Spinach sowing positioning method and system based on image data analysis

Abstract

The present invention discloses a spinach sowing positioning method and system based on image data analysis, which relates to the technical field of spinach sowing positioning. The method comprises the following steps: obtaining a soil image, determining a screenshot size according to an actual area, and intercepting a plurality of image blocks for analysis; analyzing the soil color and texture characteristics of each block, and calculating an average value to construct a soil comprehensive characteristic expression model; adjusting model parameters to obtain a soil comprehensive characteristic expression model for comparison and a corresponding sowing interval; classifying the models, establishing a reference expression model set, analyzing and determining marker factor parameters of characteristic factors, and constructing a reference expression model library; analyzing a currently captured soil image, constructing a current soil comprehensive characteristic expression model, and determining a comparison model with the highest degree of fit by comparing it with models in the reference expression model library, thereby accurately determining the position of spinach sowing. The technical solution of the present invention improves the accuracy and efficiency of sowing, and provides strong support for intelligent agriculture.

Description

Technical Field

[0001] The present invention relates to the technical field of spinach sowing positioning, and in particular to a spinach sowing positioning method and system based on image data analysis. Background Art

[0002] In modern agricultural production, precise and efficient seeding is a key step in ensuring healthy crop growth and increasing yields. Especially in the cultivation of leafy vegetables like spinach, uniform seed distribution plays a crucial role in subsequent growth management, resource utilization, and ultimately yield. However, traditional seeding methods, whether manual or simple mechanized, face challenges such as insufficient positioning accuracy, low efficiency, and difficulty adapting to complex and changing field environments.

[0003] Traditional seeding methods rely primarily on farmers' planting experience and simple mechanical devices, but this approach is inadequate for varying soil conditions, weather variations, and crop growth requirements. Due to a lack of effective positioning methods, seed distribution is often uneven, resulting in dense seeding in some areas and sparse seeding in others. This uneven seeding density not only increases the difficulty of field management, such as ensuring accurate weeding, irrigation, and fertilization, but can also lead to wasted resources and reduced crop yields. Summary of the Invention

[0004] The purpose of the present invention is to provide a positioning method and system that can accurately locate the planting position of spinach.

[0005] The present invention discloses a spinach sowing positioning method based on image data analysis, comprising:

[0006] Step S100: photographing the entire sowing soil area to obtain a sowing soil image. Based on the actual area of ​​the sowing soil area, determining a random screenshot size for the sowing soil image to capture a number of soil image blocks for analysis.

[0007] Step S200: Analyze each soil image block for analysis, determine the soil color characteristics and soil texture characteristics corresponding to each soil image block for analysis, and calculate the average soil color characteristics, average soil texture characteristics, and average soil structure characteristics of a plurality of soil image blocks for analysis;

[0008] Step S300: constructing a soil comprehensive characteristic expression model, adjusting parameters of the soil comprehensive characteristic expression model according to average soil color characteristics and average soil texture characteristics to obtain a soil comprehensive characteristic expression model for comparison, and configuring a corresponding spinach sowing interval for the soil comprehensive characteristic expression model based on the spinach sowing experiment record;

[0009] Step S400, performing a primary classification on the soil comprehensive characteristic expression models for comparison with the same spinach sowing interval, and performing a secondary classification on the soil comprehensive characteristic expression models based on the similarity between the soil comprehensive characteristic expression models for comparison, to obtain a reference expression model set;

[0010] Step S500, analyzing the soil comprehensive characteristic performance model for comparison in the reference performance model set, determining the marker factor parameter of each characteristic factor, marking the reference performance model set with the marker factor parameter, and constructing a reference performance model library from the plurality of reference performance model sets;

[0011] Step S600: Analyze the currently captured sowing soil image, and construct a current soil comprehensive characteristic expression model based on the analysis results, determine the characteristic factor parameters in the current soil comprehensive characteristic expression model, and find the corresponding reference expression model set in the reference expression model library based on the parameter difference value of the characteristic factor parameters and the corresponding marker factor parameters, compare each comparison soil comprehensive characteristic expression model in the reference expression model set with the current soil comprehensive characteristic expression model, determine the degree of consistency between the two based on the comparison results, and determine an adapted comparison soil comprehensive characteristic expression model based on the degree of consistency, and determine the position of spinach sowing based on the spinach sowing interval corresponding to the comparison soil comprehensive characteristic expression model.

[0012] In some embodiments disclosed herein, a method for determining soil color characteristics and soil texture characteristics of each soil image block for analysis includes:

[0013] Methods for determining soil color characteristics include:

[0014] Step S201, performing color analysis on the soil image block for analysis, determining a color histogram of the soil image block for analysis, and associating the color histogram with the soil image block for analysis;

[0015] Methods for determining soil texture characteristics include:

[0016] Step S202: Based on the data records of soil particle size and soil texture, a plurality of corresponding soil particle size parameters and a plurality of soil texture parameters are set for each historical grayscale change vector comparison template, the historical grayscale change vector comparison template with the set soil particle size parameters and soil texture parameters is recorded as grayscale change vector comparison data, and the plurality of grayscale change vector comparison data are constructed into a grayscale change vector comparison database;

[0017] Step S203, matching the real-time grayscale change vector comparison template using the grayscale change vector comparison database, and determining the soil particle size parameters and soil texture parameters corresponding to the real-time grayscale change vector comparison template based on the matching result;

[0018] The method for constructing a grayscale change vector comparison template includes:

[0019] Grayscale processing is performed on the soil block for analysis, and a number of marker pixels are randomly selected, the marker grayscale value of the marker pixel is analyzed, and the side grayscale value of the side pixel next to the marker pixel is analyzed, and based on the positions of the marker pixel and the side pixel, the marker grayscale value and the side grayscale value are constructed into a grayscale value matrix;

[0020] A grayscale change vector judgment template is set for the grayscale value matrix. The grayscale change vector judgment template includes a plurality of template units corresponding to the positions of matrix factors in the grayscale value matrix, and vector pointing lines are set between the template units.

[0021] Based on the grayscale change vector, the vector pointing line in the judgment template is calculated, the vector change value between the corresponding matrix factors in the grayscale value matrix is ​​calculated, and the vector change value is associated with the corresponding vector pointing line to obtain the grayscale change vector comparison template.

[0022] In some embodiments disclosed herein, a method for constructing a soil comprehensive characteristic representation model includes:

[0023] Step S301: constructing a soil particle simulation sphere for soil particles, and adjusting the diameter of the soil particle simulation sphere based on several soil particle size parameters;

[0024] In step S302, a number of soil particle simulation spheres are associated and combined to form a soil particle simulation sphere array, and based on a number of soil texture parameters, the distance between the soil particle simulation spheres in the soil particle simulation sphere array is adjusted to form an adjusted soil particle simulation sphere array, which is recorded as a soil comprehensive characteristic expression model.

