the microbial metabolic activity intensity value and the root expansion rate and leaf pigment deposition change at the same spatial location, forming an integrated dataset containing biological characteristics and corresponding plant growth characteristics. Synergistic and antagonistic effects were analyzed on the associated data. The synergistic effect was that areas with high microbial metabolic activity showed significant plant root expansion and increased leaf pigment deposition, indicating that the soil environment was in a benign biological activity state. The antagonistic effect was that microbial metabolic activity and plant growth response diverged. Based on the results of synergistic and antagonistic effects assessment, the health status level of soil biological activity is further determined. Soil biological activity status is divided into healthy, sub-healthy, and alert levels. The healthy level corresponds to significant synergistic effects and all parameters are at excellent levels. The sub-healthy level corresponds to weak synergistic effects and slight deviations in a single parameter. The alert level corresponds to no obvious synergistic or antagonistic effects but key parameters are close to the critical state. The health status level assessment results of all spatial regions are integrated and combined with the geographic information system to generate a visualized comprehensive evaluation report of soil biological activity.

[0010] This invention also provides a method for detecting the quality of garden soil based on multi-source data, specifically including the following steps: S1. Multiple sets of bioimpedance sensors are implanted in the garden soil to be tested, and bioimpedance spectrum data collected by the bioimpedance sensors are obtained. S2. Based on the bioimpedance spectrum data, determine the distribution of metabolic activity of microbial communities in the soil; S3. Plant indicator plants in the soil and monitor the morphological change response of the indicator plants during their growth cycle; S4. Based on the distribution and morphological change response of the agent activity, generate a comprehensive assessment report of soil biological activity.

[0011] The beneficial effects of this invention are as follows: This invention uses a bioimpedance sensor to measure bioimpedance and monitor plant growth response, establishing an evaluation system from microbial metabolic activity to plant growth status. It can directly reflect the metabolic activity of the microbial community, and verifies the actual effect of soil biological activity through plant growth. Through multi-band impedance phase angle characteristic analysis, it realizes non-destructive detection of soil microbial community metabolic activity and can accurately obtain soil microbial activity data. Attached Figure Description

[0012] Figure 1 This is a system structure diagram of the present invention; Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0013] 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.

