Soil nutrient evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a soil nutrient evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: determining nutrient data corresponding to a plurality of sampling points in a soil monitoring area according to a remote sensing image corresponding to the soil monitoring area; for each nutrient data, determining a soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and the fuzzy membership degree corresponding to the nutrient data; and determining a soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation indexes corresponding to the plurality of sampling points. According to the method, through entropy weights and fuzzy membership degrees of nutrient data corresponding to a plurality of sampling points, the problems of fuzziness and randomness in soil nutrient evaluation are solved, the accuracy and reliability of a soil nutrient evaluation result are improved, in addition, the whole process is not limited to a specific region or a specific type of forest, and the accuracy and reliability of the soil nutrient evaluation result are improved. And the compatibility and feasibility of the soil nutrient evaluation process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a soil nutrient evaluation method, device, electronic equipment and storage medium. Background Art

[0002] Soil nutrient is one of the important indicators for evaluating soil quality and agricultural production potential. However, in the process of comprehensive evaluation of soil nutrient, due to the complexity of the soil itself and the many influencing factors involved in the evaluation process, the entire evaluation process is full of ambiguity and randomness.

[0003] The traditional soil nutrient evaluation method is based on the virtual cloud theory, using a cloud model combined with principal component analysis, using basic soil fertility indicators to construct a comprehensive cloud model of forest soil fertility, and conducting an intelligent comprehensive evaluation of forest soil fertility based on the evaluation set established by the cloud model. The entire process is often difficult to accurately and comprehensively reflect the true state of soil nutrients, resulting in the final soil nutrient evaluation results being inaccurate, and the entire process is only applicable to specific areas or specific types of forests, and has certain limitations for other ecosystems and geographical environments. Summary of the invention

[0004] The present invention provides a soil nutrient evaluation method, device, electronic device and storage medium to solve the problem that the prior art is difficult to accurately and comprehensively reflect the true state of soil nutrients, resulting in the final soil nutrient evaluation result being inaccurate, and only applicable to specific areas or specific types of forests, and having certain limitations for other ecosystems and geographical environments. The entropy weight and fuzzy membership of nutrient data corresponding to multiple sampling points are realized to solve the fuzziness and randomness problems in soil nutrient evaluation, and improve the accuracy and reliability of soil nutrient evaluation results. In addition, the entire process is not limited to specific areas or specific types of forests, and the compatibility and feasibility of the soil nutrient evaluation process are improved.

[0005] The invention provides a soil nutrient evaluation method, comprising the following steps.

[0006] According to the remote sensing image corresponding to the soil monitoring area, the nutrient data corresponding to each of the plurality of sampling points in the soil monitoring area are determined.

[0007] For each nutrient data: determine the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data.

[0008] The soil nutrient evaluation result of the soil monitoring area is determined according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

[0009] According to a soil nutrient evaluation method provided by the present invention, the soil comprehensive evaluation index of the corresponding sampling point is determined according to the entropy weight and fuzzy membership corresponding to the nutrient data, including: classifying the nutrient data to obtain a first-level result; determining a first characteristic parameter according to an evaluation range of the first-level result, and determining a preset constant as a second characteristic parameter; inputting the first characteristic parameter and the second characteristic parameter into a forward cloud generator to obtain the fuzzy membership output by the forward cloud generator; and determining the soil comprehensive evaluation index according to the fuzzy membership and the entropy weight.

[0010] According to a soil nutrient evaluation method provided by the present invention, the soil comprehensive evaluation index of the corresponding sampling point is determined according to the entropy weight and fuzzy membership corresponding to the nutrient data, including: fuzzy conversion is performed on the entropy weight and fuzzy membership corresponding to the nutrient data to obtain fuzziness; and the soil comprehensive evaluation index is determined according to the grade standard vector and the fuzziness.

[0011] According to a soil nutrient evaluation method provided by the present invention, the entropy weight and fuzzy membership corresponding to the nutrient data are fuzzily converted to obtain the fuzziness; and the soil comprehensive evaluation index is determined according to the grade standard vector and the fuzziness, including: determining the first product result of the entropy weight and the fuzzy membership as the fuzziness; determining the second product result of the fuzziness and the grade standard vector, and determining the sum result of all elements in the fuzziness; determining the soil comprehensive evaluation index according to the second product result and the sum result.