[0025] In some embodiments disclosed herein, a method for adjusting the diameter of a soil particle simulation sphere based on several soil particle size parameters includes:

[0026] Step S3011: constructing a spinach sowing interval variation sequence based on the spinach sowing experiment record, wherein the spinach sowing interval variation sequence includes a plurality of spinach sowing intervals, and the spinach sowing intervals are arranged according to a monotonic variation trend;

[0027] Step S3012: constructing a soil particle size change sequence for each soil particle size parameter, wherein the soil particle size change sequence includes a plurality of soil particle size parameters, and the soil particle size parameters are arranged according to a monotonic change trend;

[0028] Step S3013: Based on the spinach sowing experiment records, a correspondence between the spinach sowing interval in the spinach sowing interval variation sequence and the soil particle size parameter in the soil particle size variation sequence is established, and the correspondence between the spinach sowing interval and the soil particle size parameter is segmentally associated to form a correspondence between the spinach sowing interval segments and the soil particle size parameter segments;

[0029] The method for segmentally associating the corresponding relationship between the spinach sowing interval and the soil particle size parameter includes:

[0030] The spinach sowing interval change sequence is analyzed. If the change amount of adjacent spinach sowing intervals is greater than or equal to a preset value, the intervals between the spinach sowing intervals are identified as interval boundary points. Based on the interval boundary points, several spinach sowing interval segments in the spinach sowing interval change sequence are determined. Based on the correspondence between the spinach sowing intervals and the soil particle size parameters in the spinach sowing interval segments, the correspondence between the spinach sowing interval segments and the soil particle size parameter segments is determined. The spinach sowing interval segments mapped to the several soil particle size parameter segments under different performance states are analyzed to establish a soil particle size parameter segment group-spinach sowing interval segment correspondence table.

[0031] Step S3014: setting a standard diameter for the soil particle simulation sphere, dividing the standard diameter evenly to obtain a plurality of diameter segments, and associating each spinach sowing interval segment with a corresponding diameter segment according to the order of the spinach sowing interval segments;

[0032] Step S3015: Acquire several current soil particle size parameters, determine the soil particle size parameter segment to which each soil particle size parameter belongs, substitute the determined soil particle size parameter segment into the soil particle size parameter segment group-spinach sowing interval segment correspondence table, determine the corresponding spinach sowing interval segment, and based on the correspondence between the spinach sowing interval segments and the diameter segments, determine a first adjusted reference diameter of the soil simulation sphere. The first adjusted reference diameter is the total length of the diameter segment before the determined diameter segment.

[0033] Step S3016, performing an influence characteristic analysis on each soil particle size parameter, and based on the analysis results, determining a correction method for the first adjustment reference diameter to obtain a second adjustment reference diameter, and using the second adjustment reference diameter to adjust the diameter of the soil particle simulation sphere.

[0034] In some embodiments disclosed herein, the correction method for the first adjustment reference diameter includes:

[0035] Step S30161: Determine the mapping sub-segment of each soil particle size parameter segment in the soil particle size parameter segment group that is equivalent to the spinach sowing interval segment, determine the sub-segment position of the obtained soil particle size parameter in the mapping sub-segment, and record the front section of the sub-segment position as the front section of the mapping sub-segment;

[0036] Step S30162: Calculate the first segment occupancy ratio of the mapping subsegment relative to the soil particle size parameter segment, and calculate the second segment occupancy ratio of the front segment of the mapping subsegment relative to the mapping subsegment. Based on the first segment occupancy ratio and the second segment occupancy ratio, determine a diameter segment intercept of the diameter segment, and add the diameter segment intercept to the first adjusted reference diameter to obtain a second adjusted reference diameter.

[0037] The expression for calculating the second adjusted reference diameter is:

[0038] ;

[0039] in, For the second adjustment reference diameter, For the first adjustment reference diameter, is the conversion adjustment coefficient of the diameter segment interception segment length, d is the length of the diameter segment, is the first segment occupancy ratio corresponding to the i-th type of soil particle size parameter, is the second segment occupancy ratio corresponding to the i-th type of soil particle size parameter, c is the adjustment constant affecting the segment occupancy ratio, and n is the total number of types of soil particle size parameters.

[0040] In some embodiments disclosed herein, a method for adjusting the distance between soil particle simulation spheres in an array of soil particle simulation spheres based on a number of soil texture parameters includes:

[0041] Step S3021: Analyze the spinach sowing experiment record, and construct a spinach sowing interval change sequence based on the analysis result, wherein the spinach sowing interval change sequence includes a plurality of spinach sowing intervals, and the spinach sowing intervals are arranged according to a monotonic change trend;

[0042] Step S3022: Analyze the spinach sowing interval variation sequence. If there are adjacent spinach sowing intervals whose variation is greater than or equal to a preset value, identify the intervals between the spinach sowing intervals as interval boundaries. Based on the interval boundaries, determine a number of spinach sowing interval segments in the spinach sowing interval variation sequence.

[0043] Step S3023, based on the analysis of the spinach sowing experiment records, determining a plurality of soil texture parameter groups corresponding to each spinach sowing interval segment, wherein the soil texture parameter groups include a plurality of soil texture parameters;

[0044] Step S3024, evenly dividing the distances between the soil particle simulation spheres to obtain a plurality of inter-sphere distance segments, and establishing a correspondence between the inter-sphere distance segments and the spinach sowing intervals based on the order of the spinach sowing intervals in the spinach sowing interval variation sequence;

[0045] Step S3025: Obtain several current soil texture parameters and substitute them into different soil texture parameter groups. If the texture parameter difference between each soil texture parameter of the same type is less than or equal to a preset value, the soil texture parameter group is determined to be a reference soil texture parameter group.

[0046] Step S3026, determine the spinach sowing interval segment corresponding to the reference soil texture parameter group, and based on the correspondence between the inter-ball distance segment and the spinach sowing interval, determine the inter-ball distance segment corresponding to the reference soil texture parameter group, and the total length of several inter-ball distance segments before the inter-ball distance segment is recognized as the distance between the soil particle simulation spheres.

[0047] In some embodiments disclosed herein, a method for determining the similarity between the soil comprehensive performance models for comparison includes:

[0048] Step S401, determining characteristic factors of a soil comprehensive characteristic representation model for comparison, the characteristic factors including the spherical size of the soil particle simulation spheres and the spatial size of the model space jointly constructed between the soil particle simulation spheres;

[0049] Step S402 , setting a plurality of sphere size comparison intervals for sphere size, and setting a plurality of space size comparison intervals for space size, and randomly combining the sphere size comparison intervals and space size comparison intervals to obtain a plurality of size comparison interval groups;

[0050] Step S403 , classifying the soil comprehensive performance models for comparison based on the size comparison interval group to which the sphere size and the spatial size of the soil comprehensive performance model for comparison belong, to obtain a reference performance model set.

[0051] In some embodiments disclosed herein, a method for analyzing a comparative soil comprehensive characteristic performance model in a reference performance model set to determine a marker factor parameter for each characteristic factor includes:

[0052] Step S501, determining characteristic factors to be analyzed, including the spherical size of the soil particle simulation spheres and the spatial size of the model space jointly constructed between the soil particle simulation spheres;

[0053] Step S502: Analyze each reference representation model in the reference representation model set and define a number of marker sphere sizes, including establishing a sphere size reference line, mapping each sphere size onto the sphere size reference line in the form of a sphere size mapping point, analyzing the clustering characteristics of the sphere size mapping points, and if the number of sphere size mapping points within a preset size interval is greater than or equal to a preset value, then identify the midpoint of the size interval as the marker sphere size mapping point, and identify the sphere size corresponding to the marker sphere size mapping point as the marker sphere size;

[0054] Step S503 : determining the spatial size corresponding to each sphere mapping point in the preset size interval, and calculating the average value of these spatial sizes, which is recorded as the marker spatial size.