[0014] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0015] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0016] like Figure 1 This embodiment provides: a garden soil quality testing system based on multi-source data, comprising: Data acquisition module: Multiple sets of bioimpedance sensors are implanted in the garden soil to be tested to acquire bioimpedance spectrum data collected by the bioimpedance sensors; In this embodiment of the invention, the data acquisition module needs to be specifically described. The data acquisition module implants multiple sets of bioimpedance sensors into different depth layers of the garden soil to be tested. The bioimpedance sensors are composed of excitation electrodes and detection electrodes, and their deployment depth needs to cover the main biological activity areas of the soil profile. After the bioimpedance sensor is deployed, an AC excitation signal is applied to the excitation electrode through an impedance analyzer. The frequency range of the excitation signal needs to cover the key frequency band that can reflect the polarization characteristics of the media inside and outside the microbial cell. It usually extends from the low frequency band that reflects the cell membrane capacitance effect to the high frequency band that reflects the conductivity characteristics of the intracellular fluid. The voltage amplitude of the excitation signal needs to be controlled within a safe range that can obtain a sufficient measurement signal-to-noise ratio without causing electrical breakdown damage to the soil microorganisms. When the AC excitation signal passes through the soil medium, the microbial cells in the soil will undergo a polarization reaction due to the dielectric properties, causing the response signal received by the detection electrode to change relative to the AC excitation signal. The system synchronously collects the amplitude ratio and phase difference between the response signal and the excitation signal at different frequencies. The system sets the frequency scanning range and step interval to generate a frequency sequence containing several discrete frequency points. When the AC excitation signal outputs a sinusoidal electrical signal of a specific frequency, the data acquisition unit is started synchronously, and the excitation signal acquisition channel and the response signal acquisition channel are captured simultaneously. The two signals are synchronously acquired at the same sampling rate to ensure that waveform data of the complete signal cycle is acquired. After data acquisition is completed, the acquired excitation signal waveform and response signal waveform are converted from time-domain waveform data to frequency-domain spectrum data through fast Fourier transform. Frequency points are located on the frequency-domain spectrum data obtained after fast Fourier transform. By comparing the magnitude of each frequency point, the frequency point closest to the preset excitation frequency is determined. The preset excitation frequency is each frequency point in the frequency sequence contained in the AC excitation signal. Within the neighborhood of the frequency point, the amplitude spectrum is locally refined using a parabolic interpolation algorithm to determine the accurate location of the actual fundamental frequency. The specific calculation formula for the parabolic interpolation algorithm is as follows: ; in, This indicates the precise location of the actual fundamental frequency. Indicates the initial frequency point position. Indicates frequency resolution. , , This represents the amplitude at three adjacent frequency points. At a given fundamental frequency point, the corresponding spectral line position is located in the frequency domain spectrum data obtained by the Fast Fourier Transform. The real and imaginary part values ​​at this spectral line are read from the frequency domain spectrum data of the excitation signal and the frequency domain spectrum data of the response signal, respectively, and combined to form the corresponding original complex spectral value. The complex spectral value contains the amplitude and phase information of the corresponding signal. The extracted complex spectral value is normalized to obtain the standardized fundamental component data. Based on the frequency domain data obtained by transformation, the amplitude ratio of the response signal to the excitation signal at the excitation frequency is calculated to obtain the amplitude ratio data at that frequency point. The specific calculation formula for the amplitude ratio is as follows: ; in, Indicates the amplitude ratio, The modulus of the complex spectrum of the normalized response signal is represented by... This represents the modulus of the complex spectrum of the normalized excitation signal. This represents the real part of the complex spectrum of the normalized response signal. This represents the imaginary part of the complex spectrum of the normalized response signal. This represents the real part of the complex spectrum value of the normalized excitation signal. The imaginary part of the normalized complex spectrum value of the excitation signal is represented by the phase difference between the fundamental components of the two signals, and the phase difference data at this frequency point is obtained by calculating the phase difference data. The specific formula for calculating the phase difference is as follows: ; in, Indicates phase difference, This represents the phase angle of the response signal. This represents the phase angle of the excitation signal. Represents the arctangent function. This represents the ratio of the imaginary part to the real part of the complex spectrum of the response signal. This represents the ratio of the imaginary part to the real part of the complex spectrum value of the excitation signal; The amplitude ratio and phase difference data calculated at each frequency point are temporarily stored, and it is determined whether the measurement of all frequency points in the frequency sequence has been completed. If not, the signal acquisition and processing steps are automatically switched to the next frequency point and repeated until the entire frequency sequence is traversed. The amplitude ratio and phase difference data corresponding to all frequency points are integrated in frequency order to form complete bioimpedance spectrum data. The collected bioimpedance spectrum data were preprocessed, including removing background interference caused by non-biological conductors in the soil and compensating for measurement errors caused by electrode polarization effects. The bioimpedance spectrum data obtained after preprocessing retained electrical characteristic information related to soil microbial activity.

