Zonal Survey and Assessment System for Evaluation and Protection of Cultivated Land Quality Based on the Red Line of Black Soil

Abstract

This invention relates to the field of land assessment technology, and discloses a zoning survey and assessment system for the evaluation and protection of arable land quality based on the red line of black soil. It includes an arable land area identification module to segment arable land areas from aerial images, an assessment point marking module to mark regional assessment points within the arable land areas, a regional yield prediction module to predict regional yield using an arable land yield prediction model, and an arable land quality assessment module to analyze the arable land quality grade of the arable land area and formulate strategies for improving arable land quality. This invention achieves a modular approach to the analysis and assessment of arable land quality in black soil, provides a distinguishing basis for data collection locations, and enables corresponding adjustments to data collection locations for arable land areas with different structural forms. It avoids the limitations of using a single location for data collection in arable land areas with different structural forms, achieving a dynamic correlation between data collection locations and the structural form of arable land areas.

Description

Technical Field

[0001] This invention relates to the field of land assessment technology, and more specifically, to a zoning survey and assessment system based on the evaluation and protection of the red line for the quality of cultivated land in black soil. Background Technology

[0002] Black soil, as a precious soil resource, possesses extremely high agricultural productivity. In order to understand the quality of arable land in a timely and accurate manner, it is necessary to establish a scientific evaluation system and implement regional survey operations to achieve an accurate assessment of the quality of arable land in black soil.

[0003] The patent application with publication number CN117132175A discloses a method for evaluating the quality of newly reclaimed arable land based on satellite remote sensing data sources. It uses the normalized vegetation index of satellite remote sensing in multiple phases of satellite remote sensing images within a predetermined period to enclose the "bell-shaped" area as an evaluation index. By comparing the size of the "bell-shaped" area of ​​newly reclaimed arable land plots with that of existing arable land, the quality of newly reclaimed arable land is evaluated, achieving the technical effect of comprehensively evaluating the quality of newly reclaimed arable land.

[0004] In current surveys and assessments of black soil arable land quality, the increase or decrease in arable land area is analyzed and compared, and data is collected from fixed points within the arable land area to analyze and assess the overall arable land quality. However, since arable land areas are typically large, this large-scale data collection and analysis method results in a heavy burden of data collection and calculation. Furthermore, due to inherent structural deviations between arable land areas with different structural forms, the data collected from fixed points may lack objectivity and accuracy, exhibiting certain limitations. It fails to achieve a dynamic correlation between data collection locations and the structural form of the arable land area, thus failing to accurately reflect the arable land quality and reducing the precision and reliability of black soil arable land quality assessments.

[0005] In view of this, the present invention proposes a zoning survey and assessment system based on the evaluation and protection of the red line for the quality of cultivated land in black soil to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a zoning survey and assessment system for the evaluation and protection of black soil arable land quality based on the red line, comprising:

[0007] The cultivated land area identification module is used to acquire aerial images of black soil, identify the regional boundary lines from the aerial images, segment the cultivated land areas from the aerial images using the regional boundary lines as dividing lines, and mark the regional boundaries of the cultivated land areas.

[0008] The assessment point marking module is used to establish a central line between two adjacent area boundaries, determine the positional relationship between the central line and the cultivated land area, identify the regional attributes of the cultivated land area, including regular and irregular areas, and mark regional assessment points in the cultivated land area according to the different regional attributes.

[0009] The regional yield prediction module is used to collect farmland quality data at regional assessment points and predict the regional yield of farmland areas through the farmland yield prediction model.

[0010] The arable land quality assessment module is used to perform quality red line analysis on the predicted regional yield, analyze the arable land quality level of the arable land area, and formulate arable land quality improvement strategies.

[0011] Further, the steps for dividing arable land areas are as follows:

[0012] Computer vision technology was used to identify the field ridges and paths in the aerial images, and lines were drawn along the locations of the field ridges and paths to draw the boundary lines of the area.

[0013] Using the regional boundary lines as dividing lines, the overhead image is divided into adjacent sub-regions, and the intersection of the boundary lines of two adjacent regions is recorded as the dividing point;

[0014] In the overhead image, the sub-regions whose outer edges are all regional boundaries are denoted as cultivated land regions, resulting in A cultivated land regions.

[0015] Furthermore, the methods for identifying regular and irregular regions are as follows:

[0016] The included angle formed by any two adjacent boundaries in cultivated land area A is recorded as the boundary angle. The angle of the boundary angle is measured one by one to obtain the included angle value.

[0017] When the included angle is less than 90 degrees, the boundary angle is recorded as an irregular angle;

[0018] Mark each of the A cultivated land areas one by one The midpoint of each region boundary is used to connect the midpoints of two adjacent region boundaries to generate... The connection between them;

[0019] When the connecting line is located outside the cultivated land area, the positional relationship is non-inclusive.

[0020] When the connecting line is located inside the cultivated land area, the positional relationship is one of inclusion.

[0021] When irregular angles and non-inclusive relationships coexist in a cultivated land area, the regional attribute of the cultivated land area is classified as an irregular area.

[0022] When irregular angles and non-inclusive relationships do not exist simultaneously in a cultivated land area, the regional attribute of the cultivated land area is classified as a regular region.

[0023] Furthermore, when the cultivated land area is a regular area, the steps for marking the regional assessment points are as follows:

[0024] The distance between any two adjacent midpoints in the cultivated land area was measured one by one to obtain... A first distance value, and The average distance is calculated by summing the first distance values.

[0025] Randomly select a boundary point in the cultivated land area as the starting point, draw auxiliary lines inwards from the cultivated land area, record the intersection of the auxiliary lines and the boundary of the area as the endpoint, and record the part of the auxiliary lines between the starting point and the endpoint as the extension line.

[0026] The areas on both sides of the extension line in the cultivated land area are designated as the first area and the second area, respectively. The position of the end point of the extension line is continuously adjusted until the area of ​​the first area and the second area are the same size, at which point the position of the end point of the extension line is stopped.

[0027] Draw a circle with the midpoint of the extension line as the center and half the distance from the mean as the radius;

[0028] Measure all points on the circle one by one until... The point-to-point distance values ​​of each boundary point are calculated, and the point corresponding to the minimum point-to-point distance value is recorded as the regional assessment point. One regional assessment point.

