A method and system for dynamically evaluating arable land quality by fusing AI modeling

Abstract

The application is suitable for the field of cultivated land quality evaluation and dynamic monitoring technology, and provides a cultivated land quality dynamic evaluation method and system fusing AI modeling, the method comprising: if there is a preset mode, generating a burying threshold according to a burying comprehensive index corresponding to a critical interval, obtaining a burying comprehensive index of a target cultivated land area, and correcting the cultivated land quality score according to the difference between the burying comprehensive index and the burying threshold. The application dynamically corrects the cultivated land quality score based on the burying threshold, so that the score not only reflects the state of the current cultivation stage, but also can predict the delayed influence caused by the residual film burying, thereby making up for the deficiency of the prior art that only performs static evaluation in a single stage. The application effectively solves the problem that the burying difference under different slope conditions is not recognized by the existing evaluation system, makes the cultivated land quality evaluation more accurate and reliable, can provide reliable basis for cultivated land grade division, farmland management subsidy issuance and operation planning adjustment, and has remarkable popularization and application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cultivated land quality evaluation and dynamic monitoring, and particularly relates to a cultivated land quality dynamic evaluation method and system fusing AI modeling. BACKGROUND

[0002] In the existing cultivated land quality evaluation technology, a comprehensive evaluation system based on multiple dimensions such as soil fertility, plough layer thickness, soil conservation capacity, irrigation and drainage conditions, and surface pollution conditions is generally used. The cultivated land quality score is calculated by collecting the soil physical and chemical indicators and environmental parameters of the current stage of the land plot. This kind of evaluation system can accurately reflect the static state of the cultivated land at a certain time period, and is the mainstream method widely used in the field of agricultural management and cultivated land protection. At the same time, some studies also focus on the influence of plastic film residues on the cultivated land environment, and try to include the residue content as a pollution index in the evaluation system, so as to improve the environmental adaptability of the evaluation. However, most of these existing methods are limited to static observation, and only record the number, coverage or surface residue of the plastic film, and lack in-depth analysis of the dynamic behavior of the plastic film in the plowing process.

[0003] In actual production, especially in the slope cultivated land widely distributed in hilly areas, mountainous areas and slope transformation areas, the mechanical action of agricultural machinery on the soil surface will cause different degrees of plastic film compression, burial and migration, and this process is closely related to the slope. The same type of agricultural machinery will show different operation trajectories, pressure depths and soil turning modes under different slope conditions, so that the burial degree of the plastic film in the soil shows significant differences. The existing technology does not systematically identify the differences in plastic film burial caused by slope differences, and does not explore the delayed effects that may be caused by the differences in the subsequent crop growth stage. The actual investigation in the field shows that the phenomena of decreased emergence rate, poor uniformity of emergence or weak growth potential of seedlings in some areas are often attributed to the differences in seeding depth, meteorological conditions, uneven supply of fertilizer and water, etc. in the existing evaluation system, and the difference in the burial degree of the plastic film in the previous stage is not considered as a possible cause.

[0004] The existing cultivated land quality evaluation technology lacks dynamic correlation analysis capability across stages, and cannot establish a quantifiable link between the plastic film burial in the plowing stage and the growth performance in the subsequent emergence stage, so it cannot identify when the plastic film burial reaches a critical level that can have a substantial impact on crops, nor can it make forward-looking adjustments to the score in advance. Especially in the context of slope cultivated land, the differences in plastic film burial caused by different slopes have been ignored by the existing system for a long time, making it difficult for the static score to reflect the potential risks in the subsequent stage, and making the cultivated land quality evaluation lagging and limited. SUMMARY

[0005] The purpose of the present application is to provide a cultivated land quality dynamic evaluation method and system integrating AI modeling, aiming to solve the problems raised in the background art.

[0006] The present application is implemented as a cultivated land quality dynamic evaluation method integrating AI modeling, which comprises:

[0007] Obtaining the cultivated land quality score of the target cultivated land area after the end of the preset cultivation stage, and calling several sample cultivated land areas with the same cultivated land background characteristics but different slopes from the database;

[0008] Obtaining the burying comprehensive index of the sample cultivated land area caused by the disturbance of agricultural machinery operation on the existing film residue in the preset cultivation stage, and determining whether the burying comprehensive index presents a preset correlation trend with the increase of slope;

[0009] If there is a correlation trend, obtaining the overall crop emergence index of the sample cultivated land area in the subsequent crop emergence stage, and constructing an association trend curve based on the burying comprehensive index and the corresponding overall crop emergence index;

[0010] Determining whether the association trend curve meets the preset mode, which is that when the burying comprehensive index is in the low value interval, the overall crop emergence index remains stable, and when the burying comprehensive index exceeds a certain critical interval, the overall crop emergence index shows a downward trend;

[0011] If there is a preset mode, generating a burying threshold value according to the burying comprehensive index corresponding to the critical interval, obtaining the burying comprehensive index of the target cultivated land area, and correcting the cultivated land quality score according to the difference between the burying comprehensive index and the burying threshold value.

[0012] As a further limitation of the embodiment of the present application, the same cultivated land background characteristics means that the sample cultivated land area and the target cultivated land area are consistent in soil properties, crop types, cultivation systems, agricultural operation conditions and weather conditions in the planting stage, or the difference is within a preset tolerance threshold.

