Accurate control system and method for cooperative fertilization and seeding

Abstract

The invention relates to the technical field of sowing control, in particular to a precise control system and method for cooperative fertilization and sowing, and the method comprises the following steps: monitoring soil humidity distribution data of a target farmland by using a soil humidity sensor, and calculating a soil permeability coefficient based on the soil humidity distribution data in combination with a three-dimensional terrain model and a soil permeability coefficient; constructing a soil humidity gradient model by adopting a finite element analysis method; based on the soil humidity gradient model and in combination with target fertilizer characteristics, establishing a fertilizer diffusion model, simulating the diffusion range and concentration distribution of the fertilizer under the soil humidity gradient condition, and outputting the fertilizer diffusion radius and the concentration gradient distribution of the fertilizer in soil through multivariate regression analysis; and based on the fertilizer diffusion model, calculating the fertilization amount and the fertilization depth in combination with the crop demand parameters. According to the method, the soil humidity gradient model is established, and fertilizer waste and environmental pollution caused by non-uniform fertilization or excessive fertilization amount are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of sowing control, and in particular to a precise control system and method for coordinated fertilization and sowing. Background Art

[0002] With the advancement of agricultural production modernization, precision fertilization, as an important part of precision agriculture, aims to optimize the amount and method of fertilizer application based on real-time data on soil, crops and environmental conditions, so as to improve fertilizer utilization efficiency and reduce the environmental burden caused by excessive fertilizer application.

[0003] However, in the actual fertilization process, the dynamic changes in soil moisture have an important impact on the diffusion range and distribution of fertilizers. Soil moisture not only determines the dissolution rate and penetration depth of fertilizers, but also directly affects the diffusion path and concentration distribution of fertilizers in the soil. Traditional fertilization methods usually ignore this key factor, resulting in uneven fertilizer application, serious waste, and even affecting the normal growth of crops.

[0004] The existing technology has failed to fully consider the impact of soil moisture gradient on fertilizer diffusion, and lacks real-time monitoring and dynamic adjustment capabilities. For example, some studies use soil moisture sensor monitoring data to make fertilization decisions, but due to the complex spatial distribution and temporal changes of soil moisture, the existing technology still has great limitations in predicting fertilizer diffusion paths and optimizing fertilization parameters.

[0005] In addition, most existing technologies focus on optimizing the amount of fertilizer applied, lack accurate simulation and control of the fertilizer diffusion process, and are unable to effectively fine-tune the fertilization depth and amount according to different soil moisture conditions, resulting in unsatisfactory fertilization effects and unbalanced crop nutrient supply. Summary of the invention

[0006] The invention provides a precise control system and method for coordinated fertilization and sowing.

[0007] A method for precise control of coordinated fertilization and sowing, comprising the following steps:

[0008] S1, soil moisture gradient modeling: soil moisture distribution data of target farmland is monitored using soil moisture sensors, and a soil moisture gradient model is constructed using finite element analysis method based on soil moisture distribution data combined with a three-dimensional terrain model and soil permeability coefficient;

[0009] S2, Fertilizer diffusion model construction: Based on the soil moisture gradient model and combined with the target fertilizer characteristics, a fertilizer diffusion model is established to simulate the diffusion range and concentration distribution of fertilizer under soil moisture gradient conditions. Through multivariate regression analysis, the fertilizer diffusion radius and its concentration gradient distribution in the soil are output. The target fertilizer characteristics include dissolution rate and diffusion coefficient;

[0010] S3, precise fertilization parameter calculation: based on the fertilizer diffusion model and combined with crop demand parameters, calculate the amount of fertilizer and fertilization depth.

[0011] Optionally, the S1 specifically includes:

[0012] S11, soil moisture data collection: Distributed soil moisture sensors are arranged in the target farmland to monitor the soil moisture at different depths (such as the surface layer, the root zone) and different locations (such as different slopes, near ditches) in real time. The collected data is expressed as θ(x, y, z, t), which represents the soil moisture at the target farmland location (x, y, z) at time t, where x, y and z are the ground plane coordinates and soil depth coordinates, respectively;

[0013] S12, 3D terrain modeling: Use remote sensing data (or lidar data) to build a 3D terrain model of the target farmland. The 3D terrain model includes the slope and undulation of the farmland, reflecting the impact of the terrain on soil moisture distribution;

[0014] S13, soil permeability characteristics analysis: according to the type of target farmland soil, the soil permeability coefficient is analyzed and obtained, and the permeability coefficient k(x, y, z) of different soil types is obtained, which represents the permeability coefficient of the soil at the position (x, y, z) in the target farmland;

[0015] S14, taking soil moisture and soil permeability as input, a mathematical model is established to simulate the flow process of soil moisture in the target farmland, and a soil moisture gradient model is constructed based on finite element analysis. (x,y,z).

