A dynamic prediction method for soil oxygen in precision agriculture

Abstract

This invention discloses a method for dynamic soil oxygen prediction for precision agriculture, belonging to the technical field of soil oxygen prediction. The method comprises the following steps: S1. collecting key parameters at different soil depths within a study area; S2. calculating the soil oxygen diffusion coefficient based on the key parameters at different soil depths within the study area; S3. calculating the soil root respiration rate; S4. constructing a soil oxygen consumption model; and S5. performing iterative calculations to obtain soil oxygen concentration distributions at different times and depths. This method can address the issue of soil oxygen concentration being affected by complex factors such as moisture, temperature, porosity, root respiration, and microbial activity, providing a scientific basis for precision agricultural irrigation, soil health assessment, and optimization of aeration irrigation strategies.

Description

Technical Field

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

[0002] In agricultural production and soil ecological environment management, soil oxygen concentration is a key factor affecting crop root growth and microbial activity. Oxygen supply is complexly influenced by soil moisture, temperature, pore structure, and microbial activity. Especially in high-moisture environments, soil pores are filled with water, limiting oxygen diffusion and hindering root respiration, which in turn affects crop growth and yield.

[0003] However, current soil oxygen monitoring and prediction methods have numerous limitations. Traditional soil oxygen measurements primarily rely on field sensor sampling. While this method can capture data within a specific spatial and temporal range, large-scale dynamic predictions are difficult due to high data acquisition costs and limited spatial coverage. Furthermore, existing soil oxygen prediction models are mostly based on deterministic numerical methods (such as finite difference and finite element methods), but they fail to adequately consider the dynamic changes in environmental factors (such as moisture and temperature), resulting in large prediction errors. Furthermore, these methods typically require a large amount of high-precision basic data as input. However, in actual agricultural production, soil oxygen data with high spatial and temporal resolution is difficult to obtain, limiting the applicability of these models.

[0004] On the other hand, while stochastic modeling methods (such as statistical regression and random forests) can account for the uncertainty of environmental variables, their description of the physical mechanisms of soil oxygen diffusion and consumption is relatively limited, making it difficult to generalize across different soil types and climatic conditions. Consequently, there is currently no predictive model that can simultaneously account for environmental uncertainty and the physical mechanisms of oxygen diffusion, limiting the development of precision agricultural management and ecological and environmental monitoring. Summary of the Invention

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

[0006] The technical solution of the present invention is: a soil oxygen dynamic prediction method for precision agriculture comprises the following steps:

[0007] S1. Collect key parameters of soil at different depths within the study area;

[0008] S2. Calculate the soil oxygen diffusion coefficient based on key parameters at different soil depths within the study area;

[0009] S3. Calculate soil root respiration rate based on key parameters at different soil depths within the study area;

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

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

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

[0013] Furthermore, in S2, the soil oxygen diffusion coefficient The calculation formula is:

[0014] ;

[0015] Where, represents the soil basic diffusion coefficient, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at represents the parameter that controls the effect of soil moisture on the diffusion coefficient, represents the parameter that controls the effect of soil temperature on the diffusion coefficient, Represents the exponential function.

[0016] Furthermore, in S3, the soil root respiration rate The calculation formula is:

[0017] ;

[0018] Where, represents the oxygen consumption rate of the roots under conditions without water limitation, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at Indicates the degree of influence of soil moisture on the respiration rate of crop roots. Indicates the degree of influence of temperature change on the respiration rate of crop roots. Represents the exponential function.

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

[0020] ;

[0021] Where, represents the basic microbial respiration rate at standard moisture and temperature, Indicates the intensity of the effect of controlling water on the respiration rate of microorganisms, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at represents the exponential function, Indicates the degree of influence of controlled temperature on the respiration rate of microorganisms.

[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-atmosphere interface as a Dirichlet boundary, and the bottom boundary as a Neumann no-flux boundary, to generate a closed state below the root system.

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

[0025] S43, performing mesh refinement processing on the mesh that meets any one of the first condition, the second condition, and the third condition according to the spatial gradient;

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

[0027] S45. After the mesh is refined, the control equation is iteratively calculated, and the time step is dynamically adjusted during the iterative calculation process;

[0028] S46, calculate the difference between the result of each iteration and the measured value of the study area, if the difference satisfies , then the soil oxygen diffusion coefficient, root respiration rate and soil oxygen consumption model are weighted adjusted, otherwise the iterative calculation results are output to obtain the soil oxygen concentration distribution at different times and depths; It represents the difference between the iterative calculation result and the measured value of the study area. Indicates setting a threshold.