[0012] According to a soil nutrient evaluation method provided by the present invention, the first characteristic parameter includes: an expected value and a target entropy value; determining the first characteristic parameter according to the evaluation range of the first level result includes: determining a level boundary minimum value and a level boundary maximum value according to the evaluation range of the first level result; determining the expected value and the target entropy value according to the level boundary minimum value and the level boundary maximum value.

[0013] According to a soil nutrient evaluation method provided by the present invention, the soil comprehensive evaluation index of the corresponding sampling point is determined according to the entropy weight and fuzzy membership corresponding to the nutrient data, including: normalizing the nutrient data to obtain target nutrient data; determining the entropy weight according to the entropy value of the target nutrient data; and determining the soil comprehensive evaluation index according to the entropy weight and the fuzzy membership.

[0014] According to a soil nutrient evaluation method provided by the present invention, the soil nutrient evaluation result of the soil monitoring area is determined according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points, including: for each soil comprehensive evaluation index, the soil comprehensive evaluation index is graded to obtain a second-level result; all the second-level results are spatialized to obtain a soil nutrient grade distribution map, and the soil nutrient grade distribution map is determined as the soil nutrient evaluation result of the soil monitoring area.

[0015] The present invention also provides a soil nutrient evaluation device, comprising the following modules.

[0016] The data processing module is used to determine the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: determine the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data.

[0017] The nutrient evaluation module is used to determine the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

[0018] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the soil nutrient evaluation methods described above when executing the computer program.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the soil nutrient evaluation method as described in any one of the above is implemented.

[0020] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the soil nutrient evaluation method as described above is implemented.

[0021] The soil nutrient evaluation method, device, electronic device and storage medium provided by the present invention determine the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: determine the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data; determine the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points. This method aims to solve the fuzziness and randomness problems in soil nutrient evaluation through the entropy weight and fuzzy membership of the nutrient data corresponding to each of the multiple sampling points, improve the accuracy and reliability of the soil nutrient evaluation results, and in addition, the whole process is not limited to a specific area or a specific type of forest, which improves the compatibility and feasibility of the soil nutrient evaluation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of the process of the soil nutrient evaluation method provided by the present invention.

[0024] Figure 2a It is a schematic diagram of the forward cloud generator provided by the present invention.

[0025] Figure 2b It is a schematic diagram of a cloud map of target nutrient data provided by the present invention.

[0026] Figure 2c It is a schematic diagram of the soil nutrient grade distribution map provided by the present invention.

[0027] Figure 3 It is a structural schematic diagram of the soil nutrient evaluation device provided by the present invention.

[0028] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be noted that the execution subject involved in the embodiment of the present invention may be a soil nutrient evaluation device or an electronic device. Optionally, the electronic device may include: a computer, a mobile terminal, a wearable device, etc.

[0031] The embodiment of the present invention is further described below by taking an electronic device as an example.

[0032] Figure 1 It is a schematic diagram of the process of the soil nutrient evaluation method provided by the present invention, such as Figure 1 As shown, the method includes the following steps 101 to 103.

[0033] Step 101: Determine nutrient data corresponding to each of a plurality of sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area.

[0034] Among them, the soil monitoring area refers to a specific geographical area used for detailed monitoring and analysis of soil nutrient characteristics.

[0035] Remote sensing images refer to image data obtained by observing the soil monitoring area from a long distance through remote sensing technology (such as sensors carried by satellites and aircraft).

[0036] Sampling points refer to specific locations selected within the soil monitoring area for collecting soil samples for nutrient data analysis.

[0037] Nutrient data refers to the data on soil nutrient content obtained by analyzing soil samples. Optionally, nutrient data may include multiple sub-nutrient data, which may include: soil organic carbon (SOC), total nitrogen (TN), total potassium (TK) and total phosphorus (TP).

[0038] After determining the soil monitoring area, the electronic equipment can obtain the remote sensing image corresponding to the soil monitoring area. At the same time, the soil monitoring area is sampled to obtain multiple sampling points. Then, based on the remote sensing image, each sampling point is analyzed to obtain the corresponding nutrient data, providing data support for the subsequent comprehensive analysis of soil nutrient status.

[0039] It should be noted that the m sampling points mentioned above can be used as m evaluation units, m≥2, and the nutrient data corresponding to each of the n sampling points can be used as n nutrient evaluation indicators, n≥2, where each nutrient evaluation indicator can include 4 sub-nutrient evaluation indicators, namely organic matter (i.e. soil organic carbon), total nitrogen, total potassium and total phosphorus. Based on this, the m evaluation units and the 4n sub-nutrient evaluation indicators can construct an initial soil nutrient evaluation matrix.