[0016] Optionally, the mathematical model established to simulate the flow process of soil moisture in the target farmland is described by Darcy's law:

[0017] Among them, J is the water flux, which represents the amount of water flowing through a unit area, k(x,y,z) is the soil permeability coefficient, which describes the flow capacity of water in the soil, is the spatial rate of change of the known humidity distribution θ(x,y,z).

[0018] Optionally, the finite element analysis is expressed using the equation: Where Ω is the spatial area of ​​the target farmland, representing the entire target farmland range. For the three-dimensional finite element model, the soil moisture gradient is expressed in three-dimensional space as: in, is the rate of change of soil moisture over time, Q(x,y,z,t) represents the source of moisture per unit volume (precipitation, irrigation, or evaporation);

[0019] Based on the results of finite element analysis, the distribution of soil moisture is obtained through numerical solution, and the output is θ(x, y, z). This result represents the distribution of soil moisture at different positions and depths in the target farmland. The gradient change of soil moisture is expressed by the following formula:

[0020] in, and Represent the rate of change of soil moisture in the x, y, and z directions respectively.

[0021] Optionally, the construction of the fertilizer diffusion model in S2 specifically includes:

[0022] S21, experimentally determine the dissolution rate R of the target fertilizer f and the diffusion coefficient D f , where the dissolution rate R f Describes the dissolution rate of fertilizer in water, the diffusion coefficient D f Describes the diffusion capacity of fertilizers in water, which is affected by the humidity gradient and soil permeability k(x,y,z);

[0023] S22, Fertilizer diffusion model construction: based on soil moisture gradient model Combined with the target fertilizer characteristics, a fertilizer diffusion model is established, and the diffusion process is expressed as:

[0024]

[0025] Among them, C f (x,y,z,t) represents the fertilizer concentration at soil position (x,y,z) at time t, Represents the diffusion behavior of fertilizer, which is affected by the diffusion coefficient D f and soil moisture gradient The impact of represents the transport behavior of fertilizer in the direction of water flow, v is the water flow rate, S f The dissolution source strength of the fertilizer is expressed by the dissolution rate R f Decision, S. f =R f ·θ(x,y,z), indicating that the soil moisture θ(x,y,z) in the target area has a direct impact on the dissolution rate of the fertilizer, that is, the higher the soil moisture, the higher the fertilizer dissolution rate S f The bigger;

[0026] S23, multivariate regression analysis: Using historical experimental data and measured data, combined with the fertilizer diffusion model, a multivariate regression model was established using multivariate regression analysis to convert the humidity gradient Permeability coefficient k(x,y,z) and fertilizer characteristics R f ,Df As input variables, the relationship between fertilizer diffusion range and concentration distribution under soil moisture gradient conditions is fitted;

[0027] S24, diffusion path and concentration distribution output:

[0028] Fertilizer diffusion radius R d (x, y, z), describing the diffusion range of fertilizer under different humidity gradient conditions;

[0029] Fertilizer concentration distribution Reflects the spatial concentration variation characteristics of fertilizer in the soil.

[0030] Optionally, in the multivariate regression model:

[0031] Input variables include soil moisture gradient Soil permeability k(x,y,z), dissolution rate R f and the diffusion coefficient D f ;

[0032] Output variables include fertilizer diffusion range and fertilizer concentration distribution;

[0033] The relationship fitted by the multivariate regression model is expressed as:

[0034] Among them, f1 and f2 are fitted multivariate functions, which are used to describe the relationship between the input variables and the diffusion range and concentration distribution. These two functions can be fitted by multivariate linear regression.

