Farmland soil data digital analysis method and system

Abstract

The invention discloses a farmland soil data digital analysis method and system, and the method comprises the steps: obtaining a farmland image, and dividing the image into different planting regions according to plants; based on the images of different planting areas, dividing the images into a plurality of sub-areas, obtaining soil samples in each sub-area, and analyzing the samples to obtain soil fertility in the sub-areas; based on the land fertility of different time nodes in each sub-region, obtaining a land fertility change condition; and comprehensively evaluating and analyzing the soil fertility change condition of the planting area based on the soil fertility change condition in each sub-area. The farmland soil fertility monitoring system has the advantages that accurate monitoring and dynamic tracking of farmland soil fertility are realized through image analysis and soil sample collection, and scientific decision support is provided for agricultural production. Through analysis of the soil fertility change trend, optimization of a fertilization scheme is facilitated, the land utilization efficiency is improved, and sustainable agricultural development is promoted.

Description

Technical Field

[0001] The present invention relates to farmland soil analysis, and in particular to a farmland soil data digital analysis method and system. Background Art

[0002] A digital analysis method and system for farmland soil data, which obtains farmland images and divides the images into regions based on plant types, and further subdivides each planting area into several sub-regions. Soil samples in each sub-region are analyzed to obtain soil fertility data and monitor its trend over time. By comparing the changing trends of soil fertility in different sub-regions, if the changing trends are consistent, the changes in soil fertility in the entire region can be inferred; if the trends are inconsistent, the changing trends of adjacent regions are further analyzed, and the changes in soil fertility are analyzed and judged by assigning different weights. This method can accurately evaluate and predict changes in farmland soil fertility and optimize agricultural management and fertilization strategies.

[0003] The current farmland soil data analysis methods on the market mainly include remote sensing technology analysis, geographic information system combined with data mining analysis and other means. Remote sensing technology is gradually applied to soil analysis. Using satellites or drones to obtain high-resolution soil image data, it can quickly monitor soil quality, humidity, temperature and other indicators over a large area, with strong timeliness and spatial coverage. At the same time, GIS technology combined with big data analysis and machine learning algorithms can perform spatial analysis of soil data in different regions and predict soil change trends. This method can provide accurate farmland management decision support and help farmers optimize agricultural management measures such as fertilization and irrigation. Modern farmland soil data analysis methods tend to be diversified and intelligent, combining advanced sensing technology, data processing technology and analysis models to provide agriculture with more scientific and accurate management solutions. Summary of the invention

[0004] In order to improve the existing farmland soil data analysis method, a farmland soil data digital analysis method and system are provided. This method realizes the accurate monitoring and dynamic tracking of farmland soil fertility through image analysis and soil sample collection. By analyzing the changing trend of soil fertility, it helps to optimize the fertilization plan, improve land use efficiency and promote sustainable agricultural development.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for digital analysis of farmland soil data, characterized by comprising:

[0007] Obtain farmland images and divide the images into different planting areas according to the plants planted;

[0008] Based on the images of different planting areas, the areas are divided into several sub-areas, soil samples are obtained from each sub-area, and the samples are analyzed to obtain the soil fertility in the sub-area;

[0009] Based on the soil fertility at different time points in each sub-region, obtain the soil fertility change situation;

[0010] Based on the changes in soil fertility in each sub-area, a comprehensive evaluation and analysis of the changes in soil fertility in the planting area was conducted.

[0011] Preferably, the images based on different planting areas are divided into several sub-areas, soil samples in each sub-area are obtained, and the samples are analyzed to obtain the soil fertility in the sub-area, which specifically includes:

[0012] Based on farmland images, the important outline nodes of various types of planted areas are marked according to fixed pixels;

[0013] Based on the important nodes of the contour, the nodes are connected into continuous contour lines through spatial interpolation method to obtain the soil contour map. The formula is: L(k) = (1-k)P i +kP i+1 ,k∈[0,1], where P i and P i+1 are the coordinates of adjacent nodes;

[0014] Based on the soil contour map, it is divided into several grid areas of equal size, and soil samples are obtained in each area;

[0015] Based on soil samples from each area, the pH value and organic matter content were analyzed to obtain the soil fertility of each area.