[0013] As a further limitation of the embodiment of the present application, the calculation process of the burying comprehensive index comprises: obtaining the burying depth index and burying density index of the film residue disturbed by agricultural machinery operation in the sample cultivated land area in the preset cultivation stage, and performing weighted processing or nonlinear combination on the burying depth index and burying density index according to the preset weight coefficient or combination function, to generate the burying comprehensive index for representing the burying degree of the film residue.

[0014] As a further limitation of the technical scheme of the embodiment of the present application, the preset correlation trend refers to: a sample sequence is formed by ordering a plurality of sample cultivated regions according to the corresponding slope size from small to large, and the corresponding burying comprehensive index in the sample sequence increases monotonously with the increase of the slope.

[0015] As a further limitation of the technical scheme of the embodiment of the present application, the overall crop emergence index refers to an index for characterizing the emergence condition of the sample cultivated region at the crop emergence stage, including at least one of the number of emergence per unit area, the emergence rate, the emergence uniformity or the seedling growth potential, or a comprehensive emergence index obtained by weighted combination of the above indexes.

[0016] As a further limitation of the technical scheme of the embodiment of the present application, if there is a preset mode, the step of generating a burying threshold value according to the burying comprehensive index corresponding to the critical interval, obtaining the burying comprehensive index of the target cultivated region, and correcting the cultivated land quality score according to the difference between them includes:

[0017] After determining that there is a preset mode, the critical interval where the stable trend changes to the downward trend in the correlation trend curve is identified, and the average value of the burying comprehensive index corresponding to all sample cultivated regions in the critical interval is calculated;

[0018] The average value is set as the burying threshold value, the burying comprehensive index of the target cultivated region is obtained, and the difference between the burying comprehensive index of the target cultivated region and the burying threshold value is quantified to generate a correction factor;

[0019] The correction factor is used to correct the cultivated land quality score to obtain an optimized cultivated land quality score, and the optimized cultivated land quality score is applied to the output of the dynamic evaluation result of the cultivated land quality of the target cultivated region.

[0020] As a further limitation of the technical scheme of the embodiment of the present application, when correcting the cultivated land quality score, a preset correction function is used, and the correction function is: ;

[0021] Wherein, refers to the optimized cultivated land quality score, refers to the initial cultivated land quality score, refers to the minimum acceptable value of the cultivated land quality score, refers to the burying comprehensive index of the target cultivated region, refers to the burying threshold value, refers to the correction factor, refers to a preset control correction strength coefficient.

[0022] A cultivated land quality dynamic evaluation system integrating AI modeling, the system comprising:

[0023] The sample region acquisition module is configured to acquire a farmland quality score of the target farmland region after a preset cultivation stage ends, and to retrieve from a database a plurality of sample farmland regions that have the same farmland background characteristics as the target farmland region but different slopes;

[0024] The burying index calculation module is configured to acquire a burying comprehensive index of the sample farmland region caused by disturbance of existing mulch film residues by agricultural machinery operation in the preset cultivation stage, and to determine whether the burying comprehensive index presents a preset correlation trend with an increase in the slope;

[0025] The emergence index acquisition module is configured to, when the correlation trend exists, acquire an overall crop emergence index of the sample farmland region in a subsequent crop emergence stage, and to construct a correlation trend curve based on the burying comprehensive index and the corresponding overall crop emergence index.

[0026] The pattern recognition module is configured to determine whether the correlation trend curve satisfies a preset pattern, the preset pattern being that the overall crop emergence index remains stable when the burying comprehensive index is in a low value range, and the overall crop emergence index presents a downward trend when the burying comprehensive index exceeds a certain critical range.

[0027] The score correction module is configured to, when the preset pattern exists, generate a burying threshold according to the burying comprehensive index corresponding to the critical range, acquire the burying comprehensive index of the target farmland region, and correct the farmland quality score according to the difference between the burying comprehensive index and the burying threshold.

[0028] As a further limitation of the technical scheme of the embodiment of the present application, the same farmland background characteristics refer to that the sample farmland region and the target farmland region are consistent in soil properties, crop types, cultivation systems, agricultural operation conditions, and weather conditions in the planting stage, or the differences are within a preset tolerance threshold.

[0029] As a further limitation of the technical scheme of the embodiment of the present application, the calculation process of the burying comprehensive index includes: acquiring a burying depth index and a burying density index of the mulch film residues disturbed by agricultural operation in the preset cultivation stage in the sample farmland region, and performing weighted processing or nonlinear combination on the burying depth index and the burying density index according to a preset weight coefficient or a combination function, to generate a burying comprehensive index for representing the burying degree of the mulch film residues.

[0030] Compared with the prior art, the present application has the following beneficial effects:

[0031] The present application realizes the prospective quantitative analysis of the relationship between the disturbance mode of slope farmland, the degree of residual film burying and the emergence performance of subsequent crops by constructing a cross-stage dynamic farmland quality evaluation system based on the burying comprehensive index. By establishing a trend identification model for the burying characteristics of different slope samples, and further constructing a correlation trend curve between the burying degree and the overall crop emergence index, the present application can accurately identify the degree at which residual film burying starts to cause substantial decline in the emergence stage, and generate a burying threshold with physical meaning accordingly. Based on the burying threshold, the farmland quality score is dynamically corrected, so that the score not only reflects the state of the current cultivation stage, but also can predict the delayed influence caused by residual film burying, thereby making up for the shortcomings of the prior art that only performs static evaluation in a single stage. The present application effectively solves the problem that the burying differences under different slope conditions are not recognized by the existing evaluation system, so that the farmland quality evaluation is more accurate and reliable, which can provide reliable basis for farmland grade division, farmland management subsidy issuance and operation planning adjustment, and has significant popularization and application value. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 A flowchart of the method provided by the embodiment of the present application is shown.