[0035] Optionally, the precision fertilization parameter calculation in S3 specifically includes:

[0036] S31, obtaining crop demand parameters: according to the growth stage, root development characteristics and nutrient demand curve of the target crop, the crop demand parameters for fertilizer are obtained. The crop demand parameters include:

[0037] Crop root depth: Maximum root depth D root , which varies with the growth stage of the crop;

[0038] Crop nutrient requirement curve: describes the nutrient absorption requirements of crops at different growth stages, expressed as a time function N crop (t);

[0039] S32, Fertilizer application amount calculation: Calculate the fertilizer application amount M based on crop demand parameters and fertilizer diffusion path prediction model f , taking into account the nutrient requirements of crops, the range of fertilizer diffusion, and the soil moisture distribution:

[0040] M f =∫ A Cf (x,y,z)dA, where A is the area of ​​the fertilization area, is the fertilizer concentration distribution, ∫ A It represents the integration of fertilizer concentration in the farmland area A, and dA represents the small area element;

[0041] S33, fertilization depth calculation: according to the root depth D of the crop root and soil moisture gradient to determine the optimal depth D for fertilization p , minimize the root depth D root and ideal fertilization depth.

[0042] Optionally, the minimum root depth D root And the ideal fertilization depth is expressed as: Among them, D root is the maximum depth of the crop root system, is a ratio that characterizes the relative efficiency of the diffusion coefficient and soil permeability. is the soil moisture gradient.

[0043] Optionally, the time function N crop (t) Take it into account when calculating the amount of fertilizer to adjust the fertilizer concentration C f (x,y,z):C f (x,y,z,t)=C f (x,y,z)·N crop (t), the fertilizer application amount calculation is updated as:

[0044] M f =∫ A C f (x,y,z)·N crop (t)dA.

[0045] A coordinated fertilization and sowing precision control system, used to implement the above-mentioned coordinated fertilization and sowing precision control method, includes the following modules:

[0046] Soil moisture monitoring module: includes multiple distributed soil moisture sensors, which monitor soil moisture data at different depths and locations in the target farmland in real time and transmit the data to the central control unit for preprocessing;

[0047] 3D terrain modeling module: constructs a 3D terrain model of farmland through remote sensing data to reflect the slope and undulating characteristics of the farmland;

[0048] Soil moisture gradient modeling module: Based on soil moisture monitoring data, three-dimensional terrain model and soil permeability characteristics, the finite element analysis method is used to calculate the gradient change of soil moisture and generate a soil moisture gradient model;

[0049] Fertilizer diffusion path prediction module: Combined with the soil moisture gradient model and fertilizer characteristics, the fertilizer diffusion model is used to predict the diffusion range and concentration distribution of fertilizers under different humidity conditions, and the fertilizer application strategy is optimized through multivariate regression analysis to output the fertilizer diffusion radius and concentration gradient distribution map;

[0050] Precision fertilization parameter calculation module: Based on the fertilizer diffusion path prediction model and combined with the crop demand parameters, the required fertilizer amount and fertilization depth are calculated, and the calculation results are transmitted to the fertilization execution unit, which executes the fertilizer amount and fertilization depth.

[0051] Beneficial effects of the present invention:

[0052] In response to the problem that fertilizer diffusion is affected by soil moisture gradient, the soil moisture gradient model is combined with the fertilizer diffusion model to monitor the distribution of soil moisture in real time and accurately predict the diffusion path and concentration changes of fertilizer in the soil. By utilizing the finite element analysis method, a soil moisture gradient model is established, and combined with the soil permeability characteristics, the dissolution rate and diffusion coefficient of the fertilizer, the diffusion behavior of the fertilizer under different soil moisture conditions is accurately simulated, thus avoiding fertilizer waste and environmental pollution caused by uneven fertilization or excessive fertilization.

[0053] The present invention constructs a soil moisture gradient model, combines crop demand, soil permeability and fertilizer characteristics, accurately simulates the diffusion path and concentration distribution of fertilizer in the soil, and then optimizes the amount and depth of fertilizer application. Under different soil moisture and terrain conditions, the method can reduce excessive application of fertilizer, ensure that fertilizer is accurately applied to the crop root area, improve fertilizer utilization efficiency, reduce fertilizer waste, and reduce environmental pollution.