[0055] In some embodiments disclosed herein, a method for determining the degree of agreement between a comparison soil comprehensive characteristic expression model and a current soil comprehensive characteristic expression model includes:

[0056] Step S601, determining the comparison sphere size and the comparison space size of the soil comprehensive characteristic expression model, and determining the current sphere size and the current space size of the current soil comprehensive characteristic expression model;

[0057] Step S602: Compare the current sphere size with the comparison sphere size, and construct a first comparison operator based on the comparison results. Compare the current space size with the comparison space size, and construct a second comparison operator based on the comparison results. Randomly select several other comprehensive feature representation models for comparison, and analyze the difference scale characteristics between them and the current comprehensive feature representation model.

[0058] Step S603: Based on the difference scale feature, the first comparison operator and the second comparison operator are modified to obtain the degree of agreement between the comparison soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model;

[0059] The expression for calculating the degree of fit is:

[0060] ;

[0061] Where W is the degree of fit, To preset the maximum degree of fit, is the weight coefficient of the sphere size parameter difference, is the difference in sphere size parameters, is the weight coefficient of the spatial size parameter difference, is the difference of spatial size parameters, is the difference scale judgment function between the xth randomly selected comparison soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model. If the difference in spherical size parameters and spatial size parameters of the two are both less than or equal to the preset value, then Output 1, otherwise output 0, P is the difference feature impact adjustment coefficient, and b is the difference feature impact adjustment constant.

[0062] In some embodiments disclosed herein, a spinach sowing positioning system based on image data analysis includes:

[0063] The first module is used to capture the entire sowing soil area to obtain a sowing soil image, determine the size of a random screenshot of the sowing soil image based on the actual area of ​​the sowing soil area, and capture a number of soil image blocks for analysis;

[0064] The second module is used to analyze each soil image block for analysis, determine the soil color characteristics and soil texture characteristics corresponding to each soil image block for analysis, and calculate the average soil color characteristics, average soil texture characteristics, and average soil structure characteristics of multiple soil image blocks for analysis;

[0065] The third module is used to construct a soil comprehensive characteristic performance model. The soil comprehensive characteristic performance model is adjusted according to the average soil color characteristics and the average soil texture characteristics to obtain a soil comprehensive characteristic performance model for comparison. Based on the spinach sowing experiment records, the corresponding spinach sowing interval is configured according to the soil comprehensive characteristic performance model for comparison;

[0066] The fourth module is used to classify the soil comprehensive characteristic performance models for comparison with the same spinach sowing interval, and to classify the soil comprehensive characteristic performance models for comparison based on the similarity between the soil comprehensive characteristic performance models for comparison to obtain a reference performance model set;

[0067] The fifth module is used to analyze the soil comprehensive characteristic performance model for comparison in the reference performance model set, determine the marker factor parameter of each characteristic factor, mark the reference performance model set with the marker factor parameter, and construct a reference performance model library from the plurality of reference performance model sets;

[0068] The sixth module is used to analyze the currently taken sowing soil image, and construct the current soil comprehensive characteristic expression model based on the analysis results, and determine the characteristic factor parameters in the current soil comprehensive characteristic expression model, and based on the parameter difference value of the characteristic factor parameters and the corresponding marker factor parameters, find the corresponding reference expression model set in the reference expression model library, and compare each comparison soil comprehensive characteristic expression model in the reference expression model set with the current soil comprehensive characteristic expression model, and determine the degree of consistency between the two based on the comparison results, and determine the suitable comparison soil comprehensive characteristic expression model based on the degree of consistency, and determine the position of spinach sowing based on the spinach sowing interval corresponding to the comparison soil comprehensive characteristic expression model.

[0069] The present invention discloses a spinach sowing positioning method and system based on image data analysis, which relates to the technical field of spinach sowing positioning. The method comprises the following steps: obtaining a soil image, determining a screenshot size according to an actual area, and intercepting a plurality of image blocks for analysis; analyzing the soil color and texture characteristics of each block, and calculating an average value to construct a soil comprehensive characteristic expression model; adjusting model parameters to obtain a soil comprehensive characteristic expression model for comparison and a corresponding sowing interval; classifying the models, establishing a reference expression model set, analyzing and determining marker factor parameters of characteristic factors, and constructing a reference expression model library; analyzing a currently captured soil image, constructing a current soil comprehensive characteristic expression model, and determining a comparison model with the highest degree of fit by comparing it with models in the reference expression model library, thereby accurately determining the position of spinach sowing. The technical solution of the present invention improves the accuracy and efficiency of sowing, and provides strong support for intelligent agriculture.

[0070] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a diagram of the steps of a spinach sowing positioning method based on image data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0073] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solutions of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should have the common meanings understood by those skilled in the art of the present invention.

[0074] The present invention discloses a spinach sowing positioning method based on image data analysis, see Figure 1 ,include:

[0075] In step S100, the sowing soil area is photographed as a whole to obtain a sowing soil image. Based on the actual area of ​​the sowing soil area, the screenshot size of the sowing soil image is determined to capture a number of soil image blocks for analysis.

[0076] This step captures the entire soil area to obtain an image containing information such as soil color and texture. The screenshot size is then determined based on the actual area of ​​the soil, ensuring that the image block captured for analysis is representative but not too large, which would inefficiently capture the image. This randomized approach captures different parts of the soil area, providing diverse data for subsequent analysis and helping to more accurately reflect the overall characteristics of the soil.

[0077] In step S200 , each soil image block for analysis is analyzed to determine the soil color feature and soil texture feature corresponding to each soil image block for analysis, and the average soil color feature and average soil texture feature of a plurality of soil image blocks for analysis are calculated.

[0078] In this step, image processing techniques are used to extract the color and texture features of each soil image block used for analysis. Color features reflect soil color information, such as depth and brightness, while texture features describe physical properties such as surface roughness and particle distribution. Calculating the average of these features helps eliminate local variations and obtain more stable and reliable comprehensive soil characteristics. These features will provide key data support for the subsequent construction of a comprehensive soil characteristic representation model.

[0079] In some embodiments disclosed herein, a method for determining soil color characteristics and soil texture characteristics of each soil image block for analysis includes:

[0080] Methods for determining soil color characteristics include:

[0081] Step S201 : performing color analysis on the soil image block for analysis, determining a color histogram of the soil image block for analysis, and associating the color histogram with the soil image block for analysis.

[0082] Color is a key attribute in describing soil appearance, reflecting information such as soil composition, moisture, and oxidation level. In this step, color analysis of the analytical soil image blocks is performed to obtain their color histograms. A color histogram is a statistical method that calculates the frequency of occurrence of various colors in an image and provides a visual representation of soil color distribution. Associating the color histogram with the corresponding analytical soil image blocks provides foundational data for subsequent feature extraction and model building.

