Forestry disease and insect pest distribution area determination method

Abstract

The invention belongs to the field of forestry insect diseases, relates to a data analysis technology, and particularly relates to a forestry insect disease distribution area determination method, which is used for solving the problem of non-ideal insect disease prevention and control effect caused by adopting a single division mode to divide insect disease areas in the prior art. A remote sensing image of a forestry coverage area is obtained through an unmanned aerial vehicle remote sensing technology, and whether the pest state of the forestry coverage area meets the requirement is judged through the remote sensing image. When the requirements are not met, carrying out regional division processing on the forestry coverage region by adopting division modes of grid division, annular division and random division in sequence to obtain grid division regions, grid division values, annular division regions, annular division values, random division regions and random division values; according to the method, a plurality of division modes are adopted to perform area division processing on the forestry coverage area, the division area with the highest coverage rate on the pest and disease damage area is finally screened out, and the division accuracy of the forestry pest and disease damage area is improved.

Description

Technical Field

[0001] The invention belongs to the field of forestry pests and diseases, relates to data analysis technology, and specifically is a method for determining the distribution area of ​​forestry pests and diseases. Background Art

[0002] As an important part of the ecosystem, forestry plays an irreplaceable role in maintaining ecological balance, providing ecological services, and promoting economic development. However, the invasion of pests and diseases has always been a severe challenge facing forestry development.

[0003] There is an inseparable relationship between the selection of forestry pest and disease control measures and the results of pest and disease area division. After the pest and disease area is divided, it is necessary to analyze the distribution characteristics based on the division results, and then select appropriate pest and disease control measures based on the distribution characteristics; however, the existing technology generally adopts a single division method to divide the pest and disease area, which leads to the low accuracy of the pest and disease area division results, which in turn affects the accuracy of feature analysis and the matching degree of the selection of pest and disease control measures; ultimately leading to unsatisfactory pest and disease control effects.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a method for determining the distribution area of ​​forestry pests and diseases, which is used to solve the problem that the existing technology uses a single division method to divide the pest and disease areas, resulting in unsatisfactory pest and disease control effects;

[0006] The technical problem to be solved by the present invention is: how to provide a method for determining the distribution area of ​​forestry pests and diseases, which can use multiple division methods to divide pest and disease areas, improve the accuracy of pest and disease area division and the effect of pest and disease control.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for determining the distribution area of ​​forestry pests and diseases, comprising the following steps:

[0009] Step 1: Detect pests and diseases in forestry-covered areas: Obtain remote sensing images of forestry-covered areas through UAV remote sensing technology, and use the remote sensing images to determine whether the pest and disease status of the forestry-covered areas meets the requirements. If not, proceed to step 2;

[0010] Step 2: Use grid division, ring division and random division to divide the forestry covered area into regions to obtain grid division areas, grid division values, ring division areas, ring division values, random division areas and random division values;

[0011] Step 3: Screening and analyzing the division methods of forestry coverage areas: numerically compare the grid division values ​​obtained by square division, the ring division values ​​obtained by ring division, and the random division values ​​obtained by random division: mark the division method corresponding to the minimum value as the screening method, mark the division area obtained by the screening method as the screening area, and mark the screening area as the screening-related area or the screening-irrelevant area according to the coverage coefficient of the screening area;

[0012] Step 4: Analyze the distribution of pests and diseases in forest-covered areas and mark the distribution characteristics of pests and diseases in forest-covered areas; send the distribution characteristics of pests and diseases in forest-covered areas and all screened areas to the mobile phone terminal of the manager.

[0013] Furthermore, the specific process of determining whether the pest and disease status of the forestry covered area meets the requirements includes: enlarging the remote sensing image into a pixel grid image and performing grayscale transformation, retrieving the grayscale range of forestry pests and diseases, and comparing the grayscale value of the pixel grid in the remote sensing image with the grayscale range: marking the pixel grid whose grayscale value is within the grayscale range as a pest grid; marking the pixel grid whose grayscale value is outside the grayscale range as a normal grid; marking the ratio of the number of pest grids to the total number of pixel grids in the remote sensing image as the pest coefficient of the forestry covered area, and comparing the pest coefficient with a preset pest threshold: if the pest coefficient is less than the pest threshold, it is determined that the pest and disease status of the forestry covered area meets the requirements; if the pest coefficient is greater than or equal to the pest threshold, it is determined that the pest and disease status of the forestry covered area does not meet the requirements.

