Soil oxygen dynamic prediction method for precision agriculture

Abstract

The invention discloses a soil oxygen dynamic prediction method for precision agriculture, and belongs to the technical field of soil oxygen prediction, and the method comprises the following steps: S1, collecting key parameters of different soil depths in a research area, and S2, calculating a soil oxygen diffusion coefficient according to the key parameters of different soil depths in the research area; s3, calculating the respiration rate of the soil root system; s4, constructing a soil oxygen consumption model; and S5, carrying out iterative calculation to obtain soil oxygen concentration distribution at different moments and different depths. The method can solve the problem that the oxygen concentration of the soil is influenced by complex factors such as moisture, temperature, porosity, root respiration and microbial activity, and provides a scientific basis for precise agricultural irrigation, soil health assessment and oxygenation irrigation strategy optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of soil oxygen prediction, and particularly relates to a method for dynamically predicting soil oxygen for precision agriculture. Background Art

[0002] In agricultural production and soil ecological environment management, soil oxygen concentration is one of the key factors affecting crop root growth and microbial activity. The supply of oxygen is affected by the complex interaction of soil moisture, temperature, pore structure, and microbial activity. Especially in high-moisture environments, soil pores are filled with water, restricting oxygen diffusion and root respiration, thereby affecting crop growth and yield.

[0003] However, current soil oxygen monitoring and prediction methods have many limitations. Traditional soil oxygen measurement mainly relies on on-site sensor sampling. Although it can obtain data within a certain spatio-temporal range, due to the high cost of data collection and limited spatial coverage, it is difficult to conduct large-scale dynamic prediction. In addition, most existing soil oxygen prediction models are based on deterministic numerical methods (such as finite difference method, finite element method, etc.), but they do not adequately consider the dynamic changes of environmental factors (such as moisture and temperature, etc.), resulting in large prediction errors. Moreover, these methods usually require a large amount of high-precision basic data as input, and in actual agricultural production, it is difficult to obtain soil oxygen data with high spatio-temporal resolution, limiting the applicability of the models.

[0004] On the other hand, although stochastic model methods (such as statistical regression, random forest, etc.) can consider the uncertainty of environmental variables, their description of the physical mechanisms of soil oxygen diffusion and consumption is relatively limited, and it is difficult to generalize and apply under different soil types and climate conditions. Therefore, there is currently no prediction model that can simultaneously take into account environmental uncertainty and the physical mechanism of oxygen diffusion, which limits the development of precision agriculture management and ecological environment monitoring. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a method for dynamically predicting soil oxygen for precision agriculture.

[0006] The technical solution of the present invention is: A method for dynamically predicting soil oxygen for precision agriculture includes the following steps:

[0007] S1. Collect key parameters at different soil depths in the research area;

[0008] S2. Calculate the soil oxygen diffusion coefficient according to the key parameters at different soil depths in the research area;

[0009] S3. Calculate the soil root respiration rate according to the key parameters at different soil depths in the research area;

[0010] S4. Construct a soil oxygen consumption model based on the key parameters at different soil depths within the research area;

[0011] S5. Perform iterative calculations based on the soil oxygen diffusion coefficient, root respiration rate, and soil oxygen consumption model to obtain the soil oxygen concentration distribution at different times and different depths.

[0012] Furthermore, in S1, the key parameters include the soil oxygen concentration, soil water content, soil temperature, soil porosity, crop root respiration rate, and microbial respiration rate at different depths.

[0013] Furthermore, in S2, the soil oxygen diffusion coefficient is calculated by the formula:

[0014] ;

[0015] In the formula, represents the soil basic diffusion coefficient, represents the soil water content at time and soil depth ; represents the soil temperature at time and soil depth ; represents the parameter controlling the influence of soil water on the diffusion coefficient, represents the parameter controlling the influence of soil temperature on the diffusion coefficient, represents the exponential function.

[0016] Furthermore, in S3, the soil root respiration rate is calculated by the formula:

[0017] ;

[0018] In the formula, represents the oxygen consumption rate of roots under no water limitation conditions, represents the soil water content at time and soil depth ; represents the soil temperature at time and soil depth ; represents the degree of influence of soil water on the crop root respiration rate, represents the degree of influence of temperature change on the crop root respiration rate, represents the exponential function.

[0019] Furthermore, in S4, the expression of the soil oxygen consumption model is:

[0020] ;

[0021] In the formula, represents the basic microbial respiration rate under standard moisture and temperature, represents the influence intensity of controlled moisture on the microbial respiration rate, represents at time and soil depth the soil water content at, represents at time and soil depth the soil temperature at, represents the exponential function, represents the influence degree of controlled temperature on the microbial respiration rate.

