A Construction Method and System for a Relationship Model between Soil Moisture Content and Fertilizer Application Rate

Abstract

The present invention provides a method and system for constructing a relationship model between soil moisture content and fertilization amount, which relates to the technical field of soil moisture content model construction. By constructing a multiple linear regression model, the present invention comprehensively analyzes the relationship between the proportion of soil particle components, environmental data, and fertilization amount to accurately predict the fertilization amount. A method combining stage-by-stage fertilization and real-time monitoring is adopted to accurately calculate the fertilizer moisture penetration ratio and dynamically correct it using environmental data. This method effectively solves the problem of the lack of data-driven precise fertilization guidance in the prior art, improves the scientificity and precision of fertilization strategies. Ultimately, it optimizes the utilization efficiency of fertilizer resources, reduces environmental pollution, promotes the sustainable development of agricultural production, and achieves the goal of precision agriculture.

Description

Technical Field

[0001] The present invention relates to the technical field of soil moisture model construction, and specifically to a method and system for constructing a relationship model between soil moisture and fertilization amount. Background Art

[0002] In modern agricultural production, precise fertilization and water management are the keys to improving crop yields and resource utilization efficiency. However, due to the complexity of soil composition and environmental conditions, traditional fertilization methods often struggle to accurately match actual needs, leading to fertilizer waste or poor crop growth.

[0003] In the prior art, the publication number CN109118382B discloses a method and application for constructing a relationship model between soil moisture and fertilization amount. Although this solution studies the relationship between soil parameters and fertilizer usage, filling the gap in research on the impact of soil moisture parameters on fertilization amount, it does not consider factors such as soil quality differences and environmental differences in different regions on the water penetration and utilization rate of fertilizers, resulting in inaccurate final recommended fertilization amounts and being not suitable for large-scale promotion. Therefore, how to construct a model that can comprehensively analyze soil composition, environmental data, and fertilization amount to achieve precise fertilization guidance has become an important technical issue in modern agricultural production.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for constructing a relationship model between soil moisture and fertilization amount to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for constructing a relationship model between soil moisture and fertilization amount, the specific steps including:

[0008] S1: Divide several groups of sampling areas with equal areas in different geological regions, collect soil samples from each sampling area, determine the component proportions of different types of soil particles in each sampling area, then divide each sampling area into a test area and a control area on average, and conduct fertilization experiments on the test area;

[0009] S2: Collect environmental data and soil data for each sampling area, generate the penetration ratio of fertilizer moisture based on the fertilization amount and soil data in the test area during the fertilization experiment, and correct it using the soil data of the control area;

[0010] S3: Construct a multiple linear regression model to study the relationships among the proportion of soil particle components, environmental data, fertilizer application rate, and soil data. Divide the collected proportion of soil particle components, environmental data, fertilizer application rate, and soil data into a training set and a validation set. Use the training set to train the multiple linear regression model, and use the validation set to validate the multiple linear regression model;

[0011] S4: Collect the proportion of soil particle components, environmental data, and soil data of the actual sowing area, substitute them into the multiple linear regression model, calculate the corresponding fertilizer application rate, and recommend it as the optimal fertilizer application rate.

[0012] Preferably, the environmental data includes environmental temperature and environmental humidity The soil data includes the experimental soil humidity control soil humidity Where the superscript i represents the number of the sampling area, i = 1, 2, 3, …, M, M is the total number of sampling areas, and the subscript j represents the number of the sampled data, j = 1, 2, 3, …, N, N is the total number of sampled data. The divided areas and sampling depths of the experimental area and the control area are the same.

[0013] Preferably, the proportion of soil particle components is generated by the sieving method. The specific operation steps are as follows:

[0014] When collecting soil samples, collect equal amounts of soil in the experimental area and the control area respectively, mix them as the soil sample of this sampling area, and then select the corresponding low-mesh sieve and high-mesh sieve based on user settings, and use the low-mesh sieve and the high-mesh sieve to sieve the soil sample in turn. Label the soil residue on the low-mesh sieve as large particles, the soil residue on the high-mesh sieve as medium particles, and the soil passing through the high-mesh sieve as small particles;

[0015] Measure the weights of the large particles, medium particles, and small particles respectively, compare them with the total weight of the soil sample, calculate the proportion of the large particles, medium particles, and small particles in the soil sample, and label them as δ1 i , δ2 i , δ3 i , where 0 ≤ δ1 i , 2 i , 3 i ≤ 1, and δ1 i + 2 i + 3 i = 1.