[0029] Furthermore, in S43, the first condition is specifically: the soil moisture content change rate between adjacent nodes satisfies Where, Indicates the Soil moisture content at the layer, Indicates the Soil moisture content at the layer, represents the depth interval, Indicates setting the soil moisture threshold;

[0030] In S43, the second condition is specifically: the oxygen concentration change rate between adjacent nodes satisfies Where, Indicates the Soil oxygen concentration at the layer point, Indicates the Soil oxygen concentration at the layer point, Indicates setting the 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 Where, Indicates the The root respiration rate of the layer Indicates the The root respiration rate of the layer Indicates the rate of change of root respiration rate.

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

[0033] ;

[0034] Where, Indicates time and soil depth The oxygen concentration at represents the soil oxygen diffusion coefficient, represents the soil root respiration rate, Represents a soil oxygen depletion model.

[0035] Furthermore, in S45 , the method for dynamically adjusting the time step is: calculating a local change rate factor, and when the local change rate factor exceeds a set change rate threshold, reducing the time step, otherwise increasing the time step.

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

[0037] ;

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

[0039] The beneficial effects of the present invention are as follows: the present invention designs a soil oxygen dynamic prediction method for precision agriculture, which is used to predict changes in soil oxygen concentration under different environmental conditions; the present invention can solve the problem that 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 agricultural irrigation, soil health assessment and optimization of oxygen-enhanced irrigation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flowchart of the soil oxygen dynamics prediction method for precision agriculture. DETAILED DESCRIPTION

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

[0042] like Figure 1 As shown, the present invention provides a soil oxygen dynamic prediction method for precision agriculture, comprising the following steps:

[0043] S1. Collect key parameters of soil at different depths within the study area;

[0044] S2. Calculate the soil oxygen diffusion coefficient based on key parameters at different soil depths within the study area;

[0045] S3. Calculate soil root respiration rate based on key parameters at different soil depths within the study area;

[0046] S4. Construct a soil oxygen consumption model based on key parameters at different soil depths within the study area;

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

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

[0049] In the embodiment of the present invention, in S2, soil porosity determines the soil's gas exchange capacity. Higher porosity generally means smoother oxygen diffusion. Pore size and distribution significantly influence the diffusion coefficient in different soil types. Soil moisture content is a key factor affecting soil oxygen diffusion. When soil moisture is excessive, it fills the pores, reducing gas flow and thus lowering the oxygen diffusion coefficient. Conversely, when the soil is too dry, the pore moisture decreases, and the gas diffusion capacity increases. Soil temperature is another important factor affecting gas diffusion. As temperature rises, the movement of gas molecules accelerates, enhancing diffusion capacity. Especially in environments with higher soil temperatures, the oxygen diffusion coefficient is generally higher. Different soil types (such as sand, clay, and loam) have different effects on gas diffusion. Sand, due to its larger particles and pores, has a higher oxygen diffusion coefficient, while clay, due to its smaller pores, has poorer diffusion.

[0050] Considering these factors, soil moisture It will fill the pores in the soil, reduce the space for gas diffusion, and thus reduce the diffusion coefficient of oxygen. When the soil moisture content increases, the oxygen diffusion coefficient tends to decrease. By introducing Can accurately simulate the effect of water changes on the diffusion coefficient, It is the influence coefficient of water on oxygen diffusion coefficient, which is used in the model to describe the regulatory effect of water changes on oxygen diffusion capacity. It is usually a negative value. 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 higher temperatures, the movement speed of oxygen molecules is accelerated and the diffusion capacity is enhanced. Here, an exponential function is used. represents the effect of temperature on the diffusion coefficient, is the temperature influence coefficient, which was obtained through later field experiments.

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

[0052] ;

[0053] Where, represents the soil basic diffusion coefficient, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at represents the parameter that controls the effect of soil moisture on the diffusion coefficient, represents the parameter that controls the effect of soil temperature on the diffusion coefficient, Represents the exponential function.

[0054] Determined by gas diffusion test; Affects soil porosity and soil oxygen mobility; Affects the diffusion rate of oxygen molecules in the soil; 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 regression analysis.

[0055] In the embodiment of the present invention, in S3, the influence of moisture is regarded as a nonlinear limiting effect on oxygen consumption, and an exponential decay term is used here. Represents the relationship between water changes and uses a similar exponential decay function to represent the effect of temperature on root respiration; soil root respiration rate The calculation formula is:

[0056] ;

[0057] Where, represents the oxygen consumption rate of the roots under conditions without water limitation, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at Indicates the degree of influence of soil moisture on the respiration rate of crop roots. Indicates the degree of influence of temperature change on the respiration rate of crop roots. Represents the exponential function.

