Methods and apparatus for determining nitrogen fertilizer application rate for Leymus chinensis forage grasses

Abstract

This application provides a method and apparatus for determining the nitrogen fertilizer application rate for *Leymus chinensis* forage. The method includes: collecting growth data and biological samples of target *Leymus chinensis* forage within a target area; determining the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target *Leymus chinensis* forage using the biological samples; constructing a first critical nitrogen dilution curve based on the aboveground dry matter and nitrogen concentration data; and determining the nitrogen fertilizer application rate for the target area based on the belowground dry matter, the first critical nitrogen dilution curve, and the growth data. This method enables precise application of nitrogen fertilizer during the production of *Leymus chinensis* forage, ensuring yield and root growth while reducing fertilization costs and environmental pollution.

Description

Technical Field

[0001] This application relates to the field of nitrogen diagnostic technology, and in particular to a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage. This application also relates to a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage, a computing device, and a computer-readable storage medium. Background Technology

[0002] Elymus, a genus of grasses, is a perennial forage grass belonging to the Poaceae family. It is highly resilient and adaptable, widely distributed in temperate regions globally, and extensively found in most parts of northern my country, as well as high-altitude and high-latitude areas such as the Qinghai-Tibet Plateau. Due to its vigorous vegetative growth, high crude protein content, tolerance to poor soil, and high forage value, it is an important forage species used in the restoration of natural grasslands and the establishment of artificial grasslands in my country.

[0003] Fertilization is one of the main factors affecting forage production and quality, especially for the growth of gramineous forage grasses, which plays an irreplaceable role. At present, there is a widespread phenomenon of excessive application of nitrogen fertilizer, which not only results in low nitrogen fertilizer utilization and a significant increase in production costs, but also causes a series of environmental problems due to nitrogen volatilization and leaching, such as water pollution and soil acidification, which are detrimental to sustainable agricultural development. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses, to address the technical deficiencies in the prior art. Embodiments of this application also provide a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses, a computing device, and a computer-readable storage medium.

[0005] According to a first aspect of the embodiments of this application, a method for determining the amount of nitrogen fertilizer to be applied to *Leymus chinensis* forage grasses is provided, comprising:

[0006] Collect growth data and biological samples of the target Leymus chinensis forage grass within the target area;

[0007] The aboveground dry matter, belowground dry matter, and nitrogen concentration of the target Leymus chinensis forage grass were determined using the biological samples.

[0008] Based on the aboveground dry matter and nitrogen concentration data, a first critical nitrogen dilution curve is constructed;

[0009] The amount of nitrogen fertilizer to be applied to the target area is determined based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data.

[0010] Optionally, the growth data and biological samples of the target *Leymus* species within the target area collected include:

[0011] At preset time points, samples are taken from the target area of ​​the *Leymus* species forage grasses with different amounts of nitrogen fertilizer applied, and the growth data and biological samples are obtained.

[0012] Optionally, determining the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target *Leymus* species using the biological sample includes:

[0013] The above-ground biological samples in the biological samples were dried to obtain above-ground dry samples;

[0014] The underground biological samples in the biological sample were dried to obtain underground dried samples;

[0015] Based on the aboveground dry matter sample, the aboveground dry matter mass and nitrogen concentration data are determined, and the underground dry matter mass is determined based on the underground dry matter mass.

[0016] Optionally, determining the nitrogen fertilizer application rate for the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data includes:

[0017] Determine the seed yield data of the target *Leymus* forage grass included in the growth data;

[0018] Based on the seed yield data and the first critical nitrogen dilution curve, a first yield-nitrogen nutrition curve is constructed.

[0019] Based on the underground dry matter mass and the first critical nitrogen dilution curve, a first root nitrogen nutrition curve is constructed.

[0020] The amount of nitrogen fertilizer applied is determined based on the first yield nitrogen nutrient curve and the first root nitrogen nutrient curve.

[0021] Optionally, constructing a first yield-nitrogen nutrient curve based on the seed yield data and the first critical nitrogen dilution curve includes:

[0022] Determine a first correlation curve between the seed yield data and the aboveground dry matter mass;

[0023] Based on the first correlation curve and the first critical nitrogen dilution curve, the first yield nitrogen nutrition curve is determined.

[0024] Optionally, constructing the first root nitrogen nutrition curve based on the underground dry matter mass and the first critical nitrogen dilution curve includes:

[0025] Determine a second correlation curve between the underground dry matter mass and the aboveground dry matter mass;

[0026] The first root nutrient curve is determined based on the second correlation curve and the first critical nitrogen dilution curve.