[0016] Preferably, the method for generating the penetration ratio of fertilizer moisture and correcting it with environmental data is:

[0017] Divide the water-soluble fertilizer into several equal parts and apply it to the test area stage by stage to obtain the change curve between the test soil humidity and the stage time. When the test soil humidity stabilizes, apply the next portion of the water-soluble fertilizer, and repeat this step until the finally stabilized test soil humidity is reached. z represents the sampling number corresponding to the test soil humidity being 1.1 times the control soil humidity. Count the number of portions of the water-soluble fertilizer applied, calculate the fertilizer water penetration ratio corresponding to each test area, and then take the average to calculate the penetration ratio τ of the fertilizer water. The calculation method is as follows:

[0018]

[0019] In the formula respectively represent the test soil humidity and the control soil humidity before the water-soluble fertilizer is applied. respectively represent the test soil humidity and the control soil humidity at the final stabilization, and z ∈ j.

[0020] Preferably, the multiple linear regression model can be expressed as:

[0021]

[0022] In the formula, M f represents the fertilization amount, and k 1 ~k 6 respectively represent the linear parameters related to the large particle proportion, medium particle proportion, small particle proportion, environmental temperature, environmental humidity, and fertilization amount. Substitute the training set into the multiple linear regression model to fit and generate the values of the linear parameters k 1 ~k 6 Then substitute the large particle proportion, medium particle proportion, small particle proportion, environmental temperature, environmental humidity, and finally stabilized test soil humidity in the validation set into the multiple linear regression model to solve the fertilization amount calculated by the multiple linear regression model, and compare it with the actual fertilization amount in the validation set. When the error between the two is less than 5%, it is considered that the model optimization is completed.

[0023] Preferably, after the model optimization is completed, substitute the large particle proportion, medium particle proportion, small particle proportion, environmental temperature, environmental humidity, and finally stabilized test soil humidity in the actual sowing area into the multiple linear regression model to solve the final recommended fertilization amount, where the finally stabilized test soil humidity is taken as 1.1 times the finally stabilized control soil humidity.

[0024] Preferably, when applying the water-soluble fertilizer to the actual sowing area, fertilize the entire actual sowing area with P times the recommended fertilization amount, where the calculation method of the P value is:

[0025]

[0026] In the formula, S represents the area of the sampling area, S ° represents the area of the actual sowing area.

[0027] A system for constructing a relationship model between soil moisture and fertilization amount, the construction system adopts the above construction method, and includes:

[0028] A sensing module, the sensing module includes a first sensing unit and a second sensing unit, the first sensing unit is electrically connected to the data analysis module, and is used to collect environmental data and soil data of the sampling area, and the second sensing unit is electrically connected to the comprehensive processing module, and is used to collect environmental data and soil data of the actual sowing area;

[0029] A screening module, the screening module is electrically connected to the data analysis module, and is used to screen the soil samples and generate the component proportion of different types of soil particles;

[0030] A data analysis module, the data analysis module is electrically connected to the comprehensive processing module, and is used to generate the penetration ratio of fertilizer moisture according to the fertilization amount and soil data of the test area, and then correct it using the environmental soil data of the control area;

[0031] A comprehensive processing module, the comprehensive processing module is used to construct a multiple linear regression model and perform corresponding optimization and verification, and then substitute the component proportion of soil particles, environmental data, and soil data of the actual sowing area into the multiple linear regression model, calculate the corresponding fertilization amount and recommend it as the optimal fertilization amount.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] The present invention constructs a multiple linear regression model, comprehensively analyzes the relationship between the component proportion of soil particles, environmental data and fertilization amount, and accurately predicts the fertilization amount. By combining stage fertilization with real-time monitoring, the penetration ratio of fertilizer moisture is accurately calculated and dynamically corrected using environmental data. This method effectively solves the problem of lack of data-driven precise fertilization guidance in the prior art, improves the scientificity and precision of fertilization strategies. Finally, it optimizes the utilization efficiency of fertilizer resources, reduces environmental pollution, promotes the sustainable development of agricultural production, and realizes the goal of precision agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a schematic diagram of the overall method flow of the present invention;

[0035] Figure 2 is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to specific embodiments.