[0022] Furthermore, S4 includes the following sub-steps:

[0023] S41. Divide the vertical soil profile in the study area into several nodes, set the top soil and atmosphere interface as the Dirichlet boundary, set the bottom boundary as the Neumann zero-flux boundary, and generate a closed state in the area below the root system;

[0024] S42. Calculate the spatial gradient in the closed state of the area below the root system;

[0025] S43. According to the spatial gradient, perform grid refinement on the grids that meet any one of the first condition, the second condition, and the third condition;

[0026] S44. Construct a control equation based on the soil oxygen diffusion coefficient, the soil root respiration rate, and the soil oxygen consumption model;

[0027] S45. After grid refinement, perform iterative calculations on the control equation and dynamically adjust the time step during the iterative calculation process;

[0028] S46. Calculate the difference between the result of each iterative calculation and the measured value in the study area. If the difference satisfies , then perform weighted adjustment on the soil oxygen diffusion coefficient, the root respiration rate, and the soil oxygen consumption model, otherwise output the result of the iterative calculation to obtain the soil oxygen concentration distribution at different times and different depths; where, represents the difference between the result of the iterative calculation and the measured value in the study area, represents the set threshold.

[0029] Furthermore, in S43, the first condition is specifically: the change rate of soil water content between adjacent nodes satisfies ; In the formula, represents the Soil moisture content at the layer point, Indicates the Soil moisture content at the layer point, Indicates the depth interval, Indicates the set soil moisture content threshold;

[0030] In S43, the second condition is specifically: the oxygen concentration change rate between adjacent nodes satisfies ; In the formula, Indicates the Soil oxygen concentration at the layer point, Indicates the Soil oxygen concentration at the layer point, Indicates the set oxygen concentration change rate threshold;

[0031] In S43, the third condition is specifically: the change rate of the root respiration rate in the root zone satisfies ; In the formula, Indicates the Root respiration rate at the layer, Indicates the Root respiration rate at the layer, Indicates the change rate of the root respiration rate.

[0032] Furthermore, in S44, the expression of the control equation is:

[0033] ;

[0034] In the formula, Indicates at time And soil depth The oxygen concentration at the location, Indicates the soil oxygen diffusion coefficient, Indicates the soil root respiration rate, Indicates the soil oxygen consumption model.

[0035] Furthermore, in S45, the method for dynamically adjusting the time step is: calculate the local change rate factor, and when the local change rate factor exceeds the set change rate threshold, reduce the time step, otherwise increase the time step.

[0036] Furthermore, the local change rate factor The calculation formula is:

[0037] ;

[0038] In the formula, Indicates the oxygen concentration, Indicates the time, Indicates the soil depth.

[0039] The beneficial effects of the present invention are as follows: The present invention designs a method for dynamically predicting soil oxygen in precision agriculture, which is used to predict the changes in soil oxygen concentration under different environmental conditions. The present invention can solve the problem that the soil oxygen concentration is affected by complex factors such as moisture, temperature, porosity, root respiration, and microbial activity, and provide a scientific basis for precision agriculture irrigation, soil health assessment, and optimization of oxygenation irrigation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of a method for dynamically predicting soil oxygen in precision agriculture. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0042] As Figure 1 shown, the present invention provides a method for dynamically predicting soil oxygen in precision agriculture, including the following steps:

[0043] S1. Collect key parameters at different soil depths in the research area;

[0044] S2. Calculate the soil oxygen diffusion coefficient according to the key parameters at different soil depths in the research area;

[0045] S3. Calculate the soil root respiration rate according to the key parameters at different soil depths in the research area;

[0046] S4. Construct a soil oxygen consumption model according to the key parameters at different soil depths in the research area;

[0047] S5. Perform iterative calculations according to the soil oxygen diffusion coefficient, root respiration rate, and soil oxygen consumption model to obtain the soil oxygen concentration distribution at different times and different depths.

[0048] In the embodiment of the present invention, in S1, considering that in actual field experiments, soil moisture and temperature are dynamically changing, and different soil types and environmental conditions in the experiments may cause changes in parameters (such as oxygen diffusion coefficient and root respiration rate, etc.), it is necessary to comprehensively collect key parameters related to oxygen diffusion, consumption, and environmental conditions. The key parameters include soil oxygen concentration, soil moisture content, soil temperature, soil porosity, crop root respiration rate, and microbial respiration rate at different depths.