[0027] Optionally, determining the nitrogen fertilizer application rate based on the first yield nitrogen nutrient curve, the first root nitrogen nutrient curve, and the growth data includes:

[0028] By superimposing the first yield nitrogen nutrient curve and the first root nitrogen nutrient curve, a fertilization curve relating nitrogen nutrient index and comprehensive growth index is obtained, wherein the comprehensive growth index is relating seed yield data and underground dry matter mass.

[0029] The amount of nitrogen fertilizer applied is determined using the fertilization curve.

[0030] Optionally, determining the nitrogen fertilizer application rate for the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data includes:

[0031] Determine the leaf area index data of the target *Leymus* species included in the growth data;

[0032] Determine a third correlation curve between the leaf area index data and the aboveground dry matter;

[0033] Based on the third correlation curve and the first critical nitrogen dilution curve, construct the second critical nitrogen dilution curve;

[0034] The amount of nitrogen fertilizer applied is determined based on the second critical nitrogen dilution curve, the underground dry matter mass, and the growth data.

[0035] Optionally, determining the nitrogen fertilizer application rate based on the second critical nitrogen dilution curve, the underground dry matter mass, and the growth data includes:

[0036] Determine the seed yield data of the target *Leymus* forage grass included in the growth data;

[0037] Based on the seed yield data and the second critical nitrogen dilution curve, a second yield-nitrogen nutrition curve is constructed.

[0038] Based on the underground dry matter mass and the second critical nitrogen dilution curve, a second root nitrogen nutrition curve is constructed;

[0039] The amount of nitrogen fertilizer applied is determined based on the second yield nitrogen nutrient curve and the second root nitrogen nutrient curve.

[0040] According to a second aspect of the embodiments of this application, a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses is provided, comprising:

[0041] The data acquisition module is configured to collect growth data and biological samples of target Leymus chinensis forage grasses within the target area.

[0042] The first determining module is configured to determine the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target *Leymus* species of forage grass using the biological sample.

[0043] The construction module is configured to construct a first critical nitrogen dilution curve based on the aboveground dry matter mass and the nitrogen concentration data;

[0044] The second determining module is configured to determine the amount of nitrogen fertilizer to be applied to the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data.

[0045] According to a third aspect of the embodiments of this application, a computing device is provided, comprising:

[0046] Memory and processor;

[0047] The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses.

[0048] According to a fourth aspect of the present application, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage.

[0049] According to a fifth aspect of the present application, a chip is provided that stores a computer program, which, when executed by the chip, implements the steps of the method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage.

[0050] The method for determining nitrogen fertilizer application for *Leymus chinensis* forage grasses provided in this application involves collecting growth data and biological samples of target *Leymus chinensis* forage grasses within a target area; determining the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target *Leymus chinensis* forage grasses using the biological samples; constructing a first critical nitrogen dilution curve based on the aboveground dry matter and nitrogen concentration data; and determining the nitrogen fertilizer application amount for the target area based on the belowground dry matter, the first critical nitrogen dilution curve, and the growth data. This establishes a critical nitrogen concentration dilution curve for *Leymus chinensis* forage grass seed production, enabling precise application of nitrogen fertilizer during the production process, ensuring seed yield and root growth while reducing fertilization costs and environmental pollution. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses, provided in one embodiment of this application.

[0053] Figure 2 This is a schematic diagram of the first critical nitrogen dilution curve of a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses according to an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of the third correlation curve of a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses according to an embodiment of this application;

[0055] Figure 4 This is a schematic diagram of the second critical nitrogen dilution curve of a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses according to an embodiment of this application;

[0056] Figure 5 This is a schematic diagram of a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grass, provided in one embodiment of this application.

[0057] Figure 6 This is a structural block diagram of a computing device provided in one embodiment of this application. Detailed Implementation

[0058] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0059] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0060] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first.

[0061] This application provides a method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage. This application also relates to a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage, a computing device, and a computer-readable storage medium, which will be described in detail in the following embodiments.

[0062] It should be noted that the minimum nitrogen concentration corresponding to the peak crop growth is the critical nitrogen concentration. When the nitrogen concentration in the above-ground parts is below the critical nitrogen concentration, crop growth is limited by nitrogen nutrient availability; when it is above the critical nitrogen concentration, the crop's nitrogen nutrient supply exceeds its needs, and crop growth is no longer limited by nitrogen. Therefore, in terms of the economical and efficient use of nitrogen fertilizer by crops, the critical nitrogen concentration is the most suitable concentration. From the perspective of critical nitrogen concentration, the "critical nitrogen dilution curve" has been proposed, namely (Nc = aW). -b The critical nitrogen concentration (CNC) dilution curve reflects the relationship between nitrogen concentration in the plant and aboveground biomass. Furthermore, due to differences in growth rate, adaptation area, and nutrient requirement, the established CNC dilution curves vary considerably among different crops, thus exhibiting species specificity. Nc represents the critical nitrogen concentration (%) in the aboveground plant, and W represents the aboveground biomass (t·hm²). -2 ), where 'a' represents the plant dry matter content as 1 t·hm. -2 The plant nitrogen concentration at time b represents the dilution factor, and represents the relationship between the critical nitrogen concentration and the decrease in plant dry matter.