[0037] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings as understood by those of ordinary skill in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0038] Embodiment:

[0039] Please refer to Figures 1 to 2 , the present invention provides a technical solution:

[0040] A method for constructing a relationship model between soil moisture and fertilization amount, the specific steps include:

[0041] S1: Divide several groups of sampling areas with equal areas in different geological regions, collect soil samples from each sampling area, determine the component ratios of different types of soil particles in each sampling area, and then divide each sampling area into a test area and a control area, and conduct a fertilization test on the test area. When fertilizing, water-soluble fertilizers are used to study the influence of the fertilization amount of water-soluble fertilizers on soil moisture during fertilization.

[0042] The environmental data includes environmental temperature and environmental humidity The soil data includes test soil moisture control soil moisture Where the superscript i represents the number of the sampling area, i = 1, 2, 3,..., M, M is the total number of sampling areas, the subscript j represents the number of the sampled data, j = 1, 2, 3,..., N, N is the total number of sampled data. The divided areas and sampling depths of the test area and the control area are the same, and the sampling depth can be determined according to the root distribution depth of specific crops. It can be understood that when measuring environmental data and soil data, several sensors are set in the test area and the control area to detect, and the average value of multiple groups of sensors is used as the corresponding soil data and environmental data of the test area or the control area to reduce data errors.

[0043] The sieving method is used to generate the composition ratio of soil particles. The specific steps are as follows:

[0044] When collecting soil samples, equal amounts of soil are collected from the test area and the control area, respectively, and the mixed soil is used as the soil sample of the sampling area. Then, the corresponding low-mesh sieve and high-mesh sieve are selected based on the user's settings, and the soil samples are sieved using the low-mesh sieve and the high-mesh sieve in turn. The soil residue on the low-mesh sieve is calibrated as large particles, the soil residue on the high-mesh sieve is calibrated as medium particles, and the soil passing through the high-mesh sieve is calibrated as small particles;

[0045] The weights of large particles, medium particles, and small particles were measured respectively and compared with the total weight of the soil sample. The proportions of large particles, medium particles, and small particles in the soil sample were calculated and calibrated as δ1 i , δ2 i , δ3 i , where 0≤δ1 i ,δ2 i ,δ3 i ≤1, and δ1 i +δ2 i +δ3 i =1.

[0046] Because the proportion of different types of soil particles in the soil will have a certain impact on the soil's ability to lock in water, it will lead to large differences in soil moisture at the same depth even if the same amount of fertilizer is applied. When the proportion of large particles in the soil is high, it appears blocky or plate-like. This type of soil usually has more gravel, which speeds up the penetration of water-soluble fertilizers during fertilization, that is, water permeability increases and water retention decreases. When the proportion of medium particles is high, the soil appears clumpy. This type of soil usually has more organic matter and has good water permeability and water retention. When the proportion of small particles is high, the soil appears granular. This type of soil usually has more gravel, poor water permeability, but good water retention.

[0047] In this step, weight measurement and proportion calculation are used to ensure the scientificity and accuracy of the soil composition ratio, providing a reliable data basis for subsequent data analysis and model building. The screening method is simple to operate, convenient for large-scale sampling and processing, and improves experimental efficiency. It is suitable for large-scale soil composition analysis in different geological regions. The accurate soil particle composition ratio can help the model better explain the impact of soil particle characteristics on soil moisture conditions, thereby improving the explanatory power and applicability of the model.

[0048] S2: Collect environmental data and soil data of each sampling area, generate the fertilizer water penetration ratio based on the fertilizer application amount and soil data of the test area in the fertilization experiment, and correct it using the soil data of the control area.

[0049] The method for generating the penetration ratio of fertilizer moisture and correcting it using environmental data is as follows:

[0050] Divide the water-soluble fertilizer into several equal parts and apply it to the test area stage by stage to obtain the change curve between the test soil humidity and the stage time. When the test soil humidity stabilizes, apply the next part of the water-soluble fertilizer, and repeat this step until the finally stabilized test soil humidity z represents the sampling number corresponding to the test soil humidity when it is 1.1 times the control soil humidity. Count the number of parts of the water-soluble fertilizer applied, calculate the fertilizer moisture penetration ratio corresponding to each test area, and then take the average to calculate the fertilizer moisture penetration ratio τ. The calculation method is as follows:

[0051]

[0052] In the formula respectively represent the test soil humidity and the control soil humidity before applying the water-soluble fertilizer, respectively represent the test soil humidity and the control soil humidity at the final stabilization, z ∈ j.