[0049] In the embodiments of the present invention, in S2, the porosity of the soil determines the gas exchange capacity of the soil. A higher porosity generally means smoother oxygen diffusion. Among different soil types, the size and distribution of pores have a significant impact on the diffusion coefficient. Soil moisture content is a key factor affecting soil oxygen diffusion. When there is too much soil moisture, the moisture will fill the pores in the soil, reducing gas flow and thus decreasing the oxygen diffusion coefficient. On the contrary, when the soil is too dry, the moisture in the pores decreases, and the gas diffusion ability will increase. Soil temperature is another important factor affecting gas diffusion. When the temperature rises, the movement rate of gas molecules speeds up, and the diffusion ability increases. Especially in an environment with a relatively high soil temperature, the oxygen diffusion coefficient is usually large. Different soil types (such as sandy soil, clay soil, and loamy soil) will have different effects on gas diffusion. Sandy soil has a higher oxygen diffusion coefficient due to its larger particles and larger pores, while clay soil has poor diffusivity due to its smaller pores.

[0050] Considering these influencing factors, soil moisture will fill the pores of the soil, reducing the space for gas diffusion, and thus decreasing the oxygen diffusion coefficient. When the soil moisture content increases, the oxygen diffusion coefficient shows a downward trend. By introducing it is possible to accurately simulate the influence of moisture changes on the diffusion coefficient, which is the influence coefficient of moisture on the oxygen diffusion coefficient and is used in the model to describe the regulatory effect of moisture changes on the oxygen diffusion ability. It is usually negative, and the larger the value, the greater the decrease in the diffusion coefficient. Temperature affects oxygen diffusion by increasing the average kinetic energy of gas molecules. At a higher temperature, the movement speed of oxygen molecules speeds up, and the diffusion ability increases. Here, the exponential function is used to represent the influence of temperature on the diffusion coefficient. is the temperature influence coefficient, which is obtained through later field tests.

[0051] Soil oxygen diffusion coefficient The calculation formula is as follows:

[0052] ;

[0053] In the formula, represents the soil basic diffusion coefficient, represents the soil moisture content at time and soil depth , represents the soil temperature at time and soil depth , represents the parameter controlling the influence of soil moisture on the diffusion coefficient, represents the parameter controlling the influence of soil temperature on the diffusion coefficient. represents an exponential function.

[0054] Determined by gas diffusion test; Affects the soil porosity and the mobility of soil oxygen; Affects the diffusion rate of soil oxygen molecules; and respectively represent the effects of controlling soil moisture and soil temperature on the diffusion coefficient, and the values are obtained through field test data and using regression analysis.

[0055] In the embodiment of the present invention, in S3, the influence of moisture is regarded as a non-linear limiting effect on oxygen consumption, and an exponential decay term represents the relationship of moisture change, and a similar exponential decay function is used to represent the influence of temperature on root respiration; the soil root respiration rate The calculation formula is:

[0056] ;

[0057] In the formula, represents the oxygen consumption rate of roots under the condition of no moisture limitation, represents at time and soil depth the soil moisture content at, represents at time and soil depth the soil temperature at, represents the degree of influence of soil moisture on the root respiration rate of crops, represents the degree of influence of temperature change on the root respiration rate of crops, represents an exponential function.

[0058] In the embodiment of the present invention, in S4, the activities of microorganisms are affected by soil moisture, temperature, and organic matter decomposition. In order to accurately provide the adjustment ability between the above relationships, influence coefficients and are introduced in the model to further improve the adaptability, flexibility, and generality of the model under different soil conditions. For example, in areas with high temperature and high humidity, the activity of microorganisms may be stronger, so needs to be increased, while in arid or extremely cold areas, the activity of microorganisms is low, and and values need to be reduced. The soil oxygen consumption model The expression is:

[0059] ;

[0060] In the formula, Represents the basic microbial respiration rate under standard moisture and temperature, Represents the influence intensity of controlled moisture on the microbial respiration rate, Represents at time and soil depth the soil water content at the location, Represents at time and soil depth the soil temperature at the location, Represents the exponential function, Represents the influence degree of controlled temperature on the microbial respiration rate.

[0061] Calibrated through field experiments; Use the exponential decay function to represent the inhibitory effect of moisture on the microbial oxygen consumption rate; and Calibrated through field experiments; Use the exponential growth function to represent the promoting effect of temperature on the microbial respiration rate.