[0063] The critical nitrogen concentration dilution curve, derived from the concept of critical nitrogen concentration, is a key concept in plant nutrition and fertilization, focusing on the relationship between nitrogen application rate and crop productivity. It reflects the relationship between nitrogen content within the crop and aboveground biomass. The critical nitrogen concentration dilution curve can accurately diagnose the dynamic nitrogen nutrient status of the crop, determining the optimal nitrogen application rate to maximize crop yield. To gain a deeper understanding of crop nitrogen nutrient status, the concept of the nitrogen nutrition index (NNI) was developed. NNI is the ratio of the actual nitrogen concentration in the aboveground parts of the crop to the critical nitrogen concentration, expressed by the formula NNI = Nt / Nc, where Nt represents the measured value of nitrogen concentration in the aboveground parts of the crop, and Nc is the critical nitrogen concentration value obtained using the critical nitrogen concentration dilution model with the same aboveground biomass. NNI quantitatively reflects the nitrogen nutrient status within the crop. When NNI is close to 1, it indicates that the nitrogen nutrient level in the crop is optimal; a value greater than 1 indicates excessive nitrogen nutrient; and a value less than 1 indicates insufficient nitrogen nutrient. Critical nitrogen dilution curves and NNI can quantitatively and dynamically describe changes in nitrogen nutrition status during crop growth, and can be used for nitrogen diagnosis and precise management of nitrogen fertilizer during crop growth stages.

[0064] In modern agricultural production, establishing critical nitrogen dilution curve models plays a crucial role in achieving precision nitrogen management in crop production. Critical nitrogen dilution curves enable faster and more convenient determination of optimal nitrogen application rates, thus achieving precise nitrogen management, and their theoretical value is increasingly recognized. In recent years, people have placed increasing emphasis on nitrogen management, and research on critical nitrogen dilution curves has made rapid progress. Different types of crops exhibit varying physiological and ecological characteristics and growth environments, resulting in significant differences in the a and b parameters of the critical nitrogen dilution curve under different regional environmental conditions.

[0065] Figure 1 The flowchart illustrates a method for determining the nitrogen fertilizer application rate for *Leymus chinensis* forage grasses according to an embodiment of this application, specifically including the following steps:

[0066] Step S102: Collect growth data and biological samples of the target Leymus chinensis forage grass within the target area;

[0067] Step S104: Using the biological samples, determine the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target *Leymus* species forage grass;

[0068] Step S106: Construct a first critical nitrogen dilution curve based on the aboveground dry matter mass and nitrogen concentration data;

[0069] Step S108: Determine the amount of nitrogen fertilizer to be applied to the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data.

[0070] The target area is a man-made planting area of ​​*Leymus chinensis* forage grasses, where the climate and soil environment are relatively stable, and the a and b parameters of the critical nitrogen dilution curve are also relatively stable. The process of collecting growth data and biological samples of the target *Leymus chinensis* forage grasses allows for the timed and quantitative planting of the target *Leymus chinensis* forage grasses within the target area in practical applications. Control groups with different nitrogen fertilizer application rates are also set up, and biological samples and growth data are collected at the same time points.

[0071] Based on this, after collecting growth data and biological samples, the aboveground dry matter, underground dry matter, and nitrogen concentration data of the target *Leymus* species in control groups with different fertilization amounts can be determined through the biological samples. Then, based on the obtained aboveground dry matter and nitrogen concentration data, a critical nitrogen concentration dilution curve based on biomass can be established, that is, the first critical nitrogen dilution curve can be constructed. Finally, the most suitable fertilization amount for plant growth can be determined through underground dry matter, the first critical nitrogen dilution curve, and growth data.

[0072] It should be noted that *Leymus* species are perennial forage plants, and their growth depends on the amount of root growth from the previous year. A robust root system contributes to growth in the following year. Therefore, from a farmer's perspective, the cultivation of *Leymus* species requires attention to both seed yield and root growth. Thus, by introducing underground dry matter mass and incorporating both underground dry matter mass and seed yield as factors in determining fertilization amounts, a targeted nitrogen fertilizer application model for *Leymus* species has been established.