[0040] Optionally, the electronic device determines the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area based on the remote sensing image corresponding to the soil monitoring area, which may include: the electronic device inputs the remote sensing image corresponding to the soil monitoring area into a soil nutrient inversion model to obtain the nutrient data corresponding to each of the multiple sampling points output by the soil nutrient inversion model.

[0041] Among them, the soil nutrient inversion model is a mathematical model or calculation model that uses remote sensing technology, especially hyperspectral remote sensing data, to infer or estimate the soil nutrient content. The soil nutrient inversion model is trained based on remote sensing image samples corresponding to soil monitoring area samples and nutrient data samples.

[0042] After the electronic device determines the remote sensing image corresponding to the soil monitoring area, it can input the remote sensing image as input data into the soil nutrient inversion model, identify the remote sensing image through the soil nutrient inversion model, and obtain the nutrient data corresponding to multiple sampling points. The whole process can improve the efficiency of soil nutrient monitoring while being able to perform nutrient monitoring without direct contact with soil samples, avoiding the damage to soil structure caused by traditional soil sampling methods, which is conducive to protecting the soil ecological environment.

[0043] Step 102: for each nutrient data: determine the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership degree corresponding to the nutrient data.

[0044] Among them, entropy weight is a weighting method. The entropy value of the nutrient data is calculated according to the impact of the change of nutrient data on the whole, and then the corresponding weight is determined. The weight is mainly determined by analyzing the amount of information carried by the nutrient data, which can effectively avoid the uncertainty that may be introduced by human factors in the traditional subjective weighting method.

[0045] Fuzzy membership can be used to describe the relationship between the nutrient data of the sampling point and the set nutrient standard (i.e., the grade standard vector described below), and can more comprehensively reflect the actual situation of soil nutrients at the sampling point.

[0046] The soil comprehensive evaluation index is a comprehensive indicator used to quantitatively evaluate soil nutrient status.

[0047] For each nutrient data, the electronic device can first determine the entropy weight and fuzzy membership corresponding to the nutrient data, and then calculate the entropy weight and fuzzy membership to obtain the soil comprehensive evaluation index of the corresponding sampling point. Among them, the introduction of entropy weight makes the determination of weight more objective and avoids the deviation caused by subjective factors; while the introduction of fuzzy membership takes into account the fuzziness and uncertainty of the corresponding nutrient data, making the evaluation result (i.e., soil comprehensive evaluation index) closer to the actual situation, and thus can comprehensively and accurately reflect the overall quality of the soil at the corresponding sampling point.

[0048] Based on this, the electronic device can obtain as many comprehensive soil evaluation indexes as there are nutrient data.

[0049] It should be noted that, since each nutrient data includes multiple sub-nutrient data, the number of entropy weights, fuzzy memberships, and the number of soil comprehensive evaluation indexes corresponding to each nutrient data are also multiple, and are the same as the number of sub-nutrient data.

[0050] In some embodiments, the electronic device determines the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data, which may include: at least one of implementation method 1, implementation method 2 and implementation method 3.

[0051] Implementation method 1: The electronic device normalizes the nutrient data to obtain target nutrient data; the electronic device determines the entropy weight according to the entropy value of the target nutrient data; the electronic device determines the soil comprehensive evaluation index according to the entropy weight and the fuzzy membership.

[0052] Among them, entropy is a key indicator to measure the disorder or uncertainty of information. If the entropy value of the target nutrient data is large, it means that the data variation range of the target nutrient data is large, the degree of information disorder is high, and the uncertainty is large. Therefore, the weight of the target nutrient data will be relatively small. On the contrary, if the entropy value of the target nutrient data is small, it means that the data of the target nutrient data is relatively stable, the amount of information is large, and the impact on the overall evaluation is also large. Therefore, the weight of the target nutrient data will be relatively large.

[0053] After obtaining the initial soil nutrient evaluation matrix, the electronic device can normalize the initial soil nutrient evaluation matrix to obtain the target soil nutrient evaluation matrix; then determine the entropy value of each target element in the target soil nutrient evaluation matrix, and then determine the entropy weight of each target element. Based on this, the entire process also realizes the normalization of the nutrient data, and determines the entropy weight of the target nutrient data based on the entropy value of the normalized nutrient data, that is, the entropy value of the target nutrient data. Then, the electronic device determines the soil comprehensive evaluation index of the corresponding sampling point based on the entropy weight and the fuzzy membership corresponding to the nutrient data.