[0062] In the embodiment of the present invention, S4 includes the following sub-steps:

[0063] S41. Divide the vertical soil profile in the study area into several nodes, set the top soil-atmosphere interface as the Dirichlet boundary, set the bottom boundary as the Neumann zero-flux boundary, and generate a closed state in the area below the root system;

[0064] S42. Calculate the spatial gradient in the closed state of the area below the root system;

[0065] S43. According to the spatial gradient, perform grid refinement on the grids that meet any one of the first condition, the second condition, and the third condition;

[0066] S44. Construct the control equation according to the soil oxygen diffusion coefficient, the soil root respiration rate, and the soil oxygen consumption model;

[0067] S45. After grid refinement, perform iterative calculation on the control equation and dynamically adjust the time step during the iterative calculation process;

[0068] S46. Calculate the difference between the result of each iterative calculation and the measured value in the study area. If the difference satisfies , then perform weighted adjustment on the soil oxygen diffusion coefficient, the root respiration rate, and the soil oxygen consumption model, otherwise output the result of the iterative calculation to obtain the soil oxygen concentration distribution at different times and different depths; where, represents the difference between the result of the iterative calculation and the measured value in the study area, represents the set threshold.

[0069] In the embodiment of the present invention, in S43, the first condition is specifically that the change rate of soil moisture content between adjacent nodes satisfies ; in the formula, represents the soil moisture content of the -th layer point, represents the soil moisture content of the -th layer point, represents the depth interval, represents the set soil moisture content threshold;

[0070] In S43, the second condition is specifically that the change rate of oxygen concentration between adjacent nodes satisfies ; in the formula, represents the soil oxygen concentration of the -th layer point, represents the soil oxygen concentration of the -th layer point, represents the set oxygen concentration change rate threshold;

[0071] In S43, the third condition is specifically that the change rate of root respiration rate in the root zone satisfies ; in the formula, represents the root respiration rate of the -th layer, represents the root respiration rate of the -th layer, represents the change rate of root respiration rate.

[0072] In the embodiment of the present invention, in S44, the soil oxygen concentration is affected by environmental factors such as soil moisture, temperature, porosity, root respiration rate, and microbial activity, and these factors have randomness and spatio-temporal variation characteristics. To improve the prediction accuracy, the Monte Carlo method is introduced for random sampling to simulate the oxygen diffusion and consumption processes under different environmental conditions, and to improve the applicability and stability of the model. The number of sampling times N is set to 10,000 times. According to the probability distribution of variables, a data set of each parameter is randomly generated, and the corresponding oxygen diffusion coefficient is calculated. The finite difference method (FDM) or the finite element method (FEM) is used to solve the oxygen diffusion equation, and the spatio-temporal distribution data of oxygen concentration under different soil environments is obtained, reducing the influence of environmental variable uncertainty on the prediction results. The expression of the control equation is:

[0073] ;

[0074] In the formula, represents the oxygen concentration at time and soil depth , represents the soil oxygen diffusion coefficient, represents the soil root respiration rate, represents the soil oxygen consumption model.

[0075] To describe the change of oxygen concentration with time and depth, it is necessary to establish a coupled oxygen diffusion-consumption model based on the Monte-Carlo method, including the oxygen diffusion process and the oxygen consumption term. The left side of the control equation represents the change of oxygen concentration with time. The first term on the right side is the diffusion term of oxygen, and the second term is the oxygen consumption term. By solving this equation, the change law of oxygen concentration with variables such as time and depth in the soil can be obtained. The left side represents the change rate of oxygen concentration with time. According to Fick's law, the flow rate of a substance is proportional to the concentration gradient (i.e., the rate of change of concentration). At the same time, considering that the concentration gradient changes with the soil position, therefore, the present invention uses the right represents the second-order spatial derivative of oxygen concentration, representing the acceleration of oxygen concentration change (the curvature of oxygen concentration change), so as to more accurately simulate the change of oxygen concentration in time and space. The second term on the right side represents the consumption of oxygen.

[0076] In the embodiment of the present invention, in S45, the method for dynamically adjusting the time step is: calculate the local change rate factor. When the local change rate factor exceeds the set change rate threshold, reduce the time step, otherwise increase the time step.

[0077] In the embodiment of the present invention, the local change rate factor is calculated by the formula:

[0078] ;

[0079] In the formula, represents the oxygen concentration, represents the time, represents the soil depth.

[0080] Those of ordinary skill in the art will realize that the embodiments described herein are for helping readers understand the principles of the present invention and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations without departing from the essence of the present invention according to the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A method for predicting soil oxygen dynamics in precision agriculture, characterized in that, It includes the following steps: S1. Collect key parameters at different soil depths in the study area; S2. Calculate the soil oxygen diffusion coefficient according to the key parameters at different soil depths in the study area; S3. Calculate the soil root respiration rate according to the key parameters at different soil depths in the study area; S4. Construct a soil oxygen consumption model according to the key parameters at different soil depths in the study area; S5. Perform iterative calculations based on the soil oxygen diffusion coefficient, root respiration rate, and soil oxygen consumption model to obtain the soil oxygen concentration distribution at different times and different depths.