[0073] In summary, the above methods aim to maximize nitrogen fertilizer utilization, reduce nitrogen fertilizer application, and mitigate environmental pollution. Furthermore, after obtaining the first critical nitrogen dilution curve, in subsequent target *Leymus chinensis* forage planting scenarios, based on the long growth cycle of *Leymus chinensis*, multiple fertilization applications can be rationally adopted to improve nitrogen use efficiency. This avoids the problem of low crop nitrogen use efficiency and prevents nitrogen fertilizer-related issues such as leaching, volatilization, denitrification, and soil erosion, thereby avoiding air pollution caused by ammonia (NH3), nitrous oxide (N2O), and other nitrogen oxides (NOx), as well as the impact of nitrates on groundwater. Optimized fertilization management ensures improved agricultural productivity, reduced nitrogen fertilizer loss, and the realization of sustainable agricultural development and a virtuous cycle of the ecological environment.

[0074] Furthermore, the process of collecting growth data and biological samples of the target *Leymus* species within the target area is specifically implemented as follows in this embodiment:

[0075] At preset time points, samples are taken from the target area of ​​the *Leymus* species forage grasses with different amounts of nitrogen fertilizer applied, and the growth data and biological samples are obtained.

[0076] For example, taking sampling in a real-world application scenario, if the target elytra species are identified as *Elymus esculenta* and *Elymus esculenta* var. *demonatus*, and *Elymus esculenta* var. *qingmu 1*, then from May to September of several years, the target elytra species are sown in rows within the target area with a row spacing of 30 cm and a seeding rate of 30 kg·hm². -2 Apply 3-4 cm deep, at a rate of 60 kg / hm² during the greening stage. -2 P2O5 (superphosphate, containing 16% P2O5) was used. In addition, multiple control groups were set up, with nitrogen fertilizer application gradients of 0, 45, 90, 135, 180, and 225 kg N·hm⁻². -2 Furthermore, the application of nitrogen fertilizer is limited to urea (containing 46% nitrogen), with 50% applied during the greening and jointing stages respectively. The fertilization period is one day before rainfall, and there is no artificial irrigation during the planting period, thereby ensuring a consistent growth environment for the target Leymus chinensis forage grass.

[0077] Based on this, management was carried out using a split-plot experiment. The main plot was for variety, and the subplot was for nitrogen fertilizer level. Each treatment was repeated three times. Each plot was 3m × 5m in size, with a plot spacing of 1m, for a total of 36 plots. Subsequently, based on weather factors, corresponding time points were selected to sample the target *Leymus* forage grasses planted in the above manner. The sampling process involved cutting two 50cm strips at ground level in each plot to obtain biological samples.

[0078] Furthermore, the process of determining the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target *Leymus* species using the biological samples is specifically implemented as follows in this embodiment:

[0079] The aboveground biological samples in the biological sample are dried to obtain aboveground dry matter samples; the underground biological samples in the biological sample are dried to obtain underground dry matter samples; based on the aboveground dry matter samples, the aboveground dry matter mass and the nitrogen concentration data are determined, and based on the underground dry matter mass, the underground dry matter mass is determined.

[0080] Following the previous example, for the cleaned aboveground and belowground biological samples, the aboveground dry matter and belowground dry matter samples can be obtained by blanching in an oven at 105℃ for 30 minutes, followed by drying at 65℃ to constant weight. Furthermore, after obtaining the aboveground dry matter mass, the relative aboveground dry matter (RDM) can be calculated as: actual yield of different nitrogen treatments at harvest / maximum aboveground dry matter mass of each treatment at the same growth stage. Nitrogen concentration data can be determined by grinding the aboveground dry matter sample, selecting a quantitative sample powder, igniting the powder, and reducing nitrogen from its oxidized state to nitrogen gas through a redox reaction. The nitrogen gas content is then measured to obtain the nitrogen concentration data. Additionally, the nitrogen nutrition index (NNI) can be calculated, which is the ratio of the measured nitrogen concentration (Nt) in the aboveground parts of the crop to the critical nitrogen concentration (Nc) calculated based on the critical nitrogen concentration dilution curve. The formula is: NNI = Nt / Nc. When NNI is close to 1, it indicates that the nitrogen nutrition level in the crop is in a suitable state; when it is greater than 1, it indicates that the nitrogen content is too high; when it is less than 1, it indicates that the nitrogen content is too low.

[0081] Furthermore, in the process of constructing the first critical nitrogen dilution curve based on aboveground dry matter and nitrogen concentration data, a first initial critical nitrogen dilution curve can be constructed based on the aboveground dry matter and nitrogen concentration data, the first coefficient of determination of the first initial critical nitrogen dilution curve can be determined, and it can be determined whether the first coefficient of determination is greater than the preset first coefficient of determination threshold. If so, the first initial critical nitrogen dilution curve is used as the first critical nitrogen dilution curve. If not, abnormal data points in the first initial critical nitrogen dilution curve are identified, the aboveground dry matter and nitrogen concentration data associated with the abnormal data points are removed, and the first critical nitrogen dilution curve is constructed based on the aboveground dry matter and nitrogen concentration data after the removal process.