[0083] Methods for determining soil texture characteristics include:

[0084] Step S202, based on the data records of soil particle size and soil texture, set corresponding soil particle size parameters and soil texture parameters for each historical grayscale change vector comparison template, record the historical grayscale change vector comparison template with the soil particle size parameters and soil texture parameters as grayscale change vector comparison data, and construct the grayscale change vector comparison data into a grayscale change vector comparison database.

[0085] Soil texture features describe physical properties such as surface roughness, particle distribution, and pore structure, and are crucial for understanding soil physical properties and predicting soil behavior. In this step, by constructing a grayscale change vector comparison database and using it to match real-time grayscale change vector comparison templates, we can determine soil particle size and texture parameters, thereby extracting soil texture features.

[0086] Step S203 : Matching the real-time grayscale change vector comparison template with the grayscale change vector comparison database, and determining the soil particle size parameters and soil texture parameters corresponding to the real-time grayscale change vector comparison template based on the matching result.

[0087] The method for constructing a grayscale change vector comparison template includes:

[0088] The soil block for analysis is grayscaled, and several marker pixels are randomly selected. The marker grayscale value of the marker pixel is analyzed, and the side grayscale value of the side pixel next to the marker pixel is analyzed. Based on the positions of the marker pixel and the side pixel, the marker grayscale value and the side grayscale value are constructed into a grayscale value matrix.

[0089] A grayscale change vector judgment template is set for the grayscale value matrix. The grayscale change vector judgment template includes a number of template units corresponding to the positions of matrix factors in the grayscale value matrix, and vector pointing lines are set between the template units.

[0090] Based on the grayscale change vector, the vector pointing line in the judgment template is calculated, the vector change value between the corresponding matrix factors in the grayscale value matrix is ​​calculated, and the vector change value is associated with the corresponding vector pointing line to obtain the grayscale change vector comparison template.

[0091] Step S300: construct a soil comprehensive characteristic expression model, adjust the parameters of the soil comprehensive characteristic expression model according to the average soil color characteristics and the average soil texture characteristics, and obtain a soil comprehensive characteristic expression model for comparison. Based on the spinach sowing experiment record, the corresponding spinach sowing interval is configured for the soil comprehensive characteristic expression model for comparison.

[0092] Based on the characteristic data extracted in step S200, a comprehensive soil characteristic representation model is constructed. This model aims to describe the comprehensive characteristics of the soil through mathematical methods. By adjusting model parameters, such as color feature weights and texture feature weights, the model can more accurately reflect the actual state of the soil. Furthermore, based on the spinach sowing experiment records, the model is configured with corresponding spinach sowing intervals, enabling the model to guide the selection of spinach sowing locations in practical applications.

[0093] In some embodiments disclosed herein, a method for constructing a soil comprehensive characteristic representation model includes:

[0094] Step S301 : constructing a soil particle simulation sphere for soil particles, and adjusting the diameter of the soil particle simulation sphere based on a number of soil particle size parameters.

[0095] Step S301 is the first step in building a comprehensive soil feature representation model. Its core is to simulate the morphology of soil particles. In real soil, particles vary in shape, but to simplify the model and capture key features, soil particles are typically abstracted into simple geometric shapes, such as spheres.

[0096] Constructing a sphere to simulate soil particles: First, a basic sphere model is selected as the simulation object of soil particles. The sphere model has the advantages of being easy to handle and able to reflect the basic shape of particles.

[0097] Adjusting the simulated sphere diameter: Next, the simulated sphere diameter is finely adjusted based on the soil particle size parameters (such as mean diameter and standard deviation) previously extracted from the actual soil image. This step ensures that the simulated sphere size is consistent with that of real soil particles, thereby more accurately reflecting the physical properties of the soil.

[0098] In some embodiments disclosed herein, a method for adjusting the diameter of a soil particle simulation sphere based on several soil particle size parameters includes:

[0099] Step S3011: constructing a spinach sowing interval variation sequence based on the spinach sowing experiment record. The spinach sowing interval variation sequence includes a plurality of spinach sowing intervals, and the spinach sowing intervals are arranged according to a monotonic variation trend.

[0100] Step S3012: constructing a soil particle size variation sequence for each soil particle size parameter. The soil particle size variation sequence includes a plurality of soil particle size parameters, and the soil particle size parameters are arranged according to a monotonic variation trend.

[0101] Step S3013, based on the spinach sowing experiment record, establish the correspondence between the spinach sowing interval in the spinach sowing interval change sequence and the soil particle size parameters in the soil particle size change sequence, and perform segment association on the correspondence between the spinach sowing interval and the soil particle size parameters to form a correspondence between the spinach sowing interval segments and the soil particle size parameter segments.

[0102] The method for segmentally associating the corresponding relationship between the spinach sowing interval and the soil particle size parameter includes:

[0103] The spinach sowing interval change sequence is analyzed. If the change in adjacent spinach sowing intervals is greater than or equal to a preset value, the spinach sowing intervals are identified as interval boundary points. Based on the interval boundary points, several spinach sowing interval segments in the spinach sowing interval change sequence are determined. Based on the correspondence between the spinach sowing intervals and the soil particle size parameters in the spinach sowing interval segments, the correspondence between the spinach sowing interval segments and the soil particle size parameter segments is determined. The spinach sowing interval segments mapped by several soil particle size parameter segments under different performance states are analyzed, and a soil particle size parameter segment group-spinach sowing interval segment correspondence table is established.

[0104] Step S3014: set a standard diameter for the soil particle simulation sphere, divide the standard diameter evenly to obtain a number of diameter segments, and associate each spinach sowing interval segment with a corresponding diameter segment according to the order of the spinach sowing interval segments.

[0105] Step S3015, obtain several current soil particle size parameters, and determine the soil particle size parameter segment to which each soil particle size parameter belongs, and substitute the determined soil particle size parameter segment into the soil particle size parameter segment group---spinach sowing interval segment correspondence table, determine the corresponding spinach sowing interval segment, and based on the correspondence between the spinach sowing interval segment and the diameter segment, determine the first adjustment reference diameter of the soil simulation sphere, the first adjustment reference diameter is the total length of the diameter segment before the determined diameter segment.

[0106] Step S3016, performing an influence characteristic analysis on each soil particle size parameter, and based on the analysis results, determining a correction method for the first adjustment reference diameter to obtain a second adjustment reference diameter, and using the second adjustment reference diameter to adjust the diameter of the soil particle simulation sphere.

[0107] In some embodiments disclosed herein, the correction method for the first adjustment reference diameter includes:

[0108] Step S30161, determine the mapping sub-segment of each soil particle size parameter segment in the soil particle size parameter segment group that is relatively equivalent to the spinach sowing interval segment, and judge the sub-segment position of the obtained soil particle size parameter in the mapping sub-segment, and record the front segment of the sub-segment position as the front segment of the mapping sub-segment.

[0109] Step S30162, calculate the first segment occupancy ratio of the mapping sub-segment relative to the soil particle size parameter segment, and calculate the second segment occupancy ratio of the front segment of the mapping sub-segment relative to the mapping sub-segment, based on the first segment occupancy ratio and the second segment occupancy ratio, determine the diameter segment interception segment of the diameter segment, and add the diameter segment interception segment and the first adjusted reference diameter to obtain the second adjusted reference diameter.