[0053] In the setting of the above test conditions, by dividing the water-soluble fertilizer into several equal parts and applying it in stages, the change of soil humidity can be observed in detail to ensure the accuracy of the calculation of the penetration ratio. And the final boundary condition, that is, the finally stabilized test soil humidity The setting of this step is because when the soil humidity after fertilization is between 0.9 times and 1.1 times the soil temperature without fertilization, the fertilization amount and the growth trend of the crop show a linear proportional relationship. This research result is prior art, and the specific reasons will not be elaborated here. Therefore, when the finally stabilized test soil humidity At this time, the corresponding fertilization amount is the optimal fertilization amount that can promote the growth of the crop. On this basis, reducing the fertilization amount will slow down the growth of the crop, and increasing the fertilization amount will reduce the promotion effect on the growth of the crop, and the cost performance is relatively low.

[0054] The calculation formula of the fertilizer moisture penetration ratio, Here refers to the fertilizer moisture penetration ratio corresponding to each test area, where represents the humidity change generated by the combined action of the water-soluble fertilizer and the environment on the test soil humidity after fertilization, represents the humidity change generated by the environment on the control soil humidity after fertilization. It is equivalent to taking the control area as a reference to indirectly obtain the influence of the environment on the test area, so as to obtain the proportion of the influence of the water-soluble fertilizer in the humidity change of the control soil.

[0055] In this step, by calculating the infiltration ratio in detail and correcting the environmental data, the prediction accuracy of the multiple linear regression model for the fertilization amount is improved, ensuring the comprehensiveness and accuracy of the model input data, enhancing the applicability and reliability of the model, enabling better optimization of the fertilization plan, improving the implementation effect of the overall plan, and achieving the goal of precision agriculture.

[0056] S3: Construct a multiple linear regression model to study the relationships among the proportion of soil particle components, environmental data, fertilization amount, and soil data. Divide the collected proportion of soil particle components, environmental data, fertilization amount, and soil data into a training set and a validation set. Use the training set to train the multiple linear regression model and use the validation set to validate the multiple linear regression model.

[0057] The multiple linear regression model can be expressed as:

[0058]

[0059] In the formula, M f represents the fertilization amount, and k 1 ~k 6 respectively represent the linear parameters related to the proportion of large particles, the proportion of medium particles, the proportion of small particles, environmental temperature, environmental humidity, and fertilization amount. Substitute the training set into the multiple linear regression model to fit and generate the values of the linear parameters k 1 ~k 6 Then substitute the proportion of large particles, the proportion of medium particles, the proportion of small particles, environmental temperature, environmental humidity, and the finally stabilized experimental soil humidity in the validation set into the multiple linear regression model to solve the fertilization amount calculated by the multiple linear regression model and compare it with the actual fertilization amount in the validation set. When the error between the two is less than 5%, it is considered that the model optimization is completed.

[0060] In this step, the training method of the multiple linear regression model is an existing technology and will not be elaborated here. Using the multiple linear regression model, the effects of multiple variables on soil humidity can be comprehensively considered, improving the prediction accuracy of the model. By fitting and generating the linear parameters k 1 ~k 6 , ensure that the weight of each variable in the model is optimal, thereby improving the prediction accuracy. By comprehensively analyzing the relationships among the proportion of soil particle components, environmental data, fertilization amount, and soil data, the scientificity and precision of the fertilization strategy can be significantly improved, accurate fertilization amount prediction can be carried out, the utilization efficiency of fertilizers can be enhanced, waste can be reduced, and the benefits of agricultural production can be increased.

[0061] S4: Collect the proportion of soil particle components, environmental data, and soil data in the actual sowing area, substitute them into the multiple linear regression model, calculate the corresponding fertilization amount, and recommend it as the optimal fertilization amount.