[0058] In the embodiment of the present invention, in S4, the activity of microorganisms is affected by soil moisture, temperature and organic matter decomposition. In order to accurately provide the regulation ability between the above relationships, the influence coefficient is introduced into the model. and , further improve the adaptability, flexibility and versatility of the model in different soil conditions. For example, in areas with high temperature and high humidity, the activity of microorganisms may be stronger, so it is necessary to increase In arid or extremely cold areas, microbial activity is low and needs to be reduced. and Numerical. Soil oxygen depletion model The expression is:

[0059] ;

[0060] Where, represents the basic microbial respiration rate at standard moisture and temperature, Indicates the intensity of the effect of controlling water on the respiration rate of microorganisms, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at represents the exponential function, Indicates the degree of influence of controlled temperature on the respiration rate of microorganisms.

[0061] Calibrated through field trials; An exponential decay function was used to represent the inhibitory effect of moisture on the oxygen consumption rate of microorganisms; and Calibrated through field trials; An exponential growth function is used to represent the promoting effect of temperature on microbial respiration rate.

[0062] In this 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 a Dirichlet boundary, and the bottom boundary as a Neumann no-flux boundary, to generate a closed state below the root system.

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

[0065] S43, performing mesh refinement processing on the mesh that meets any one of the first condition, the second condition, and the third condition according to the spatial gradient;

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

[0067] S45. After the mesh is refined, the control equation is iteratively calculated, and the time step is dynamically adjusted during the iterative calculation process;

[0068] S46, calculate the difference between the result of each iteration and the measured value of the study area, if the difference satisfies , then the soil oxygen diffusion coefficient, root respiration rate and soil oxygen consumption model are weighted adjusted, otherwise the iterative calculation results are output to obtain the soil oxygen concentration distribution at different times and depths; It represents the difference between the iterative calculation result and the measured value of the study area. Indicates setting a threshold.

[0069] In the embodiment of the present invention, in S43, the first condition is specifically: the soil moisture content change rate between adjacent nodes satisfies Where, Indicates the Soil moisture content at the layer, Indicates the Soil moisture content at the layer, represents the depth interval, Indicates setting the soil moisture threshold;

[0070] In S43, the second condition is specifically: the oxygen concentration change rate between adjacent nodes satisfies Where, Indicates the Soil oxygen concentration at the layer point, Indicates the Soil oxygen concentration at the layer point, Indicates setting the oxygen concentration change rate threshold;

[0071] In S43, the third condition is specifically: the change rate of the root respiration rate in the root zone satisfies Where, Indicates the The root respiration rate of the layer Indicates the The root respiration rate of the layer Indicates the rate of change 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 spatiotemporal variation characteristics. To improve the prediction accuracy, the Monte Carlo method is introduced for random sampling to simulate the oxygen diffusion and consumption process under different environmental conditions, thereby improving the applicability and stability of the model. The sampling number N is set to 10,000 times, and according to the probability distribution of the variables, the data sets of each parameter are randomly generated, and the corresponding oxygen diffusion coefficient is calculated. The finite difference method (FDM) or finite element method (FEM) is used to solve the oxygen diffusion equation, and the spatiotemporal distribution data of oxygen concentration under different soil environments are obtained, which reduces the impact of environmental variable uncertainty on the prediction results. The expression of the control equation is:

[0073] ;

[0074] Where, Indicates time and soil depth The oxygen concentration at represents the soil oxygen diffusion coefficient, represents the soil root respiration rate, Represents a soil oxygen depletion model.

[0075] In order to describe the change of oxygen concentration with time and depth, it is necessary to establish an oxygen diffusion-consumption coupling model based on the Monte-Carlo method, which includes 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 oxygen diffusion term, and the second term is the oxygen consumption term. By solving this equation, the change pattern of oxygen concentration in the soil with time, depth and other variables can be obtained. Indicates the rate of change of oxygen concentration over 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). Considering that the concentration gradient changes with the change of soil position, the present invention uses the right It represents the second-order spatial derivative of oxygen concentration, which indicates the acceleration of oxygen concentration change (the curvature of oxygen concentration change), thereby more accurately simulating the change of oxygen concentration in space and time. Indicates oxygen consumption.

[0076] In an embodiment of the present invention, in S45 , the method for dynamically adjusting the time step is: calculating a local change rate factor, and when the local change rate factor exceeds a set change rate threshold, reducing the time step; otherwise, increasing the time step.