[0016] Preferably, analyzing the pH value and organic matter content of soil samples in each region to obtain soil fertility in each region specifically includes:

[0017] Based on the soil samples obtained in each area, a certain amount of soil samples were taken, mixed with water at a ratio of 2.5:1 to obtain a mixed solution, and the pH value of the soil samples was measured using a standardized pH electrode;

[0018] Take a certain amount of soil sample, add an excess of potassium dichromate solution under acidic conditions, and carry out oxidation reaction. The amount of potassium dichromate consumed is proportional to the organic matter content in the soil. The content of soil organic carbon is calculated based on the amount of potassium dichromate consumed.

[0019] The formula is:

[0020]

[0021] Among them, C organic is the organic carbon content in the soil sample, Vtitration is the volume of sodium thiosulfate consumed in titration (mL), N Na2S2O3 is the concentration of sodium thiosulfate solution (mol / L), 0.003 is a constant representing the ratio of the amount of potassium dichromate that can be reduced by each mole of sodium thiosulfate solution, and 100 is a conversion factor that converts the result from mass calculated in the unit "g" to percentage;

[0022] Based on soil samples, obtain the content of nitrogen, phosphorus, ammonia and potassium compounds;

[0023] Based on the pH value and organic matter content data of the soil samples obtained, an assessment of soil fertility is given.

[0024] Preferably, obtaining the soil fertility change based on the soil fertility at different time points in each sub-region specifically includes:

[0025] Based on the soil fertility P at different time points in each sub-area t , forming a time series {P(t1),P(t2),...,P(t n )};

[0026] Based on the soil fertility P at different time points in the same sub-region t , compare the difference in soil fertility between the two, and obtain the changes in soil fertility in the current sub-area.

[0027] Preferably, the soil fertility P at different time points in the same sub-region is t , compare the difference in soil fertility between the two, and obtain the soil fertility changes in the current sub-region, including:

[0028] Standardization processing is performed based on soil pH and organic matter content data at different time points;

[0029] Based on the standardized soil data, calculate the change rate at different time nodes;

[0030] Based on the change rate of each soil data, the soil fertility is comprehensively evaluated by weighted average method, and the formula is:

[0031] S=α1·C organic +α2·N+·P+α4·NH+α5·K

[0032] Among them, α1, α2, α3, and α4 are weight factors, which are set according to the relative importance of various soil factors to soil fertility;

[0033] Based on the comprehensive evaluation results of soil fertility, the changing trend of soil fertility in the current sub-region is obtained.

[0034] Preferably, the comprehensive evaluation and analysis of the soil fertility changes in the planting area based on the soil fertility changes in each sub-area specifically includes:

[0035] Count the soil fertility change trends of each sub-area in the planting area. If the change trends are consistent, the soil fertility change status of the planting area can be determined;

[0036] Based on the inconsistency of the change trends, the change trends of the adjacent areas of the area are obtained, and weights are given as influencing factors to analyze and determine the change trends of the area.

[0037] Preferably, based on the inconsistency of the change trends, obtaining the change trends of the adjacent areas of the area, assigning weights as influencing factors to analyze and determine the change trends of the area specifically includes:

[0038] Obtain the changing trend of soil fertility in adjacent areas and assign different weight values ​​based on the area of ​​adjacent areas;

[0039] Based on the distribution of change trends in the region and the weight change trends of adjacent regions, the change trend of the region is comprehensively judged. The calculation formula is:

[0040]

[0041] Among them, T0 is the proportion of the expansion of the area, γ is the weight proportion of the area, and λ i is the additional weight of each adjacent region, is the changing trend of adjacent regions, and n is the total number of adjacent regions.