[0082] In this process, a power-law function is fitted based on the changes in nitrogen concentration data and aboveground dry matter mass to establish a critical nitrogen dilution curve model, i.e., the first initial critical nitrogen dilution curve. It should be noted that the power-law function fitting can be performed using existing statistical analysis and graphing software; the specific data processing tools used depend on the actual application scenario, and this embodiment does not impose any limitations. Continuing with the above example, such as... Figure 2 The schematic diagram of the first critical nitrogen dilution curve for a method to determine the nitrogen fertilizer application rate for *Leymus chinensis* forage grasses is shown, yielding Nc = 2.5993 DM. -0.198 Where DM represents aboveground dry matter. From the trend of the fitted curve, the nitrogen content of the plant decreases as aboveground dry matter increases, yielding the first coefficient of determination R0. 2 It is 0.6559.

[0083] Furthermore, the process of determining the nitrogen fertilizer application rate for the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and growth data is specifically implemented as follows in this embodiment:

[0084] The seed yield data of the target *Leymus* species included in the growth data is determined; a first yield nitrogen nutrition curve is constructed based on the seed yield data and the first critical nitrogen dilution curve; a first root nitrogen nutrition curve is constructed based on the underground dry matter mass and the first critical nitrogen dilution curve; and the nitrogen fertilizer application rate is determined based on the first yield nitrogen nutrition curve and the first root nitrogen nutrition curve.

[0085] Furthermore, the process of constructing the first yield-nitrogen nutrient curve based on seed yield data and the first critical nitrogen dilution curve is specifically implemented in this embodiment as follows:

[0086] A first correlation curve is determined between the seed yield data and the aboveground dry matter; based on the first correlation curve and the first critical nitrogen dilution curve, the first yield nitrogen nutrition curve is determined.

[0087] Seed yield data can be determined by sampling during the seed maturity period of the target Leymus chinensis forage grass. Following the example above, during the seed maturity period of the target Leymus chinensis forage grass, two 50cm strips are cut in each plot, dried, threshed, and the seed yield is measured. In addition, the relative seed yield (RSY) can be calculated based on the medium yield: actual seed yield under nitrogen fertilizer application / maximum seed yield × 100%.

[0088] Based on this, the correlation between nitrogen nutrient index and relative aboveground dry matter (RDM) and relative seed yield (RSY) is determined using the above data. The correlation curve between the two is constructed by fitting linear or quadratic functions. Continuing with the previous example, as NNI increases, both RDM and RSY tend to increase. Moreover, NNI and RDM and RSY have certain linear or quadratic relationships at different sampling periods. Further fitting yield nitrogen nutrient curve is obtained. Similarly, the determination coefficient of the first yield nitrogen nutrient curve can be used to determine whether data needs to be removed or adjusted. The specific implementation process is similar to that of the first critical nitrogen dilution curve mentioned above, and will not be described in detail in this embodiment.

[0089] Furthermore, in this embodiment, the process of constructing the first root nitrogen nutrient curve based on the underground dry matter mass and the first critical nitrogen dilution curve is specifically implemented as follows:

[0090] A second correlation curve is determined between the underground dry matter mass and the aboveground dry matter mass; based on the second correlation curve and the first critical nitrogen dilution curve, the first root nutrient curve is determined.

[0091] The process of constructing the first root nutrient curve is similar to the process of constructing the first yield nitrogen nutrient curve. The difference is that the first yield nitrogen nutrient curve represents the relationship between the aboveground dry matter and the seed, while the first root nutrient curve represents the relationship between the aboveground dry matter and the root growth.

[0092] Furthermore, the process of determining the nitrogen fertilizer application rate based on the first yield nitrogen nutrient curve, the first root nitrogen nutrient curve, and growth data is specifically implemented as follows in this embodiment:

[0093] By superimposing the first yield nitrogen nutrient curve and the first root nitrogen nutrient curve, a fertilization curve relating nitrogen nutrient index and comprehensive growth index is obtained, wherein the comprehensive growth index is relating seed yield data and underground dry matter mass; the amount of nitrogen fertilizer applied is determined by the fertilization curve.

[0094] In the process of superimposing the first yield nitrogen nutrient curve and the first root nitrogen nutrient curve, the proportion of the corresponding curve in the superimposed result can be adjusted by the corresponding superimposition coefficient. For example, if the user only focuses on seed yield and not root growth, the superimposition coefficient of the first yield nitrogen nutrient curve can be set to 1, while the superimposition coefficient of the first root nitrogen nutrient curve can be set to 0. Conversely, for the first year of planting of Leymus chinensis forage grass, if the user pays more attention to the survival rate of the forage grass in the following year and ensures planting stability, the superimposition coefficient of the first root nitrogen nutrient curve can be set higher, while the superimposition coefficient of the first yield nitrogen nutrient curve can be set lower.