[0014] Furthermore, in step 2, the specific process of dividing the forest-covered area into regions using a grid division method includes: making a number of longitudinal tangent lines perpendicular to the bottom edge and a number of transverse tangent lines perpendicular to the side edge on the remote sensing image of the forest-covered area, dividing the remote sensing image into a number of grid areas by the longitudinal tangent lines and the transverse tangent lines, calculating the ratio of the number of insect-pest grids to the number of pixel grids in the grid area and marking it as a grid coverage coefficient of the grid area, and marking the grid area as a grid-related area or a grid-irrelevant area according to the grid coverage coefficient.

[0015] Furthermore, the process of obtaining the grid divided area includes: calculating the sum of the grid coverage coefficients of all grid unrelated areas and marking them as grid performance values, rotating all transverse and longitudinal lines 360 degrees around the center point of the forest coverage area, and updating the marking results of the unrelated areas and the values ​​of the grid performance values ​​in real time during the rotation process, and after the rotation is completed, marking the minimum value of the grid performance value during the rotation process as the grid division value; marking the grid area where the distribution status of the transverse and longitudinal lines corresponding to the grid division value is divided as the grid division area.

[0016] Furthermore, in step 2, the specific process of dividing the forest-covered area into regions by adopting the annular division method includes: on the remote sensing image of the forest-covered area, a circle is drawn with the center point of the forest-covered area as the center and r1 as the radius, and the obtained circular area is marked as the central area; then, a number of circles are drawn in the outer circle of the central area with the center point of the forest-covered area as the center, and each circle forms an annular area with the closest circle to the inner circle, and the area value of each annular area is equal to the area value of the central area; the divided central area and the annular area are marked as analysis areas, the ratio of the number of insect pest grids to pixel grids in the analysis area is calculated and marked as the analysis coverage coefficient of the analysis area, and the analysis area is marked as an analysis-related area or an analysis-irrelevant area according to the analysis coverage coefficient.

[0017] Furthermore, the process of obtaining the annular division area includes: calculating the sum of the analysis coverage coefficients of all analysis-irrelevant areas and marking it as the annular performance value; then indenting the forestry covered area: adjusting the radius r1 of the central area to r2, r2 = t1 k *r2, where t1 is the proportional coefficient, 0.95≤t1≤0.98; k is the number of indentation processing, and the annular performance value after each indentation processing is recorded; when the number of indentation processing reaches M1, the minimum value of the annular performance values ​​obtained by all indentation processing is marked as the annular division value, and the analysis area where the indentation processing state corresponding to the annular division value completes the segmentation is marked as the annular division area.

[0018] Furthermore, in step 2, the specific process of dividing the forest-covered area by random division includes: randomly dividing the remote sensing image of the forest-covered area into a number of random areas of equal area, and then calculating the ratio of the number of insect-pest grids to the number of pixel grids in the random area and marking it as the random coverage coefficient of the random area, and marking the random area as a random related area or a random unrelated area through the random coverage coefficient.

[0019] Furthermore, the process of obtaining the randomly divided area includes: marking the sum of the random coverage coefficients of all random unrelated areas as the random performance value of the random division; then randomly dividing M2 times in the same way, recording the random performance value of each random division, marking the minimum value of the random performance value as the random division value, and marking the random area obtained by random division corresponding to the random division value as the randomly divided area.

[0020] Furthermore, in step four, the specific process of marking the distribution characteristics of pests and diseases in forestry covered areas includes: obtaining the distribution coefficient FB of the forestry covered area, and comparing the distribution coefficient FB of the forestry covered area with a preset distribution threshold FBmax: if the distribution coefficient FB is less than the distribution threshold FBmax, the distribution characteristics of pests and diseases in the forestry covered area are marked as scattered; if the distribution coefficient FB is greater than or equal to the distribution threshold FBmax, the distribution characteristics of pests and diseases in the forestry covered area are marked as concentrated.