[0054] The present invention, by introducing a crop demand time function, can dynamically adjust the amount of fertilizer according to the fertilizer demand of crops at different growth stages. In the critical period of crop growth (such as flowering period and fruit expansion period), the amount of fertilizer can be accurately calculated according to the nutrient demand of crops to avoid excessive or insufficient fertilization, thereby ensuring that crops can obtain the best nutrient supply at each growth stage, thereby improving the growth rate and yield of crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic diagram of a control method flow chart of an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of control system functional modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0059] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0060] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0061] like Figure 1 As shown, a method for precise control of coordinated fertilization and sowing includes the following steps:

[0062] S1, soil moisture gradient modeling: soil moisture distribution data of target farmland is monitored using soil moisture sensors, and a soil moisture gradient model is constructed using finite element analysis method based on soil moisture distribution data combined with a three-dimensional terrain model and soil permeability coefficient;

[0063] S2, Fertilizer diffusion model construction: Based on the soil moisture gradient model and combined with the target fertilizer characteristics, a fertilizer diffusion model is established to simulate the diffusion range and concentration distribution of fertilizer under soil moisture gradient conditions. Through multivariate regression analysis, the fertilizer diffusion radius and its concentration gradient distribution in the soil are output. The target fertilizer characteristics include dissolution rate and diffusion coefficient;

[0064] S3, precise fertilization parameter calculation: based on the fertilizer diffusion model and combined with crop demand parameters, calculate the amount of fertilizer and fertilization depth.

[0065] S1 specifically includes:

[0066] S11, soil moisture data collection: Distributed soil moisture sensors are arranged in the target farmland to monitor the soil moisture at different depths (such as the surface layer, the root zone) and different locations (such as different slopes, near ditches) in real time. The collected data is expressed as θ(x, y, z, t), which represents the soil moisture at the target farmland location (x, y, z) at time t, where x, y and z are the ground plane coordinates and soil depth coordinates, respectively;

[0067] S12, 3D terrain modeling: Use remote sensing data (or lidar data) to build a 3D terrain model of the target farmland. The 3D terrain model includes the slope and undulation of the farmland, reflecting the impact of the terrain on the soil moisture distribution. The 3D terrain model is represented by h(x, y), which is the ground height at the target farmland (x, y). The slope S(x, y) is used to further describe the terrain characteristics, which is defined as the spatial gradient of the terrain elevation:

[0068] in, and Represent the slope gradient in the x and y directions respectively;

[0069] S13, soil permeability characteristics analysis: according to the type of target farmland soil, the soil permeability coefficient is analyzed and obtained. Soil permeability determines the propagation speed and distribution of water in the soil. Through experiments or existing literature data, the permeability coefficient k(x,y,z) of different soil types can be obtained, which represents the permeability coefficient of the soil at the position (x,y,z) in the target farmland;

[0070] S14, taking soil moisture and soil permeability as input, a mathematical model is established to simulate the flow process of soil moisture in the target farmland. The finite element analysis method can accurately calculate the water exchange, permeability and distribution between different soil layers in three-dimensional space, and further derive the gradient change of soil moisture. Based on finite element analysis, a soil moisture gradient model is constructed. The model reflects the distribution characteristics of soil moisture in various areas of farmland and its spatial variation law. The model can provide accurate soil moisture gradient data.

[0071] A mathematical model was established to simulate the flow of soil moisture in the target farmland and described by Darcy's law:

[0072] Among them, J is the water flux, which represents the amount of water flowing through a unit area, k(x,y,z) is the soil permeability coefficient, which describes the flow capacity of water in the soil, It is the spatial change rate of the known moisture distribution θ(x,y,z). It is used as an intermediate variable in the water flow simulation in Darcy's law. Darcy's law describes the ultimate goal of simulating the water flow process and inferring the soil moisture gradient at each point (x,y,z) in the farmland. That is, the soil moisture gradient model.

[0073] Finite element analysis uses the equation: Where Ω is the spatial area of ​​the target farmland, representing the entire target farmland range. For the three-dimensional finite element model, the soil moisture gradient is expressed in three-dimensional space as: in, is the rate of change of soil moisture over time, Q(x,y,z,t) represents the source of moisture per unit volume (precipitation, irrigation, or evaporation);

[0074] Based on the results of finite element analysis, the distribution of soil moisture is obtained through numerical solution, and the output is θ(x, y, z). This result represents the distribution of soil moisture at different positions and depths in the target farmland. The gradient change of soil moisture is expressed by the following formula:

[0075] in, and Represent the rate of change of soil moisture in the x, y, and z directions respectively.