[0033] Figure 2 A flowchart of the method provided by the embodiment of the present application is shown.

[0034] Figure 3 An application architecture diagram of the system provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0036] Figure 1 A flowchart of the method provided by the embodiment of the present application is shown.

[0037] Specifically, a farmland quality dynamic evaluation method fusing AI modeling, the method specifically comprises the following steps:

[0038] Step S100, obtaining the farmland quality score of the target farmland region after the end of the preset cultivation stage, and calling a plurality of sample farmland regions with the same farmland background characteristics but different slopes from the database.

[0039] The same ploughing background characteristics refer to the sample ploughing region and the target ploughing region being consistent in soil properties, crop types, farming systems, farming operation conditions, and weather conditions at the planting stage, or the differences being within a preset tolerance threshold.

[0040] In the embodiments of the present application, the target ploughing region is usually a ploughing field with a certain slope characteristic. Ploughing fields with slopes exist in large areas in China, typically distributed in the hilly region of the Loess Plateau, the valley region of Southwest China, the arid slope ploughing region of Northwest China, and part of the mountainous and hilly agricultural region. Such slope ploughing land is very common in agricultural production, and the popularization rate of mechanical operation is continuously improving, but the slope conditions themselves can affect the travel trajectory, operation stability, and disturbance mode of the ploughing layer of the farm machinery. Therefore, the present application takes the ploughing region with slope characteristics as the main research object, which has high universality and practical significance.

[0041] The preset ploughing stage is a primary surface disturbance stage with the participation of farm machinery, such as mechanical land preparation, shallow rotary ploughing, or ditching operation stage. At the beginning of this stage, most of the mulch residues left on the ploughing surface in the last planting cycle are still exposed or distributed in a shallow form; and at the end of this stage, due to the passage of the farm machinery device (such as rotary plough, ditcher, combined land preparation machine, etc.) through the ploughing surface layer, the working parts of the device will form a certain turning, covering, chopping, or moving effect on the soil, thereby causing part of the residual film to be buried into the soil surface layer. This stage is a typical and necessary step in ploughing operation, and the residual film disturbance effect in this stage is obvious.

[0042] The ploughing quality score is an index commonly used in the existing technology for the classification of ploughing quality grades or the dynamic monitoring system of ploughing quality. In the existing technology, the ploughing quality score is usually constructed based on multiple dimensions such as soil fertility conditions, ploughing layer thickness, soil conservation ability, surface flatness, soil organic matter content, soil particle structure, irrigation and drainage conditions, and production environment pollution conditions, and a comprehensive score is formed through weight combination or index model. It is reasonable to perform ploughing quality scoring at the end of the preset ploughing stage, because this stage has completed the preliminary land preparation, and the ploughing layer structure, disturbance state, and residual film condition all have a certain stability, which is suitable as a periodic quality assessment node.

[0043] The present application is based on the research findings of the present inventors that although the existing technology refers to the amount or coverage of mulch residues when performing ploughing quality scoring, the consideration is relatively shallow, mainly limited to the amount of residual film pollution itself, and does not pay attention to the delayed effects of the degree of residual film burial caused by farm machinery disturbance. Especially in ploughing regions with slope characteristics, the farm machinery has weak stability on the slope surface, and the cutting angle, wheel pressure depth, and disturbance trajectory of the farm machinery change significantly with the slope, thereby causing differences in the degree of residual film burial in ploughing land with the same background conditions but different slopes.

[0044] Further research shows that this difference will be reflected in the subsequent crop emergence stage as a prologue and epilogue relationship, because the difference in burying degree will affect the difficulty of seedling root breaking through the soil, the change of local soil physical and chemical structure, the blocking effect of mulch fragments in the emergence layer and other factors. However, when the existing technology observes a slight decline in the number of emergence, the uniformity of emergence or the growth potential of seedlings at the emergence stage, it is often attributed to objective factors such as climate deviation, seeding depth error, seed quality difference, local uneven fertilization, and will not trace back to determine whether it is related to the strength of residual film burying in the previous stage. Therefore, when the degree of residual film burying is different in regions with different slope degrees, if the possible substantial impact on the subsequent emergence stage can be identified in the current preset cultivation stage, the quality score of the cultivated land can be prospectively corrected in the current stage, making the score more in line with the real state of the cultivated land, and providing more accurate basis for subsequent cultivated land classification, compensation, management scheme formulation, etc.

[0045] In the embodiment of the present application, the database can be the existing data resources in the agricultural production information platform, the agricultural operation monitoring system, the remote sensing monitoring data set, the digital archive of the plot or the regional cultivated land quality monitoring system. The database usually stores a large amount of historical cultivated land sample data, including the slope data of the plot, the soil property data, the crop planting record, the agricultural operation trajectory data, the residual film monitoring data and the emergence stage monitoring data, etc. The database of the present application belongs to the extension or integration form of the existing technology, and is not limited to the specific storage method, structure or source of the database.