[0042] Furthermore, a farmland soil data digital analysis system is characterized by comprising:

[0043] Image acquisition module: The image acquisition module is mainly used to acquire image data of the planting area through satellites or drones;

[0044] A contour construction module, wherein the contour construction module is mainly used to obtain a soil contour map of the same type based on the types of crops planted in each planting area;

[0045] A chemical analysis module, which is mainly used to analyze the chemical properties of the soil and obtain soil fertility;

[0046] A comparison module, which is mainly used to obtain the change trend of soil fertility in each sub-area by comparing the change of soil fertility at each time node;

[0047] Comprehensive module: The comprehensive module is mainly used to integrate the change trends in each sub-area to obtain the change trends of soil fertility in each planting area;

[0048] A storage module, the storage module is mainly used to store contour function data of each time node and change trend data of each area;

[0049] The processing module is mainly used for information transmission processing between various modules and calculation of profiles and change trends.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] By acquiring farmland images and dividing them into different planting areas according to crop types, refined management of cultivated land can be achieved. The planting area is further subdivided into multiple sub-areas, and soil samples are collected and analyzed separately to ensure the accuracy and representativeness of the data. By tracking the changes in soil fertility at different time points, the dynamic change trend of the land can be clearly reflected, providing timely and comprehensive decision-making support for soil management. Based on the consistency analysis of soil fertility change trends, the change patterns in sub-areas can be effectively identified. In the case of inconsistent soil fertility change trends, the method also combines the data of adjacent areas for weighted analysis, which improves the accuracy and reliability of the overall analysis. This method not only helps agricultural producers optimize fertilization plans and improve land use efficiency, but also provides a scientific basis for agricultural environmental protection, thereby promoting sustainable agricultural development. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of farmland soil analysis of the method and system proposed in the present invention;

[0053] Figure 2 A schematic diagram of the plant area division of the method and system proposed in the present invention;

[0054] Figure 3 A schematic diagram of soil fertility measurement of the method and system proposed in the present invention;

[0055] Figure 4 A schematic diagram of soil fertility changes of the method and system proposed by the present invention;

[0056] Figure 5 A schematic diagram of soil fertility difference calculation of the method and system proposed by the present invention;

[0057] Figure 6 A schematic diagram of comprehensive soil fertility changes of the method and system proposed in the present invention;

[0058] Figure 7 A unified schematic diagram of the changing trend of the method and system proposed by the present invention;

[0059] Figure 8 This is a schematic diagram of the electronic device in this solution;

[0060] Fig. 9This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0061] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0062] A farmland soil data digital analysis system, comprising:

[0063] Image acquisition module: The image acquisition module is mainly used to acquire image data of the planting area through satellites or drones;

[0064] A contour construction module, wherein the contour construction module is mainly used to obtain a soil contour map of the same type based on the types of crops planted in each planting area;

[0065] A chemical analysis module, which is mainly used to analyze the chemical properties of the soil and obtain soil fertility;

[0066] A comparison module, which is mainly used to obtain the change trend of soil fertility in each sub-area by comparing the change of soil fertility at each time node;

[0067] Comprehensive module: The comprehensive module is mainly used to integrate the change trends in each sub-area to obtain the change trends of soil fertility in each planting area;

[0068] A storage module, the storage module is mainly used to store contour function data of each time node and change trend data of each area;

[0069] The processing module is mainly used for information transmission processing between various modules and calculation of profiles and change trends.

[0070] See also Figure 1 As shown, a method for digital analysis of farmland soil data includes:

[0071] Step 1: Obtain farmland images and divide the images into different planting areas according to the plants planted;

[0072] Step 2: Based on the images of different planting areas, divide them into several sub-areas, obtain soil samples in each sub-area, and analyze the samples to obtain the soil fertility in the sub-area;

[0073] Step 3: Based on the soil fertility at different time points in each sub-region, obtain the soil fertility change situation;

[0074] Step 4: Based on the changes in soil fertility in each sub-area, comprehensively evaluate and analyze the changes in soil fertility in the planting area.