[0076] Based on the above calculation results, the soil moisture gradient model is finally obtained

[0077] The construction of the fertilizer diffusion model in S2 specifically includes:

[0078] S21, experimentally determine the dissolution rate R of the target fertilizer f and the diffusion coefficient D f , where the dissolution rate R f Describes the dissolution rate of fertilizer in water, the diffusion coefficient D f Describes the diffusion capacity of fertilizers in water, which is affected by the humidity gradient and soil permeability k(x,y,z);

[0079] S22, Fertilizer diffusion model construction: based on soil moisture gradient model Combined with the target fertilizer characteristics, a fertilizer diffusion model is established, and the diffusion process is expressed as:

[0080] The above is a simplified representation (subsequent related parameters will also be simplified), which can be expanded to:

[0081]

[0082] Among them, C f (x,y,z,t) represents the fertilizer concentration at soil position (x,y,z) at time t, Represents the diffusion behavior of fertilizer, which is affected by the diffusion coefficient D f and soil moisture gradient The impact of represents the transport behavior of fertilizer in the direction of water flow, v is the water flow rate, S f The dissolution source strength of the fertilizer is expressed by the dissolution rate R f Decision, S. f =R f ·θ(x,y,z), indicating that the soil moisture θ(x,y,z) in the target area has a direct impact on the dissolution rate of the fertilizer, that is, the higher the soil moisture, the higher the fertilizer dissolution rate S f The bigger;

[0083] The above simulation data are used as training data to fit the multivariate regression model. The diffusion model requires numerical solution of partial differential equations, which is computationally intensive and has low real-time performance. The multivariate regression model simplifies the complex physical process into statistical relationships by fitting the data generated by the diffusion model, thereby achieving rapid prediction.

[0084] S23, multivariate regression analysis: Using historical experimental data and measured data, combined with the fertilizer diffusion model, a multivariate regression model was established using multivariate regression analysis to convert the humidity gradient Permeability coefficient k(x,y,z) and fertilizer characteristics R f ,D f As input variables, the relationship between fertilizer diffusion range and concentration distribution under soil moisture gradient conditions is fitted;

[0085] S24, diffusion path and concentration distribution output:

[0086] Fertilizer diffusion radius R d (x, y, z), describing the diffusion range of fertilizer under different humidity gradient conditions;

[0087] Fertilizer concentration distribution Reflects the spatial concentration variation characteristics of fertilizer in the soil.

[0088] The fertilizer diffusion radius and concentration gradient output by the model are used to generate a concentration distribution map through a three-dimensional visualization tool to intuitively display the fertilizer diffusion characteristics in different areas and provide a reference for precise fertilization.

[0089] In the multivariate regression model:

[0090] Input variables include soil moisture gradient Soil permeability k(x,y,z), dissolution rate R fand the diffusion coefficient D f ;

[0091] Output variables include fertilizer diffusion range and fertilizer concentration distribution;

[0092] The relationship fitted by the multivariate regression model is expressed as:

[0093] Among them, f1 and f2 are fitted multivariate functions, which are used to describe the relationship between the input variables and the diffusion range and concentration distribution. These two functions can be fitted by multivariate linear regression;

[0094] Use multiple linear regression to fit the following relationship:

[0095] Target variable (output variable):

[0096] Fertilizer diffusion range R d : represents the radius of fertilizer diffusion;

[0097] Fertilizer concentration distribution C f : Indicates the fertilizer concentration at a certain location;

[0098] Input variable (characteristic variable): soil moisture gradient Soil permeability k, fertilizer dissolution rate R f , Fertilizer diffusion coefficient D f ;

[0099] The goal is to fit the following linear relationship: Y = W0 + W1X1 + W2X2 + ... + W n X n , where Y: target variable (R d or C f ); X1, X2, …, X n : Input variables W0: bias term (intercept); W1, W2, …, W n : The regression coefficient of the characteristic variable, reflecting the influence of each input variable on Y.