[0095] Based on this, by superimposing the first yield nitrogen nutrition curve and the first root nitrogen nutrition curve, the fertilization curve obtained represents the comprehensive effect of nitrogen fertilizer application on the seed yield and root growth of Leymus chinensis forage grasses.

[0096] Furthermore, the process of determining the nitrogen fertilizer application rate for the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data is specifically implemented as follows in this embodiment:

[0097] The leaf area index data of the target *Leymus* species included in the growth data is determined; a third correlation curve between the leaf area index data and the aboveground dry matter is determined; a second critical nitrogen dilution curve is constructed based on the third correlation curve and the first critical nitrogen dilution curve; and the amount of nitrogen fertilizer applied is determined based on the second critical nitrogen dilution curve, the underground dry matter, and the growth data.

[0098] Furthermore, the process of determining the nitrogen fertilizer application rate based on the second critical nitrogen dilution curve, the underground dry matter mass, and the growth data is specifically implemented as follows in this embodiment:

[0099] The seed yield data of the target *Leymus* species included in the growth data is determined; a second yield nitrogen nutrition curve is constructed based on the seed yield data and the second critical nitrogen dilution curve; a second root nitrogen nutrition curve is constructed based on the underground dry matter content and the second critical nitrogen dilution curve; and the nitrogen fertilizer application rate is determined based on the second yield nitrogen nutrition curve and the second root nitrogen nutrition curve.

[0100] The process of determining the amount of nitrogen fertilizer to be applied through the first critical nitrogen dilution curve undoubtedly requires sampling of the target Leymus chinensis forage grass. However, the sampling process is destructive and cannot provide timely feedback on growth information during the critical growth period of the crop. Therefore, by collecting leaf area index data and constructing a second critical nitrogen dilution curve based on the leaf area index data, non-destructive sampling can be achieved.

[0101] Based on this, the data collection process for leaf area index (LAI) follows the previous example. A time point with good visibility can be selected, and areas with relatively uniform growth can be chosen from each plot. Multiple rows can be selected for measurement. During the measurement process, a laser beam is emitted by a relevant sensor. After the laser beam passes through the target Leymus chinensis forage planting area, it is received by the receiver. The raw data undergoes preprocessing such as filtering, noise reduction, and segmentation. Then, based on the preprocessed image, the LAI data of the target Leymus chinensis forage is detected.

[0102] The third correlation curve between leaf area index data and aboveground dry matter was determined using linear or quadratic fitting, with the fitting results as follows: Figure 3 The provided method for determining nitrogen fertilizer application rate for *Leymus chinensis* forage grasses includes a third correlation curve diagram. This diagram shows that as the LAI (Lower Intake Index) increases, the aboveground dry matter mass also increases, exhibiting a positive correlation. The equation is DM = 1488.9LAI + 275.96. A second critical nitrogen dilution curve is then established by fitting the changes in nitrogen content and LAI. Figure 4The schematic diagram of the second critical nitrogen dilution curve for a method of determining nitrogen fertilizer application rate for *Leymus chinensis* forage grasses is shown, with the equation Nc = 2.9637LAI. -0.403 Therefore, 2.9637% represents the nitrogen concentration that the plant needs to absorb when the unit LAI increases. The critical nitrogen concentration of Leymus chinensis forage grass shows a gradual decreasing trend in nitrogen content as LAI increases.

[0103] In summary, by using the above non-destructive approval methods, a critical nitrogen concentration dilution curve model was established between leaf area index, vegetation index, and nitrogen concentration. The application of this model can serve as a basis for determining the critical nitrogen concentration corresponding to a certain growth stage or biomass of crops. When the nitrogen concentration is higher than the critical nitrogen concentration, fertilization is not required; when the concentration is lower than the critical nitrogen concentration, topdressing is necessary.

[0104] Corresponding to the above method embodiments, this application also provides an embodiment of a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses. Figure 5 A schematic diagram of a device for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage grasses, according to an embodiment of this application, is shown. Figure 5 As shown, the device includes:

[0105] The acquisition module 502 is configured to acquire growth data and biological samples of the target Leymus chinensis forage grass within the target area;

[0106] The first determining module 504 is configured to determine the aboveground dry matter, belowground dry matter, and nitrogen concentration data of the target Leymus chinensis forage grass through the biological sample.