[0062] After the model optimization is completed, the proportion of large particles, medium particles, small particles, ambient temperature, ambient humidity, and the finally stabilized experimental soil humidity in the actual sowing area are substituted into the multiple linear regression model to solve the final recommended fertilization amount, where the finally stabilized experimental soil humidity is taken as 1.1 times the finally stabilized control soil humidity.

[0063] When applying water-soluble fertilizer to the actual sowing area, the entire actual sowing area is fertilized at P times the recommended fertilization amount, where the calculation method of the P value is:

[0064]

[0065] In the formula, S represents the area of the sampling area, and S ′ represents the area of the actual sowing area. It can be understood that the area of the test area is half of the area of the sampling area, and the recommended fertilization amount calculated by the multiple linear regression model is for the area of the test area. Therefore, for the actual sowing area, it needs to be adjusted according to the area ratio.

[0066] A construction system for a relationship model between soil moisture and fertilization amount, the construction system adopts the above construction method, and includes:

[0067] A sensing module, the sensing module includes a first sensing unit and a second sensing unit, the first sensing unit is electrically connected to the data analysis module and is used to collect the environmental data and soil data of the sampling area, and the second sensing unit is electrically connected to the comprehensive processing module and is used to collect the environmental data and soil data of the actual sowing area;

[0068] A screening module, the screening module is electrically connected to the data analysis module and is used to screen the soil samples and generate the component proportions of different types of soil particles;

[0069] A data analysis module, the data analysis module is electrically connected to the comprehensive processing module and is used to generate the penetration ratio of fertilizer moisture according to the fertilization amount and soil data of the test area, and then correct it using the environmental soil data of the control area;

[0070] A comprehensive processing module, the comprehensive processing module is used to construct a multiple linear regression model and perform corresponding optimization and verification, and then substitute the soil particle component proportions, environmental data, and soil data of the actual sowing area into the multiple linear regression model to calculate the corresponding fertilization amount and recommend it as the optimal fertilization amount.

[0071] In summary, the present invention constructs a multiple linear regression model to comprehensively analyze the relationship between the proportion of soil particle components, environmental data, and fertilization amount, and accurately predict the fertilization amount. By combining staged fertilization with real-time monitoring, the method accurately calculates the fertilizer water penetration ratio and dynamically corrects it using environmental data. This method effectively solves the problem of the lack of data-driven precise fertilization guidance in the prior art, improves the scientificity and precision of fertilization strategies. Ultimately, it optimizes the utilization efficiency of fertilizer resources, reduces environmental pollution, promotes the sustainable development of agricultural production, and achieves the goal of precision agriculture.