[0110] The expression for calculating the second adjusted reference diameter is:

[0111] .

[0112] in, For the second adjustment reference diameter, For the first adjustment reference diameter, is the conversion adjustment coefficient of the diameter segment interception segment length, d is the length of the diameter segment, is the first segment occupancy ratio corresponding to the i-th type of soil particle size parameter, is the second segment occupancy ratio corresponding to the i-th type of soil particle size parameter, c is the adjustment constant affecting the segment occupancy ratio, and n is the total number of types of soil particle size parameters.

[0113] In step S302, a number of soil particle simulation spheres are associated and combined to form a soil particle simulation sphere array, and based on a number of soil texture parameters, the distance between the soil particle simulation spheres in the soil particle simulation sphere array is adjusted to form an adjusted soil particle simulation sphere array, which is recorded as a soil comprehensive characteristic expression model.

[0114] Step S302 is to further simulate the arrangement and interaction between soil particles based on step S301 to form a soil model with a specific texture.

[0115] Combining simulated spheres to form an array: The soil particle simulated spheres obtained in step S301 are combined according to a certain rule or random distribution to form a simulated sphere array. This array can be regarded as a simplified and representative soil sample.

[0116] Adjusting the distances between simulated spheres: Next, the distances between soil particles in the simulated sphere array are finely adjusted based on previously extracted soil texture parameters (such as porosity and inter-particle contact patterns). These adjustments are intended to mimic the spatial relationships and interactions between particles in real soil, thereby reflecting the soil's texture and structural characteristics.

[0117] Forming a comprehensive soil characteristic model: After the above adjustments, the resulting array of simulated soil particle spheres is the comprehensive soil characteristic model. This model takes into account not only the size of soil particles but also the arrangement and interactions between particles, thus more comprehensively reflecting the comprehensive characteristics of the soil.

[0118] In some embodiments disclosed herein, a method for adjusting the distance between soil particle simulation spheres in an array of soil particle simulation spheres based on a number of soil texture parameters includes:

[0119] Step S3021: Analyze the spinach sowing experiment record, and construct a spinach sowing interval change sequence based on the analysis result. The spinach sowing interval change sequence includes a plurality of spinach sowing intervals, and the spinach sowing intervals are arranged according to a monotonic change trend.

[0120] Step S3022: Analyze the spinach sowing interval variation sequence. If the variation of adjacent spinach sowing intervals is greater than or equal to a preset value, the intervals between the spinach sowing intervals are identified as interval boundary points. Based on the interval boundary points, determine several spinach sowing interval segments in the spinach sowing interval variation sequence.

[0121] Step S3023: Based on the analysis of the spinach sowing experiment record, several soil texture parameter groups corresponding to each spinach sowing interval are determined, and the soil texture parameter groups include several soil texture parameters.

[0122] Step S3024, evenly divide the distance between the soil particle simulation spheres to obtain a number of inter-sphere distance segments, and establish a correspondence between the inter-sphere distance segments and the spinach sowing intervals based on the arrangement order of the spinach sowing intervals in the spinach sowing interval change sequence.

[0123] Step S3025: Obtain several current soil texture parameters and substitute them into different soil texture parameter groups. If the texture parameter difference between each soil texture parameter of the same type is less than or equal to a preset value, the soil texture parameter group is determined to be a reference soil texture parameter group.

[0124] Step S3026, determine the spinach sowing interval segment corresponding to the reference soil texture parameter group, and based on the correspondence between the inter-ball distance segment and the spinach sowing interval, determine the inter-ball distance segment corresponding to the reference soil texture parameter group, and the total length of several inter-ball distance segments before the inter-ball distance segment is recognized as the distance between the soil particle simulation spheres.

[0125] In step S400, the soil comprehensive characteristic expression models for comparison with the same spinach sowing interval are classified once, and the soil comprehensive characteristic expression models for comparison are classified twice based on the similarity between the soil comprehensive characteristic expression models for comparison to obtain a reference expression model set.

[0126] This step simplifies model management and retrieval by comparing and classifying comprehensive soil performance models. Initial classification is performed based on spinach sowing intervals, grouping models with similar sowing intervals. Secondary classification further refines the categories based on similarities between models, such as feature similarity and sowing interval proximity. This classification creates a reference performance model set, with models within each set sharing similar performance characteristics, facilitating rapid matching of the most appropriate model in subsequent practical applications.

[0127] In some embodiments disclosed herein, a method for determining the similarity between the soil comprehensive performance models for comparison includes:

[0128] Step S401 : determining characteristic factors of a soil comprehensive characteristic representation model for comparison, wherein the characteristic factors include the spherical size of the soil particle simulation sphere and the spatial size of the model space jointly constructed between the soil particle simulation spheres.

[0129] In step S402 , a plurality of sphere size comparison intervals are set for the sphere size, and a plurality of space size comparison intervals are set for the space size, and the sphere size comparison intervals and the space size comparison intervals are randomly combined to obtain a plurality of size comparison interval groups.

[0130] Step S403 , classifying the soil comprehensive performance models for comparison based on the size comparison interval group to which the sphere size and the spatial size of the soil comprehensive performance model for comparison belong, to obtain a reference performance model set.

[0131] Step S500: Analyze the soil comprehensive characteristic performance model for comparison in the reference performance model set, determine the marker factor parameters of each characteristic factor, mark the reference performance model set with the marker factor parameters, and construct several reference performance model sets into a reference performance model library.

[0132] This step thoroughly analyzes each model in the reference performance model set to determine the signature factor parameters within its characteristic factors. These signature factor parameters are highly representative and discriminatory, accurately reflecting the model's characteristic performance. These signature factor parameters are then used to tag the model, creating a unique identity. Integrating these tagged models into a reference performance model library significantly improves model retrieval speed and matching accuracy.

[0133] In some embodiments disclosed herein, a method for analyzing a comparative soil comprehensive characteristic performance model in a reference performance model set to determine a marker factor parameter for each characteristic factor includes:

[0134] Step S501 : determining characteristic factors to be analyzed, including the spherical size of the soil particle simulation spheres and the spatial size of the model space jointly constructed between the soil particle simulation spheres.

[0135] Step S502 analyzes each reference expression model in the reference expression model set and defines a number of marker sphere sizes, including establishing a sphere size reference line, mapping each sphere size onto the sphere size reference line in the form of a sphere size mapping point, and analyzing the aggregation characteristics of the sphere size mapping points. If the number of sphere size mapping points within a preset size range is greater than or equal to a preset value, the midpoint of the size range is identified as the marker sphere size mapping point, and the sphere size corresponding to the marker sphere size mapping point is identified as the marker sphere size.

[0136] Step S503 : determining the spatial size corresponding to each sphere mapping point in the preset size interval, and calculating the average value of these spatial sizes, which is recorded as the marker spatial size.