[0075] See also Figure 2 As shown, based on the images of different planting areas, they are divided into several sub-areas, soil samples are obtained in each sub-area, and the samples are analyzed to obtain the soil fertility in the sub-area, including:

[0076] Based on farmland images, the important outline nodes of various types of planted areas are marked according to fixed pixels;

[0077] Based on the important nodes of the contour, the nodes are connected into continuous contour lines through spatial interpolation method to obtain the soil contour map. The formula is: L(k) = (1-k)P i +kP i+1 ,k∈[0,1], where P i and P i+1 are the coordinates of adjacent nodes;

[0078] Based on the soil contour map, it is divided into several grid areas of equal size, and soil samples are obtained in each area;

[0079] Based on soil samples from each area, the pH value and organic matter content were analyzed to obtain the soil fertility of each area.

[0080] See also Figure 3 As shown in the figure, based on the soil samples in each area, the pH value and organic matter content are analyzed to obtain the soil fertility of each area, including:

[0081] Based on the soil samples obtained in each area, a certain amount of soil samples were taken, mixed with water at a ratio of 2.5:1 to obtain a mixed solution, and the pH value of the soil samples was measured using a standardized pH electrode;

[0082] Take a certain amount of soil sample, add an excess of potassium dichromate solution under acidic conditions, and carry out oxidation reaction. The amount of potassium dichromate consumed is proportional to the organic matter content in the soil. The content of soil organic carbon is calculated based on the amount of potassium dichromate consumed.

[0083] The formula is:

[0084]

[0085] Among them, C organic is the organic carbon content in the soil sample, V titration is the volume of sodium thiosulfate consumed in titration (mL), N Na2S2O3 is the concentration of sodium thiosulfate solution (mol / L), 0.003 is a constant representing the ratio of the amount of potassium dichromate that can be reduced by each mole of sodium thiosulfate solution, and 100 is a conversion factor that converts the result from mass calculated in the unit "g" to percentage;

[0086] Based on soil samples, obtain the content of nitrogen, phosphorus, ammonia and potassium compounds;

[0087] Based on the pH value and organic matter content data of the soil samples obtained, an assessment of soil fertility is given.

[0088] Specifically, the analysis of nitrogen content is carried out by determining the content of ammonium nitrogen and nitrate nitrogen in the soil by using the Kjeldahl method or the distillation method, and by adding an acid solvent, such as a mixture of sodium chloride and sulfuric acid solution, and measuring the phosphate content in the solution using a spectrophotometer. The solution is extracted with potassium chloride, and the concentration of potassium ions is measured by a flame photometer. For the measurement of other trace elements, atomic absorption spectroscopy or inductively coupled plasma spectroscopy is used for determination.

[0089] See also Figure 4 As shown in the figure, based on the soil fertility at different time points in each sub-area, the soil fertility change is obtained, including:

[0090] Based on the soil fertility P at different time points in each sub-area t , forming a time series {P(t1),P(t2),...,P(t n )};

[0091] Based on the soil fertility P at different time points in the same sub-region t , compare the difference in soil fertility between the two, and obtain the changes in soil fertility in the current sub-area.

[0092] See also Figure 5 As shown in Figure 2, soil fertility P at different time points in the same sub-region t , compare the difference in soil fertility between the two, and obtain the soil fertility changes in the current sub-region, including:

[0093] Standardization processing is performed based on soil pH and organic matter content data at different time points;

[0094] Based on the standardized soil data, calculate the change rate at different time nodes;

[0095] Based on the change rate of each soil data, the soil fertility is comprehensively evaluated by weighted average method, and the formula is:

[0096] S=α1·C organic +α2·N+·P+α4·NH+α5·K

[0097] Among them, α1, α2, α3, and α4 are weight factors, which are set according to the relative importance of various soil factors to soil fertility;

[0098] Based on the comprehensive evaluation results of soil fertility, the changing trend of soil fertility in the current sub-region is obtained.