[0046] It is of great significance to screen the sample cultivated land region in step S100. By limiting the sample cultivated land region to a plurality of plots with the same cultivated land background characteristics as the target cultivated land region, the influence of soil property differences, crop type differences, cultivation system differences, agricultural operation condition differences and climate differences on the residual film burying method and the subsequent emergence performance can be effectively reduced, and the comparability of the burying comprehensive index change and the accuracy of the trend analysis can be improved. The same cultivated land background can include soil texture, soil fertility grade, crop type, planting system, agricultural machine model and operation condition, weather condition in the operation period, etc. The reason for adopting a more stringent screening condition is to ensure that the relationship between the change of the burying comprehensive index obtained in the subsequent analysis and the slope factor has statistical and practical significance, and will not be masked or weakened by multiple interference factors. The screening of the present application relies on big data, and the database contains a large amount of historical cultivation samples and monitoring indexes. The AI model or data query screening algorithm can efficiently screen out the samples that meet the conditions. If there is a lack of big data support, it is difficult to meet the sample screening requirements of multiple dimensional background consistency, and it is also difficult to form a reliable and universal trend analysis of burying, so the big data environment is one of the important foundations for the effective implementation of the present application.

[0047] Further, the cultivated land quality dynamic evaluation method based on the fusion AI modeling further includes the following steps:

[0048] In step S200, a burying comprehensive index of the sample cultivated land region disturbed by the agricultural machinery operation in the preset farming stage is obtained, and whether the burying comprehensive index presents a preset correlation trend with the increase of the slope is determined.

[0049] The calculation process of the burying comprehensive index includes: obtaining a burying depth index and a burying density index of the film residue disturbed by the agricultural machinery operation in the sample cultivated land region in the preset farming stage, and performing weighted processing or nonlinear combination on the burying depth index and the burying density index according to a preset weight coefficient or a combination function to generate the burying comprehensive index for representing the burying degree of the film residue.

[0050] The preset correlation trend refers to that a sample sequence is formed by arranging a plurality of sample cultivated land regions in order of the corresponding slope size from small to large, and the corresponding burying comprehensive index in the sample sequence presents a monotonic growth trend with the increase of the slope.

[0051] In the embodiment of the present application, the burying comprehensive index is used to quantify the degree of burying of the film residue into the soil by the agricultural machinery disturbance after the end of the preset farming stage, and its acquisition method is based on the monitoring means that can be realized by the prior art. Specifically, the burying comprehensive index is composed of a burying depth index and a burying density index, wherein the burying depth index is used to represent the burying depth range of the residual film fragments relative to the surface of the plough layer, and the burying density index is used to represent the number, coverage area or volume ratio of the residual film fragments that are buried in the unit area. Both types of indexes can be obtained by the mature methods in the prior art, for example, the distribution depth of the residual film in the soil can be obtained by using high-resolution soil profile image analysis, shallow geology radar scanning, infrared imaging, multispectral remote sensing inversion or by using a handheld soil profile scanner, and the burying depth and number distribution characteristics can be obtained by image segmentation, target recognition and statistical analysis of the collected data; at the same time, the number and density of the residual film at different depths can also be obtained by the experimental method of plot sampling and layered screening analysis. The extraction of the above burying depth index and burying density index belongs to the technical ability covered by the prior art in the field. The present application performs weighted operation or nonlinear combination on the two types of indexes by using a preset weight coefficient or a combination function, so that the obtained burying comprehensive index can more comprehensively reflect the real situation of the residual film burying.

[0052] The construction significance of the burying comprehensive index lies in that the burying degree of the residual film in the soil after the agricultural machine operation is reflected as a whole through a single index. The burying depth and density of the residual film will jointly affect the subsequent changes of the tillage layer structure, the emergence resistance of seedlings and the local microenvironment of the soil, and therefore, it is difficult to accurately describe the actual influence of the residual film burying by simply relying on the single depth or density information. The present application unifies the two by the form of the comprehensive index, which is helpful to provide a characterization parameter with better comparability and higher stability for the subsequent growth performance analysis in the emergence stage.

[0053] By judging whether the burying comprehensive index presents a preset correlation trend with the increase of the slope, whether the burying degree of the residual film has a systematic change rule under different slope conditions can be identified. The determination method of the preset correlation trend is that a sample tillage area is sorted according to the corresponding slope from small to large to form a sample sequence, and whether the burying comprehensive index in the sample sequence presents a monotonic increasing trend with the increase of the slope is judged. When the slope is larger, the agricultural machine is more likely to have abnormal cutting angle, wheel pressure deviation, machine jumping and the like due to the decrease of the driving stability, so that the migration, burying and covering behaviors of the residual film fragments in the soil layer are more significant. Therefore, if the sample data satisfy the monotonic increasing trend, it indicates that there is a stable statistical rule between the burying degree of the residual film and the slope.

[0054] The satisfaction of the preset correlation trend has important significance, which verifies whether the slope factor is the key driving factor causing the difference in the burying of the residual film. The judgment directly responds to the core research point mentioned in the previous step S100, that is, the burying degree of the residual film in the tillage area with different slopes will be different under the same background condition due to the different disturbance modes of the agricultural machine, and this difference may be reflected in the subsequent emergence stage through the changes of the emergence quantity and the growth of the seedlings. Only when it is confirmed that there is a stable trend between the burying comprehensive index and the slope, the correlation trend curve constructed based on the burying comprehensive index and the index in the emergence stage can have reliability, so as to provide a reliable basis for further determining the critical interval, constructing the burying threshold and prospectively correcting the quality score of the tillage land. If the burying comprehensive index does not present a trend change with the slope, it indicates that the slope is not the main factor affecting the burying of the residual film, and in this case, continuing the subsequent steps may lead to the deviation of the results from the true situation, so the setting of the trend determination step plays a key role in ensuring the scientificity and accuracy of the whole process of the present application.