[0054] Optionally, the target soil nutrient evaluation matrix can be normalized using the formula: X=(x ij ) m×4n Calculated.

[0055] Where X represents the target soil nutrient evaluation matrix; x ij It represents the element in the i-th row and j-th column in the initial soil nutrient evaluation matrix, that is, the sampling value of the j-th sub-nutrient evaluation index of the i-th evaluation unit.

[0056] Optionally, the entropy value of the target element in the i-th row and j-th column in the target soil nutrient evaluation matrix can be calculated using the first calculation formula: Calculated.

[0057] Among them, H j Represents the entropy value of the target element in the i-th row and j-th column; , represents the initial weight.

[0058] Optionally, the entropy weight of the target element in the i-th row and j-th column may be calculated using the second calculation formula: Calculated.

[0059] Among them, W j Represents the entropy weight of the target element in the i-th row and j-th column.

[0060] Implementation method 2: The electronic device classifies the nutrient data to obtain a first-level result; the electronic device determines a first characteristic parameter according to the evaluation range of the first-level result, and determines a preset constant as a second characteristic parameter; the electronic device inputs the first characteristic parameter and the second characteristic parameter into a forward cloud generator to obtain a fuzzy membership output by the forward cloud generator; the electronic device determines a comprehensive soil evaluation index according to the fuzzy membership and entropy weight.

[0061] Among them, the forward cloud generator is a fuzzy cloud model (Cloud Fuzzy Model) used to determine the fuzzy membership.

[0062] Optionally, the first characteristic parameter may include: expected value E x and the target entropy value E n .

[0063] Optionally, the second characteristic parameter is the super entropy value H e .

[0064] The electronic device can classify the nutrient data according to the nutrient index levels shown in Table 1, obtain the first-level result, and determine the evaluation range of the first-level result (B min , B max ), where B min Indicates the minimum value of the level boundary, B max Represents the maximum value of the level boundary; then, the evaluation range is calculated to obtain the first characteristic parameter, and at the same time, a preset constant is obtained and the preset constant is directly determined as the second characteristic parameter; then, the first characteristic parameter and the second characteristic parameter are used as cloud model characteristic parameters and input into the forward cloud generator (such as Figure 2a ), the cloud model characteristic parameters are calculated by the forward cloud generator to obtain a cloud map of target nutrient data (as shown in Figure 2b As shown in the figure, the cloud map is then analyzed to obtain the fuzzy membership degree; finally, the soil comprehensive evaluation index of the corresponding sampling point is determined based on the fuzzy membership degree and the entropy weight.

[0065] For example, Table 1:

[0066] As can be seen from Table 1, the nutrient index levels can be divided into six levels, namely level 1, level 2, level 3, level 4, level 5 and level 6, and the corresponding scores are 6, 5, 4, 3, 2 and 1 respectively.

[0067] It should be noted that Figure 2b The cloud map shown includes four sub-cloud maps, namely (a) soil organic carbon SOC sub-cloud map, (b) total nitrogen TN sub-cloud map, (c) total potassium TK sub-cloud map and (d) total phosphorus TP sub-cloud map. The fuzzy membership degree corresponding to each sub-cloud map is the maximum value of the evaluation range corresponding to the first-level result of the corresponding nutrient evaluation index.

[0068] Optionally, the super entropy value can adopt the first formula: H e =k is calculated.

[0069] Wherein, k represents a preset constant obtained through experience.

[0070] In some embodiments, the electronic device determines the first characteristic parameter based on the evaluation scope of the first level results, which may include: the electronic device determines the minimum level boundary and the maximum level boundary based on the evaluation scope of the first level results; the electronic device determines the expected value and the target entropy value based on the minimum level boundary and the maximum level boundary.

[0071] After determining the evaluation range of the first-level results, the electronic device can determine the minimum level boundary and the maximum level boundary corresponding to the evaluation range; then, the electronic device calculates these two maximum values ​​to obtain the expected value and the target entropy value, so as to prepare for the subsequent determination of the fuzzy membership with higher accuracy.

[0072] Optionally, the expected value can be calculated using the second formula: x =(B min +B max ) / a is calculated.