[0029] Furthermore, when the cultivated land area is irregular, the steps for marking regional assessment points are as follows:

[0030] Measure one by one The first distance value between each boundary point and its two adjacent boundary points, and the... The average of the two first distance values ​​at each boundary point is calculated. Each boundary distance value;

[0031] Pass The location of each dividing point, along a line perpendicular to... Draw auxiliary lines extending into the cultivated land area in the direction of the boundary of the area where each dividing point is located, to obtain... The perpendicular line;

[0032] One-third of the boundary distance value is recorded as the marker distance value, and in... Mark the distance on the perpendicular bisector. Each boundary point is marked with a distance value, thus obtaining... One regional assessment point.

[0033] Furthermore, the farmland quality data includes topsoil depth, soil volume, soil pH, organic matter content, total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content;

[0034] The steps for collecting the topsoil depth value are as follows:

[0035] Soil samples were taken from cultivated land areas using soil samplers. Soil samples were collected from each assessment point in the area. The soil samples were photographed and then converted to grayscale to obtain... Individual sample grayscale images;

[0036] Mark them one by one The pixel values ​​of all pixels in a sample grayscale image are used to identify target pixels, with those values ​​being less than a preset value.

[0037] Along the vertical direction of the sample grayscale image, the distance from the target pixel to the upper surface of the soil sample is measured one by one and recorded as the point-to-surface distance value. The target pixel corresponding to the maximum point-to-surface distance value is recorded as the edge pixel, and the area between the upper surface of the soil sample and the edge pixel is recorded as the topsoil layer to be identified.

[0038] Using the calibrated interval depth as a standard, soil hardness was measured one by one using an insertion-type soil hardness meter. The hardness values ​​at different depths within the topsoil to be identified are determined, and the previous depth position where the hardness value first exceeds the preset hardness threshold is recorded as the topsoil position.

[0039] Read the data one by one using a soil hardness meter. The values ​​at each tillage layer location are obtained. Individual depth values, and The depth of the tillage layer is calculated by averaging the individual depth values.

[0040] Furthermore, the steps for collecting soil volume values ​​are as follows:

[0041] Will Soil samples from each assessment site were placed in a drying device for heating and drying. The weight of the soil samples was measured periodically at preset intervals to obtain the weight values.

[0042] Until the soil sample's weight no longer changes, this static weight is recorded as the soil weight value, thus obtaining... One soil weight value;

[0043] Query the sampling volume of the soil sampler and... After comparing the soil weight value with the sampling volume of the soil sampler, the following calculations were performed. Individual capacity value;

[0044] After removing the maximum and minimum values ​​of the sub-capacity values, the remaining values ​​are... The soil capacity value is calculated by averaging the sum of the individual capacity values.

[0045] Furthermore, the steps for predicting regional output are as follows:

[0046] Multiple sets of farmland quality data and regional yield data are collected in advance. A set of farmland quality data is labeled as a set of feature vectors, and regional yield is converted into labels corresponding to the feature vectors.

[0047] Each feature vector corresponds to a label, forming a set of training data, and the labeled training data is divided into a training set and a test set;

[0048] The feature vector is used as the input of the machine learning model, and the regional yield corresponding to the cultivated land quality data is used as the output of the machine learning model. The machine learning model is trained using the training set and tested using the test set. When the mean of the prediction error of all training data in the test set is less than the preset error threshold, the cultivated land yield prediction model is obtained.

[0049] The collected farmland quality data is input into the farmland yield prediction model to predict the regional yield corresponding to the farmland quality data.

[0050] Furthermore, the quality grades of arable land include high-quality, medium-quality, and low-quality grades. The analysis steps for high-quality, medium-quality, and low-quality grades are as follows:

[0051] When the predicted regional yield is greater than or equal to the preset first yield threshold value, the cultivated land area is classified as high quality.

[0052] When the predicted regional output is less than the preset first output threshold and the predicted regional output is greater than or equal to the preset second output threshold, the cultivated land area is classified as medium quality.

[0053] When the predicted regional yield is less than the preset second yield threshold, the cultivated land area is classified as low-quality.

[0054] Furthermore, the steps for formulating strategies to improve arable land quality are as follows:

[0055] If the quality grade of cultivated land is medium or low, the cultivated land quality data will be compared with the corresponding red line value.

[0056] When the tillage depth value is greater than or less than the depth red line value, a strategy to increase or decrease the tillage depth shall be formulated.

[0057] When the soil capacity value is greater than or less than the capacity red line value, a strategy to increase or decrease the soil capacity should be formulated.

[0058] When the soil pH value is greater than or less than the pH red line value, a strategy to increase or decrease the soil pH value should be formulated.

[0059] When the organic matter content is greater than or less than the organic matter red line value, a strategy to increase or decrease the organic matter content should be formulated.

[0060] When the total nitrogen content is greater than or less than the total nitrogen red line value, a strategy to increase or decrease the total nitrogen content shall be formulated.

[0061] When the available phosphorus content is greater than or less than the available phosphorus red line value, a strategy to increase or decrease the available phosphorus content shall be formulated.

[0062] When the available potassium content is greater than or less than the red line value for available potassium, a strategy to increase or decrease the available potassium content should be formulated.

[0063] When the content of slow-release potassium is greater than or less than the red line value of slow-release potassium, a strategy to increase or decrease the slow-release potassium should be formulated.

[0064] The technical effects and advantages of this invention are as follows: A zonal survey and assessment system for evaluating and protecting arable land quality based on the red line for black soil:

[0065] (1): By identifying the regional boundary line, accurate boundary limits can be provided for the identification and determination of cultivated land areas. This allows for the orderly division and segmentation of large areas of black soil, achieving the effect of breaking down the whole into parts for the analysis and evaluation of black soil cultivated land quality. This avoids the huge burden of overall analysis and evaluation of black soil cultivated land quality. Furthermore, by effectively identifying cultivated land areas with different structural forms, a basis for distinguishing and limiting the data collection location for subsequent cultivated land quality analysis and evaluation can be provided. This enables the corresponding adjustment of the data collection location for cultivated land areas with different structural forms, avoiding the limitations of using a single location for data collection in cultivated land areas with different structural forms. This achieves the effect of dynamic correlation between the data collection location and the structural form of cultivated land areas.