[0107] Construction module 506 is configured to construct a first critical nitrogen dilution curve based on the aboveground dry matter mass and the nitrogen concentration data;

[0108] The second determining module 508 is configured to determine the amount of nitrogen fertilizer to be applied to the target area based on the underground dry matter mass, the first critical nitrogen dilution curve, and the growth data.

[0109] In an optional embodiment, the acquisition module 502 is further configured to:

[0110] At preset time points, samples are taken from the target area of ​​the *Leymus* species forage grasses with different amounts of nitrogen fertilizer applied, and the growth data and biological samples are obtained.

[0111] In an optional embodiment, the first determining module 504 is further configured to:

[0112] The aboveground biological samples in the biological sample are dried to obtain aboveground dry matter samples; the underground biological samples in the biological sample are dried to obtain underground dry matter samples; based on the aboveground dry matter samples, the aboveground dry matter mass and the nitrogen concentration data are determined, and based on the underground dry matter mass, the underground dry matter mass is determined.

[0113] In an optional embodiment, the second determining module 508 is further configured to:

[0114] The seed yield data of the target *Leymus* species included in the growth data is determined; a first yield nitrogen nutrition curve is constructed based on the seed yield data and the first critical nitrogen dilution curve; a first root nitrogen nutrition curve is constructed based on the underground dry matter mass and the first critical nitrogen dilution curve; and the nitrogen fertilizer application rate is determined based on the first yield nitrogen nutrition curve and the first root nitrogen nutrition curve.

[0115] In an optional embodiment, the second determining module 508 is further configured to:

[0116] A first correlation curve is determined between the seed yield data and the aboveground dry matter; based on the first correlation curve and the first critical nitrogen dilution curve, the first yield nitrogen nutrition curve is determined.

[0117] In an optional embodiment, the second determining module 508 is further configured to:

[0118] A second correlation curve is determined between the underground dry matter mass and the aboveground dry matter mass; based on the second correlation curve and the first critical nitrogen dilution curve, the first root nutrient curve is determined.

[0119] In an optional embodiment, the second determining module 508 is further configured to:

[0120] By superimposing the first yield nitrogen nutrient curve and the first root nitrogen nutrient curve, a fertilization curve relating nitrogen nutrient index and comprehensive growth index is obtained, wherein the comprehensive growth index is relating seed yield data and underground dry matter mass; the amount of nitrogen fertilizer applied is determined by the fertilization curve.

[0121] In an optional embodiment, the second determining module 508 is further configured to:

[0122] The leaf area index data of the target *Leymus* species included in the growth data is determined; a third correlation curve between the leaf area index data and the aboveground dry matter is determined; a second critical nitrogen dilution curve is constructed based on the third correlation curve and the first critical nitrogen dilution curve; and the amount of nitrogen fertilizer applied is determined based on the second critical nitrogen dilution curve, the underground dry matter, and the growth data.

[0123] In an optional embodiment, the second determining module 508 is further configured to:

[0124] The seed yield data of the target *Leymus* species included in the growth data is determined; a second yield nitrogen nutrition curve is constructed based on the seed yield data and the second critical nitrogen dilution curve; a second root nitrogen nutrition curve is constructed based on the underground dry matter content and the second critical nitrogen dilution curve; and the nitrogen fertilizer application rate is determined based on the second yield nitrogen nutrition curve and the second root nitrogen nutrition curve.

[0125] The device for determining nitrogen fertilizer application rate for *Leymus chinensis* forage grass provided in this application collects growth data and biological samples of target *Leymus chinensis* forage grass within a target area; uses the biological samples to determine the aboveground dry matter and nitrogen concentration data of the target *Leymus chinensis* forage grass; constructs a first critical nitrogen dilution curve based on the aboveground dry matter and nitrogen concentration data; and determines the nitrogen fertilizer application rate for the target area based on the first critical nitrogen dilution curve and the growth data. This achieves precise application of nitrogen fertilizer during the production process of *Leymus chinensis* forage grass, ensuring yield and root growth while reducing fertilization costs and environmental pollution.

[0126] The above is a schematic scheme of a device for determining the nitrogen fertilizer application rate of *Leymus chinensis* forage grass according to this embodiment. It should be noted that the technical solution of this device for determining the nitrogen fertilizer application rate of *Leymus chinensis* forage grass belongs to the same concept as the technical solution of the aforementioned method for determining the nitrogen fertilizer application rate of *Leymus chinensis* forage grass. Details not described in detail in the technical solution of the device for determining the nitrogen fertilizer application rate of *Leymus chinensis* forage grass can be found in the description of the technical solution of the aforementioned method for determining the nitrogen fertilizer application rate of *Leymus chinensis* forage grass. Furthermore, the components in the device embodiment should be understood as functional modules necessary to implement each step of the program flow or each step of the method; these functional modules are not actual functional divisions or separations. The device claim defined by such a set of functional modules should be understood as a functional module architecture that primarily implements the solution through the computer program described in the specification, and not as a physical device that primarily implements the solution through hardware.