2. The method for predicting soil oxygen dynamics for precision agriculture according to claim 1, wherein In S1, the key parameters include the soil oxygen concentration, soil moisture content, soil temperature, soil porosity, crop root respiration rate, and microbial respiration rate at different depths.

3. The method for predicting soil oxygen dynamics for precision agriculture according to claim 1, characterized in that, In S2, the soil oxygen diffusion coefficient is calculated by the following formula: ； Wherein, represents the soil base diffusion coefficient, represents at time and soil depth the soil moisture content at the location, represents at time and soil depth the soil temperature at the location, represents the influence parameter of controlling soil moisture on the diffusion coefficient, represents the influence parameter of controlling soil temperature on the diffusion coefficient, represents the exponential function.

4. The method for predicting soil oxygen dynamics for precision agriculture according to claim 1, wherein In S3, the soil root respiration rate is calculated by the following formula: ； In the formula, represents the oxygen consumption rate of the root system under the condition of no water limitation, represents at time and soil depth the soil water content, represents at time and soil depth the soil temperature, represents the influence degree of soil moisture on the root respiration rate of crops, represents the influence degree of temperature change on the root respiration rate of crops, represents the exponential function.

5. The method for predicting soil oxygen dynamics for precision agriculture according to claim 1, wherein In S4, the soil oxygen consumption model has the following expression: ； Wherein, represents the basic microbial respiration rate under standard moisture and temperature, represents the influence intensity of moisture control on the microbial respiration rate, represents at time and soil depth the soil moisture content at the location, represents at time and soil depth the soil temperature at the location, represents the exponential function, represents the influence degree of temperature control on the microbial respiration rate.

6. The method for predicting soil oxygen dynamics for precision agriculture according to claim 1, characterized in that, S4 includes the following sub-steps: S41. Divide the vertical soil profile in the study area into several nodes, set the top soil-atmosphere interface as the Dirichlet boundary, and set the bottom boundary as the Neumann zero-flux boundary to generate a closed state in the area below the roots; S42. Calculate the spatial gradient in the closed state of the area below the roots; S43. Refine the grid for the grids that meet any one of the first condition, the second condition, and the third condition according to the spatial gradient; S44. Construct a control equation according to the soil oxygen diffusion coefficient, soil root respiration rate, and soil oxygen consumption model; S45. After grid refinement, perform iterative calculations on the control equation and dynamically adjust the time step during the iterative calculation process; S46. Calculate the difference between the calculation result of each iteration and the measured value in the study area. If the difference satisfies , then perform weighted adjustment on the soil oxygen diffusion coefficient, root respiration rate, and soil oxygen consumption model. Otherwise, output the iterative calculation result to obtain the soil oxygen concentration distribution at different times and different depths. Among them, represents the difference between the iterative calculation result and the measured value in the study area, and represents the set threshold.

7. The method for predicting soil oxygen dynamics for precision agriculture according to claim 6, characterized in that, In S43, the first condition is specifically: the change rate of soil moisture content between adjacent nodes satisfies ; in the formula, represents the soil moisture content of the -th layer point, represents the soil moisture content of the -th layer point, represents the depth interval, represents the set soil moisture content threshold; In S43, the second condition is specifically: the oxygen concentration change rate between adjacent nodes satisfies ; in the formula, represents the soil oxygen concentration at the layer point, represents the soil oxygen concentration at the layer point, represents the set oxygen concentration change rate threshold; In S43, the third condition is specifically: the change rate of the root respiration rate in the root zone satisfies ; in the formula, represents the root respiration rate of the th layer, represents the root respiration rate of the th layer, represents the change rate of the root respiration rate.

8. The method for predicting soil oxygen dynamics for precision agriculture according to claim 6, wherein In S44, the expression of the control equation is: ； In the formula, represents the oxygen concentration at time and soil depth, represents the soil oxygen diffusion coefficient, represents the soil root respiration rate, represents the soil oxygen consumption model.

9. The method for predicting soil oxygen dynamics for precision agriculture according to claim 6, wherein In S45, the method for dynamically adjusting the time step is: calculate the local change rate factor, and when the local change rate factor exceeds the set change rate threshold, reduce the time step, otherwise increase the time step.

10. The method for predicting soil oxygen dynamics for precision agriculture according to claim 9, characterized in that, The local change rate factor has the following calculation formula: ； In the formula, represents the oxygen concentration, represents the time, represents the soil depth.

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