[0021] Furthermore, the process of obtaining the distribution coefficient FB of the forestry coverage area includes: forming an associated cluster from the mutually contacting screened related areas, marking the screened related areas outside the associated cluster as independent areas; marking the number of screened related areas in the associated cluster as the associated value of the associated cluster, summing and averaging the associated values ​​of all associated clusters to obtain the associated data GL, marking the number of associated clusters as cluster data JQ, and marking the number of independent areas as independent data DL; through the formula The distribution coefficient FB of the forest coverage area is obtained, where c1, c2 and c3 are all proportional coefficients, and c3>c2>c1>1.

[0022] The present invention has the following beneficial effects:

[0023] 1. Carry out pest and disease detection in forestry-covered areas, detect and evaluate the pest and disease status in forestry-covered areas based on remote sensing images, and differentiate forestry-covered areas based on the evaluation results, and divide the areas with unqualified pest and disease status into pest and disease areas;

[0024] 2. Use multiple division methods to divide forestry-covered areas. Each division method is dynamic. While using specific division logic to divide, the proportion of pest and disease coverage areas that are not included in the pest and disease areas is counted and analyzed. Finally, the division area with the highest coverage rate of pest and disease areas is selected to improve the accuracy of the division of forestry pest and disease areas;

[0025] 3. Conduct distribution analysis on the status of pests and diseases in forest-covered areas, and conduct distribution analysis on the areas divided by the most accurate division method to ensure the reliability of data for the analysis of pest and disease distribution characteristics in forest-covered areas, and then select appropriate pest and disease control measures and other processing decisions based on the results of the distribution characteristic analysis to ensure the effectiveness of pest and disease control. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only 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.

[0027] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0029] like Figure 1 As shown, a method for determining the distribution area of ​​forestry pests and diseases comprises the following steps:

[0030] Step S1: Detect pests and diseases in forestry-covered areas: obtain remote sensing images of forestry-covered areas through UAV remote sensing technology, enlarge the remote sensing images into pixel grid images and perform grayscale transformation, retrieve the grayscale range of forestry pests and diseases, and compare the grayscale value of the pixel grid in the remote sensing image with the grayscale range: mark the pixel grids with grayscale values ​​within the grayscale range as pest grids; mark the pixel grids with grayscale values ​​outside the grayscale range as normal grids; mark the ratio of the number of pest grids to the total number of pixel grids in the remote sensing image as forest pests and diseases. The pest coefficient of the forestry-covered area is compared with the preset pest threshold: if the pest coefficient is less than the pest threshold, it is determined that the pest status of the forestry-covered area meets the requirements; if the pest coefficient is greater than or equal to the pest threshold, it is determined that the pest status of the forestry-covered area does not meet the requirements, and step 2 is executed; the pest status of the forestry-covered area is detected and evaluated based on remote sensing images, and the forestry-covered area is differentially marked according to the evaluation results, and the areas with unqualified pest status are divided into pest and disease areas;

[0031] Step S2: using grid division, ring division and random division to divide the forestry covered area into regions;

[0032] Step S21: Divide the forestry covered area by grid division: make a number of longitudinal tangent lines perpendicular to the bottom edge and a number of transverse tangent lines perpendicular to the side edge on the remote sensing image of the forestry covered area, divide the remote sensing image into a number of grid areas by the longitudinal tangent lines and the transverse tangent lines, calculate the ratio of the number of insect-infested grids to the number of pixel grids in the grid area and mark it as the grid coverage coefficient of the grid area, and compare the grid coverage coefficient with a preset coverage threshold: if the grid coverage coefficient is less than the coverage threshold, mark the corresponding grid area as a grid-irrelevant area; if the grid coverage coefficient is less than the coverage threshold, mark the corresponding grid area as a grid-irrelevant area; if the grid coverage coefficient is less than the coverage threshold, mark the corresponding grid area as a grid-irrelevant area. If the number is greater than or equal to the coverage threshold, the corresponding grid area is marked as the grid-related area; the sum of the grid coverage coefficients of all grid-irrelevant areas is calculated and marked as the grid performance value, all transverse and longitudinal lines are rotated 360 degrees around the center point of the forestry coverage area, and the marking results of the irrelevant areas and the values ​​of the grid performance values ​​are updated in real time during the rotation process. After the rotation is completed, the minimum value of the grid performance value during the rotation process is marked as the grid division value; the grid area where the distribution state of the transverse and longitudinal lines corresponding to the grid division value is divided is marked as the grid division area;