[0066] (2): By using multi-dimensional data collection for analysis and evaluation of cultivated land areas and combining it with cultivated land yield prediction models for intelligent prediction, the regional yield of cultivated land areas can be predicted quickly and accurately, providing an accurate data basis for the analysis and evaluation of cultivated land quality in cultivated land areas. It can also provide targeted cultivated land quality improvement strategies for low-quality cultivated land areas, ultimately achieving the dual effect of accurate analysis and evaluation of cultivated land quality and formulation of suggestions for improving cultivated land quality, thus achieving the ultimate goal of evaluation and protection of the red line for cultivated land quality in black soil. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the modules of the zoning survey and assessment system for the evaluation and protection of black soil arable land quality red line provided in Embodiment 1 of the present invention.

[0068] Figure 2 This is a flowchart illustrating the zoning survey and assessment method for evaluating and protecting arable land quality based on the red line of black soil, as provided in Embodiment 2 of the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example 1: Please refer to Figure 1 As shown in this embodiment, the zoning survey and assessment system for the evaluation and protection of arable land quality based on the red line of black soil includes:

[0071] The cultivated land area identification module acquires aerial images of black soil, identifies the regional boundary lines from the aerial images, uses the regional boundary lines as dividing lines to segment cultivated land areas, and marks the regional boundaries of cultivated land areas.

[0072] Aerial images are images taken from a high-altitude perspective that contain large areas of black soil. They can provide an image basis for the subsequent zonal survey and assessment of the quality evaluation and protection of black soil, thereby enabling a series of evaluation and assessment processes.

[0073] Specifically, when acquiring aerial images, an aircraft equipped with a camera flies along a planned route for the black soil area that needs to be assessed and evaluated for farmland quality protection, and then captures high-definition images of the black soil from above using the camera equipment; for example, the aircraft is a drone and the camera equipment is a high-definition camera.

[0074] After obtaining aerial images of the black soil, it is necessary to identify and distinguish the large areas of black soil contained in the aerial images, and accurately identify the arable land areas used for zoning surveys and assessments for arable land quality evaluation and protection.

[0075] When determining cultivated land areas, it is necessary to first identify the area boundaries in the aerial images, and ensure that the area boundaries can provide a basis for segmenting cultivated land areas at different locations in the aerial images, so as to ensure the accuracy of cultivated land area segmentation.

[0076] The steps for dividing arable land areas are as follows:

[0077] Computer vision technology was used to identify the field ridges and paths in the aerial images, and lines were drawn along the locations of the field ridges and paths to draw the boundary lines of the area.

[0078] Using the regional boundary lines as dividing lines, the overhead image is divided into adjacent sub-regions, and the intersection of the boundary lines of two adjacent regions is recorded as the dividing point;

[0079] In the overhead image, the sub-regions whose outer edges are all regional boundaries are denoted as cultivated land regions, resulting in A cultivated land regions.

[0080] After obtaining the cultivated land areas, it is necessary to identify and mark the boundaries of each cultivated land area so that the boundaries can both define the outer edge of the cultivated land area and serve as the dividing point between two adjacent cultivated land areas.

[0081] Specifically, when marking the boundary of a region, the boundary line of the region adjacent to the cultivated land region is taken as the target boundary line, and the boundary point of the region adjacent to the cultivated land region is taken as the segment point of the target boundary line. The target boundary line is segmented by the segment point, and the segmented target boundary line is recorded as the boundary of the cultivated land region.

[0082] It should be noted that since a cultivated land area is a land shape with a closed structure, the number of boundaries of a cultivated land area is at least three, and the number of boundaries may vary depending on the specific shape of each cultivated land area.

[0083] The assessment point marking module establishes a connecting line between the midpoints of two adjacent area boundaries, determines the positional relationship between the connecting line and the cultivated land area, identifies the regional attributes of the cultivated land area, and marks regional assessment points in the cultivated land area based on the regional attributes.

[0084] After identifying the arable land areas and their boundaries, further analysis can then be conducted on each arable land area based on the actual needs for arable land quality evaluation and assessment.

[0085] Before further analysis and identification of cultivated land areas, it is necessary to first identify the regional attributes of cultivated land areas so that the regional attributes can represent the area structure and morphology of each different cultivated land area, thereby providing a basis for subsequent further analysis and evaluation.

[0086] Specifically, regional attributes include regular regions and irregular regions. Regular regions refer to cultivated land areas whose overall shape and structure are relatively regular and flat, without serious depressions, protrusions, or sharp edges. Irregular regions refer to cultivated land areas whose overall shape and structure are not relatively regular and flat, with serious depressions, protrusions, or sharp edges.

[0087] The method for identifying regular and irregular regions is as follows:

[0088] The included angle formed by any two adjacent boundaries in cultivated land area A is recorded as the boundary angle. The angle of the boundary angle is measured one by one to obtain the included angle value.

[0089] When the included angle is less than 90 degrees, it indicates that the boundaries of the two regions at the boundary angle are in a relatively sharp shape, and the boundary angle is recorded as an irregular angle.

[0090] Mark each of the A cultivated land areas one by one The midpoint of each region boundary is used to connect the midpoints of two adjacent region boundaries to generate... The connection between them;

[0091] Identify one by one The positional relationship between the connecting lines and the corresponding cultivated land areas;

[0092] When the middle line is outside the cultivated land area, the positional relationship between the middle line and the cultivated land area is non-inclusive. When the middle line is outside the cultivated land area, it means that the extension directions of the regional boundaries of the two midpoints of the middle line are quite different, and the directional consistency between the two regional boundaries is poor. Therefore, the structural form of the cultivated land area is relatively irregular.

[0093] When the connecting line is inside the cultivated land area, the positional relationship between the connecting line and the cultivated land area is one of inclusion.

[0094] When irregular angles and non-inclusive relationships coexist in a cultivated land area, it indicates that the structural form of the cultivated land area is not a regular and complete structure, and the regional attribute of the cultivated land area is then classified as an irregular area.

