Non-destructive determination method of soil water content and saturation based on color and ultrasonic technology

Abstract

The application discloses a kind of soil water content and saturation nondestructive determination method based on color and ultrasonic technology, soil water content determination method: photograph soil, carry out image preprocessing, obtain the R, G, B diagram of soil photo;Obtain the effective fractal dimension in image, obtain the discrete probability distribution diagram of fractal dimension;Establish the relationship between fitting parameter W, S and K and each fractal dimension of R, G, B level in soil image;With fitting parameter W, S and K as independent variable, with measured soil water content as dependent variable, linear regression analysis is carried out using least square method, and the estimation model of soil water content is established.Saturation determination method: the soil to be measured is detected by acoustic detector to obtain the soil porosity ratio e, and the saturation is calculated based on the porosity ratio e, the specific gravity of soil particles and the measured water content.The application has the characteristics of high precision, clear physical meaning of model parameters, strong applicability, fast testing speed and simple operation.

Description

Technical Field

[0001] This invention belongs to the field of geotechnical engineering technology and relates to a non-destructive method for determining the moisture content and saturation of soil based on color and ultrasonic technology. Background Technology

[0002] Moisture content and saturation are two fundamental physical indicators of soil, and their changes significantly affect soil strength, deformation, and seepage characteristics. During roadbed and slope construction, excessively high soil moisture content or saturation can easily reduce the soil's bearing capacity and shear strength, leading to engineering disasters such as uneven subgrade settlement and slope instability. Therefore, determining the moisture content and saturation of soil is of great importance.

[0003] In the field of geotechnical engineering, there are many methods for measuring soil moisture content and saturation. Existing methods for moisture content determination mainly include traditional methods such as the drying method, as well as newer methods such as frequency domain reflectance (FDR), remote sensing, and ground-penetrating radar (GPR). While the traditional drying method is an internationally recognized standard method with advantages such as high accuracy and simple operation, it is a laboratory test that requires considerable time and effort to dry the soil. Furthermore, the traditional method involves damaging the soil sample, making it difficult to measure soil moisture content without disturbing the soil in the field. The frequency domain reflectance method, while offering advantages such as high accuracy, versatility in testing materials, and good sensitivity, is susceptible to influences such as soil salinity at low frequencies, leading to significant errors. Remote sensing methods measure soil moisture content over large scales, have a wide applicability, and offer high accuracy, but are susceptible to weather and climate influences, increasing the risk of errors, and are also relatively expensive.

[0004] In saturation testing, traditional methods indirectly determine saturation by measuring the moisture content and porosity of specimens indoors. Newer methods include X-ray attenuation technology and optical transmission methods. However, existing technology (publication number: CN107782640A) discloses a method for detecting the uniformity of water content and calculating the diffusion coefficient of rock specimens based on digital images. This process is overly cumbersome, requiring significant on-site soil measurement and affecting accuracy. While traditional methods offer high precision, they require damaging the soil sample during testing, and drying the soil takes considerable time, making them unsuitable for on-site measurements. X-ray attenuation technology and optical transmission methods can be conducted without damaging the soil sample. However, X-ray attenuation technology has a slow reaction time to changes in water saturation, making accurate saturation measurement impossible; while optical transmission methods can only measure semi-transparent or multi-transparent porous media. Therefore, it is necessary to propose a more efficient, accurate, and non-destructive method for determining soil moisture content and saturation. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a non-destructive method for determining soil moisture content based on color and ultrasonic technology. This method features high accuracy, clear physical meaning of model parameters, strong applicability, fast testing speed, and simple operation, offering a new approach for determining soil moisture content on-site.

[0006] Another objective of this invention is to provide a non-destructive method for determining soil saturation.

[0007] The technical solution adopted in this invention is a non-destructive method for determining soil moisture content based on color and ultrasonic technology, comprising the following steps:

[0008] Step 1: Take photos of the soil, perform image preprocessing, and obtain the R, G, and B images of the soil photos;

[0009] Step 2: Obtain the effective fractal dimension in the image and obtain the discrete probability distribution map of the fractal dimension;

[0010] Step 3: Establish the relationship between the fitting parameters W, S, and K and the respective fractal dimensions of R, G, and B levels in the soil image;

[0011] Step 4: Using the fitting parameters W, S, and K as independent variables and the measured soil moisture content as the dependent variable, a linear regression analysis is performed using the least squares method to establish an estimation model for the soil moisture content.