[0017] Data analysis module: Based on the bioimpedance spectrum data, determine the distribution of metabolic activity of microbial communities in the soil; In this embodiment of the invention, the data analysis module needs to be specifically described. The data analysis module decomposes the bioimpedance spectrum data into multiple frequency bands and divides the complete impedance spectrum into a first frequency range and a second frequency range. The first frequency range corresponds to the low-frequency response dominated by the microbial cell membrane polarization effect, and the second frequency range mainly corresponds to the high-frequency response dominated by the intracellular liquid ion conduction effect. After completing the frequency interval division, all frequency points and their corresponding phase angle raw data of the first and second frequency intervals are obtained respectively. The validity of the phase angle dataset of each frequency interval is verified to check for abnormal data points caused by measurement interference. If abnormal values ​​are found, the data is repaired by interpolation method based on the phase angle change trend of adjacent frequency points. Statistical feature calculation is performed on the phase angle datasets of each frequency interval that have passed the verification. The arithmetic mean of all phase angles in the phase angle dataset is calculated as the feature value representing the central trend. The dispersion of all phase angles in the phase angle dataset relative to its mean is calculated as the variance feature value. All phase angles in the phase angle dataset are sorted according to their numerical values, and the phase angles located at the preset specific quantile values ​​are extracted as distribution feature values. The mean, variance, and specific quantile values ​​calculated for each frequency interval are standardized and combined to generate the corresponding impedance phase angle feature components. The impedance phase angle feature components are input into a preset microbial metabolic activity correlation model. This model selects representative ecological regions across the country, collects standard soil samples, and performs two key measurements simultaneously on each sample: first, using traditional microbial culture and respiration intensity measurement methods to obtain accurate baseline values ​​for metabolic activity intensity; second, using a bioimpedance sensor to acquire the bioimpedance spectrum data of the corresponding soil sample. This constructs an initial database containing multi-band impedance phase angle features and their corresponding baseline values ​​for metabolic activity intensity. The input impedance phase angle feature components are weighted, and a weight coefficient is assigned to each feature component based on the historical correlation strength between each phase angle feature and microbial metabolic activity. Features with higher correlation to metabolic activity are assigned larger weight coefficients. The model then linearly combines the weighted feature components to calculate the sum of the weighted features. The specific calculation formula is as follows: ; Where Z represents the weighted feature composite value, This represents the weighting coefficient of the i-th phase angle feature. Let represent the i-th standardized impedance phase angle characteristic component, and n represent the total number of phase angle characteristics. The weighted sum of characteristics is input into the regression analysis function for nonlinear transformation. Based on the transformation result, the calculated value of metabolic activity intensity corresponding to the measurement points at different soil depths is output. It should be noted that the phase angle values ​​corresponding to commonly used quantiles such as the 25th percentile, 50th percentile, and 75th percentile are extracted after sorting the dataset by pre-setting specific quantile values. These quantile values ​​are used to describe the symmetry, tail features, and other key distribution characteristics of the phase angle distribution.

[0018] It should be noted that the fast Fourier transform, mean, variance, nonlinear transformation and linear regression algorithm in this invention are existing technologies and will not be described in detail here.