[0137] Step S600: Analyze the currently captured sowing soil image, and construct a current soil comprehensive characteristic expression model based on the analysis results, determine the characteristic factor parameters in the current soil comprehensive characteristic expression model, and find the corresponding reference expression model set in the reference expression model library based on the parameter difference value of the characteristic factor parameters and the corresponding marker factor parameters, compare each comparison soil comprehensive characteristic expression model in the reference expression model set with the current soil comprehensive characteristic expression model, determine the degree of consistency between the two based on the comparison results, and determine an adapted comparison soil comprehensive characteristic expression model based on the degree of consistency, and determine the position of spinach sowing based on the spinach sowing interval corresponding to the comparison soil comprehensive characteristic expression model.

[0138] In practical applications, an image of the soil area currently being sown is captured and a comprehensive soil characteristic model is constructed. This model is then compared with models in a library of reference performance models, and the differences between the characteristic factor parameters are calculated to identify the most matching reference performance model set. Subsequently, the models within the set are compared one by one to determine the comparison soil comprehensive characteristic performance model that best matches the current soil comprehensive characteristic performance model. Finally, the spinach sowing interval corresponding to this model is used to precisely guide the sowing location of the spinach. This process enables intelligent and precise decision-making on sowing locations, helping to improve sowing efficiency and crop growth quality.

[0139] In some embodiments disclosed herein, a method for determining the degree of agreement between a comparison soil comprehensive characteristic expression model and a current soil comprehensive characteristic expression model includes:

[0140] Step S601: determining the comparison sphere size and the comparison space size of the soil comprehensive characteristic expression model, and determining the current sphere size and the current space size of the current soil comprehensive characteristic expression model.

[0141] Step S602: Compare the current sphere size with the comparison sphere size, and construct a first comparison operator based on the comparison result; compare the current space size with the comparison space size, and construct a second comparison operator based on the comparison result; randomly select several other comprehensive feature representation models for comparison, and analyze the difference scale characteristics between them and the current comprehensive feature representation model.

[0142] Step S603: Based on the difference scale feature, the first comparison operator and the second comparison operator are modified to obtain the degree of agreement between the comparison soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model.

[0143] The expression for calculating the degree of fit is:

[0144] .

[0145] Where W is the degree of fit, To preset the maximum degree of fit, is the weight coefficient of the sphere size parameter difference, is the difference in sphere size parameters, is the weight coefficient of the spatial size parameter difference, is the difference of spatial size parameters, is the difference scale judgment function between the xth randomly selected comparison soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model. If the difference in spherical size parameters and spatial size parameters of the two are both less than or equal to the preset value, then Output 1, otherwise output 0, P is the difference feature impact adjustment coefficient, and b is the difference feature impact adjustment constant.

[0146] In some embodiments disclosed herein, a spinach sowing positioning system based on image data analysis includes:

[0147] The first module is used to capture the entire sowing soil area to obtain a sowing soil image, determine the size of a random screenshot of the sowing soil image based on the actual area of ​​the sowing soil area, and capture a number of soil image blocks for analysis;

[0148] The second module is used to analyze each soil image block for analysis, determine the soil color characteristics and soil texture characteristics corresponding to each soil image block for analysis, and calculate the average soil color characteristics, average soil texture characteristics, and average soil structure characteristics of multiple soil image blocks for analysis;

[0149] The third module is used to construct a soil comprehensive characteristic performance model. The soil comprehensive characteristic performance model is adjusted according to the average soil color characteristics and the average soil texture characteristics to obtain a soil comprehensive characteristic performance model for comparison. Based on the spinach sowing experiment records, the corresponding spinach sowing interval is configured according to the soil comprehensive characteristic performance model for comparison;

[0150] The fourth module is used to classify the soil comprehensive characteristic performance models for comparison with the same spinach sowing interval, and to classify the soil comprehensive characteristic performance models for comparison based on the similarity between the soil comprehensive characteristic performance models for comparison to obtain a reference performance model set;

[0151] The fifth module is used to analyze the soil comprehensive characteristic performance model for comparison in the reference performance model set, determine the marker factor parameter of each characteristic factor, mark the reference performance model set with the marker factor parameter, and construct a reference performance model library from the plurality of reference performance model sets;

[0152] The sixth module is used to analyze the currently taken sowing soil image, and construct the current soil comprehensive characteristic expression model based on the analysis results, and determine the characteristic factor parameters in the current soil comprehensive characteristic expression model, and based on the parameter difference value of the characteristic factor parameters and the corresponding marker factor parameters, find the corresponding reference expression model set in the reference expression model library, and compare each comparison soil comprehensive characteristic expression model in the reference expression model set with the current soil comprehensive characteristic expression model, and determine the degree of consistency between the two based on the comparison results, and determine the suitable comparison soil comprehensive characteristic expression model based on the degree of consistency, and determine the position of spinach sowing based on the spinach sowing interval corresponding to the comparison soil comprehensive characteristic expression model.

[0153] The present invention discloses a spinach sowing positioning method and system based on image data analysis, which relates to the technical field of spinach sowing positioning. The method comprises the following steps: obtaining a soil image, determining a screenshot size according to an actual area, and intercepting a plurality of image blocks for analysis; analyzing the soil color and texture characteristics of each block, and calculating an average value to construct a soil comprehensive characteristic expression model; adjusting model parameters to obtain a soil comprehensive characteristic expression model for comparison and a corresponding sowing interval; classifying the models, establishing a reference expression model set, analyzing and determining marker factor parameters of characteristic factors, and constructing a reference expression model library; analyzing a currently captured soil image, constructing a current soil comprehensive characteristic expression model, and determining a comparison model with the highest degree of fit by comparing it with models in the reference expression model library, thereby accurately determining the position of spinach sowing. The technical solution of the present invention improves the accuracy and efficiency of sowing, and provides strong support for intelligent agriculture.

[0154] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A spinach sowing positioning method based on image data analysis, characterized in that: include: Step S100: photographing the entire sowing soil area to obtain a sowing soil image. Based on the actual area of ​​the sowing soil area, determining a random screenshot size for the sowing soil image to capture a number of soil image blocks for analysis. Step S200 , analyzing each soil image block for analysis, determining the soil color characteristics and soil texture characteristics corresponding to each soil image block for analysis, and calculating the average soil color characteristics and average soil texture characteristics of a plurality of soil image blocks for analysis; Step S300: constructing a soil comprehensive characteristic expression model, adjusting parameters of the soil comprehensive characteristic expression model according to average soil color characteristics and average soil texture characteristics to obtain a soil comprehensive characteristic expression model for comparison, and configuring a corresponding spinach sowing interval for the soil comprehensive characteristic expression model based on the spinach sowing experiment record; Step S400, performing a primary classification on the soil comprehensive characteristic expression models for comparison with the same spinach sowing interval, and performing a secondary classification on the soil comprehensive characteristic expression models based on the similarity between the soil comprehensive characteristic expression models for comparison, to obtain a reference expression model set; Step S500, analyzing the soil comprehensive characteristic performance model for comparison in the reference performance model set, determining the marker factor parameter of each characteristic factor, marking the reference performance model set with the marker factor parameter, and constructing a reference performance model library from the plurality of reference performance model sets; Step S600: Analyze the currently captured sowing soil image, and construct a current soil comprehensive characteristic expression model based on the analysis results, determine characteristic factor parameters in the current soil comprehensive characteristic expression model, and find a corresponding reference expression model set in a reference expression model library based on parameter difference values ​​between the characteristic factor parameters and corresponding marker factor parameters. Compare each comparison soil comprehensive characteristic expression model in the reference expression model set with the current soil comprehensive characteristic expression model, determine the degree of agreement between the two based on the comparison results, and determine an appropriate comparison soil comprehensive characteristic expression model based on the degree of agreement, and determine the position location of the spinach sowing based on the spinach sowing interval corresponding to the comparison soil comprehensive characteristic expression model. Methods for constructing a comprehensive soil characteristic model include: Step S301: constructing a soil particle simulation sphere for soil particles, and adjusting the diameter of the soil particle simulation sphere based on several soil particle size parameters; Step S302: Associating and combining a plurality of soil particle simulation spheres to form a soil particle simulation sphere array, and adjusting the distances between the soil particle simulation spheres in the soil particle simulation sphere array based on a plurality of soil texture parameters to form an adjusted soil particle simulation sphere array, which is recorded as a soil comprehensive characteristic representation model; Among them, the characteristic factors include the spherical size of the soil particle simulation sphere and the spatial size of the model space jointly constructed between the soil particle simulation spheres; the marker factor parameter is the marker space size.