[0033] Step S22: The forestry covered area is divided into regions by adopting an annular division method: on the remote sensing image of the forestry covered area, a circle is drawn with the center point of the forestry covered area as the center and r1 as the radius, and the obtained circular area is marked as the central area, and then a number of circles are drawn in the outer circle of the central area with the center point of the forestry covered area as the center, and each circle and the circle closest to the inner circle form an annular area, and the area value of each annular area is equal to the area value of the central area; the divided central area and the annular area are marked as analysis areas, the ratio of the number of insect pest grids to pixel grids in the analysis area is calculated and marked as the analysis coverage coefficient of the analysis area, and the analysis coverage coefficient is compared with the coverage threshold: if the analysis coverage coefficient is less than the coverage threshold, the corresponding analysis area is marked as an analysis irrelevant area; if the analysis coverage coefficient is greater than or equal to the coverage threshold, the corresponding analysis area is marked as an analysis relevant area; the sum of the analysis coverage coefficients of all analysis irrelevant areas is calculated and marked as the annular performance value; and then the forestry covered area is indented: the radius r1 of the central area is adjusted to r2, r2=t1 k *r2, where t1 is the proportional coefficient, 0.95≤t1≤0.98; k is the number of indentation processing, and the annular performance value after each indentation processing is recorded; when the number of indentation processing reaches M1, the minimum value of the annular performance values ​​obtained by all indentation processing is marked as the annular division value, and the analysis area where the indentation processing state corresponding to the annular division value is segmented is marked as the annular division area;

[0034] Step S23: adopting random division to divide the forestry covered area into regions: randomly divide the remote sensing image of the forestry covered area into a number of random areas of equal area, then calculate the ratio of the number of insect pest grids to the number of pixel grids in the random area and mark it as the random coverage coefficient of the random area, compare the random coverage coefficient with the coverage threshold: if the random coverage coefficient is less than the coverage threshold, then mark the corresponding random area as a random irrelevant area; if the random coverage coefficient is greater than or equal to the coverage threshold, then mark the corresponding random area as a random relevant area, and mark the sum of the random coverage coefficients of all random irrelevant areas as the random performance value of random division; then adopt the same method to randomly divide M2 ​​times, record the random performance value of each random division, mark the minimum value of the random performance value as the random division value, and mark the random area obtained by random division corresponding to the random division value as the random division area;

[0035] In step S2, each division method is a dynamic division. While using a specific division logic for division, the proportion of the area covered by pests and diseases that are not included in the pest and disease area is counted and analyzed, and finally the division area with the highest coverage rate of pest and disease areas is selected to improve the accuracy of the division of forestry pest and disease areas.

[0036] Step S3: Screening and analyzing the division methods of forestry coverage areas: numerically comparing the square division values ​​obtained by square division, the ring division values ​​obtained by ring division, and the random division values ​​obtained by random division: marking the division method corresponding to the minimum value as the screening method, marking the division area obtained by the screening method as the screening area, and marking the screening area as a screening-related area or a screening-irrelevant area according to the coverage coefficient of the screening area;

[0037] Step S4: Perform distribution analysis on the pest and disease status in forestry-covered areas: form an associated cluster from the mutually contacting screened areas, and mark the screened areas outside the associated cluster as independent areas; mark the number of screened areas in the associated cluster as the associated value of the associated cluster, sum and average the associated values ​​of all associated clusters to obtain the associated data GL, mark the number of associated clusters as cluster data JQ, and mark the number of independent areas as independent data DL; by formula Obtain the distribution coefficient FB of the forestry covered area, where c1, c2 and c3 are all proportional coefficients, and c3>c2>c1>1; compare the distribution coefficient FB of the forestry covered area with the preset distribution threshold FBmax: if the distribution coefficient FB is less than the distribution threshold FBmax, the distribution characteristics of pests and diseases in the forestry covered area are marked as dispersed; if the distribution coefficient FB is greater than or equal to the distribution threshold FBmax, the distribution characteristics of pests and diseases in the forestry covered area are marked as concentrated; send the distribution characteristics of pests and diseases in the forestry covered area and all the screened related areas to the mobile phone terminal of the manager; perform distribution analysis on the divided areas obtained by the division method with the highest division accuracy to ensure the data reliability of the distribution characteristics analysis of pests and diseases in the forestry covered area, and then select appropriate pest and disease control measures and other processing decisions according to the results of the distribution characteristics analysis to ensure the pest and disease control effect.