[0012] Furthermore, in step 1, image preprocessing includes image extraction, image segmentation, image brightness normalization, and filtering and noise reduction to better preserve the color information of the image and enhance its contrast.

[0013] Furthermore, in step 2, the fractal dimension is determined according to equations (1)-(2):

[0014]

[0015]

[0016] In the formula: D i Let i be the fractal dimension, and i represent the color type, specifically R, G, B; A (x，y) Z represents the number of boxes containing the maximum values ​​of R, G, and B on the image covered by the small grid at position (x, y) in the original image meshing; (x，y) N is the number of boxes that fall on the image covered by the minimum values ​​of R, G, and B in the (x, y) grid; r The number of boxes needed to completely cover the (x, y) grid. The division ratio; G represents the height of the box; G represents the R, G, and B series numbers corresponding to each pixel in the image; the original image of the soil is L×L pixels in size, and the original image is meshed into small S×S pixels.

[0017] Furthermore, in step 3, the relationship between the combined parameters W and S and their respective fractal dimensions is specifically as follows:

[0018]

[0019]

[0020]

[0021] In the formula: W is the fitting parameter of R in the soil image; S is the fitting parameter of G in the soil image; K is the fitting parameter of B in the soil image; D R D G D B Let r, g, and b be the fractal dimensions of images R, G, and B, respectively; and let r, g, and b be the pixel values ​​of images R, G, and B, respectively. max g max b max These represent the maximum values ​​of R, G, and B pixels in the image, respectively; γ is a control parameter during the transformation process; a light correction parameter γ is proposed in the fitting parameter function to ensure that the on-site soil imaging effect is not affected by the on-site light, and to cover more image color information and details.

[0022] Furthermore, in step 4, the soil moisture content estimation model is specifically as follows:

[0023] w = a + bW c +dexp(-S)+eK f (7)

[0024] In the formula: W is the fitting parameter of R in the soil image; S is the fitting parameter of G in the soil image; K is the fitting parameter of B in the soil image, where a, b, c, d, e, and f are model coefficients.

[0025] Furthermore, in step 2, the method for obtaining the discrete probability distribution map of the fractal dimension is as follows: Based on the obtained fractal dimension D... i Find the maximum and minimum values ​​in the data, calculate the range, divide the data into several groups, divide the range by the number of groups to get the width of the group interval, count the number of pixels in each group, and draw a rectangle for each group with the group interval as the x-axis and the number of pixels as the y-axis. The height of each rectangle represents the corresponding number of pixels.

[0026] A non-destructive method for determining soil saturation includes the following steps:

[0027] Step (1): The soil porosity e is obtained by testing the soil under test with an acoustic wave detector.

[0028] Step (2) involves establishing the porosity e and saturation S.r The conversion formula indirectly determines the soil saturation S. r As shown in equation (9):

[0029]

[0030] In the formula: S r d represents the soil saturation; w represents the soil moisture content; d represents the soil moisture content. s This refers to the specific gravity of soil particles.

[0031] Furthermore, in step (1), the formula for calculating the soil void ratio e is as follows:

[0032]

[0033] In the formula: Δt is the measured wavelength time difference; Δt ma Δt represents the time difference of soil wavelength. f ν is the time difference of water wavelength in the soil; v is the correction coefficient for calculating porosity based on the time difference of sound waves.

[0034] Furthermore, the correction coefficient v is obtained by correcting the porosity calculated by equation (8) with the porosity calculated by the traditional dry weight method.

[0035] The beneficial effects of this invention are:

[0036] 1. This invention analyzes the fractal dimension D of the soil images R, G, and B by taking on-site photographs and processing them with a computer. i The formula function related to soil moisture content can be used to obtain the soil moisture content of the measured area. It has high accuracy and strong applicability, which greatly frees up manpower.

[0037] 2. This invention improves the efficiency and accuracy of on-site testing by using acoustic wave detection and computer processing of the soil in the tested area to indirectly determine the soil saturation using water content and void ratio.