[0073] Among them, B min Indicates the minimum value of the grade boundary; B max Represents the maximum value of the level boundary; a represents the first constant, which is usually 2.

[0074] Optionally, the target entropy value can be calculated using the third formula: E n =(B min -B max ) / b is calculated.

[0075] Wherein, b represents the second constant, which is usually taken as 2.335.

[0076] It should be noted that if the evaluation scope of the first-level results only involves one level boundary, it is necessary to first clarify the E without numerical boundary. x Then, calculate according to the above first and third formulas respectively. When the target nutrient data is greater than the E corresponding to the highest level Yi x , 1 is taken as the degree of certainty.

[0077] In addition, when implementation method 1 and implementation method 2 are combined, the precise conversion of qualitative concepts and evaluation indicators can be achieved by combining cloud theory (i.e., using a forward cloud generator) and fuzzy mathematics principles (i.e., determining the fuzziness) to obtain a soil comprehensive evaluation index with higher accuracy and reliability, which is closer to the actual situation.

[0078] Implementation method 3: The electronic device performs fuzzy conversion on the entropy weight and fuzzy membership corresponding to the nutrient data to obtain the fuzziness; and determines the soil comprehensive evaluation index according to the grade standard vector and the fuzziness.

[0079] Among them, fuzziness is a comprehensive evaluation value used to reflect the fuzziness of soil nutrient data.

[0080] The grade standard vector is a pre-set standard used to convert fuzziness into a specific evaluation grade. The grade standard vector is usually a vector containing multiple nutrient index grades, each nutrient index grade corresponds to an evaluation range, which is used to represent different levels or states of soil nutrients.

[0081] After determining the entropy weight and fuzzy membership corresponding to the nutrient data, the electronic device can calculate the entropy weight and fuzzy membership to obtain the fuzziness, and then determine the soil comprehensive evaluation index in combination with the grade standard vector.

[0082] In some embodiments, the electronic device performs fuzzy conversion on the entropy weight and fuzzy membership corresponding to the nutrient data to obtain the fuzziness; and determines the soil comprehensive evaluation index based on the grade standard vector and the fuzziness, which may include: the electronic device determines the first product result of the entropy weight and the fuzzy membership as the fuzziness; the electronic device determines the second product result of the fuzziness and the grade standard vector, and determines the sum result of all elements in the fuzziness; the electronic device determines the soil comprehensive evaluation index based on the second product result and the sum result.

[0083] Optionally, the ambiguity can be calculated using the first objective formula: B=W×R.

[0084] Among them, B represents a fuzzy matrix with 1 row and 6 columns, the rows represent nutrient evaluation indicators, and the columns represent nutrient indicator levels (such as the 6 levels shown in Table 1); W represents a 1-row and 4-column matrix composed of the entropy weights of each sub-nutrient data in the corresponding sampling point, and the columns represent sub-nutrient evaluation indicators (i.e. soil organic carbon, total nitrogen, total potassium and total phosphorus); R represents a fuzzy membership matrix with 4 rows and 6 columns, the rows represent nutrient evaluation indicators, and the columns represent nutrient indicator levels.

[0085] Optionally, the soil comprehensive evaluation index is the soil nutrient fuzzy cloud comprehensive index (CFCI), and the second objective formula can be used: Calculated.

[0086] Among them, A represents a 6-row 1-column matrix composed of grade standard vectors, and the rows represent the vectors corresponding to the nutrient index grades. In addition, A also represents the grade score, which is equivalent to the weight.

[0087] Based on the above process, the electronic device can determine the CFCI corresponding to each sampling point, realizing the accurate quantification and graded evaluation of soil nutrient status.

[0088] Optionally, after step 102, the method may further include: the electronic device importing all CFCIs into a Geographical Information System (GIS) database.

[0089] Specifically, the electronic device saves all CFCIs in a Shapefile file in a GIS database according to the latitude and longitude of the soil monitoring area, or saves all CFCIs in a tiff file in a GIS database according to the pixels of the sampling points.

[0090] Shapefile is a commonly used vector data format in GIS databases, which can store the spatial location and attribute information of geographic elements. Through Shapefile, researchers can easily perform spatial query, analysis, visualization and other operations on GIS.

[0091] tiff file is a flexible image file format that supports multiple color modes, compression methods and tag information. In GIS databases, tiff files are often used to store raster data, that is, matrix data composed of pixel values. Through tiff files, researchers can conduct more detailed analysis and visualization of the spatial distribution of soil nutrients.