[0038] A method for determining the distribution area of ​​forestry pests and diseases, during operation, pest and disease detection is performed on forestry covered areas: remote sensing images of forestry covered areas are obtained by using unmanned aerial vehicle remote sensing technology, and whether the pest and disease status of the forestry covered areas meets the requirements is determined by using image processing technology. If the requirements are not met, the forestry covered areas are divided into regions by using grid division, ring division and random division in sequence, and the grid division values ​​obtained by square division, the ring division values ​​obtained by ring division and the random division values ​​obtained by random division are numerically compared to obtain screening related areas or screening irrelevant areas; distribution analysis of the pest and disease status of the forestry covered areas is performed according to the screening related objects.

[0039] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0040] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of sample data and sets a corresponding distribution coefficient for each group of sample data; substitutes the set distribution coefficient and the collected sample data into the formula, and any three formulas constitute a three-variable linear equation system. The calculated coefficients are screened and averaged, and the values ​​of c1, c2, and c3 are obtained to be 2.15, 2.83, and 3.82, respectively;

[0041] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding distribution coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the distribution coefficient is proportional to the value of the associated data.

[0042] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0043] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for determining the distribution area of ​​forestry pests and diseases, characterized in that: The following steps are involved: Step 1: Detect pests and diseases in forestry-covered areas: Obtain remote sensing images of forestry-covered areas through UAV remote sensing technology, and use the remote sensing images to determine whether the pest and disease status of the forestry-covered areas meets the requirements. If not, proceed to step 2; Step 2: Use grid division, ring division and random division to divide the forestry covered area into regions to obtain grid division areas, grid division values, ring division areas, ring division values, random division areas and random division values; Step 3: Screening and analyzing the division methods of forestry coverage areas: numerically compare the grid division values ​​obtained by square division, the ring division values ​​obtained by ring division, and the random division values ​​obtained by random division: mark the division method corresponding to the minimum value as the screening method, mark the division area obtained by the screening method as the screening area, and mark the screening area as the screening-related area or the screening-irrelevant area according to the coverage coefficient of the screening area; Step 4: Analyze the distribution of pests and diseases in forestry-covered areas and mark the distribution characteristics of pests and diseases in forestry-covered areas; The distribution characteristics of pests and diseases in forestry-covered areas and all screened areas are sent to the mobile phone terminals of managers.

2. A method for determining the distribution area of ​​forestry pests and diseases according to claim 1, characterized in that: The specific process of determining whether the pest and disease status of forestry-covered areas meets the requirements includes: enlarging the remote sensing image into a pixel grid image and performing grayscale transformation, retrieving the grayscale range of forestry pests and diseases, and comparing the grayscale value of the pixel grid in the remote sensing image with the grayscale range: marking the pixel grid whose grayscale value is within the grayscale range as a pest grid; marking the pixel grid whose grayscale value is outside the grayscale range as a normal grid; marking the ratio of the number of pest grids to the total number of pixel grids in the remote sensing image as the pest coefficient of the forestry-covered area, and comparing the pest coefficient with a preset pest threshold: if the pest coefficient is less than the pest threshold, it is determined that the pest and disease status of the forestry-covered area meets the requirements; if the pest coefficient is greater than or equal to the pest threshold, it is determined that the pest and disease status of the forestry-covered area does not meet the requirements.

3. A method for determining the distribution area of ​​forestry pests and diseases according to claim 2, characterized in that: In step 2, the specific process of dividing the forest-covered area by using the grid division method includes: making a number of longitudinal tangent lines perpendicular to the bottom edge and a number of transverse tangent lines perpendicular to the side edge on the remote sensing image of the forest-covered area, dividing the remote sensing image into a number of grid areas by the longitudinal tangent lines and the transverse tangent lines, calculating the ratio of the number of insect-pest grids to the number of pixel grids in the grid area and marking it as the grid coverage coefficient of the grid area, and marking the grid area as a grid-related area or a grid-irrelevant area by the grid coverage coefficient.