[0038] 3. To eliminate the influence of lighting variations on soil imaging, this invention proposes a lighting correction parameter γ using gamma correction in the fitting parameter function. This makes it applicable to different indoor and outdoor environmental conditions, especially in engineering sites where it can ensure sufficient accuracy. Thus, in practical field applications, soil imaging results will not be affected by external weather, lighting, or other environmental factors. The corrected fitting parameters encompass more image color information and details, resulting in higher accuracy and greater reliability. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the method for indirectly determining the soil moisture content and saturation in an embodiment of the present invention.

[0041] Figure 2 This is the original photograph of the soil in an embodiment of the present invention.

[0042] Figure 3 This is the R-image extracted from the soil image in this embodiment of the invention.

[0043] Figure 4 This is the G-image extracted from the soil image in this embodiment of the invention.

[0044] Figure 5 This is Image B, extracted from the soil image in this embodiment of the invention.

[0045] Figure 6 This is a histogram showing the frequency distribution of the fractal dimension R value of the soil in this embodiment of the invention.

[0046] Figure 7 This is a histogram showing the frequency distribution of the fractal dimension G value of the soil in this embodiment of the invention.

[0047] Figure 8 This is a histogram showing the frequency distribution of the fractal dimension B value of the soil in an embodiment of the present invention.

[0048] Figure 9 This is a rendering of the soil moisture content model in an embodiment of the present invention.

[0049] Figure 10 This is a diagram showing the effect of the porosity correction parameters of the soil in an embodiment of the present invention.

[0050] Figure 11 This is a diagram showing the effect of soil saturation correction parameters in an embodiment of the present invention.

[0051] Figure 12 This is a diagram showing the on-site setup for measuring saturation according to the present invention.

[0052] Figure 12 In the diagram, 1. Soil, 2. Area to be tested, 3. Pit, 4. Acoustic transducer, 5. Connecting wire, 6. Acoustic detector. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] Moisture content and saturation are two fundamental physical indicators of soil, and their changes have a significant impact on soil strength, seepage characteristics, and compaction properties. In slope and road construction, excessively high soil moisture content or saturation can easily lead to reduced soil strength and slope stability, thereby inducing geological disasters such as landslides and debris flows, causing significant losses to people and the nation. This invention's embodiments analyze the fractal dimension D of soil images R, G, and B. i The soil moisture content is indirectly determined by a formula function related to soil moisture content. At the same time, the soil saturation is measured by an acoustic detector. The soil moisture content and saturation can be determined quickly and accurately.

[0055] Example 1,

[0056] Non-destructive methods for determining soil moisture content based on color and ultrasonic technologies, such as Figure 1 As shown, please follow these steps:

[0057] S1-1, Image Preprocessing

[0058] Standard rectangular specimens (80mm long × 80mm wide × 160mm high) were prepared from red clay on-site and treated with different moisture contents. Soil photographs were taken using a Daheng MER2-2000-6GM industrial camera. Figure 2 As shown. Subsequent image preprocessing includes image extraction, image segmentation, image brightness normalization, and filtering / denoising. (See attached image.) Figure 3-5 As shown, the R, G, and B maps extracted from the soil image in the embodiment can be intuitively obtained from the map, which shows the R, G, and B levels of the image region.

[0059] To reduce the impact of image information extraction errors caused by differences in shooting time, lighting, and angle, a photo with the most suitable brightness is selected as the benchmark photo. The brightness of the image is then normalized, and filtering and noise reduction are used to better preserve the color information of the image, enhance the contrast of the image, and reduce the adverse effects of natural lighting on the image.

[0060] S1-2, Extraction of Fitting Parameters

[0061] To obtain the effective fractal dimension of an image, MATLAB software was used to analyze the image ( Figure 2After preprocessing, discrete functions are used to define the R, G, and B of the image. The original image of the soil is L×L pixels in size, and the original image is gridded into small grids of S×S pixels. The horizontal and vertical coordinates are used to determine the corresponding positions of the grids, and (x, y) represents the fixed position in the gridding of the original image. As shown in equations (1)-(2).