[0100] Data preparation:

[0101] X1: Soil moisture gradient

[0102] X2: soil permeability k(x,y,z);

[0103] X3: Fertilizer dissolution rate R f ;

[0104] X4: Fertilizer diffusion coefficient D f ;

[0105] Y: target variable (diffusion radius Rd or concentration C f ).

[0106] Use multiple linear regression to fit the following model:

[0107]

[0108] or: Here, W0 is the intercept, which represents the target value when all input variables are zero, and W1, W2, W3, and W4 are regression coefficients, which represent the linear influence of each input variable on the target variable.

[0109] The regression coefficients are estimated using the least squares method to minimize the loss function.

[0110] The precise fertilization parameter calculation in S3 specifically includes:

[0111] S31, obtaining crop demand parameters: according to the growth stage, root development characteristics and nutrient demand curve of the target crop, the crop demand parameters for fertilizer are obtained. The crop demand parameters include:

[0112] Crop root depth: Maximum root depth D root , which varies with the growth stage of the crop;

[0113] Crop nutrient requirement curve: describes the nutrient absorption requirements of crops at different growth stages, expressed as a time function N crop (t), such as the absorption rate of nutrients such as nitrogen, phosphorus, and potassium;

[0114] S32, Fertilizer application amount calculation: Calculate the fertilizer application amount M based on crop demand parameters and fertilizer diffusion path prediction model f , taking into account the nutrient requirements of crops, the range of fertilizer diffusion, and the soil moisture distribution:

[0115] M f =∫ A C f (x,y,z)dA, where A is the area of ​​the fertilization area, is the fertilizer concentration distribution, ∫ A It represents the integration of fertilizer concentration in the farmland area A, dA represents the small area element, which is obtained according to the fertilizer diffusion model and represents the fertilizer concentration at different locations. By integrating the fertilizer concentration in the fertilization area, the total fertilizer application amount in the fertilization area is obtained;

[0116] S33, fertilization depth calculation: according to the root depth D of the crop root and soil moisture gradient to determine the optimal depth D for fertilization p , minimize the root depth D rootand the ideal fertilization depth, which should ensure that the fertilizer can effectively penetrate into the root zone of the crop, taking into account the infiltration characteristics of the soil layer.

[0117] Minimize root depth D root And the ideal fertilization depth is expressed as: Among them, D root is the maximum depth of the crop root system, is a ratio that characterizes the relative efficiency of the diffusion coefficient and soil permeability. is the soil moisture gradient.

[0118] Time function N crop (t) Take it into account when calculating the amount of fertilizer to adjust the fertilizer concentration C f (x, y, z), in practical applications, the crop nutrient demand curve should be used as a regulating factor to influence the distribution of concentration, combining the crop demand at time t with the fertilizer concentration: C f (x,y,z,t)=C f (x,y,z)·N crop (t), the fertilizer application amount calculation is updated as:

[0119] M f =∫ A C f (x,y,z)·N crop (t)dA.

[0120] like Figure 2 As shown, a coordinated fertilization and sowing precision control system is used to implement the above-mentioned coordinated fertilization and sowing precision control method, including the following modules:

[0121] Soil moisture monitoring module: includes multiple distributed soil moisture sensors, which monitor soil moisture data at different depths and locations in the target farmland in real time and transmit the data to the central control unit for preprocessing;

[0122] 3D terrain modeling module: constructs a 3D terrain model of farmland through remote sensing data to reflect the slope and undulating characteristics of the farmland;

[0123] Soil moisture gradient modeling module: Based on soil moisture monitoring data, three-dimensional terrain model and soil permeability characteristics, the finite element analysis method is used to calculate the gradient change of soil moisture and generate a soil moisture gradient model;

[0124] Fertilizer diffusion path prediction module: Combined with the soil moisture gradient model and fertilizer characteristics, the fertilizer diffusion model is used to predict the diffusion range and concentration distribution of fertilizers under different humidity conditions, and the fertilizer application strategy is optimized through multivariate regression analysis to output the fertilizer diffusion radius and concentration gradient distribution map;

[0125] Precision fertilization parameter calculation module: Based on the fertilizer diffusion path prediction model and combined with the crop demand parameters, the required fertilizer amount and fertilization depth are calculated, and the calculation results are transmitted to the fertilization execution unit, which executes the fertilizer amount and fertilization depth.