[0095] When irregular angles and non-inclusive relationships do not exist simultaneously in a cultivated land area, it indicates that the structural form of the cultivated land area is a regular and complete structure, and the regional attribute of the cultivated land area is a regular area.

[0096] After identifying the regional attributes of cultivated land areas, it is necessary to locate and mark the regional assessment points within the cultivated land areas based on the specific morphological structure of regular and irregular areas. This will enable the regional assessment points to serve as data collection points for subsequent zoning surveys and assessments of cultivated land quality evaluation and protection, thereby limiting the location of all subsequent collected analysis and assessment data.

[0097] The steps for marking regional assessment points are as follows:

[0098] When the cultivated land area is a regular area, the location distribution of the regional assessment points in the cultivated land area is relatively regular.

[0099] The distance between any two adjacent midpoints in the cultivated land area was measured one by one to obtain... A first distance value, and The average distance is calculated by summing the first distance values.

[0100] The expression for the distance to the mean is:

[0101] ;

[0102] In the formula, The mean distance For the first The first distance value;

[0103] Randomly select a boundary point in the cultivated land area as the starting point, draw auxiliary lines inwards from the cultivated land area, record the intersection of the auxiliary lines and the boundary of the area as the endpoint, and record the part of the auxiliary lines between the starting point and the endpoint as the extension line.

[0104] The areas on either side of the extension line in the cultivated land area are designated as the first area and the second area, respectively. The position of the endpoint of the extension line is continuously adjusted until the area of ​​the first area and the second area are the same, at which point the adjustment of the endpoint of the extension line is stopped. The area of ​​the first area and the second area can be calculated using area calculation tools in computer vision technology, or by counting the number of pixels in the first area and the second area. That is, when the number of pixels in the first area and the second area are the same, the area of ​​the first area and the second area are the same.

[0105] Draw a circle with the midpoint of the extension line as the center and half the distance from the mean as the radius;

[0106] Measure all points on the circle one by one until... The point-to-point distance values ​​of each boundary point are calculated, and the point corresponding to the minimum point-to-point distance value is recorded as the regional assessment point. The location of regional assessment points can be defined in a relatively regular position within a regular cultivated land area, thereby achieving a regular multi-point determination effect for cultivated land quality assessment within the cultivated land area.

[0107] When the cultivated land area is an irregular area, the location distribution of the regional assessment points in the cultivated land area is relatively irregular.

[0108] Measure one by one The first distance value between each boundary point and its two adjacent boundary points, and the... The average of the two first distance values ​​at each boundary point is calculated. Each boundary distance value;

[0109] Pass The location of each dividing point, along a line perpendicular to... Draw auxiliary lines extending into the cultivated land area in the direction of the boundary of the area where each dividing point is located, to obtain... The perpendicular line;

[0110] One-third of the boundary distance value is recorded as the marker distance value, and in... Mark the distance on the perpendicular bisector. Each boundary point is marked with a distance value, thus obtaining... Regional assessment points. The location of regional assessment points can be defined in a relatively regular shape within irregular cultivated land areas, ensuring that the regional assessment points can provide a relatively regular point marking effect in cultivated land areas with irregular structures.

[0111] It should be noted that when the cultivated land area is a regular area or an irregular area, the marking process and method of regional assessment points within the cultivated land area are inconsistent. Therefore, when marking regional assessment points within the cultivated land area, it is necessary to mark them according to the regional attributes of the cultivated land area.

[0112] The regional yield prediction module collects farmland quality data at regional assessment points and uses a farmland yield prediction model to predict the regional yield corresponding to the farmland quality data.

[0113] Once regional assessment points are marked within the cultivated land area, these points can be used as data collection locations within the cultivated land area to collect cultivated land quality data.

[0114] Farmland quality data is a diverse set of data that can be used for the analysis and evaluation of farmland quality in farmland areas. In addition, farmland quality data can directly affect the corresponding farmland yield in farmland areas, thus providing diversified and multi-dimensional data support for farmland quality in farmland areas.

[0115] Farmland quality data include topsoil depth, soil volume, soil pH, organic matter content, total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content.

[0116] The topsoil depth value refers to the soil depth that is provided for the growth of crop roots and stems when the cultivated land is actually cultivated at the location of the regional assessment point. When the topsoil depth value is too large or too small, it will have a negative impact on the yield of crops in the cultivated land area.

[0117] Specifically, the steps for collecting the tillage depth value are as follows:

[0118] Soil samples were taken from cultivated land areas using soil samplers. Soil samples were collected from each assessment point in the area. The soil samples were photographed and then converted to grayscale to obtain... Individual sample grayscale images;

[0119] Mark them one by one The pixel values ​​of all pixels in a sample grayscale image are used to identify target pixels. Pixels with pixel values ​​less than a preset pixel value are marked as target pixels. The preset pixel value refers to the maximum value of the pixel value marked as a target pixel. This can limit the size of the pixel value of black soil in the grayscale image, thereby achieving the preliminary identification and judgment effect of the depth of black soil at the cultivated height in the sample grayscale image.

[0120] Along the vertical direction of the sample grayscale image, the distance from the target pixel to the upper surface of the soil sample is measured one by one and recorded as the point-to-surface distance value. The target pixel corresponding to the maximum point-to-surface distance value is recorded as the edge pixel, and the area between the upper surface of the soil sample and the edge pixel is recorded as the topsoil layer to be identified.

[0121] Using the calibrated interval depth as a standard, soil hardness was measured one by one using an insertion-type soil hardness meter. The soil hardness tester measures the hardness values ​​at different depths within the topsoil layer and marks the depth at which the hardness value first exceeds a preset hardness threshold as the topsoil layer location. The calibrated interval depth limits the insertion depth of the soil hardness tester to ensure that it maintains a constant insertion depth for insertion-type hardness detection within the soil sample. For example, the calibrated interval depth is 1 cm. The preset hardness threshold refers to the minimum soil hardness value corresponding to the topsoil layer location, thereby achieving the effect of distinguishing the hardness of topsoil and non-topsoil layers. Specifically, since crop roots extend into the topsoil layer, the soil hardness within the topsoil layer is lower than that within the non-topsoil layer.