[0062]

[0063]

[0064] In the formula: D i Let i be the fractal dimension, and i represent the color type, specifically R, G, or B.

[0065] A (x，y) The maximum value of R, G, and B values ​​on the image covered by the small grid at position (x, y) in the original image meshing is the number of boxes that fall on the image, i.e., the minimum number of R, G, and B levels estimated for the image region; the R, G, and B value map is a fractal surface in three-dimensional space.

[0066] Z (x，y) The number of boxes that fall on the minimum values ​​of R, G, and B in the image covered by the (x, y) grid.

[0067] N r The number of boxes needed to completely cover the (x, y) grid. The division ratio; Let G be the height of the box; make the number of divisions in the length, width and height directions the same; G is the R, G and B level corresponding to each pixel in the image. By preprocessing the image with MATLAB software, the R, G and B values ​​of the three primary color components of each pixel in the image can be obtained. The RGB color mode uses the RGB model to assign an intensity value in the range of 0 to 255 to the RGB components of each pixel in the image, with a total of 256 levels.

[0068] The obtained fractal dimension D i Find the maximum and minimum values, calculate the range (maximum value - minimum value), divide the data into several groups, divide the range by the number of groups to get the group interval width, and count the number of pixels in each group. In a Cartesian coordinate system, draw a rectangular plot of each group with the group interval as the x-axis and the number of pixels as the y-axis, where the height of each rectangle represents the corresponding number of pixels; for example... Figure 6-8 The figure shows the frequency distribution histograms of the R, G, and B fractal dimensions of the soil in the embodiment.

[0069] S1-3, based on the three fitting parameters W, S, and K in the established soil moisture content estimation model, and the respective fractal dimensions D of levels R, G, and B in the existing image. i The relationship between them is shown in equations (3)-(5).

[0070]

[0071]

[0072]

[0073] In the formula: W is the fitting parameter of R in the soil image; S is the fitting parameter of G in the soil image; K is the fitting parameter of B in the soil image; D R D G D B Let r, g, and b be the fractal dimensions of images R, G, and B, respectively; and let r, g, and b be the pixel values ​​of images R, G, and B, respectively. max g max b max These represent the maximum values ​​of the R, G, and B pixels in the image, respectively; γ is a control parameter during the transformation process. When γ > 1, the overall brightness of the image decreases, resulting in a dark visual mode; when γ < 1, the overall brightness of the image increases, resulting in a bright visual mode. Table 1 shows the results of fitting parameters (W, S, K) for the three sets of R, G, and B images of the soil in the embodiment.

[0074] RGB color values ​​cannot be simply added together. In this embodiment of the invention, the gamma correction method is used to establish a ray function for the red clay specimen of the present invention through formulas (3)-(5). A ray correction parameter γ is proposed in the fitting parameter function to ensure that the on-site soil imaging effect is not affected by the on-site light, and to cover more image color information and details, with higher accuracy and reliability. It also ensures that the measurement method of this embodiment of the invention is not limited to indoor tests, but can also be applied to on-site soil construction.

[0075] Table 1. Results of fitting parameters for three sets of R, G, and B images of soil in the embodiments of the present invention.

[0076] sample W S K 1 13.724 0.5218 2.299 2 13.8465 0.5237 2.3184 3 13.797 0.5191 2.3124 4 13.7834 0.5210 2.3161 5 13.9158 0.5336 2.3671 6 13.9283 0.5330 2.3867 7 14.0868 0.5329 2.4021 8 14.0441 0.5317 2.4097 9 14.0232 0.5334 2.4227 10 14.1441 0.5331 2.4478 11 14.0489 0.5272 2.5679 12 14.3538 0.5376 2.5353 13 14.3673 0.5396 2.5983 14 14.5658 0.5471 2.6047 15 15.1381 0.6091 2.8039 16 14.8914 0.5733 2.6623 17 15.0282 0.5889 2.7131 18 15.1036 0.6036 2.7612 19 15.0271 0.6064 2.7681 20 15.1414 0.6174 2.824

[0077] Spearman correlation analysis was performed on the sample data of the image fitting parameters R, G, and B to reveal the relationship between the fitting parameters and the moisture content. The definition (6) is expressed as follows:

[0078]

[0079] In the formula: n is the characteristic X i Y i The number of samples, X i and Y i These are the ranks of the two variables (fitting parameters and moisture content) sorted by size (or quality), and in this example, n is 20; d i For X i With Y iThe difference in rank (order) of the i-th value in each sample, i.e., d i =(X i -Y i p is the correlation coefficient, ranging from (-1, 1). When the absolute value of the correlation coefficient p of the image fitting parameters of the visible R, G, and B plots is greater than 0.05, it indicates that there is a large correlation between these three sets of fitting parameters and the soil moisture content, indicating that the prediction equation established in this embodiment of the invention is relatively reasonable.

[0080] S1-4, Soil Moisture Content Estimation Model

[0081] With three significant features (W, S, K) as independent variables and measured soil moisture content as dependent variable, a linear regression analysis was performed using the least squares method to establish an estimation model for soil moisture content, as shown in equation (7).

[0082] w = a + bW c +dexp(-S)+eK f (7)

[0083] In the formula: W is the fitting parameter of R in the soil image; S is the fitting parameter of G in the soil image; K is the fitting parameter of B in the soil image. The model coefficients are a = 0.2307, b = 4582.6921, c = -2.3458, d = 0.0405, e = -0.0079, and f = 3. Their values ​​depend on the soil type. The method for determining them is to first determine the moisture content through an indoor drying test, and then fit the known fitting parameters W, S, and K of R, G, and B with the determined moisture content using a formula.

[0084] The moisture content estimation model specimens were prepared from on-site red clay into standard cuboid specimens (80mm long × 80mm wide × 160mm high). The moisture content of the specimens was determined by the drying method, and the results were compared with those calculated by the moisture content estimation model. Figure 9 The image shown is a rendering of the soil moisture content model in the embodiment. The maximum relative error does not exceed 5%, which indicates that the soil moisture content estimation model has high accuracy.

[0085] The relationships between the various image fitting parameters and the soil moisture content in this example, as well as the relationships between the various image fitting parameters, were obtained based on previous tests. Therefore, the fractal dimension D of R, G, and B was established through the images. i For fractal dimension D iLight correction was applied to obtain three fitting parameters (R, G, B) for the soil image, and the interdependence between these three fitting parameters and the soil moisture content was established. Furthermore, the value of the ag coefficient in the soil moisture content estimation model was varied according to the type of soil measured, thereby improving the versatility of the soil moisture content estimation model.

[0086] This invention uses fractal dimension to quantitatively characterize the distribution characteristics of colors R, G, and B, and establishes the relationship between soil moisture content and color. Fractal dimension reflects the effectiveness of space occupied by complex shapes. It is a measure of the irregularity of complex shapes and can better describe the roughness information of color surfaces. At the same time, the moisture content measurement is more accurate and reliable, and it is applicable to various types of soil, making it more widely applicable.

[0087] Example 2,

[0088] A non-destructive method for determining soil saturation includes:

[0089] S2-1, Using an acoustic wave detector to indirectly determine the saturation of the soil under study.

[0090] Soil samples were prepared into standard cuboid specimens (80mm long × 80mm wide × 160mm high) using red clay from the field. The soil moisture content was determined using an RS-ST01C non-metallic ultrasonic testing instrument, and the saturation of the soil was indirectly determined using an acoustic wave testing instrument. The testing personnel obtained the soil void ratio by testing the soil under test using an acoustic wave testing instrument, as shown in equation (8).

[0091]

[0092] In the formula: e is the soil void ratio; Δt is the measured wavelength time difference; Δt ma Δt represents the time difference of soil wavelength. f ν is the time difference of water wavelength in the soil; v is the correction coefficient for calculating porosity based on the time difference of sound waves.

[0093] In formula (8), v is the porosity ratio determined by this method, which is corrected by the porosity ratio calculated by drying the specimen to constant weight at room temperature using the traditional dry weight method, and then measuring the bulk density and porosity of the specimen using a densitometer. Thus, the value of v in this example is 0.885. Figure 10 The image shown is an illustration of the effect of the void ratio correction parameters on the soil in an embodiment of the present invention. 2 Greater than 0.96.