[0055] Further, the dynamic evaluation method of the quality of the tillage land based on the fusion AI modeling further comprises the following steps:

[0056] In step S300, if the correlation trend exists, the overall crop emergence index of the sample tillage area in the subsequent crop emergence stage is obtained, and a correlation trend curve is constructed based on the burying comprehensive index and the corresponding overall crop emergence index.

[0057] Step S400, judging whether the correlation trend curve meets a preset mode, the preset mode being that when the burying comprehensive index is in a low value interval, the overall crop emergence index remains stable, and when the burying comprehensive index exceeds a certain critical interval, the overall crop emergence index presents a downward trend.

[0058] The overall crop emergence index refers to an index for characterizing the emergence condition of the sample cultivated land region in the crop emergence stage, including at least one of the number of seedlings per unit area, the emergence rate, the emergence uniformity or the seedling growth potential, or a comprehensive emergence index obtained by weighted combination of the above indexes.

[0059] In the embodiment of the present application, when the step S200 judges that the burying comprehensive index of the sample cultivated land region presents a preset correlation trend with the increase of the slope, it means that the degree of residual film burying has systematic differences between different slope cultivated lands. After confirming this premise, the crop emergence of these sample cultivated land regions in the later stage can be further analyzed to explore whether the difference in burying degree will have a substantial impact on the crop emergence performance.

[0060] In the embodiment, the overall crop emergence index is used to characterize the emergence condition of the sample cultivated land region in the crop emergence stage, and its determination method belongs to the prior art. Specifically, the number of seedlings per unit area can be obtained by ground patrol, image recognition counting, aerial remote sensing statistics and the like; the emergence rate can be calculated by the ratio of the sowing amount to the actual number of seedlings; the emergence uniformity is usually calculated according to the indexes of plant spacing difference and spatial distribution uniformity; and the seedling growth potential is usually characterized by plant height, leaf area, color index and the like. Each of the above indexes can be obtained by mature technical means such as manual measurement, monitoring equipment collection, unmanned aerial vehicle imaging analysis and spectral feature extraction, and a comprehensive emergence index can be constructed by weighted processing or combination function according to the need, so as to more comprehensively reflect the overall performance of the sample cultivated land region in the emergence stage.

[0061] In constructing the correlation trend curve, the burying comprehensive index of each sample cultivated area can be taken as the independent variable, and the corresponding overall crop emergence index is taken as the dependent variable. The ordered data point sequence is formed by ordering the burying comprehensive index from small to large, and then the data fitting technology is used to construct the continuous trend relationship. Specifically, the statistical fitting methods such as linear fitting, polynomial fitting, piecewise fitting, and spline fitting in the prior art can be selected, or the ordered data points are fitted by using random forest regression, support vector regression, neural network regression, and other machine learning regression models to generate a trend curve reflecting the relationship between the change of burying degree and the change of overall crop emergence index. By fitting the above ordered data points, the trend curve can more accurately show the change direction and change amplitude of crop emergence performance when the burying comprehensive index gradually increases, thereby providing a data basis for subsequent analysis of whether the trend curve meets the preset mode and determination of the burying threshold.

[0062] In step S400, it is important to determine whether the correlation trend curve meets the preset mode. When the trend curve meets the preset mode, that is, the overall crop emergence index remains stable when the burying comprehensive index is in the low value interval, and the overall crop emergence index shows a downward trend when the burying comprehensive index exceeds a certain critical interval, it indicates that the difference in residual film burying does indeed have a delayed impact on the emergence stage. This result directly verifies the scientific hypothesis of the core research point in step S100, that is, under the same cultivated land background conditions, the difference in residual film burying caused by different farming disturbance methods in different slope cultivated areas will be reflected in the delay stage through the emergence performance, and this influence has a stage feature.

[0063] The reason why the overall crop emergence index remains basically stable when the burying comprehensive index is in the low value interval is that under the condition of low residual film burying degree, the blocking effect of residual film fragments on the seedling emergence path is weak, and the soil physical structure and water permeability have not changed significantly enough to affect the growth of seedlings. The crop can overcome the slight obstacles by relying on its own growth ability, so the overall crop emergence index will not fluctuate obviously. However, when the burying comprehensive index exceeds a certain critical interval, it indicates that the degree of residual film burying has reached a level that affects the emergence ability of seedlings. Residual film fragments may form a barrier at a critical depth, or cause local soil compaction and decreased air permeability, thereby increasing the seedling breaking resistance, leading to decreased emergence number, uneven emergence, or weakened growth potential. Therefore, the downward trend of the trend curve after exceeding a certain threshold value is a key manifestation of the substantial impact of the degree of residual film burying on the subsequent growth stage.

[0064] Through the judgment of the preset mode, it can be accurately identified that the residue film is buried to what extent to start to cause the deterioration of the crop emergence performance, thereby providing a basis for subsequent determination of the burying threshold and correction of the plough quality score. Thus, the step S400 completes the closed loop from data rule identification to scientific hypothesis verification, and lays a foundation for the dynamic evaluation mechanism proposed in the application.

[0065] Further, the plough quality dynamic evaluation method based on the fusion AI modeling further includes the following steps:

[0066] Step S500, if there is a preset mode, generating a burying threshold according to the burying comprehensive index corresponding to the critical interval, obtaining the burying comprehensive index of the target plough region, and correcting the plough quality score according to the difference between the burying comprehensive index and the burying threshold.

[0067] Specifically, Figure 2 A flowchart for correcting the plough quality score is shown.