[0019] Morphological change monitoring module: Indicator plants are planted in the soil, and the morphological change response of the indicator plants during their growth cycle is monitored; In this embodiment of the invention, the morphological change monitoring module needs to be specifically described. The morphological change monitoring module plants specific indicator plants in the soil area where bioimpedance detection has been completed. The selection of indicator plants must meet the requirements of being sensitive to changes in soil quality and having a growth cycle that matches the preset monitoring period. In the early stage of planting, the initial state of the indicator plants is holographically scanned by a three-dimensional scanning device to obtain the baseline three-dimensional data of their root system structure and leaf morphology, and to establish an initial spatial model of plant growth. During the growth cycle of the indicator plant, a three-dimensional scanning device is periodically activated at set time intervals to monitor the indicator plant throughout its entire life cycle. During the scanning process, multi-angle image acquisition and point cloud reconstruction technology are used to obtain root spatial distribution data and leaf surface morphology data of the indicator plant at the current time point. The root spatial distribution includes root length density, root and stem distribution, and root topology parameters. The leaf surface morphology data includes leaf area, leaf tilt angle, and leaf three-dimensional curvature characteristics. All acquired data are stored in the plant growth database and associated with corresponding timestamp information. Based on root system point cloud data acquired by a 3D scanning device over two consecutive acquisition cycles, a point cloud registration algorithm was used to unify the root system point cloud data at the two time points into the same coordinate system. The 3D spatial system enclosed by the root system point cloud data at the two time points was calculated separately. The convex hull of the point cloud data was calculated, and the volume was obtained by triple integration of the convex hull region. The 3D space was divided into a regular voxel grid, and the number of voxels occupied by the point cloud was counted and multiplied by the volume of a single voxel to obtain the total volume. The length data of the root system principal axis and branches were obtained through a root system skeleton extraction algorithm. The length of each edge in the skeleton was obtained by calculating the Euclidean distance between adjacent skeleton points. The path length from the root node to any node was obtained by accumulating the lengths of all edges on the path. The path lengths from all root nodes to leaf nodes were compared, and the longest path was selected as the root system principal axis. For each branch, the length of the branch was obtained by accumulating the lengths of all edges on the branch. The total root length was obtained by accumulating the lengths of all edges in the skeleton. The maximum extension length change of the root system in 3D space was calculated. The specific calculation formula is as follows: ; in, This indicates the maximum change in extension length. Indicates the first data collection cycle Root principal axis length at time Indicates the second data collection cycle The root system main axis length at time is calculated. The change in volume and the change in length are divided by the time interval between adjacent collection cycles to obtain the root system volume expansion rate and length extension rate per unit time. The root system volume expansion rate and length extension rate are weighted and fused to form a comprehensive root system expansion rate. A 3D scanning device equipped with a multispectral imaging system was used to acquire multispectral data of leaves in the visible and near-infrared bands. The focus was on acquiring optical information in the first characteristic band, which is sensitive to chlorophyll content, and the second characteristic band, which is sensitive to carotenoid content. Continuously acquired vegetation index time-series data were analyzed, and vegetation index sequences of specified leaves at multiple consecutive sampling time points were extracted. After arranging the sequences chronologically, outlier detection and removal were performed. The data sequences after outlier detection and removal were then smoothed using a moving average. The rate of change sequence of vegetation indices at adjacent time points was calculated. The specific calculation formula is as follows: ; in, This indicates the rate of change of vegetation indices over a time interval. Indicates at a point in time Smoothed vegetation index Indicates the time interval between adjacent samplings. Indicates at a point in time After smoothing the vegetation index, the mean, variance, and linear regression slope trend feature parameters of the rate of change sequence were extracted. The vegetation index change trend was evaluated based on the trend feature parameters. If the change trend showed a continuous decline and the rate of decline exceeded the preset threshold, it was determined to be a decrease in pigment deposition. If it showed a continuous increase and the rate of increase exceeded the preset threshold, it was determined to be an increase in pigment deposition. The evaluation results were quantified as the change in leaf pigment deposition. The change in leaf pigment deposition characterizes the photosynthetic efficiency and nutritional status of plants. The calculated root expansion rate and leaf pigment deposition changes were standardized and integrated into a comprehensive morphological change response dataset, which records the adaptive responses of indicator plants to changes in the soil environment throughout their growth cycle.

[0020] Soil activity assessment module: Based on the distribution and morphological change response of the proxies, a comprehensive assessment report of soil biological activity is generated.

[0021] In this embodiment of the invention, the soil activity assessment module needs to be specifically described. The soil activity assessment module performs coordinate registration and data association between the microbial metabolic activity intensity value and the root expansion rate and leaf pigment deposition change at the same spatial location to form an integrated dataset containing biological characteristics and corresponding plant growth characteristics. Synergistic and antagonistic effects were analyzed on the associated data. The synergistic effect was that areas with high microbial metabolic activity showed significant plant root expansion and increased leaf pigment deposition, indicating that the soil environment was in a benign biological activity state. The antagonistic effect was that microbial metabolic activity and plant growth response diverged. For example, areas with high metabolic activity were accompanied by inhibited plant root growth or reduced leaf pigmentation, indicating that the soil may have biotoxicity or nutrient imbalance problems. Based on the results of synergistic and antagonistic effects assessment, the health status level of soil biological activity is further determined. Soil biological activity status is divided into healthy, sub-healthy, and alert levels. The healthy level corresponds to significant synergistic effects and all parameters are at excellent levels. The sub-healthy level corresponds to weak synergistic effects and slight deviations in a single parameter. The alert level corresponds to no obvious synergistic or antagonistic effects but key parameters are close to the critical state. The health status level assessment results of all spatial regions are integrated and combined with the geographic information system to generate a visualized comprehensive evaluation report of soil biological activity.