[0094] In equation (8), Δt ma With Δt fGiven the known data, in a field environment, when the type of soil to be measured and the type of fluid within the soil pores are known, this method can be effectively used to detect the void ratio of soil. Table 2 shows the wavelength transit time for measuring common soil media in embodiments of this invention.

[0095] Table 2 Wavelength time difference for common soil media measured in the embodiments of the present invention

[0096] name Wavelength transit time (μs / m) clay 400-833.33 wet sand 1250-1666.67 Sandy clay 1111.11-3333.33 dry sand, gravel 1250-5000 saturated sand and gravel 357.14-666.67 water 625-714.29 ice 277.78-322.58 concrete 222.22-5000

[0097] S2-2, then by establishing the porosity e and saturation S r The conversion formula indirectly determines the soil saturation S. r As shown in equation (9).

[0098]

[0099] In the formula: S r d represents the soil saturation; w represents the soil moisture content; d represents the soil moisture content. s This refers to the specific gravity of soil particles.

[0100] In equation (9), the soil moisture content w is the relevant moisture content measured by S1. Table 3 shows the specific gravity reference for common soils in this invention. In equation (9), d... s Refer to Table 3 for soil particle specific gravity values.

[0101] Table 3. Specific gravity reference of common soils in the embodiments of the present invention.

[0102] name Soil particle density sand 2.68 silty soil 2.70 silty clay 2.71 Silty clay 2.72-2.73

[0103] Finally, the void ratios obtained by the traditional dry weight method and the void ratios obtained by the method of this invention are substituted into equation (9) to obtain the values ​​of the measured saturation and the predicted saturation. Then, the saturation obtained by the method of this invention is corrected and fitted to ensure that the method has high accuracy. Figure 11 The image shown is an effect diagram of the soil saturation correction parameters in an embodiment of the present invention. Figure 11 Chinese R 2 It is 0.95.

[0104] Example 3,

[0105] On-site determination of soil moisture content and saturation:

[0106] S3-1, On-site determination of moisture content

[0107] During on-site measurements, to ensure the accuracy of feature values ​​extracted from photographs, a fixed-point shooting method was used when taking photos of the soil. This ensured that the soil images were taken on the same horizontal plane, thus preventing errors caused by uneven distribution of soil moisture content.

[0108] The moisture content of the soil at the site was determined according to the steps of Example 1. This invention proposes a method for estimating the model based on the fractal dimensions of soil images (R, G, B). The estimation accuracy meets engineering requirements. The mechanical empirical model for estimating soil moisture content using fitting parameters W, S, and K related to the fractal dimensions of images R, G, and B has engineering rationality. Furthermore, in the relationship between the three fitting parameters W, S, and K in the soil moisture content estimation model and the respective D-fractal dimensions of the R, G, and B levels in the existing image, a light correction parameter γ is added to the relationship to eliminate the influence of uneven lighting on the soil imaging effect. When γ > 1, the overall brightness of the image decreases, resulting in a dark visual mode; when γ < 1, the overall brightness of the image increases, resulting in a bright visual mode.

[0109] S3-2, On-site determination of saturation

[0110] like Figure 12 The diagram below shows the on-site layout for measuring saturation according to an embodiment of the present invention. During on-site measurement, to better utilize the acoustic wave detector 6 to determine the void ratio of the soil 1 under test, the soil area is divided into several (700mm × 700mm) square test areas 2. Simultaneously, a (700mm × 200mm × 200mm) rectangular pit 3 is arranged on each side of each test area 2. The testing personnel use an acoustic transducer 4 within the pit 3 to perform acoustic wave detection on the test area 2. The other square areas are arranged in the same manner with rectangular pits 3.