[0092] The entire process can integrate soil monitoring data from different sources and formats through the GIS database, realize unified data management and sharing, help avoid data islands, improve data utilization efficiency, and provide strong support for subsequent soil management, agricultural production, environmental protection, etc. In addition, the introduction of CFCI not only provides a reliable quantitative indicator for soil nutrient management, but also makes it easier for researchers to intuitively understand the overall status and grade distribution of soil nutrients, so as to formulate more scientific and reasonable soil improvement and fertilization strategies.

[0093] Step 103: Determine the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

[0094] In some embodiments, the electronic device determines the soil nutrient evaluation result of the soil monitoring area based on the soil comprehensive evaluation index corresponding to each of the multiple sampling points, which may include: for each soil comprehensive evaluation index, the electronic device classifies the soil comprehensive evaluation index to obtain a second-level result; the electronic device spatializes all second-level results to obtain a soil nutrient level distribution map, and determines the soil nutrient level distribution map as the soil nutrient evaluation result of the soil monitoring area.

[0095] For each soil comprehensive evaluation index, the electronic equipment uses the box method to divide the soil comprehensive evaluation index into 5 levels to obtain the second-level results. Among them, the 5 levels are as follows: the minimum value of 5 indicates that the soil nutrients are very lacking, the minimum value of 4 indicates that the soil nutrients are lacking, the minimum value of 3 indicates that the soil nutrients are medium, the maximum value of 2 indicates that the soil nutrients are rich, and the maximum value of 1 indicates that the soil nutrients are very rich. Then, all the second-level results are spatialized to obtain the soil nutrient level distribution map (such as Figure 2c The whole process can intuitively display the soil nutrient distribution and evaluate the membership level, providing strong support for soil nutrient management. At the same time, it also provides scientific guidance for agricultural production, improves the accuracy and reliability of soil nutrient evaluation, and promotes sustainable agricultural development.

[0096] In an embodiment of the present invention, the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area are determined according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: the soil comprehensive evaluation index of the corresponding sampling point is determined according to the entropy weight and fuzzy membership corresponding to the nutrient data; and the soil nutrient evaluation result of the soil monitoring area is determined according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points. This method aims to solve the fuzziness and randomness problems in soil nutrient evaluation through the entropy weight and fuzzy membership of the nutrient data corresponding to each of the multiple sampling points, and improves the accuracy and reliability of the soil nutrient evaluation results. In addition, the whole process is not limited to a specific area or a specific type of forest, which improves the compatibility and feasibility of the soil nutrient evaluation process.

[0097] The soil nutrient evaluation device provided by the present invention is described below. The soil nutrient evaluation device described below and the soil nutrient evaluation method described above can be referred to each other.

[0098] Figure 3 is a schematic diagram of the structure of the soil nutrient evaluation device provided by the present invention, such as Figure 3 As shown, the device includes the following data processing module 301 and nutrient evaluation module 302.

[0099] The data processing module 301 is used to determine the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: determine the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data.

[0100] The nutrient evaluation module 302 is used to determine the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

[0101] Optionally, the data processing module 301 is specifically used to classify the nutrient data to obtain a first-level result; determine a first characteristic parameter based on the evaluation range of the first-level result, and determine a preset constant as a second characteristic parameter; input the first characteristic parameter and the second characteristic parameter into a forward cloud generator to obtain the fuzzy membership output by the forward cloud generator; and determine the soil comprehensive evaluation index based on the fuzzy membership and the entropy weight.

[0102] Optionally, the data processing module 301 is specifically used to perform fuzzy conversion on the entropy weight and fuzzy membership corresponding to the nutrient data to obtain fuzziness; and determine the soil comprehensive evaluation index according to the grade standard vector and the fuzziness.

[0103] Optionally, the data processing module 301 is specifically used to determine the first product result of the entropy weight and the fuzzy membership as the fuzziness; determine the second product result of the fuzziness and the grade standard vector, and determine the sum result of all elements in the fuzziness; determine the soil comprehensive evaluation index based on the second product result and the sum result.

[0104] Optionally, the first characteristic parameter includes: an expected value and a target entropy value; a data processing module 301, specifically used to determine the minimum level boundary value and the maximum level boundary value according to the evaluation range of the first level result; and determine the expected value and the target entropy value according to the minimum level boundary value and the maximum level boundary value.