[0022] like Figure 2 This invention also provides a method for detecting the quality of garden soil based on multi-source data, specifically including the following steps: S1. Multiple sets of bioimpedance sensors are implanted in the garden soil to be tested, and bioimpedance spectrum data collected by the bioimpedance sensors are obtained. S2. Based on the bioimpedance spectrum data, determine the distribution of metabolic activity of microbial communities in the soil; S3. Plant indicator plants in the soil and monitor the morphological change response of the indicator plants during their growth cycle; S4. Based on the distribution and morphological change response of the agent activity, generate a comprehensive assessment report of soil biological activity.

[0023] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0024] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0025] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0026] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0027] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0028] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0029] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A garden soil quality testing system based on multi-source data, characterized in that, include: Data acquisition module: Multiple sets of bioimpedance sensors are implanted in the garden soil to be tested to acquire bioimpedance spectrum data collected by the bioimpedance sensors; Data analysis module: Based on the bioimpedance spectrum data, determine the distribution of metabolic activity of microbial communities in the soil; Morphological change monitoring module: Indicator plants are planted in the soil, and the morphological change response of the indicator plants during their growth cycle is monitored; Soil activity assessment module: Based on the distribution and morphological change response of proxy activity, a comprehensive assessment report of soil biological activity is generated.

2. The garden soil quality testing system based on multi-source data according to claim 1, characterized in that, The data acquisition module implants multiple sets of bioimpedance sensors into different depth layers of the garden soil to be tested. The bioimpedance sensors are composed of excitation electrodes and detection electrodes, and their deployment depth needs to cover the main biological activity areas of the soil profile. After the bioimpedance sensor is deployed, an AC excitation signal is applied to the excitation electrode through an impedance analyzer. When the AC excitation signal passes through the soil medium, the microbial cells in the soil will undergo a polarization reaction due to the dielectric properties, causing the response signal received by the detection electrode to change relative to the AC excitation signal. The system synchronously collects the amplitude ratio and phase difference between the response signal and the excitation signal at different frequencies. The system sets the frequency scanning range and step interval to generate a frequency sequence containing several discrete frequency points. When the AC excitation signal outputs a sinusoidal electrical signal of a specific frequency, the data acquisition unit is started synchronously, and the excitation signal acquisition channel and the response signal acquisition channel are captured simultaneously. The two signals are synchronously acquired at the same sampling rate to ensure that waveform data of the complete signal cycle is acquired.

3. The garden soil quality testing system based on multi-source data according to claim 2, characterized in that, After data acquisition is completed, the acquired excitation signal waveform and response signal waveform are converted from time-domain waveform data to frequency-domain spectrum data through fast Fourier transform. Frequency points are located on the frequency-domain spectrum data obtained after fast Fourier transform. By comparing the magnitude of each frequency point, the frequency point closest to the preset excitation frequency is determined. The preset excitation frequency is each frequency point in the frequency sequence contained in the AC excitation signal. Within the neighborhood of the frequency point, the amplitude spectrum is locally refined using a parabolic interpolation algorithm to determine the accurate location of the actual fundamental frequency point. At the determined fundamental frequency point, the corresponding spectral line position is located in the frequency domain spectrum data obtained by the fast Fourier transform. The real and imaginary part values ​​at this spectral line are read from the frequency domain spectrum data of the excitation signal and the frequency domain spectrum data of the response signal, respectively, and combined to form the corresponding original complex spectral value. The complex spectral value contains the amplitude and phase information of the corresponding signal. The extracted complex spectral value is normalized to obtain the standardized fundamental component data. Based on the frequency domain data obtained by conversion, the amplitude ratio of the response signal and the excitation signal at the excitation frequency is calculated to obtain the amplitude ratio data at that frequency point. At the same time, the phase difference between the fundamental components of the two signals is calculated to obtain the phase difference data at that frequency point. The amplitude ratio and phase difference data calculated at each frequency point are temporarily stored, and it is determined whether the measurement of all frequency points in the frequency sequence has been completed. If not, the signal acquisition and processing steps are automatically switched to the next frequency point and repeated until the entire frequency sequence is traversed. The amplitude ratio and phase difference data corresponding to all frequency points are integrated in frequency order to form complete bioimpedance spectrum data. The collected bioimpedance spectrum data were preprocessed, including removing background interference caused by non-biological conductors in the soil and compensating for measurement errors caused by electrode polarization effects. The bioimpedance spectrum data obtained after preprocessing retained electrical characteristic information related to soil microbial activity.