[0068] If there is a preset mode, the generating of the burying threshold according to the burying comprehensive index corresponding to the critical interval, the obtaining of the burying comprehensive index of the target plough region, and the correction of the plough quality score according to the difference between the burying comprehensive index and the burying threshold specifically include the following steps:

[0069] Step S501, after determining that there is a preset mode, identifying the critical interval in which the stable trend changes to the downward trend in the associated trend curve, and calculating the average value of the burying comprehensive index corresponding to all sample plough regions in the critical interval;

[0070] Step S502, setting the average value as the burying threshold, obtaining the burying comprehensive index of the target plough region, and quantifying the difference between the burying comprehensive index and the burying threshold to generate a correction factor;

[0071] Step S503, correcting the plough quality score by using the correction factor to obtain an optimized plough quality score, and applying the optimized plough quality score to the output of the plough quality dynamic evaluation result of the target plough region.

[0072] When correcting the plough quality score, a preset correction function is used, and the correction function is:

[0073] ;

[0074] Among them, refers to the optimized plough quality score, refers to the initial plough quality score, refers to the minimum acceptable value of the plough quality score, refers to the burying comprehensive index of the target plough region, refers to the burying threshold, refers to a correction factor, refers to a preset control correction intensity coefficient. The preset control correction intensity coefficient can be determined according to the average influence amplitude of the burying degree on the emergence performance in the historical sample data, the regional planting habit, the sensitivity of the crop to the residual film barrier and the weight requirement of the ploughing quality scoring system to the residual film factor, so that the correction amplitude is consistent with the actual production influence.

[0075] In the embodiment of the present application, the setting of step S500 is of great significance. When a stable and phased rule between the burying comprehensive index and the overall crop emergence index has been identified in the foregoing steps, and it is determined that the rule conforms to the preset mode, the ploughing quality score obtained by the target ploughing region at the end of the current preset ploughing stage can be prospectively corrected based on the rule. By adjusting the score with the potential delay influence reflected by the burying degree at the current stage, the final score can more truly reflect the actual performance that the ploughing region may present in the subsequent growth stage, so as to avoid potential risks in advance and improve the accuracy and application value of the ploughing quality evaluation. The present application realizes dynamic ploughing quality evaluation across stages by establishing a correlation between the burying comprehensive index, an early state quantity, and the growth performance at the emergence stage, thereby providing substantial benefits for ploughing management.

[0076] In the embodiment, the average value of the burying comprehensive indexes of all sample ploughing regions in the critical interval is set as the burying threshold, which is based on the comprehensive consideration of statistical stability and actual significance. The critical interval is a key section in the trend curve where the stable trend changes to a downward trend, and the burying comprehensive index of this section can reflect the critical degree at which the crop emergence performance begins to be substantially affected. Since the burying comprehensive index of a single sample may be affected by local random factors or measurement errors, simply using a single point as the threshold may lead to instability or deviation. The present application selects the average value of the critical interval, which can smooth accidental deviations, make the threshold more representative and reliable, and also more consistent with the natural fluctuation characteristics of the residual film distribution at the farmland scale.

[0077] In step S502, the correction factor is generated by quantifying the difference between the burying comprehensive index of the target ploughing region and the burying threshold, aiming to dynamically adjust the score according to the degree of deviation of the burying degree from the critical value. The higher the burying degree, the greater the possibility of causing actual negative influence in the subsequent emergence stage, so the score needs to be corrected more strongly; on the contrary, if the burying degree is close to or lower than the threshold, the correction amplitude is smaller or even no correction is needed. By generating the correction factor through the difference, the score correction process can maintain consistency and proportional relationship with the burying influence degree, making the correction result more scientific and reasonable.

[0078] The correction of the cultivated land quality score is only performed when the burying comprehensive index of the target cultivated land region is greater than the burying threshold value. When the burying comprehensive index of the target cultivated land region is less than or equal to the burying threshold value, it indicates that the degree of residual film burying has not reached the critical level of causing negative effects on the emergence stage, and the intensity of residual film burying in this region is still within the range that can be overcome by the growth ability of crops or the regulation ability of the soil environment, and will not cause substantial delay effects. In this case, the original cultivated land quality score does not need to be corrected, and the initial score obtained after the preset tillage stage ends can be directly used. By limiting the correction condition, unnecessary score adjustment can be avoided when the burying degree is low, thereby ensuring that the score correction process is reasonable and targeted, so that the optimized cultivated land quality score can not only reflect potential risks, but also avoid distortion of the evaluation results due to slight differences, and improve the stability and credibility of the score system.

[0079] In step S503, the correction factor is applied to the original cultivated land quality score to obtain an optimized cultivated land quality score, and the optimized score is output as the dynamic evaluation result of the cultivated land quality. This step not only completes the mathematical correction of the score, but also is a key link for the transition from "static evaluation" to "dynamic evaluation" of the present application. Traditional cultivated land quality evaluation usually only depends on the soil conditions and farmland conditions of the current stage, while the score correction step of the present application enables the evaluation result to reflect the cross-stage influencing factors, realizes the predictive response to subsequent potential changes, and makes the application scenarios such as cultivated land classification, farmland management subsidy, and tillage system optimization more accurate and controllable.

[0080] In the embodiment of the present application, the function for correcting the cultivated land quality score is an intuitive and effective calculation method. The function combines the proportion of the burying comprehensive index deviating from the burying threshold value and the preset control correction intensity coefficient, so that the correction amplitude is proportional to the burying degree, the calculation process is clear and transparent, and it is suitable for large-scale samples and multiple types of cultivated land regions. In addition, other forms of calculation methods can also be used, such as setting a segmented correction function, using an exponential or logarithmic decay function, applying a score adjustment function based on a machine learning model, and the like, to adapt to the application needs of different regions or crop types.