[0105] Optionally, the data processing module 301 is specifically used to normalize the nutrient data to obtain target nutrient data; determine the entropy weight according to the entropy value of the target nutrient data; and determine the soil comprehensive evaluation index according to the entropy weight and the fuzzy membership.

[0106] Optionally, the nutrient evaluation module 302 is specifically used to classify each soil comprehensive evaluation index to obtain a second-level result; spatialize all second-level results to obtain a soil nutrient grade distribution map, and determine the soil nutrient grade distribution map as the soil nutrient evaluation result of the soil monitoring area.

[0107] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the soil nutrient evaluation method, which includes: determining the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data; and determining the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

[0108] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0109] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the soil nutrient evaluation method provided by the above-mentioned methods, and the method includes: determining the nutrient data corresponding to each of the multiple sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data; determining the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

[0110] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the soil nutrient evaluation method provided by the above-mentioned methods, the method comprising: determining the nutrient data corresponding to each of the plurality of sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; for each nutrient data: determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data; determining the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the plurality of sampling points.

[0111] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A soil nutrient evaluation method, characterized in that: include: Determining nutrient data corresponding to each of a plurality of sampling points in the soil monitoring area according to a remote sensing image corresponding to the soil monitoring area; For each nutrient data: determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership degree corresponding to the nutrient data; Determining a soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation indexes corresponding to the plurality of sampling points; Determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data includes: Classifying the nutrient data to obtain a first-level result; Determine a first characteristic parameter according to the evaluation range of the first level result, and determine a preset constant as a second characteristic parameter; Inputting the first characteristic parameter and the second characteristic parameter into a forward cloud generator to obtain the fuzzy membership output by the forward cloud generator; The soil comprehensive evaluation index is determined according to the fuzzy membership degree and the entropy weight.

2. The soil nutrient evaluation method according to claim 1, characterized in that: Determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data includes: The entropy weight and fuzzy membership corresponding to the nutrient data are fuzzily transformed to obtain fuzziness; and the soil comprehensive evaluation index is determined according to the grade standard vector and the fuzziness.

3. The soil nutrient evaluation method according to claim 2, characterized in that: The entropy weight and fuzzy membership corresponding to the nutrient data are fuzzily converted to obtain fuzziness; And according to the grade standard vector and the fuzziness, the soil comprehensive evaluation index is determined, including: Determine a first product result of the entropy weight and the fuzzy membership as the fuzziness; determining a second product result of the ambiguity and the level standard vector, and determining a summation result of all elements in the ambiguity; The soil comprehensive evaluation index is determined according to the second multiplication result and the summation result.

4. The soil nutrient evaluation method according to claim 1, characterized in that: The first characteristic parameter includes: an expected value and a target entropy value; the first characteristic parameter is determined according to the evaluation range of the first level result, including: Determining a minimum level boundary value and a maximum level boundary value according to the evaluation range of the first level result; The expected value and the target entropy value are determined according to the level boundary minimum value and the level boundary maximum value.

5. The soil nutrient evaluation method according to claim 1, characterized in that: Determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership corresponding to the nutrient data includes: Normalizing the nutrient data to obtain target nutrient data; Determining an entropy weight according to the entropy value of the target nutrient data; The soil comprehensive evaluation index is determined according to the entropy weight and the fuzzy membership degree.

6. The soil nutrient evaluation method according to claim 1, characterized in that: Determining the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation indexes corresponding to the plurality of sampling points includes: For each soil comprehensive evaluation index, the soil comprehensive evaluation index is graded to obtain a second grade result; All second-level results are spatialized to obtain a soil nutrient level distribution map, and the soil nutrient level distribution map is determined as the soil nutrient evaluation result of the soil monitoring area.

7. A device for implementing the soil nutrient evaluation method according to any one of claims 1 to 6, characterized in that: include: A data processing module, used to determine the nutrient data corresponding to each of the plurality of sampling points in the soil monitoring area according to the remote sensing image corresponding to the soil monitoring area; For each nutrient data: determining the soil comprehensive evaluation index of the corresponding sampling point according to the entropy weight and fuzzy membership degree corresponding to the nutrient data; The nutrient evaluation module is used to determine the soil nutrient evaluation result of the soil monitoring area according to the soil comprehensive evaluation index corresponding to each of the multiple sampling points.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the soil nutrient evaluation method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the soil nutrient evaluation method according to any one of claims 1 to 6 is implemented.

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