[0099] It is understandable that soil data between different time nodes may be affected by seasonal changes or external environmental factors, resulting in instability or deviation in the calculation of the change rate. It is possible to consider using a smoothing algorithm, such as the moving average method, to smooth the change rate to reduce the impact of mutations. At the same time, consider setting a time window, such as each quarter or year as an analysis cycle, to reduce the interference of seasonal fluctuations.

[0100] See also Figure 6 Based on the changes in soil fertility in each sub-region, a comprehensive evaluation and analysis of the changes in soil fertility in the planting area specifically includes:

[0101] Count the soil fertility change trends of each sub-area in the planting area. If the change trends are consistent, the soil fertility change status of the planting area can be determined;

[0102] Based on the inconsistency of the change trends, the change trends of the adjacent areas of the area are obtained, and weights are given as influencing factors to analyze and determine the change trends of the area.

[0103] See also Figure 7 As shown, based on the inconsistent change trends, the change trends of the adjacent areas of the area are obtained, and the change trends of the area are determined by weighting as influencing factors. Specifically, the following are included:

[0104] Obtain the changing trend of soil fertility in adjacent areas and assign different weight values ​​based on the area of ​​adjacent areas;

[0105] Based on the distribution of change trends in the region and the weight change trends of adjacent regions, the change trend of the region is comprehensively judged. The calculation formula is:

[0106]

[0107] Among them, T0 is the proportion of the expansion of the area, γ is the weight proportion of the area, and λ i is the additional weight of each adjacent region, is the changing trend of adjacent regions, and n is the total number of adjacent regions.

[0108] Furthermore, the method according to the embodiment of the present application can also be performed by Figure 8 The electronic device architecture shown in FIG. Figure 8As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a method and system for digital analysis of farmland soil data provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 8 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 8 One or more components of an electronic device are shown.

[0109] Fig. 9 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Fig. 9 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, a method and system for digital analysis of farmland soil data according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0110] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

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

Claims

1. A method for digital analysis of farmland soil data, characterized in that: include: Obtain farmland images and divide the images into different planting areas according to the plants planted; Based on the images of different planting areas, the areas are divided into several sub-areas, soil samples are obtained from each sub-area, and the samples are analyzed to obtain the soil fertility in the sub-area; Based on the soil fertility at different time points in each sub-region, obtain the soil fertility change situation; Based on the changes in soil fertility in each sub-area, a comprehensive evaluation and analysis of the changes in soil fertility in the planting area was conducted.

2. A method for digital analysis of farmland soil data according to claim 1, characterized in that: The images based on different planting areas are divided into several sub-areas, soil samples in each sub-area are obtained, and the samples are analyzed to obtain the soil fertility in the sub-area, which specifically includes: Based on farmland images, the important outline nodes of various types of planted areas are marked according to fixed pixels; Based on the important nodes of the contour, the nodes are connected into continuous contour lines through spatial interpolation method to obtain the soil contour map. The formula is: L(k) = (1-k)P i +kP i+1 ,k∈[0,1], where P i and P i+1 are the coordinates of adjacent nodes; Based on the soil contour map, it is divided into several grid areas of equal size, and soil samples are obtained in each area; Based on soil samples from each area, the pH value and organic matter content were analyzed to obtain the soil fertility of each area.

3. A method for digital analysis of farmland soil data according to claim 2, characterized in that: The soil fertility of each region is obtained by analyzing the pH value and organic matter content of soil samples in each region, including: Based on the soil samples obtained in each area, a certain amount of soil samples were taken, mixed with water at a ratio of 2.5:1 to obtain a mixed solution, and the pH value of the soil samples was measured using a standardized pH electrode; Take a certain amount of soil sample, add an excess of potassium dichromate solution under acidic conditions, and carry out oxidation reaction. The amount of potassium dichromate consumed is proportional to the organic matter content in the soil. The content of soil organic carbon is calculated based on the amount of potassium dichromate consumed. The formula is: Among them, C organic is the organic carbon content in the soil sample, V titration is the volume of sodium thiosulfate consumed in titration (mL), N Na2S2O3 is the concentration of sodium thiosulfate solution (mol / L), 0.003 is a constant representing the ratio of potassium dichromate that can be reduced by each mole of sodium thiosulfate solution, and 100 is a conversion factor, which converts the result from mass calculated in "g" to percentage; Based on soil samples, obtain the content of nitrogen, phosphorus, ammonia and potassium compounds; Based on the pH value and organic matter content data of the soil samples obtained, an assessment of soil fertility is given.