[0081] The following is a detailed example to show the overall implementation process:

[0082] Suppose that there are five sample farmland regions in a certain area, and their burying comprehensive indexes (sorted from small to large) are 12, 15, 18, 24 and 30, and the corresponding overall crop emergence indexes are 96, 95, 94, 87 and 80 respectively. By constructing a correlation trend curve, it can be observed that the emergence indexes of the first three samples basically remain in the interval of 94 to 96, and the change is relatively stable, while the emergence index of the fourth sample drops to 87, and the emergence index of the fifth sample drops to 80, indicating that the burying comprehensive index appears a critical turning point between 18 and 24. Assuming that the critical interval is 18 to 24, the average value is 21, which can be set as the burying threshold value.

[0083] If the burying comprehensive index of the target farmland region is 27, it is higher than the burying threshold value 21, and the deviation is 6. Assuming that the original farmland quality score is 85 and the preset control correction intensity coefficient is 0.05, the deviation ratio is 6 divided by 21, which is about 0.285. Multiplying the original score by 1 minus 0.05 times 0.285, about 83.8 is obtained, which is taken as the optimized farmland quality score output. This example shows how the present application dynamically adjusts the score based on the difference in burying degree, so that the score is more in line with the actual situation of the farmland region.

[0084] Through the above steps, the present application can accurately identify the influence law of residual film burying degree in farmland regions with different slopes, and reflect the forward influence to the farmland quality score in the current stage, effectively solving the problem of the lack of cross-stage influence analysis in the prior art. The present application can significantly improve the scientificity and accuracy of dynamic evaluation of farmland quality, provide more reliable basis for farmland management decision-making, and has good application prospect and popularization value.

[0085] Further, Figure 3 The application architecture diagram of the system provided by the embodiment of the present application is shown.

[0086] In another preferred embodiment provided by the present application, a farmland quality dynamic evaluation system integrating AI modeling comprises:

[0087] The sample region acquisition module 100 is configured to acquire the farmland quality score of the target farmland region after the end of the preset cultivation stage, and call a plurality of sample farmland regions with the same farmland background characteristics but different slopes from the database.

[0088] The same farmland background characteristics refer to that the sample farmland region and the target farmland region are consistent in soil properties, crop types, cultivation systems, agricultural operation conditions and weather conditions in the planting stage, or the difference is within a preset tolerance threshold.

[0089] Further, the farmland quality dynamic evaluation system integrating AI modeling further comprises:

[0090] The burying index calculation module 200 is configured to obtain a burying comprehensive index of the film residue disturbed by the agricultural machinery operation in the preset farming stage in the sample farmland region, and determine whether the burying comprehensive index presents a preset correlation trend with the increase of the slope.

[0091] The calculation process of the burying comprehensive index includes: obtaining the burying depth index and the burying density index of the film residue disturbed by the agricultural machinery operation in the preset farming stage in the sample farmland region, and performing weighted processing or nonlinear combination on the burying depth index and the burying density index according to a preset weight coefficient or a combination function, to generate the burying comprehensive index for representing the burying degree of the film residue.

[0092] Further, the dynamic evaluation system for farmland quality based on AI modeling further includes:

[0093] The emergence index acquisition module 300 is configured to, when the correlation trend exists, obtain an overall crop emergence index of the sample farmland region in a subsequent crop emergence stage, and construct a correlation trend curve based on the burying comprehensive index and the corresponding overall crop emergence index.

[0094] Further, the dynamic evaluation system for farmland quality based on AI modeling further includes:

[0095] The pattern recognition module 400 is configured to determine whether the correlation trend curve satisfies a preset pattern, and the preset pattern is that the overall crop emergence index remains stable when the burying comprehensive index is in a low value interval, and the overall crop emergence index presents a downward trend when the burying comprehensive index exceeds a certain critical interval.

[0096] Further, the dynamic evaluation system for farmland quality based on AI modeling further includes:

[0097] The score correction module 500 is configured to, when the preset pattern exists, generate a burying threshold according to the burying comprehensive index corresponding to the critical interval, obtain the burying comprehensive index of the target farmland region, and correct the farmland quality score according to the difference between the burying comprehensive index and the burying threshold.

[0098] It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in a certain order according to the arrows, the steps are not necessarily executed in the order of the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in order, and the steps can be executed in other orders. Moreover, at least some of the steps in the embodiments can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be round-robin or alternately executed with at least some of the other steps or sub-steps or stages of the other steps.