[0127] Figure 6A structural block diagram of a computing device 600 according to an embodiment of this application is shown. The components of the computing device 600 include, but are not limited to, a memory 610 and a processor 620. The processor 620 is connected to the memory 610 via a bus 630, and a database 650 is used to store data.

[0128] The computing device 600 also includes an access device 640, which enables the computing device 600 to communicate via one or more networks 660. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 640 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0129] In one embodiment of this application, the aforementioned components of the computing device 600 and Figure 6 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 6 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0130] The computing device 600 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs. The computing device 600 can also be a mobile or stationary server.

[0131] The processor 620 is used to execute computer-executable instructions for each step of the method for determining the amount of nitrogen fertilizer applied to the *Leymus chinensis* forage grass.

[0132] The above is a schematic diagram of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-mentioned method for determining the amount of nitrogen fertilizer applied to crested wheatgrass forage belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the above-mentioned method for determining the amount of nitrogen fertilizer applied to crested wheatgrass forage.

[0133] An embodiment of this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, are used to perform the steps of the method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage.

[0134] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the method for determining the nitrogen fertilizer application rate of *Elymus spp.* for forage grasses described above. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the method for determining the nitrogen fertilizer application rate of *Elymus spp.* forage grasses described above.

[0135] An embodiment of this application also provides a chip that stores a computer program, which, when executed by the chip, implements the steps of the method for determining the amount of nitrogen fertilizer applied to *Leymus chinensis* forage.

[0136] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0138] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0139] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0140] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of this application, thereby enabling those skilled in the art to better understand and utilize this application. This application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for determining the amount of nitrogen fertilizer to be applied to a pasture grass of the genus Elymus, characterized by, The method comprises the following steps: Collecting growth data and biological samples of target Elymus grass in a target area; Determining aboveground dry matter mass, underground dry matter mass and nitrogen concentration data of the target Elymus grass through the biological samples; Constructing a first critical nitrogen dilution curve according to the aboveground dry matter mass and the nitrogen concentration data; Determining seed yield data of the target Elymus grass contained in the growth data, determining a first correlation curve between the seed yield data and the aboveground dry matter mass, determining a first yield-nitrogen curve based on the first correlation curve and the first critical nitrogen dilution curve, and determining a second correlation curve between the underground dry matter mass and the aboveground dry matter mass; Determining a first root-nitrogen curve based on the second correlation curve and the first critical nitrogen dilution curve; Superimposing the first yield-nitrogen curve and the first root-nitrogen curve to obtain a fertilization curve of correlation nitrogen nutrition index and comprehensive growth index, wherein the comprehensive growth index is correlated with the seed yield data and the underground dry matter mass, and determining the nitrogen fertilizer application amount through the fertilization curve.

2. The method of claim 1, wherein, The collecting growth data and biological samples of target Elymus grass in a target area comprises the following steps: At a preset time node, sampling the target Elymus grass with different nitrogen fertilizer amounts in the target area to obtain the growth data and the biological samples.

3. The method of claim 1, wherein, The determining aboveground dry matter mass, underground dry matter mass and nitrogen concentration data of the target Elymus grass through the biological samples comprises the following steps: Drying aboveground biological samples in the biological samples to obtain aboveground dry matter samples; Drying underground biological samples in the biological samples to obtain underground dry matter samples; Determining the aboveground dry matter mass and the nitrogen concentration data according to the aboveground dry matter samples, and determining the underground dry matter mass according to the underground dry matter mass.

4. The method of claim 1, wherein, The determining the nitrogen fertilizer application amount of the target area according to the underground dry matter mass, the first critical nitrogen dilution curve and the growth data comprises the following steps: Determining leaf area index data of the target Elymus grass contained in the growth data; Determining a third correlation curve between the leaf area index data and the aboveground dry matter mass; Constructing a second critical nitrogen dilution curve according to the third correlation curve and the first critical nitrogen dilution curve; Determining the nitrogen fertilizer application amount according to the second critical nitrogen dilution curve, the underground dry matter mass and the growth data.

5. The method of claim 4, wherein, The determining the nitrogen fertilizer application amount according to the second critical nitrogen dilution curve, the underground dry matter mass and the growth data comprises the following steps: Determining seed yield data of the target Elymus grass contained in the growth data; Constructing a second yield-nitrogen curve according to the seed yield data and the second critical nitrogen dilution curve; Constructing a second root-nitrogen curve according to the underground dry matter mass and the second critical nitrogen dilution curve; Determining the nitrogen fertilizer application amount based on the second yield-nitrogen curve and the second root-nitrogen curve.

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