[0122] Read the data one by one using a soil hardness meter. The values ​​at each tillage layer location are obtained. Individual depth values, and The depth of the tillage layer is calculated by averaging the individual depth values.

[0123] The formula for calculating the depth of the tillage layer is:

[0124] ;

[0125] In the formula, This represents the depth of the cultivated layer. For the first Sub-depth values ​​of each regional assessment point.

[0126] Soil capacity value refers to the weight of soil per unit volume in a cultivated area when it is used for actual cultivation of crops at the location of the regional assessment point. When the soil capacity value is too large or too small, it will have a negative impact on the yield of crops in the cultivated area.

[0127] Specifically, the steps for collecting soil volume values ​​are as follows:

[0128] Will Soil samples from each assessment point in the region are placed in a drying device for heating and drying. The weight of the soil samples is measured periodically at preset intervals to obtain the weight values. The preset interval is the time interval between two consecutive weighings of soil samples to achieve a timed weighing effect for soil samples.

[0129] Until the soil sample's weight no longer changes, this static weight is recorded as the soil weight value, thus obtaining... One soil weight value;

[0130] Query the sampling volume of the soil sampler and... After comparing the soil weight value with the sampling volume of the soil sampler, the following calculations were performed. Individual capacity value;

[0131] The formula for calculating the sub-capacity value is:

[0132] ;

[0133] In the formula, For the first Sub-capacity values ​​for each regional assessment point For the first Soil weight values ​​at each regional assessment point The sampling volume of the soil sampler;

[0134] After removing the maximum and minimum values ​​of the sub-capacity values, the remaining values ​​are... The soil capacity value is calculated by averaging the sum of the individual capacity values.

[0135] The formula for calculating soil volume is:

[0136] ;

[0137] In the formula, This is the soil volume value. For the first Sub-capacity values ​​for each regional assessment point.

[0138] Soil pH value refers to the acidity or alkalinity of the soil at a designated assessment point within a cultivated area when it is actually used for crop cultivation. Both excessively high and low soil pH values ​​negatively impact crop yields within the cultivated area. Soil pH values ​​are measured individually using an electronic pH meter. The pH value of each soil sample was obtained by summing them up and averaging them.

[0139] Organic matter content refers to the amount of organic matter in the soil at the location of the regional assessment point when crops are actually grown. Both excessively high and low organic matter content can negatively impact crop yields within the cultivated area. Organic matter content is measured individually using an organic matter testing kit. The organic matter content of each soil sample was determined and then averaged.

[0140] Total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content refer to the amount of total nitrogen, available phosphorus, available potassium, and slow-release potassium in the soil at the location of the regional assessment point when crops are actually cultivated. When the total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content are too high or too low, they will have a negative impact on the yield of crops in the cultivated area.

[0141] Specifically, when collecting total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content, either laboratory standard collection methods or rapid field collection methods can be used.

[0142] When using standard laboratory sampling methods, total nitrogen content was determined by the Kjeldahl method, available phosphorus content was determined by the Olsen or Bray method, available potassium content was determined by the NH4OAc-flame photometric method, and slow-release potassium content was determined by the HNO3 extraction method.

[0143] When collecting data rapidly in the field, total nitrogen content was detected using the alkaline hydrolysis diffusion method, available phosphorus content was detected using a colorimetric card or rapid analyzer, available potassium content was detected using the sodium tetraphenylborate turbidimetric method, and there was no rapid field collection method for slow-release potassium content.

[0144] It is important to note that when collecting total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content using either standard laboratory sampling methods or rapid field sampling methods, the appropriate method should be used. Each soil sample was tested individually, and... The method of averaging the accumulated test values ​​ensures that the collected data maintains accuracy and universality.

[0145] After collecting farmland quality data, the farmland quality data needs to be imported into the farmland yield prediction model, which can then quickly predict the regional yield of crops in the farmland area corresponding to the farmland quality data.

[0146] Regional yield refers to the actual yield of crops in a cultivated area. The regional yield of the same type of crop can vary depending on the quality of cultivated land. When collecting regional yield data, it is obtained by summing up the crop weights in the cultivated area.

[0147] The arable land yield prediction model is based on a machine learning model. It is obtained by collecting a large amount of different arable land quality data and corresponding regional yields, and then repeatedly training and iterating the machine learning model with the arable land quality data and regional yields. This artificial intelligence model can then accurately predict the regional yields corresponding to the arable land quality data.

[0148] Specifically, the steps for predicting regional output are as follows:

[0149] Multiple sets of arable land quality data and corresponding regional yields are collected in advance. A set of arable land quality data is labeled as a set of feature vectors to obtain multiple sets of feature vectors. Regional yields are then converted into labels corresponding to the feature vectors.

[0150] One feature vector corresponds to one label, forming a set of training data. Multiple sets of training data constitute the training set. The labeled training data is divided into a training set and a test set. 70% of the training data is used as the training set, and 30% of the training data is used as the test set.

[0151] The feature vector is used as the input of the machine learning model, and the regional yield corresponding to the cultivated land quality data is used as the output of the machine learning model. The machine learning model is trained using the training set and tested using the test set. A preset error threshold is set. When the mean of the prediction error of all training data in the test set is less than the preset error threshold, a cultivated land yield prediction model that predicts regional yield based on cultivated land quality data is obtained.

[0152] The collected farmland quality data is input into the farmland yield prediction model to predict the regional yield corresponding to the farmland quality data.

[0153] In this embodiment, the predicted regional yield provides accurate data support for the analysis and evaluation of the actual quality of cultivated land in the cultivated land area, realizing the evaluation and prediction effect of the criteria combining cultivated land quality assessment and artificial intelligence technology.

[0154] The arable land quality assessment module performs quality red line analysis on the predicted regional yield, analyzes the arable land quality level of the arable land area, and formulates arable land quality improvement strategies.

[0155] After predicting the regional yield of cultivated land, the regional yield can be used as a basis for analyzing the quality of cultivated land in the cultivated land area, thereby conducting quality red line analysis on the predicted regional yield and analyzing the quality level of cultivated land in the cultivated land area.