[0072] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0074] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, and may be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A method for constructing a soil moisture and fertilizer application relationship model, characterized in that: The specific steps include: S1: Divide several groups of sampling areas of equal area in different geological regions, collect soil samples from each sampling area, determine the composition ratio of different types of soil particles in each sampling area, and then divide each sampling area into a test area and a control area, and conduct fertilization experiments on the test area; The sieving method is used to generate the composition ratio of soil particles. The specific steps are as follows: When collecting soil samples, equal amounts of soil are collected from the test area and the control area, respectively, and the mixed soil is used as the soil sample of the sampling area. Then, the corresponding low-mesh sieve and high-mesh sieve are selected based on the user's settings, and the soil samples are sieved using the low-mesh sieve and the high-mesh sieve in turn. The soil residue on the low-mesh sieve is calibrated as large particles, the soil residue on the high-mesh sieve is calibrated as medium particles, and the soil passing through the high-mesh sieve is calibrated as small particles; The weights of large particles, medium particles, and small particles were measured respectively and compared with the total weight of the soil sample. The proportions of large particles, medium particles, and small particles in the soil sample were calculated and calibrated as δ1 i , δ2 i , δ3 i , where 0≤δ1 i ,δ2 i ,δ3 i ≤1, and δ1 i +δ2 i +δ3 i =1; S2: Collect environmental data and soil data of each sampling area, generate the fertilizer water penetration ratio based on the fertilizer application amount and soil data of the test area in the fertilization experiment, and correct it using the soil data of the control area; The method to generate the fertilizer water penetration ratio and correct it using environmental data is: The water-soluble fertilizer is divided into several portions and applied to the test area in stages to obtain the change curve between the test soil moisture and the stage time. When the test soil moisture is stable, take a sample and apply the next portion of water-soluble fertilizer. Repeat this step until the test soil moisture is finally stabilized. z represents the sampling number corresponding to the test soil moisture being 1.1 times the control soil moisture. The number of applied water-soluble fertilizers is counted, and the fertilizer water penetration ratio corresponding to each test area is calculated. The mean is then taken to calculate the fertilizer water penetration ratio τ, which is calculated as follows: In the formula They represent the test soil moisture and control soil moisture before water-soluble fertilizer is applied. denote the test soil moisture and control soil moisture at the final stable state, respectively, z∈j; S3: Construct a multivariate linear regression model to study the relationship between soil particle composition ratio, environmental data, fertilizer application amount and soil data. Divide the collected soil particle composition ratio, environmental data, fertilizer application amount and soil data into a training set and a validation set. Use the training set to train the multivariate linear regression model, and use the validation set to validate the multivariate linear regression model. The multivariate linear regression model can be expressed as: Where M f represents the amount of fertilizer, k1~k6 represent the linear parameters related to the proportion of large particles, the proportion of medium particles, the proportion of small particles, the ambient temperature, the ambient humidity, and the amount of fertilizer, respectively. The training set is substituted into the multiple linear regression model, and the values ​​of the linear parameters k1~k6 are fitted and generated. Then, the proportion of large particles, the proportion of medium particles, the proportion of small particles, the ambient temperature, the ambient humidity, and the finally stabilized test soil humidity in the validation set are substituted into the multiple linear regression model, and the amount of fertilizer calculated by the multiple linear regression model is solved, and compared with the actual amount of fertilizer in the validation set. When the error between the two is less than 5%, the model optimization is considered to be completed; S4: Collect the soil particle composition ratio, environmental data, and soil data of the actual sowing area, substitute them into the multivariate linear regression model, calculate the corresponding fertilizer amount, and recommend it as the optimal fertilizer amount.

2. The method for constructing a soil moisture and fertilizer application amount relationship model according to claim 1, characterized in that: The environmental data includes the ambient temperature and ambient humidity The soil data includes test soil moisture Control soil moisture The superscript i represents the number of the sampling area, i=1,2,3,…,M, M is the total number of sampling areas, the subscript j represents the sampled data number, j=1,2,3,…,N, N is the total number of sampled data, and the divided area and sampling depth of the test area and the control area are the same.

3. The method for constructing a soil moisture and fertilizer application amount relationship model according to claim 1, characterized in that: After the model optimization is completed, the proportion of large particles, medium particles, small particles, ambient temperature, ambient humidity, and the final stabilized test soil moisture in the actual sowing area are substituted into the multivariate linear regression model to solve the final recommended fertilizer amount, where the final stabilized test soil moisture is 1.1 times the final stabilized control soil moisture.

4. The method for constructing a soil moisture and fertilizer application amount relationship model according to claim 3, characterized in that: When applying water-soluble fertilizer to the actual sowing area, fertilize the entire actual sowing area at P times the recommended fertilizer amount, where the P value is calculated as follows: Where S represents the area of ​​the sampling area, S ′ Indicates the area of ​​the actual sowing area.

5. A system for constructing a soil moisture and fertilizer application relationship model, characterized in that: The construction system adopts the construction method according to any one of claims 1 to 4, comprising: A sensor module, the sensor module includes a first sensor unit and a second sensor unit, the first sensor unit is electrically connected to the data analysis module, and is used to collect environmental data and soil data of the sampling area, and the second sensor unit is electrically connected to the comprehensive processing module, and is used to collect environmental data and soil data of the actual sowing area; A screening module, which is electrically connected to the data analysis module and is used to screen the soil sample and generate the composition ratio of different types of soil particles; A data analysis module, which is electrically connected to the comprehensive processing module and is used to generate a fertilizer-water penetration ratio based on the fertilizer application amount and soil data of the test area, and then correct it using the soil data of the control area; The comprehensive processing module is used to construct a multivariate linear regression model and perform corresponding optimization and verification, and then substitute the soil particle composition ratio, environmental data, and soil data of the actual sowing area into the multivariate linear regression model to calculate the corresponding fertilizer amount and recommend it as the optimal fertilizer amount.

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