4. The method for digital analysis of farmland soil data according to claim 1, characterized in that: The obtaining of soil fertility change conditions based on soil fertility at different time points in each sub-region specifically includes: Based on the soil fertility P at different time points in each sub-area t , forming a time series {P(t1),P(t2),...,P(t n )}; Based on the soil fertility P at different time points in the same sub-region t , compare the difference in soil fertility between the two, and obtain the changes in soil fertility in the current sub-area.

5. A method for digital analysis of farmland soil data according to claim 4, characterized in that: The soil fertility P at different time points in the same sub-region t , compare the difference in soil fertility between the two, and obtain the soil fertility changes in the current sub-region, including: Standardization processing is performed based on soil pH and organic matter content data at different time points; Based on the standardized soil data, calculate the change rate at different time nodes; Based on the change rate of each soil data, the soil fertility is comprehensively evaluated by weighted average method, and the formula is: S=α1·C organic +α2·N+·P+α4·NH+α5·K Among them, α1, α2, α3, and α4 are weight factors, which are set according to the relative importance of various soil factors to soil fertility; Based on the comprehensive evaluation results of soil fertility, the changing trend of soil fertility in the current sub-region is obtained.

6. The method for digital analysis of farmland soil data according to claim 1, characterized in that: The comprehensive evaluation and analysis of the soil fertility changes in the planting area based on the soil fertility changes in each sub-area specifically includes: Count the soil fertility change trends of each sub-area in the planting area. If the change trends are consistent, the soil fertility change status of the planting area can be determined; Based on the inconsistency of the change trends, the change trends of the adjacent areas of the area are obtained, and weights are given as influencing factors to analyze and determine the change trends of the area.

7. A method for digital analysis of farmland soil data according to claim 6, characterized in that: The obtaining of the change trend of the adjacent regions of the region based on the inconsistency of the change trend and assigning weights as influencing factors to analyze and determine the change trend of the region specifically includes: Obtain the changing trend of soil fertility in adjacent areas and assign different weight values ​​based on the area of ​​adjacent areas; Based on the distribution of change trends in the region and the weight change trends of adjacent regions, the change trend of the region is comprehensively judged. The calculation formula is: Among them, T0 is the proportion of the expansion of the area, γ is the weight proportion of the area, and λ i is the additional weight of each adjacent region, is the changing trend of adjacent regions, and n is the total number of adjacent regions.

8. A method for digital analysis of farmland soil data is used to implement a farmland soil data digital analysis system as claimed in any one of claims 1 to 7, characterized in that: include: Image acquisition module: The image acquisition module is mainly used to acquire image data of the planting area through satellites or drones; A contour construction module, wherein the contour construction module is mainly used to obtain a soil contour map of the same type based on the types of crops planted in each planting area; A chemical analysis module, which is mainly used to analyze the chemical properties of the soil and obtain soil fertility; A comparison module, which is mainly used to obtain the change trend of soil fertility in each sub-area by comparing the change of soil fertility at each time node; Comprehensive module: The comprehensive module is mainly used to integrate the change trends in each sub-area to obtain the change trends of soil fertility in each planting area; A storage module, the storage module is mainly used to store contour function data of each time node and change trend data of each area; The processing module is mainly used for information transmission processing between various modules and calculation of profiles and change trends.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for digital analysis of farmland soil data as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, a method for digital analysis of farmland soil data according to any one of claims 1 to 7 is implemented.

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