[0156] Among them, the arable land quality grade is used to represent the results of the assessment and protection of the arable land quality red line in arable land areas, and to distinguish arable land areas with different arable land quality levels.

[0157] The quality grades of arable land include high quality, medium quality, and low quality; the arable land quality of the arable land areas corresponding to the high quality, medium quality, and low quality grades is from high to low.

[0158] The analysis steps for high-quality, medium-quality, and low-quality grades are as follows:

[0159] The predicted regional yield is compared with the preset first yield threshold value and the preset second yield threshold value. The preset first yield threshold value is greater than the preset second yield threshold value. The preset first yield threshold value and the preset second yield threshold value are used as critical values ​​for the regional yield corresponding to the high quality, medium quality and low quality grades of cultivated land areas, so as to effectively distinguish the cultivated land quality grades corresponding to regional yields of different sizes.

[0160] When the predicted regional output is greater than or equal to the preset first output threshold, the crop yield in the cultivated area is relatively high and the quality of the cultivated land is relatively good, thus the cultivated area is classified as a high-quality area.

[0161] When the predicted regional output is less than the preset first output threshold and the predicted regional output is greater than or equal to the preset second output threshold, the crop yield in the cultivated area is moderate and the quality of the cultivated land is average. Therefore, the cultivated area is classified as medium quality.

[0162] When the predicted regional yield is less than the preset second yield threshold, the crop yield in the cultivated area is low and the quality of the cultivated land is poor, thus the cultivated area is classified as a low-quality area.

[0163] After analyzing the arable land quality level of the arable land area, it is necessary to give reasonable and accurate arable land quality improvement strategies for arable land areas with low arable land quality, so that the arable land quality improvement strategies can serve as opinions for improving the arable land quality of arable land areas with low arable land quality, and play a positive and effective role in promoting the improvement of arable land quality in black soil areas.

[0164] When formulating strategies to improve arable land quality, it is necessary to clarify which arable land quality data for which arable land quality grade should be optimized and adjusted, and to formulate corresponding arable land quality improvement strategies based on specific arable land regions and arable land quality data.

[0165] Specifically, the steps for formulating strategies to improve arable land quality are as follows:

[0166] When the quality grade of arable land is high, there is no need to carry out arable land quality improvement operations in the arable land area, and no arable land quality improvement strategy is formulated.

[0167] When the quality grade of cultivated land is medium or low, it is necessary to carry out cultivated land quality improvement operations in the cultivated land area.

[0168] Each of the following values ​​was compared with the corresponding red line values: topsoil depth, soil volume, soil pH, organic matter content, total nitrogen content, available phosphorus content, available potassium content, and slow-release potassium content.

[0169] When the topsoil depth value is greater than or less than the depth red line value, it indicates that the topsoil depth of the cultivated land area is too deep or too shallow, and a strategy to increase or decrease the topsoil depth should be formulated. The depth red line value refers to the optimal value of the topsoil depth when the cultivated land area is of high quality.

[0170] When the soil capacity value is greater than or less than the capacity red line value, it indicates that the soil capacity of the cultivated land area is too large or too small, and a strategy to increase or decrease the soil capacity should be formulated. The capacity red line value refers to the optimal value of soil capacity when the cultivated land area is of high quality.

[0171] When the soil pH value is greater than or less than the pH red line value, it indicates that the soil acidity or alkalinity in the cultivated land area is too high or too low, and a strategy to increase or decrease the soil pH should be formulated. The pH red line value refers to the optimal soil pH value when the cultivated land area is of high quality.

[0172] When the organic matter content is greater than or less than the organic matter red line value, it indicates that the soil organic matter content in the cultivated land area is too high or too low, and strategies to increase or decrease organic matter should be formulated. The organic matter red line value refers to the optimal value of organic matter content when the cultivated land area is of high quality.

[0173] When the total nitrogen content is greater than or less than the total nitrogen red line value, it indicates that the total nitrogen content in the soil of the cultivated land area is too high or too low, and a strategy to increase or decrease the total nitrogen content should be formulated. The total nitrogen red line value refers to the optimal value of total nitrogen content when the cultivated land area is of high quality.

[0174] When the available phosphorus content is greater than or less than the available phosphorus red line value, it indicates that the available phosphorus content in the soil of the cultivated land area is too high or too low, and a strategy to increase or decrease the available phosphorus content should be formulated. The available phosphorus red line value refers to the optimal value of available phosphorus content when the cultivated land area is of high quality.

[0175] When the available potassium content is greater than or less than the available potassium red line value, it indicates that the available potassium content in the soil of the cultivated land area is too high or too low, and a strategy to increase or decrease the available potassium should be formulated. The available potassium red line value refers to the optimal value of available potassium content when the cultivated land area is of high quality.

[0176] When the content of slow-release potassium is greater than or less than the red line value, it indicates that the content of slow-release potassium in the soil of the cultivated land area is too high or too low, and a strategy to increase or decrease the slow-release potassium should be formulated. The red line value of slow-release potassium refers to the optimal value of slow-release potassium content when the cultivated land area is of high quality.

[0177] It should be noted that the formulated farmland quality improvement strategy is only intended as a guide for improving and optimizing farmland quality in areas with medium and low quality levels. It is used to provide targeted macro-level optimization suggestions for improving farmland quality, so as to ensure that subsequent farmland quality improvement operations in farmland areas remain accurate and reasonable.

[0178] For example, when a strategy to increase or decrease available potassium is formulated, and the available potassium content is greater than the red line value for available potassium, then the content of available potassium in the cultivated land area needs to be reduced, and vice versa; similarly, the logic corresponding to other cultivated land quality improvement strategies is the same.