[0126] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0127] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for precise control of coordinated fertilization and sowing, characterized in that: The following steps are involved: S1, soil moisture gradient modeling: soil moisture distribution data of target farmland is monitored using soil moisture sensors, and a soil moisture gradient model is constructed using finite element analysis method based on soil moisture distribution data combined with a three-dimensional terrain model and soil permeability coefficient; S2, Fertilizer diffusion model construction: Based on the soil moisture gradient model and combined with the target fertilizer characteristics, a fertilizer diffusion model is established to simulate the diffusion range and concentration distribution of fertilizer under soil moisture gradient conditions. Through multivariate regression analysis, the fertilizer diffusion radius and its concentration gradient distribution in the soil are output. The target fertilizer characteristics include dissolution rate and diffusion coefficient; S3, precise fertilization parameter calculation: based on the fertilizer diffusion model and combined with crop demand parameters, calculate the amount of fertilizer and fertilization depth.

2. A method for precise control of coordinated fertilization and sowing according to claim 1, characterized in that: The S1 specifically includes: S11, soil moisture data collection: Distributed soil moisture sensors are arranged in the target farmland to monitor the soil moisture at different depths and different positions in real time. The collected data is expressed as θ(x, y, z, t), which represents the soil moisture at the target farmland position (x, y, z) at time t, where x, y and z are the ground plane coordinates and soil depth coordinates respectively; S12, 3D terrain modeling: Use remote sensing data to build a 3D terrain model of the target farmland. The 3D terrain model includes the slope and undulation of the farmland, reflecting the impact of the terrain on soil moisture distribution; S13, soil permeability characteristics analysis: according to the type of target farmland soil, the soil permeability coefficient is analyzed and obtained, and the permeability coefficient k(x, y, z) of different soil types is obtained, which represents the permeability coefficient of the soil at the position (x, y, z) in the target farmland; S14, taking soil moisture and soil permeability as input, a mathematical model is established to simulate the flow process of soil moisture in the target farmland, and a soil moisture gradient model is constructed based on finite element analysis.

3. A method for precise control of coordinated fertilization and sowing according to claim 2, characterized in that: The mathematical model established to simulate the flow process of soil moisture in the target farmland is described by Darcy's law: Among them, J is the water flux, which represents the amount of water flowing through a unit area, k(x,y,z) is the soil permeability coefficient, which describes the flow capacity of water in the soil, is the spatial rate of change of the known humidity distribution θ(x,y,z).

4. A method for precise control of coordinated fertilization and sowing according to claim 3, characterized in that: The finite element analysis is expressed using the equation: Where Ω is the spatial area of ​​the target farmland, representing the entire target farmland range, and the soil moisture gradient is expressed in three-dimensional space as: in, is the rate of change of soil moisture with time, Q(x,y,z,t) represents the source of moisture per unit volume; Based on the results of finite element analysis, the distribution of soil moisture is obtained by numerical solution, and the output is θ(x, y, z). The gradient change of soil moisture is expressed by the following formula: in, and Represent the rate of change of soil moisture in the x, y, and z directions respectively.

5. The method for precise control of coordinated fertilization and sowing according to claim 3, characterized in that: The construction of the fertilizer diffusion model in S2 specifically includes: S21, experimentally determine the dissolution rate R of the target fertilizer f and the diffusion coefficient D f , where the dissolution rate R f Describes the dissolution rate of fertilizer in water, the diffusion coefficient D f Describes the diffusion capacity of fertilizers in water, which is affected by the humidity gradient and soil permeability k(x,y,z); S22, Fertilizer diffusion model construction: based on soil moisture gradient model Combined with the target fertilizer characteristics, a fertilizer diffusion model is established, and the diffusion process is expressed as: Among them, C f (x,y,z,t) represents the fertilizer concentration at soil position (x,y,z) at time t, Represents the diffusion behavior of fertilizer, which is affected by the diffusion coefficient D f and soil moisture gradient The impact of represents the transport behavior of fertilizer in the direction of water flow, v is the water flow rate, S f The dissolution source strength of the fertilizer is expressed by the dissolution rate R f Decide; S23, multivariate regression analysis: Using historical experimental data and measured data, combined with the fertilizer diffusion model, a multivariate regression model was established using multivariate regression analysis to convert the humidity gradient Permeability coefficient k(x,y,z) and fertilizer characteristics R f ,D f As input variables, the relationship between fertilizer diffusion range and concentration distribution under soil moisture gradient conditions is fitted; S24, diffusion path and concentration distribution output: Fertilizer diffusion radius R d (x, y, z), describing the diffusion range of fertilizer under different humidity gradient conditions; Fertilizer concentration distribution Reflects the spatial concentration variation characteristics of fertilizer in the soil.