2. The spinach sowing positioning method based on image data analysis according to claim 1, wherein The method for determining the soil color characteristics and soil texture characteristics of each soil image block for analysis includes: Methods for determining soil color characteristics include: Step S201, performing color analysis on the soil image block for analysis, determining a color histogram of the soil image block for analysis, and associating the color histogram with the soil image block for analysis; Methods for determining soil texture characteristics include: Step S202: Based on the data records of soil particle size and soil texture, a plurality of corresponding soil particle size parameters and a plurality of soil texture parameters are set for each historical grayscale change vector comparison template, the historical grayscale change vector comparison template with the set soil particle size parameters and soil texture parameters is recorded as grayscale change vector comparison data, and the plurality of grayscale change vector comparison data are constructed into a grayscale change vector comparison database; Step S203, matching the real-time grayscale change vector comparison template using the grayscale change vector comparison database, and determining the soil particle size parameters and soil texture parameters corresponding to the real-time grayscale change vector comparison template based on the matching result; The method for constructing a grayscale change vector comparison template includes: Grayscale processing is performed on the soil block for analysis, and a number of marker pixels are randomly selected, the marker grayscale value of the marker pixel is analyzed, and the side grayscale value of the side pixel next to the marker pixel is analyzed, and based on the positions of the marker pixel and the side pixel, the marker grayscale value and the side grayscale value are constructed into a grayscale value matrix; A grayscale change vector judgment template is set for the grayscale value matrix. The grayscale change vector judgment template includes a plurality of template units corresponding to the positions of matrix factors in the grayscale value matrix, and vector pointing lines are set between the template units. Based on the grayscale change vector, the vector pointing line in the judgment template is calculated, the vector change value between the corresponding matrix factors in the grayscale value matrix is ​​calculated, and the vector change value is associated with the corresponding vector pointing line to obtain the grayscale change vector comparison template.

3. The spinach sowing positioning method based on image data analysis according to claim 1, characterized in that, Methods for adjusting the diameter of the soil particle simulation sphere based on several soil particle size parameters include: Step S3011: constructing a spinach sowing interval variation sequence based on the spinach sowing experiment record, wherein the spinach sowing interval variation sequence includes a plurality of spinach sowing intervals, and the spinach sowing intervals are arranged according to a monotonic variation trend; Step S3012: constructing a soil particle size change sequence for each soil particle size parameter, wherein the soil particle size change sequence includes a plurality of soil particle size parameters, and the soil particle size parameters are arranged according to a monotonic change trend; Step S3013: Based on the spinach sowing experiment records, a correspondence between the spinach sowing interval in the spinach sowing interval variation sequence and the soil particle size parameter in the soil particle size variation sequence is established, and the correspondence between the spinach sowing interval and the soil particle size parameter is segmentally associated to form a correspondence between the spinach sowing interval segments and the soil particle size parameter segments; The method for segmentally associating the corresponding relationship between the spinach sowing interval and the soil particle size parameter includes: The spinach sowing interval change sequence is analyzed. If the change amount of adjacent spinach sowing intervals is greater than or equal to a preset value, the intervals between the spinach sowing intervals are identified as interval boundary points. Based on the interval boundary points, several spinach sowing interval segments in the spinach sowing interval change sequence are determined. Based on the correspondence between the spinach sowing intervals and the soil particle size parameters in the spinach sowing interval segments, the correspondence between the spinach sowing interval segments and the soil particle size parameter segments is determined. The spinach sowing interval segments mapped to the several soil particle size parameter segments under different performance states are analyzed to establish a soil particle size parameter segment group-spinach sowing interval segment correspondence table. Step S3014: setting a standard diameter for the soil particle simulation sphere, dividing the standard diameter evenly to obtain a plurality of diameter segments, and associating each spinach sowing interval segment with a corresponding diameter segment according to the order of the spinach sowing interval segments; Step S3015: Acquire several current soil particle size parameters, determine the soil particle size parameter segment to which each soil particle size parameter belongs, substitute the determined soil particle size parameter segment into the soil particle size parameter segment group-spinach sowing interval segment correspondence table, determine the corresponding spinach sowing interval segment, and based on the correspondence between the spinach sowing interval segments and the diameter segments, determine a first adjusted reference diameter of the soil simulation sphere. The first adjusted reference diameter is the total length of the diameter segment before the determined diameter segment. Step S3016, performing an influence characteristic analysis on each soil particle size parameter, and based on the analysis results, determining a correction method for the first adjustment reference diameter to obtain a second adjustment reference diameter, and using the second adjustment reference diameter to adjust the diameter of the soil particle simulation sphere.

4. The spinach sowing positioning method based on image data analysis according to claim 3, characterized in that: The correction methods for the first adjustment reference diameter include: Step S30161: Determine the mapping sub-segment of each soil particle size parameter segment in the soil particle size parameter segment group that is equivalent to the spinach sowing interval segment, determine the sub-segment position of the obtained soil particle size parameter in the mapping sub-segment, and record the front section of the sub-segment position as the front section of the mapping sub-segment; Step S30162: Calculate the first segment occupancy ratio of the mapping subsegment relative to the soil particle size parameter segment, and calculate the second segment occupancy ratio of the front segment of the mapping subsegment relative to the mapping subsegment. Based on the first segment occupancy ratio and the second segment occupancy ratio, determine a diameter segment intercept of the diameter segment, and add the diameter segment intercept to the first adjusted reference diameter to obtain a second adjusted reference diameter. The expression for calculating the second adjusted reference diameter is: ； in, For the second adjustment reference diameter, For the first adjustment reference diameter, is the conversion adjustment coefficient of the diameter segment interception segment length, d is the length of the diameter segment, is the first segment occupancy ratio corresponding to the i-th type of soil particle size parameter, is the second segment occupancy ratio corresponding to the i-th type of soil particle size parameter, c is the adjustment constant affecting the segment occupancy ratio, and n is the total number of types of soil particle size parameters.