[0099] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0100] The technical features of the above-mentioned embodiments can be combined in any way. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0101] The above embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but should not be understood as a limitation on the patent scope of the present application. It should be noted that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0102] The above merely describes the preferred embodiments of the present application and should not be used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for dynamic assessment of arable land quality integrating AI modeling, characterized in that, The method includes: Obtain the farmland quality score of the target farmland area after the preset farming stage, and retrieve several sample farmland areas with the same farmland background characteristics as the target farmland area but different slopes from the database. The comprehensive index of burial caused by the disturbance of existing plastic film residue by agricultural machinery operations in the sample cultivated land area during the preset cultivation stage was obtained, and it was determined whether the comprehensive index of burial showed a preset correlation trend with the increase of slope. The calculation process of the comprehensive burial index includes: obtaining the burial depth index and burial density index of the residual plastic film after disturbance by agricultural machinery operations in the sample cultivated land area during the preset cultivation stage, and weighting or nonlinearly combining the burial depth index and burial density index according to the preset weighting coefficient or combination function to generate a comprehensive burial index for characterizing the degree of burial of residual plastic film. If a correlation trend exists, the overall crop emergence index of the sample cultivated land area in the subsequent crop emergence stage is obtained, and a correlation trend curve is constructed based on the comprehensive index of burial and the corresponding overall crop emergence index. Determine whether the correlation trend curve meets the preset mode. The preset mode is: when the comprehensive index of burial is in the low value range, the overall crop seedling index remains stable; when the comprehensive index of burial exceeds a certain critical range, the overall crop seedling index shows a downward trend. If a preset mode exists, a burial threshold is generated based on the burial comprehensive index corresponding to the critical interval, the burial comprehensive index of the target cultivated land area is obtained, and the cultivated land quality score is corrected based on the difference between the index and the burial threshold.

2. The method for dynamic assessment of arable land quality integrating AI modeling as described in claim 1, characterized in that, The same arable land background characteristics refer to the fact that the sample arable land area and the target arable land area are consistent in terms of soil properties, crop type, farming system, agricultural machinery operation conditions and weather conditions during the planting stage, or that the differences are within a preset tolerance threshold.

3. The method for dynamic assessment of arable land quality integrating AI modeling as described in claim 1, characterized in that, The preset correlation trend refers to: sorting several sample farmland areas according to their corresponding slope from small to large to form a sample sequence, and the comprehensive index of burial in the sample sequence shows a monotonically increasing trend with the increase of slope.

4. The method for dynamic assessment of arable land quality integrating AI modeling as described in claim 1, characterized in that, The overall crop emergence index refers to the index used to characterize the emergence status of crops in the sample cultivated land area during the emergence stage, including at least one of the following: number of seedlings per unit area, emergence rate, seedling uniformity, or seedling growth vigor, or a comprehensive emergence index obtained by weighting and combining the above indicators.

5. The method for dynamic assessment of arable land quality integrating AI modeling according to claim 1, characterized in that, If a preset mode exists, the steps of generating a burial threshold based on the comprehensive burial index corresponding to the critical interval, obtaining the comprehensive burial index of the target cultivated land area, and correcting the cultivated land quality score based on the difference between the index and the burial threshold include: After determining that a preset pattern exists, the critical interval in the associated trend curve that changes from a stable trend to a downward trend is identified, and the average value of the comprehensive burial index corresponding to all sample cultivated land areas within the critical interval is calculated. The average value is set as the burial threshold, and the comprehensive burial index of the target cultivated land area is obtained and its difference from the burial threshold is quantified to generate a correction factor. The farmland quality score is corrected using the aforementioned correction factor to obtain an optimized farmland quality score, and the optimized farmland quality score is then applied to the output of the dynamic assessment results of farmland quality in the target farmland area.

6. The method for dynamic assessment of arable land quality integrating AI modeling according to claim 5, characterized in that, When correcting the farmland quality score, a preset correction function is used, which is: ; in, This refers to the optimized farmland quality score. This refers to the initial farmland quality score. This refers to the minimum possible value for the arable land quality score. This refers to the comprehensive index of land burial in the target arable land area. This refers to the burial threshold. This refers to the correction factor. This refers to the preset control correction strength coefficient.

7. A dynamic assessment system for arable land quality integrating AI modeling, characterized in that, The system includes: The sample area acquisition module is used to obtain the farmland quality score of the target farmland area after the preset farming stage, and retrieve several sample farmland areas from the database that have the same farmland background characteristics as the target farmland area but have different slopes. The burial index calculation module is used to obtain the comprehensive burial index of the sample cultivated land area caused by the disturbance of the existing plastic film residue by agricultural machinery operations during the preset cultivation stage, and to determine whether the comprehensive burial index shows a preset correlation trend with the increase of slope. The calculation process of the comprehensive burial index includes: obtaining the burial depth index and burial density index of the residual plastic film after disturbance by agricultural machinery operations in the sample cultivated land area during the preset cultivation stage, and weighting or nonlinearly combining the burial depth index and burial density index according to the preset weighting coefficient or combination function to generate a comprehensive burial index for characterizing the degree of burial of residual plastic film. The seedling emergence index acquisition module is used to acquire the overall crop seedling emergence index of the sample cultivated land area in the subsequent crop seedling stage when the correlation trend exists, and to construct a correlation trend curve based on the burial comprehensive index and the corresponding overall crop seedling emergence index. The pattern recognition module is used to determine whether the correlation trend curve meets the preset pattern. The preset pattern is: when the comprehensive index of burial is in the low value range, the overall crop seedling index remains stable; when the comprehensive index of burial exceeds a certain critical range, the overall crop seedling index shows a downward trend. The scoring correction module is used to generate a burial threshold based on the burial comprehensive index corresponding to the critical interval when the preset mode exists, obtain the burial comprehensive index of the target cultivated land area, and correct the cultivated land quality score based on the difference between the index and the burial threshold.

8. The dynamic assessment system for arable land quality integrating AI modeling as described in claim 7, characterized in that, The same arable land background characteristics refer to the fact that the sample arable land area and the target arable land area are consistent in terms of soil properties, crop type, farming system, agricultural machinery operation conditions and weather conditions during the planting stage, or that the differences are within a preset tolerance threshold.

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