6. A method for precise control of coordinated fertilization and sowing according to claim 5, characterized in that: In the multivariate regression model: Input variables include soil moisture gradient Soil permeability k(x,y,z), dissolution rate R f and the diffusion coefficient D f ; Output variables include fertilizer diffusion range and fertilizer concentration distribution; The relationship fitted by the multivariate regression model is expressed as: Among them, f1 and f2 are fitted multivariable functions, which are used to describe the relationship between the input variables and the diffusion range and concentration distribution.

7. The method for precise control of coordinated fertilization and sowing according to claim 5, characterized in that: The precision fertilization parameter calculation in S3 specifically includes: S31, obtaining crop demand parameters: according to the growth stage, root development characteristics and nutrient demand curve of the target crop, the crop demand parameters for fertilizer are obtained. The crop demand parameters include: Crop root depth: Maximum root depth D root , which varies with the growth stage of the crop; Crop nutrient requirement curve: describes the nutrient absorption requirements of crops at different growth stages, expressed as a time function N crop (t); S32, Fertilizer application amount calculation: Calculate the fertilizer application amount M based on crop demand parameters and fertilizer diffusion path prediction model f , taking into account the nutrient requirements of crops, the range of fertilizer diffusion, and the soil moisture distribution: M f =∫ A C f (x,y,z)dA, where A is the area of ​​the fertilization area, is the fertilizer concentration distribution, ∫ A It represents the integration of fertilizer concentration in the farmland area A, and dA represents the small area element; S33, fertilization depth calculation: according to the root depth D of the crop root and soil moisture gradient to determine the optimal depth D for fertilization p , minimize the root depth D root and ideal fertilization depth.

8. A method for precise control of coordinated fertilization and sowing according to claim 7, characterized in that: The minimum root depth D root And the ideal fertilization depth is expressed as: Among them, D root is the maximum depth of the crop root system, is a ratio that characterizes the relative efficiency of the diffusion coefficient and soil permeability. is the soil moisture gradient.

9. The method for precise control of coordinated fertilization and sowing according to claim 7, characterized in that: The time function N crop (t) Take it into account when calculating the amount of fertilizer to adjust the fertilizer concentration C f (x,y,z):C f (x,y,z,t)=C f (x,y,z)·N crop (t), the fertilizer application amount calculation is updated as: M f =∫ A C f (x,y,z)·N crop (t)dA。 10. A coordinated fertilization and sowing precision control system, used to implement a coordinated fertilization and sowing precision control method as claimed in any one of claims 19, characterized in that: Includes the following modules: Soil moisture monitoring module: includes multiple distributed soil moisture sensors, which monitor soil moisture data at different depths and locations in the target farmland in real time and transmit the data to the central control unit for preprocessing; 3D terrain modeling module: constructs a 3D terrain model of farmland through remote sensing data to reflect the slope and undulating characteristics of the farmland; Soil moisture gradient modeling module: Based on soil moisture monitoring data, three-dimensional terrain model and soil permeability characteristics, the finite element analysis method is used to calculate the gradient change of soil moisture and generate a soil moisture gradient model; Fertilizer diffusion path prediction module: Combined with the soil moisture gradient model and fertilizer characteristics, the fertilizer diffusion model is used to predict the diffusion range and concentration distribution of fertilizers under different humidity conditions, and the fertilizer application strategy is optimized through multivariate regression analysis to output the fertilizer diffusion radius and concentration gradient distribution map; Precision fertilization parameter calculation module: Based on the fertilizer diffusion path prediction model and combined with the crop demand parameters, the required fertilizer amount and fertilization depth are calculated, and the calculation results are transmitted to the fertilization execution unit, which executes the fertilizer amount and fertilization depth.

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