5. The spinach sowing positioning method based on image data analysis according to claim 1, characterized in that, The method for adjusting the distance between soil particle simulation spheres in the soil particle simulation sphere array based on a plurality of soil texture parameters includes: Step S3021: Analyze the spinach sowing experiment record, and construct a spinach sowing interval change sequence based on the analysis result, wherein the spinach sowing interval change sequence includes a plurality of spinach sowing intervals, and the spinach sowing intervals are arranged according to a monotonic change trend; Step S3022: Analyze the spinach sowing interval variation sequence. If there are adjacent spinach sowing intervals whose variation is greater than or equal to a preset value, identify the intervals between the spinach sowing intervals as interval boundaries. Based on the interval boundaries, determine a number of spinach sowing interval segments in the spinach sowing interval variation sequence. Step S3023, based on the analysis of the spinach sowing experiment records, determining a plurality of soil texture parameter groups corresponding to each spinach sowing interval segment, wherein the soil texture parameter groups include a plurality of soil texture parameters; Step S3024, evenly dividing the distances between the soil particle simulation spheres to obtain a plurality of inter-sphere distance segments, and establishing a correspondence between the inter-sphere distance segments and the spinach sowing intervals based on the order of the spinach sowing intervals in the spinach sowing interval variation sequence; Step S3025: Obtain several current soil texture parameters and substitute them into different soil texture parameter groups. If the texture parameter difference between each soil texture parameter of the same type is less than or equal to a preset value, the soil texture parameter group is determined to be a reference soil texture parameter group. Step S3026, determine the spinach sowing interval segment corresponding to the reference soil texture parameter group, and based on the correspondence between the inter-ball distance segment and the spinach sowing interval, determine the inter-ball distance segment corresponding to the reference soil texture parameter group, and the total length of several inter-ball distance segments before the inter-ball distance segment is recognized as the distance between the soil particle simulation spheres.

6. The spinach sowing positioning method based on image data analysis according to claim 1, characterized in that: Methods for determining the similarity between the soil comprehensive performance models used for comparison include: Step S401, determining characteristic factors of a soil comprehensive characteristic representation model for comparison, the characteristic factors including the spherical size of the soil particle simulation spheres and the spatial size of the model space jointly constructed between the soil particle simulation spheres; Step S402 , setting a plurality of sphere size comparison intervals for sphere size, and setting a plurality of space size comparison intervals for space size, and randomly combining the sphere size comparison intervals and space size comparison intervals to obtain a plurality of size comparison interval groups; Step S403 , classifying the soil comprehensive performance models for comparison based on the size comparison interval group to which the sphere size and the spatial size of the soil comprehensive performance model for comparison belong, to obtain a reference performance model set.

7. The spinach sowing positioning method based on image data analysis according to claim 1, characterized in that: The method of analyzing the soil comprehensive characteristic performance model for comparison with the reference performance model set and determining the marker factor parameters of each characteristic factor includes: Step S501, determining characteristic factors to be analyzed, including the spherical size of the soil particle simulation spheres and the spatial size of the model space jointly constructed between the soil particle simulation spheres; Step S502: Analyze each reference representation model in the reference representation model set and define a number of marker sphere sizes, including establishing a sphere size reference line, mapping each sphere size onto the sphere size reference line in the form of a sphere size mapping point, analyzing the clustering characteristics of the sphere size mapping points, and if the number of sphere size mapping points within a preset size interval is greater than or equal to a preset value, then identify the midpoint of the size interval as the marker sphere size mapping point, and identify the sphere size corresponding to the marker sphere size mapping point as the marker sphere size; Step S503 : determining the spatial size corresponding to each sphere mapping point in the preset size interval, and calculating the average value of these spatial sizes, which is recorded as the marker spatial size.

8. The spinach sowing positioning method based on image data analysis according to claim 1, characterized in that: Methods for determining the degree of agreement between the comparative soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model include: Step S601, determining the comparison sphere size and the comparison space size of the soil comprehensive characteristic expression model, and determining the current sphere size and the current space size of the current soil comprehensive characteristic expression model; Step S602: Compare the current sphere size with the comparison sphere size, and construct a first comparison operator based on the comparison results. Compare the current space size with the comparison space size, and construct a second comparison operator based on the comparison results. Randomly select several other comprehensive feature representation models for comparison, and analyze the difference scale characteristics between them and the current comprehensive feature representation model. Step S603: Based on the difference scale feature, the first comparison operator and the second comparison operator are modified to obtain the degree of agreement between the comparison soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model; The expression for calculating the degree of fit is: ； Where W is the degree of fit, To preset the maximum degree of fit, is the weight coefficient of the sphere size parameter difference, is the difference in sphere size parameters, is the weight coefficient of the spatial size parameter difference, is the difference of spatial size parameters, is the difference scale judgment function between the xth randomly selected comparison soil comprehensive characteristic expression model and the current soil comprehensive characteristic expression model. If the difference in spherical size parameters and spatial size parameters of the two are both less than or equal to the preset value, then Output 1, otherwise output 0, P is the difference feature impact adjustment coefficient, and b is the difference feature impact adjustment constant.

9. A spinach sowing positioning system based on image data analysis, characterized in that: The spinach sowing positioning method for executing any one of claims 1 to 8 comprises: The first module is used to capture the entire sowing soil area to obtain a sowing soil image, determine the size of a random screenshot of the sowing soil image based on the actual area of ​​the sowing soil area, and capture a number of soil image blocks for analysis; The second module is used to analyze each soil image block for analysis, determine the soil color characteristics and soil texture characteristics corresponding to each soil image block for analysis, and calculate the average soil color characteristics and average soil texture characteristics of multiple soil image blocks for analysis; The third module is used to construct a soil comprehensive characteristic performance model. The soil comprehensive characteristic performance model is adjusted according to the average soil color characteristics and the average soil texture characteristics to obtain a soil comprehensive characteristic performance model for comparison. Based on the spinach sowing experiment records, the corresponding spinach sowing interval is configured according to the soil comprehensive characteristic performance model for comparison; The fourth module is used to classify the soil comprehensive characteristic performance models for comparison with the same spinach sowing interval, and to classify the soil comprehensive characteristic performance models for comparison based on the similarity between the soil comprehensive characteristic performance models for comparison to obtain a reference performance model set; The fifth module is used to analyze the soil comprehensive characteristic performance model for comparison in the reference performance model set, determine the marker factor parameter of each characteristic factor, mark the reference performance model set with the marker factor parameter, and construct a reference performance model library from the plurality of reference performance model sets; The sixth module is used to analyze the currently taken sowing soil image, and construct the current soil comprehensive characteristic expression model based on the analysis results, and determine the characteristic factor parameters in the current soil comprehensive characteristic expression model, and based on the parameter difference value of the characteristic factor parameters and the corresponding marker factor parameters, find the corresponding reference expression model set in the reference expression model library, and compare each comparison soil comprehensive characteristic expression model in the reference expression model set with the current soil comprehensive characteristic expression model, and determine the degree of consistency between the two based on the comparison results, and determine the suitable comparison soil comprehensive characteristic expression model based on the degree of consistency, and determine the position of spinach sowing based on the spinach sowing interval corresponding to the comparison soil comprehensive characteristic expression model.

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