[0111] The saturation of the soil was determined according to the steps in Example 2. This test should be conducted after obtaining the soil moisture content test results. The soil void ratio was measured using an acoustic wave detector, and the void ratio was measured at fixed points to eliminate errors caused by a large testing area. During the field test, a model can be quickly built using document tools, and calculations can be completed in seconds using only a laptop computer on site. Figure 1 The diagram shown is a flowchart of a method for indirectly determining the water content and saturation of soil in an embodiment of the present invention.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A non-destructive method for determining soil moisture content based on color and ultrasonic technology, characterized in that, Includes the following steps: Step 1: Take photos of the soil, perform image preprocessing, and obtain the R, G, and B images of the soil photos; Step 2: Obtain the effective fractal dimension in the image and obtain the discrete probability distribution map of the fractal dimension; Step 3: Establish the relationship between the fitting parameters W, S, and K and the respective fractal dimensions of R, G, and B levels in the soil image; Step 4: Using the fitting parameters W, S, and K as independent variables and the measured soil moisture content as the dependent variable, a linear regression analysis is performed using the least squares method to establish an estimation model for soil moisture content. In step 3, the relationship between the fitting parameters W, S, and their respective fractal dimensions is specifically as follows: （3） （4） （5） In the formula: W is the fitting parameter of R in the soil image; S is the fitting parameter of G in the soil image; K is the fitting parameter of B in the soil image; D R D G D B , , and , respectively, are the fractal dimensions of images R, G, and B; r, g, and b are the pixel values ​​of images R, G, and B, respectively; r max g max b max These represent the maximum values ​​of R, G, and B pixels in the image, respectively; γ is a control parameter during the transformation process. In step 4, the soil moisture content estimation model is as follows: （7） In the formula: W is the fitting parameter of R in the soil image; S is the fitting parameter of G in the soil image; K is the fitting parameter of B in the soil image, where a, b, c, d, e, and f are model coefficients.

2. The non-destructive method for determining soil moisture content based on color and ultrasonic technology according to claim 1, characterized in that, In step 1, image preprocessing includes image extraction, image segmentation, image brightness normalization, and filtering and noise reduction.

3. The non-destructive method for determining soil moisture content based on color and ultrasonic technology according to claim 1, characterized in that, In step 2, the fractal dimension is determined according to equations (1)-(2): （1） （2） In the formula: D i Let i be the fractal dimension, and i represent the color type, specifically R, G, B; A （x，y） Z represents the number of boxes containing the maximum values ​​of R, G, and B on the image covered by the small grid at position (x, y) in the original image meshing; （x，y） N is the number of boxes that fall on the image covered by the minimum values ​​of R, G, and B in the (x, y) grid. r The number of boxes needed to completely cover the (x, y) grid. The division ratio; G represents the height of the box; G represents the R, G, and B series numbers corresponding to each pixel in the image; the original image of the soil is L×L pixels in size, and the original image is meshed into small S×S pixels.

4. The non-destructive method for determining soil moisture content based on color and ultrasonic technology according to claim 1, characterized in that, In step 2, the method for obtaining the discrete probability distribution map of the fractal dimension is as follows: Based on the obtained fractal dimension D... i Find the maximum and minimum values ​​in the data, calculate the range, divide the data into several groups, divide the range by the number of groups to get the width of the group interval, count the number of pixels in each group, and draw a rectangle for each group with the group interval as the x-axis and the number of pixels as the y-axis. The height of each rectangle represents the corresponding number of pixels.

5. A non-destructive method for determining soil saturation, characterized in that, The non-destructive method for determining soil moisture content based on color and ultrasonic technology as described in claim 1 includes the following steps: Step (1): The soil porosity e is obtained by testing the soil under test with an acoustic wave detector. Step (2) involves establishing the porosity e and saturation S. r The conversion formula indirectly determines the soil saturation S. r As shown in equation (9): （9） In the formula: S r d represents the soil saturation; w represents the soil moisture content; d represents the soil moisture content. s This refers to the specific gravity of soil particles.

6. The method for non-destructive testing of soil saturation according to claim 5, characterized in that, In step (1), the formula for calculating the soil void ratio e is: （8） In the formula: Δt is the measured wavelength time difference; Δt ma Δt represents the time difference of soil wavelength. f ν is the time difference of water wavelength in the soil; v is the correction coefficient for calculating porosity based on the time difference of sound waves.

7. The method for non-destructive testing of soil saturation according to claim 6, characterized in that, The correction coefficient v is obtained by correcting the void ratio calculated by equation (8) with the void ratio calculated by the traditional dry weight method.

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