4. The garden soil quality testing system based on multi-source data according to claim 1, characterized in that, The data analysis module decomposes the bioimpedance spectrum data into multiple frequency bands, dividing the complete impedance spectrum into a first frequency range and a second frequency range. The first frequency range corresponds to the low-frequency response dominated by the microbial cell membrane polarization effect, while the second frequency range mainly corresponds to the high-frequency response dominated by the intracellular liquid ion conduction effect. After completing the frequency interval division, all frequency points and their corresponding phase angle raw data of the first and second frequency intervals are obtained respectively. The validity of the phase angle dataset of each frequency interval is verified. Statistical feature calculation is performed on the phase angle datasets of each frequency interval that have passed the verification. The arithmetic mean of all phase angles in the phase angle dataset is calculated as the feature value representing the central trend. The dispersion of all phase angles in the phase angle dataset relative to its mean is calculated as the variance feature value. All phase angles in the phase angle dataset are sorted according to their numerical values, and the phase angles located at the preset specific quantile values ​​are extracted as distribution feature values. The mean, variance, and specific quantile values ​​calculated for each frequency interval are standardized and combined to generate the corresponding impedance phase angle feature components.

5. A garden soil quality testing system based on multi-source data according to claim 4, characterized in that, The impedance phase angle feature components are input into a preset microbial metabolic activity correlation model. The input impedance phase angle feature components are weighted and calculated. Based on the historical correlation strength between each phase angle feature and microbial metabolic activity, a weight coefficient is assigned to each feature component. Features with higher correlation to metabolic activity are given larger weight coefficients. The model linearly combines the weighted feature components and calculates the weighted feature sum. The weighted feature sum is input into a regression analysis function for nonlinear transformation. Based on the transformation result, the calculated value of metabolic activity intensity corresponding to measurement points at different soil depths is output.

6. A garden soil quality testing system based on multi-source data according to claim 1, characterized in that, The morphological change monitoring module plants specific indicator plants in the soil area where bioimpedance detection has been completed. In the early stage of planting, the initial state of the indicator plants is holographically scanned by a three-dimensional scanning device to obtain the baseline three-dimensional data of their root system structure and leaf morphology, and to establish an initial spatial model of plant growth. During the growth cycle of the indicator plant, a three-dimensional scanning device is periodically activated at set time intervals to monitor the indicator plant throughout its entire life cycle. During the scanning process, multi-angle image acquisition and point cloud reconstruction technology are used to obtain root spatial distribution data and leaf surface morphology data of the indicator plant at the current time point. The root spatial distribution includes root length density, root and stem distribution, and root topology parameters. The leaf surface morphology data includes leaf area, leaf tilt angle, and leaf three-dimensional curvature characteristics. All acquired data are stored in the plant growth database and associated with corresponding timestamp information.

7. A garden soil quality testing system based on multi-source data according to claim 6, characterized in that, Based on root system point cloud data acquired by a 3D scanning device over two consecutive acquisition cycles, a point cloud registration algorithm was used to unify the root system point cloud data at the two time points into the same coordinate system. The 3D spatial system enclosed by the root system point cloud data at the two time points was calculated separately. The convex hull of the point cloud data was calculated, and the volume was obtained by triple integration of the convex hull region. The 3D space was divided into a regular voxel grid, and the number of voxels occupied by the point cloud was counted and multiplied by the volume of a single voxel to obtain the total volume. The length data of the root system principal axis and branches were obtained through a root system skeleton extraction algorithm. The length of each edge in the skeleton was obtained by calculating the Euclidean distance between adjacent skeleton points. The path length from the root node to any node was obtained by accumulating the lengths of all edges on the path. The path lengths from all root nodes to leaf nodes were compared, and the longest path was selected as the root system principal axis. For each branch, the length of the branch was obtained by accumulating the lengths of all edges on the branch. The total root length was obtained by accumulating the lengths of all edges in the skeleton. The maximum extension length change of the root system in 3D space was calculated. The specific calculation formula is as follows: ； in, This indicates the maximum change in extension length. Indicates the first data collection cycle Root principal axis length at time Indicates the second data collection cycle The root system main axis length at time is calculated. The change in volume and the change in length are divided by the time interval between adjacent collection cycles to obtain the root system volume expansion rate and length extension rate per unit time. The root system volume expansion rate and length extension rate are weighted and fused to form a comprehensive root system expansion rate.