[0179] In this embodiment, by identifying regional boundary lines, accurate boundary definitions can be provided for the identification and determination of cultivated land areas. This allows for the orderly partitioning of large areas of black soil, achieving a piecemeal effect in the analysis and assessment of black soil cultivated land quality. This avoids the enormous burden of overall analysis and assessment of black soil cultivated land quality. Furthermore, by effectively identifying cultivated land areas with different structural forms, a distinguishing basis can be provided for the data collection locations in subsequent cultivated land quality analysis and assessment. This enables corresponding adjustments to the data collection locations for cultivated land areas with different structural forms, avoiding the use of a single location for data collection in cultivated land areas with different structural forms. This approach overcomes the limitations of existing methods, thereby achieving a dynamic correlation between data collection locations and the structural morphology of cultivated land areas. Furthermore, by utilizing multi-dimensional data collection for cultivated land area analysis and evaluation, and combining it with a cultivated land yield prediction model for intelligent forecasting, it enables rapid and accurate prediction of regional yields in cultivated land areas. This provides an accurate data foundation for the analysis and evaluation of cultivated land quality and allows for targeted strategies to improve the quality of low-quality cultivated land. Ultimately, it achieves the dual goals of accurate analysis and evaluation of cultivated land quality and the formulation of suggestions for improving cultivated land quality, thus fulfilling the ultimate objective of evaluating and protecting the red line for cultivated land quality in black soil regions.

[0180] Example 2: Please refer to Figure 2 As shown, the parts not described in detail in this embodiment are described in Embodiment 1. A zonal survey and assessment method based on the evaluation and protection of black soil arable land quality red line is provided. This method is implemented through a zonal survey and assessment based on the evaluation and protection of black soil arable land quality red line, including:

[0181] S1: Obtain an aerial image of the black soil, identify the regional boundary line from the aerial image, use the regional boundary line as the dividing line to segment the cultivated land area from the aerial image, and mark the regional boundary of the cultivated land area.

[0182] S2: Establish a central line between two adjacent area boundaries, determine the positional relationship between the central line and the cultivated land area, identify the regional attributes of the cultivated land area, and mark regional assessment points in the cultivated land area according to the different regional attributes.

[0183] S3: Collect farmland quality data at regional assessment points in the farmland area, and predict the regional yield of the farmland area through the farmland yield prediction model;

[0184] S4: Conduct a quality redline analysis on the predicted regional yield, analyze the quality level of cultivated land in the cultivated land area, and formulate strategies to improve the quality of cultivated land.

[0185] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A zoning investigation and evaluation system based on the black land quality red line evaluation protection, characterized in that, The method comprises the following steps: The cultivated land area identification module is used to obtain a low-altitude image of black land, identify a region boundary in the low-altitude image, divide the cultivated land area from the low-altitude image by taking the region boundary as a division line, and mark the region boundary of the cultivated land area; The evaluation point marking module is used to establish a middle connecting line between two adjacent region boundaries, determine the position relationship between the middle connecting line and the cultivated land area, identify the region attribute of the cultivated land area, the region attribute comprising a regular region and an irregular region, and mark the region evaluation point in the cultivated land area according to the different region attributes; When the cultivated land area is a regular region, the marking step of the region evaluation point is as follows: The distances between two adjacent midpoints in the farmland region are measured one by one to obtain a first distance value, and the first distance values are accumulated and averaged to calculate a distance mean value. The distances between two adjacent midpoints in the farmland region are measured one by one to obtain a first distance value, and the first distance values are accumulated and averaged to calculate a distance mean value. The distances between two adjacent midpoints in the farmland region are measured one by one to obtain a first distance A boundary point is randomly selected in the cultivated land area as a starting point, an auxiliary line is drawn in the direction of the inside of the cultivated land area, the intersection point of the auxiliary line and the region boundary is recorded as an ending point, and the part of the auxiliary line between the starting point and the ending point is recorded as an extension line; The regions on both sides of the extension line in the cultivated land area are recorded as a first region and a second region respectively, the position of the ending point of the extension line is continuously adjusted until the areas of the first region and the second region are consistent, and then the adjustment of the position of the ending point of the extension line is stopped; The midpoint of the extension line is taken as the center of a circle, and the radius of the circle is half of the mean distance, so that a point position circle is drawn; Measure all points on the circle one by one until... The point-to-point distance values ​​of each boundary point are calculated, and the point corresponding to the minimum point-to-point distance value is recorded as the regional assessment point. One regional assessment point; When the cultivated land area is an irregular region, the marking step of the region evaluation point is as follows: measured one by one the first distance value of the individual boundary point and the two boundary points adjacent thereto, and the two first distance values of the individual boundary point are added and averaged to calculate the individual boundary distance value; Pass The location of each dividing point, along a line perpendicular to... Draw auxiliary lines extending into the cultivated land area in the direction of the boundary of the area where each dividing point is located, to obtain... The perpendicular line; A third of the demarcation distance value is recorded as a marker distance value, and a point position of a marker distance value is marked on the median line of the demarcation point, to obtain a regional evaluation point. A third of the demarcation distance value is recorded as a marker distance value, and a point position of a marker distance value is marked on the median line of the demarcation point, to obtain a regional evaluation point. The region yield prediction module is used to collect the cultivated land quality data of the region evaluation point in the cultivated land area, and predict the region yield of the cultivated land area by a cultivated land yield prediction model; The cultivated land quality evaluation module is used to analyze the quality red line of the predicted region yield, analyze the cultivated land quality grade of the cultivated land area, and develop a cultivated land quality improvement strategy.