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

[0078] ;

[0079] Where, Indicates the oxygen concentration, Indicates time, Indicates soil depth.

[0080] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A soil oxygen dynamic prediction method for precision agriculture, characterized in that: The following steps are involved: S1. Collect key parameters of soil at different depths within the study area; S2. Calculate the soil oxygen diffusion coefficient based on key parameters at different soil depths within the study area; S3. Calculate soil root respiration rate based on key parameters at different soil depths within the study area; S4. Construct a soil oxygen consumption model based on key parameters at different soil depths within the study area; S5. Perform iterative calculations based on the soil oxygen diffusion coefficient, root respiration rate, and soil oxygen consumption model to obtain soil oxygen concentration distribution at different times and depths; The S5 comprises the following sub-steps: S51. Divide the vertical soil profile in the study area into several nodes, set the top soil-atmosphere interface as a Dirichlet boundary, and set the bottom boundary as a Neumann no-flux boundary to generate a closed state below the root system; S52. Calculate the spatial gradient in a closed state in the area below the root system; S53, performing mesh refinement processing on the mesh that meets any one of the first condition, the second condition, and the third condition according to the spatial gradient; S54. Construct a control equation based on the soil oxygen diffusion coefficient, soil root respiration rate, and soil oxygen consumption model; S55. After the mesh is refined, the control equation is iteratively calculated, and the time step is dynamically adjusted during the iterative calculation process; S56, calculate the difference between the result of each iteration and the measured value of the study area, if the difference satisfies , then the soil oxygen diffusion coefficient, root respiration rate and soil oxygen consumption model are weighted adjusted, otherwise the iterative calculation results are output to obtain the soil oxygen concentration distribution at different times and depths; It represents the difference between the iterative calculation result and the measured value of the study area. Indicates setting threshold; In the above S53, the first condition is specifically: the soil moisture content change rate between adjacent nodes satisfies Where, Indicates the Soil moisture content at the layer point, Indicates the Soil moisture content at the layer point, represents the depth interval, Indicates setting the soil moisture threshold; In the above S53, the second condition is specifically: the oxygen concentration change rate between adjacent nodes satisfies Where, Indicates the Soil oxygen concentration at the layer point, Indicates the Soil oxygen concentration at the layer point, Indicates setting the oxygen concentration change rate threshold; In the step S53, the third condition is specifically: the change rate of the root respiration rate in the root zone satisfies Where, Indicates the The root respiration rate of the layer Indicates the The root respiration rate of the layer Indicates the rate of change of root respiration rate; In the S54, the expression of the control equation is: ； Where, Indicates time and soil depth The oxygen concentration at represents the soil oxygen diffusion coefficient, represents the soil root respiration rate, represents the soil oxygen consumption model; In said S55, the method of dynamically adjusting the time step is: calculating a local change rate factor, and when the local change rate factor exceeds a set change rate threshold, reducing the time step, otherwise increasing the time step; The local rate of change factor The calculation formula is: ； Where, Indicates the oxygen concentration, Indicates time, Indicates soil depth.

2. The soil oxygen dynamic prediction method for precision agriculture according to claim 1, characterized in that: In S1, the key parameters include soil oxygen concentration at different depths, soil moisture content, soil temperature, soil porosity, crop root respiration rate and microbial respiration rate.

3. The soil oxygen dynamic prediction method for precision agriculture according to claim 1, characterized in that: The S2, soil oxygen diffusion coefficient The calculation formula is: ； Where, represents the soil basic diffusion coefficient, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at represents the parameter that controls the effect of soil moisture on the diffusion coefficient, represents the parameter that controls the effect of soil temperature on the diffusion coefficient, Represents the exponential function.

4. The soil oxygen dynamic prediction method for precision agriculture according to claim 1, characterized in that: In S3, the soil root respiration rate The calculation formula is: ； Where, represents the oxygen consumption rate of the roots under conditions without water limitation, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at Indicates the degree of influence of soil moisture on the respiration rate of crop roots. Indicates the degree of influence of temperature change on the respiration rate of crop roots. Represents the exponential function.

5. The soil oxygen dynamic prediction method for precision agriculture according to claim 1, characterized in that: The S4 soil oxygen consumption model The expression is: ； Where, represents the basic microbial respiration rate at standard moisture and temperature, Indicates the intensity of the effect of controlling water on the respiration rate of microorganisms, Indicates time and soil depth The soil moisture content at Indicates time and soil depth The soil temperature at represents the exponential function, Indicates the degree of influence of controlled temperature on the respiration rate of microorganisms.

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