8. A garden soil quality testing system based on multi-source data according to claim 6, characterized in that, A 3D scanning device equipped with a multispectral imaging system was used to acquire multispectral data of leaves in the visible and near-infrared bands. The focus was on acquiring optical information in the first characteristic band, which is sensitive to chlorophyll content, and the second characteristic band, which is sensitive to carotenoid content. Continuously acquired vegetation index time-series data were analyzed, and vegetation index sequences of specified leaves at multiple consecutive sampling time points were extracted. After arranging the sequences chronologically, outlier detection and removal were performed. The data sequences after outlier detection and removal were then smoothed using a moving average. The rate of change sequence of vegetation indices at adjacent time points was calculated. The specific calculation formula is as follows: ； in, This indicates the rate of change of vegetation indices over a time interval. Indicates a point in time Smoothed vegetation index Indicates the time interval between adjacent samplings. Indicates a point in time After smoothing the vegetation index, the mean, variance, and linear regression slope trend feature parameters of the rate of change sequence were extracted. The vegetation index change trend was evaluated based on the trend feature parameters. If the change trend showed a continuous decline and the rate of decline exceeded the preset threshold, it was determined to be a decrease in pigment deposition. If it showed a continuous increase and the rate of increase exceeded the preset threshold, it was determined to be an increase in pigment deposition. The evaluation results were quantified as the change in leaf pigment deposition. The change in leaf pigment deposition characterizes the photosynthetic efficiency and nutritional status of plants. The calculated root expansion rate and leaf pigment deposition changes were standardized and integrated into a comprehensive morphological change response dataset, which records the adaptive responses of indicator plants to changes in the soil environment throughout their growth cycle.

9. A garden soil quality testing system based on multi-source data according to claim 1, characterized in that, The soil activity assessment module performs coordinate registration and data association between the microbial metabolic activity intensity value and the root expansion rate and leaf pigment deposition change at the same spatial location, forming an integrated dataset that includes biological characteristics and corresponding plant growth characteristics. Synergistic and antagonistic effects were analyzed on the associated data. The synergistic effect was that areas with high microbial metabolic activity showed significant plant root expansion and increased leaf pigment deposition, indicating that the soil environment was in a benign biological activity state. The antagonistic effect was that microbial metabolic activity and plant growth response diverged. Based on the results of synergistic and antagonistic effects assessment, the health status level of soil biological activity is further determined. Soil biological activity status is divided into healthy, sub-healthy, and alert levels. The healthy level corresponds to significant synergistic effects and all parameters are at excellent levels. The sub-healthy level corresponds to weak synergistic effects and slight deviations in a single parameter. The alert level corresponds to no obvious synergistic or antagonistic effects but key parameters are close to the critical state. The health status level assessment results of all spatial regions are integrated and combined with the geographic information system to generate a visualized comprehensive evaluation report of soil biological activity.

10. A method for detecting the quality of garden soil based on multi-source data, applied to a garden soil quality detection system based on multi-source data as described in any one of claims 1-9, characterized in that, Includes the following steps: S1. Multiple sets of bioimpedance sensors are implanted in the garden soil to be tested, and bioimpedance spectrum data collected by the bioimpedance sensors are obtained. S2. Based on the bioimpedance spectrum data, determine the distribution of metabolic activity of microbial communities in the soil; S3. Plant indicator plants in the soil and monitor the morphological change response of the indicator plants during their growth cycle; S4. Based on the distribution and morphological change response of the agent activity, generate a comprehensive assessment report of soil biological activity.

Images