2. The zoning survey evaluation system based on the black land quality red line evaluation protection according to claim 1, characterized in that, The division step of the cultivated land area is as follows: The ridge road in the low-altitude image is identified by computer vision technology, and a line is drawn along the position of the ridge road to draw the region boundary; The low-altitude image is divided into adjacent sub-regions by taking the region boundary as a division line, and the intersection point of two adjacent region boundaries is recorded as a boundary point; In the low-altitude image, the sub-regions with the region boundary on the outside are recorded as the cultivated land area, and A cultivated land areas are obtained. 3.The system for the evaluation and protection of the division survey based on the red line of the black land quality according to claim 2, wherein, The identification method of the regular region and the irregular region is as follows: The angle of any two adjacent region boundaries in the A cultivated land areas is recorded as a boundary angle, and the angle of the boundary angle is measured to obtain an angle value; When the angle value is less than 90 degrees, the boundary angle is recorded as an irregular angle; Labeling the midpoints of the boundaries of the A farmland regions one by one, connecting the midpoints of two adjacent boundaries to generate A mid-lines. Labeling the midpoints of the boundaries of the A farmland regions one by one, connecting the midpoints of two adjacent boundaries to generate A mid-lines. Labeling the midpoints of the boundaries of the A farmland regions one by one, connecting the midpoints of two adjacent boundaries to generate A mid-lines. When the middle connecting line is on the outside of the cultivated land area, the position relationship is a non-inclusion relationship; When the middle connecting line is on the inside of the cultivated land area, the position relationship is an inclusion relationship; When the irregular angle and the non-inclusion relationship exist in the cultivated land area at the same time, the region attribute of the cultivated land area is an irregular region; When the irregular angle and the non-inclusion relationship do not exist in the cultivated land area at the same time, the region attribute of the cultivated land area is a regular region. 4.The system for the evaluation and protection of the division survey based on the red line of the black land quality according to claim 3, wherein, The cultivated land quality data comprises a cultivated layer depth value, a soil capacity value, a soil PH value, an organic matter content, a total nitrogen content, an available phosphorus content, a quick-acting potassium content and a slow-acting potassium content; The collection step of the cultivated layer depth value is as follows: The soil samples are collected from the cultivated land area and the uncultivated land area respectively by a soil sampler. The soil samples are collected from the cultivated land area and the uncultivated land area respectively by a soil sampler. The soil samples are collected from the cultivated land area and the uncultivated land area respectively by a soil sampler. Mark them one by one The pixel values ​​of all pixels in a sample grayscale image are used to identify target pixels, with those values ​​being less than a preset value. The distance value from the target pixel point to the upper surface of the soil sample is measured along the vertical direction of the sample grayscale image, and is recorded as a point-surface distance value. The target pixel point corresponding to the maximum point-surface distance value is recorded as an edge pixel point, and the region between the upper surface of the soil sample and the edge pixel point is recorded as a to-be-identified plough layer. With the standard of the calibrated interval depth, the soil hardness values of different depth positions in the to-be-identified plough layer are detected one by one by the inserted soil hardness meter, and the last depth position whose hardness value is greater than the preset hardness threshold value for the first time is recorded as the plough layer position. With the standard of the calibrated interval depth, the soil hardness values of different depth positions in the to-be-identified plough layer are detected one by one by the inserted soil hardness meter, and the last depth position whose hardness value is greater than the preset hardness threshold value for the first time is recorded as the plough layer position. Read the data one by one using a soil hardness meter. The values ​​at each tillage layer location are obtained. Individual depth values, and The depth of the tillage layer is calculated by averaging the individual depth values. 5.The system for the evaluation of the division survey based on the red line evaluation and protection of the quality of the black land according to claim 4, wherein, The soil capacity value is collected by the following steps: Will Soil samples from each assessment site were placed in a drying device for heating and drying. The weight of the soil samples was measured periodically at preset intervals to obtain the weight values. The soil weight value is obtained by recording the weight value of the soil sample when the weight value of the soil sample no longer changes until the weight value of the soil sample no longer changes. the soil weight value. The sampling volume of the soil sampler is queried, and the soil weight value is compared with the sampling volume of the soil sampler After the soil weight value is compared with the sampling volume of the soil sampler, a sub-capacity value is calculated ​ The maximum and minimum of the sub-capacity values are removed, and the remaining sub-capacity values are averaged to calculate the soil capacity value. The maximum and minimum of the sub-capacity values are removed, and the remaining sub-capacity values are averaged to calculate the soil capacity value. 6.The system for the evaluation of the division survey based on the red line evaluation and protection of the black soil arable land quality according to claim 5, wherein, The prediction step of the regional yield is as follows: A plurality of sets of farmland quality data and regional yields of the farmland region are collected in advance. One set of farmland quality data is marked as one set of feature vectors, and the regional yield is converted into a label corresponding to the feature vector. One feature vector corresponds to one label, forming a set of training data, and the labeled training data is divided into a training set and a test set. The feature vector is used as the input of the machine learning model, and the regional yield corresponding to the farmland quality data is used as the output of the machine learning model. The training set is used to train the machine learning model, and the test set is used to test the machine learning model. When the average prediction error of all training data in the test set is less than a preset error threshold, a farmland yield prediction model is obtained. The collected farmland quality data is input into the farmland yield prediction model to predict the regional yield corresponding to the farmland quality data. 7.The system for the evaluation of the division survey based on the red line evaluation and protection of the quality of the black soil arable land according to claim 6, wherein, The farmland quality grade includes a high quality grade, a medium quality grade, and a low quality grade. The analysis steps of the high quality grade, the medium quality grade, and the low quality grade are as follows: When the predicted regional yield is greater than or equal to a preset first yield red line value, the farmland region is of the high quality grade. When the predicted regional yield is less than the preset first yield red line value and greater than or equal to a preset second yield red line value, the farmland region is of the medium quality grade. When the predicted regional yield is less than the preset second yield red line value, the farmland region is of the low quality grade. 8.The system for the evaluation of the division survey based on the red line evaluation and protection of the black soil arable land quality according to claim 7, wherein, The farmland quality improvement strategy is determined by the following steps: If the farmland quality grade is the medium quality grade or the low quality grade, the farmland quality data is compared with the corresponding red line value. When the plough layer depth value is greater than or less than the depth red line value, an increasing or decreasing plough layer depth strategy is developed. When the soil capacity value is greater than or less than the capacity red line value, an increasing or decreasing soil capacity strategy is developed. When the soil PH value is greater than or less than the PH red line value, an increasing or decreasing soil PH strategy is developed. When the organic matter content is greater than or less than the organic matter red line value, an increasing or decreasing organic matter strategy is developed. When the total nitrogen content is greater than or less than the total nitrogen red line value, an increasing or decreasing total nitrogen strategy is developed. When the effective phosphorus content is greater than or less than the effective phosphorus red line value, an increasing or decreasing effective phosphorus strategy is developed. When the available potassium content is greater than or less than the available potassium red line value, an increasing or decreasing available potassium strategy is developed. When the slow-acting potassium content is greater than or less than the slow-acting potassium red line value, an increasing or decreasing slow-acting potassium strategy is developed.

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