4. A method for determining the distribution area of ​​forestry pests and diseases according to claim 3, characterized in that: The process of obtaining the grid divided area includes: calculating the sum of the grid coverage coefficients of all the grid unrelated areas and marking them as the grid performance value, rotating all the transverse and longitudinal lines 360 degrees around the center point of the forest coverage area, and updating the marking results of the unrelated areas and the values ​​of the grid performance values ​​in real time during the rotation process, and after the rotation is completed, marking the minimum value of the grid performance value during the rotation process as the grid division value; marking the grid area divided by the distribution status of the transverse and longitudinal lines corresponding to the grid division value as the grid division area.

5. A method for determining the distribution area of ​​forestry pests and diseases according to claim 4, characterized in that: In step 2, the specific process of dividing the forest-covered area by annular division includes: on the remote sensing image of the forest-covered area, a circle is drawn with the center point of the forest-covered area as the center and r1 as the radius, and the obtained circular area is marked as the central area; then, a number of circles are drawn in the outer circle of the central area with the center point of the forest-covered area as the center, and each circle forms an annular area with the closest circle to the inner circle, and the area value of each annular area is equal to the area value of the central area; the divided central area and the annular area are marked as analysis areas, the ratio of the number of insect-pest grids to the number of pixel grids in the analysis area is calculated and marked as the analysis coverage coefficient of the analysis area, and the analysis area is marked as an analysis-related area or an analysis-irrelevant area according to the analysis coverage coefficient.

6. A method for determining the distribution area of ​​forestry pests and diseases according to claim 5, characterized in that: The process of obtaining the annular division area includes: calculating the sum of the analysis coverage coefficients of all analysis-irrelevant areas and marking them as the annular performance value; then indenting the forestry coverage area: adjusting the radius r1 of the central area to r2, r2 = t1 k *r2, where t1 is the proportional coefficient, 0.95≤t1≤0.98; k is the number of indentation processing, and the annular performance value after each indentation processing is recorded; when the number of indentation processing reaches M1, the minimum value of the annular performance values ​​obtained by all indentation processing is marked as the annular division value, and the analysis area where the indentation processing state corresponding to the annular division value completes the segmentation is marked as the annular division area.

7. A method for determining the distribution area of ​​forestry pests and diseases according to claim 6, characterized in that: In step 2, the specific process of dividing the forest-covered area by random division includes: randomly dividing the remote sensing image of the forest-covered area into a number of random areas of equal area, and then calculating the ratio of the number of insect-pest grids to the number of pixel grids in the random area and marking it as the random coverage coefficient of the random area, and marking the random area as a random related area or a random unrelated area through the random coverage coefficient.

8. A method for determining the distribution area of ​​forestry pests and diseases according to claim 7, characterized in that: The process of obtaining the randomly divided area includes: marking the sum of the random coverage coefficients of all random unrelated areas as the random performance value of the random division; then randomly dividing M2 times in the same way, recording the random performance value of each random division, marking the minimum value of the random performance value as the random division value, and marking the random area obtained by random division corresponding to the random division value as the random division area.

9. A method for determining the distribution area of ​​forestry pests and diseases according to claim 8, characterized in that: In step 4, the specific process of marking the distribution characteristics of pests and diseases in the forestry covered area includes: obtaining the distribution coefficient FB of the forestry covered area, and comparing the distribution coefficient FB of the forestry covered area with a preset distribution threshold FBmax: if the distribution coefficient FB is less than the distribution threshold FBmax, then marking the distribution characteristics of pests and diseases in the forestry covered area as dispersed; If the distribution coefficient FB is greater than or equal to the distribution threshold FBmax, the distribution characteristics of pests and diseases in the forestry coverage area are marked as concentrated.

10. A method for determining the distribution area of ​​forestry pests and diseases according to claim 9, characterized in that: The process of obtaining the distribution coefficient FB of the forestry coverage area includes: forming an associated cluster from the mutually contacting screened related areas, marking the screened related areas outside the associated cluster as independent areas; marking the number of screened related areas in the associated cluster as the associated value of the associated cluster, summing and averaging the associated values ​​of all associated clusters to obtain the associated data GL, marking the number of associated clusters as cluster data JQ, and marking the number of independent areas as independent data DL; through the formula The distribution coefficient FB of the forest coverage area is obtained, where c1, c2 and c3 are all proportional coefficients, and c3>c2>c1>1.

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