Corn disease index simulation and scab spatial distribution visualization method and device

Abstract

The invention provides a corn disease index simulation and scab spatial distribution visualization method and device, and the method comprises the steps: building a disease transmission kinetic model based on the interaction between a corn host population and a pathogen vector population; parameters of the disease propagation kinetic model comprise disease propagation parameters; analyzing the environmental factor response condition of the disease propagation parameter to obtain an environment-driven corn disease index simulation result; and based on the corn disease index simulation result, determining a visual result of corn scab spatial distribution. According to the method, the environment-driven corn disease index simulation result is obtained by analyzing the response mechanism of the environment factor response function of the disease propagation parameters in the disease propagation kinetic model, and the performance of the coupled corn model is improved and perfected; by analyzing the response mechanism of the corn scab spatial distribution to the severity of the disease, a visual simulation result of dynamic change of the corn scab spatial distribution along with the severity of the disease is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of corn disease analysis, and particularly relates to a corn disease index simulation and disease spot spatial distribution visualization method and device. BACKGROUND

[0002] Corn disease process mechanism models have been widely used to simulate the occurrence and development of corn diseases. At present, mechanism models considering the interaction of host plants, pathogens and environmental factors play an important role in the quantitative simulation of the whole process of corn disease epidemic and spread. However, the current disease transmission dynamics model lacks close connection with corn phenomics and corn growth models, and cannot realize dynamic simulation of disease index and visualization of disease spot spatial distribution. SUMMARY

[0003] The present application provides a corn disease index simulation and disease spot spatial distribution visualization method and device, which solves the problem that the existing analysis of corn disease transmission cannot realize dynamic simulation of corn disease index and visualization of disease spot spatial distribution.

[0004] The present application provides a corn disease index simulation and disease spot spatial distribution visualization method, which comprises the following steps: Based on the interaction between corn host population and pathogen vector population, a disease transmission dynamics model is constructed; the parameters of the disease transmission dynamics model include disease transmission parameters; The environmental factor response of the disease transmission parameters is analyzed to obtain an environmental-driven corn disease index simulation result; Based on the corn disease index simulation result, a visualization result of corn disease spot spatial distribution is determined.

[0005] According to the corn disease index simulation and disease spot spatial distribution visualization method provided by the present application, the construction of the disease transmission dynamics model based on the interaction between the corn host population and the pathogen vector population comprises: An environmental pathogen population is constructed; Disease transmission parameters of disease-related populations are obtained; the disease-related populations include corn host population, pathogen vector population and the environmental pathogen population; the disease transmission parameters include intra-population conversion rate, inter-population contact rate and population entry and exit rate; A disease transmission dynamics model is constructed based on the disease transmission parameters.

[0006] According to the corn disease index simulation and disease spot spatial distribution visualization method provided by the present application, the construction of the disease transmission dynamics model based on the disease transmission parameters comprises: determining model construction conditions; the model construction conditions include a first condition and a second condition; the first condition is that each corn individual has the same probability of being infected; and the second condition is that the natural mortality rate of each subpopulation in the corn host population is the same; obtaining a population base number of the disease-related population; constructing a disease transmission dynamics model based on the model construction conditions, the population base number and population change information; the population change information is determined based on the disease transmission parameters.

[0007] According to the corn disease index simulation and lesion spatial distribution visualization method provided by the application, the environmental factors include temperature factors, humidity factors and day length factors; the environmental factor response of the disease transmission parameter is analyzed to obtain the environmental driving corn disease index simulation result, which includes: Based on the influence information of the environmental factors on the disease transmission parameters, an environmental response function is determined; the environmental response function includes a temperature response function, a humidity response function and a day length response function; The environmental response function is used to adjust the disease transmission parameters in the corn disease development process to obtain the environmental driving corn disease index simulation result.

[0008] According to the corn disease index simulation and lesion spatial distribution visualization method provided by the application, the corn lesion spatial distribution visualization result is determined based on the corn disease index simulation result, which includes: A corn disease model is constructed to change the degree of corn disease over time; A lesion expansion distribution rule is constructed; the lesion expansion distribution rule is a rule that the lesion expansion area and the lesion spatial distribution change with the degree of corn disease; Based on the corn disease model and the lesion expansion distribution rule, the visualization result of the corn lesion spatial distribution is determined.

[0009] According to the corn disease index simulation and lesion spatial distribution visualization method provided by the application, the visualization result of the corn lesion spatial distribution is determined based on the corn disease index simulation result, and then includes: Obtaining measured corn disease data and a target disease degree; the target disease degree is a corn disease degree without environmental driving; Based on the measured corn disease data, the target disease degree and the corn disease index simulation result, the simulation reliability of the disease transmission dynamics model is determined.

[0010] The application also provides a corn disease index simulation and lesion spatial distribution visualization device, which includes the following modules: The disease transmission dynamics model construction module is configured to construct a disease transmission dynamics model based on the interaction between the corn host population and the pathogen vector population, and parameters of the disease transmission dynamics model include disease transmission parameters. The corn disease index simulation module is configured to analyze the response of the disease transmission parameters to environmental factors to obtain an environmental-driven corn disease index simulation result. The lesion spatial distribution visualization module is configured to determine a visualization result of the spatial distribution of corn lesions based on the corn disease index simulation result.

[0011] The present application also provides an electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the corn disease index simulation and lesion spatial distribution visualization method according to any one of the above when executing the computer program.

[0012] The present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program implements the corn disease index simulation and lesion spatial distribution visualization method according to any one of the above when executed by a processor.

[0013] The present application also provides a computer program product including a computer program, and the computer program implements the corn disease index simulation and lesion spatial distribution visualization method according to any one of the above when executed by a processor.

[0014] The corn disease index simulation and lesion spatial distribution visualization method and device provided by the present application improve and perfect the performance of the coupled corn model by analyzing the response mechanism of the environmental factor response function of the disease transmission parameters in the disease transmission dynamics model to obtain an environmental-driven corn disease index simulation result, and realize the visualization simulation result of the dynamic change of the spatial distribution of corn lesions with the severity of the disease by analyzing the response mechanism of the spatial distribution of corn lesions to the severity of the disease. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0016] Figure 1 is one of the flowcharts of the corn disease index simulation and lesion spatial distribution visualization method provided by the present application.

[0017] Figure 2is a schematic diagram of a coupling corn model of an integrated disease transmission dynamics model provided by the application.

[0018] Figure 3 is a schematic diagram of a disease transmission dynamics model provided by the application.

[0019] Figure 4 is a schematic diagram of a temperature response function, a humidity response function and a growth length day response function of a disease transmission parameter provided by the application.

[0020] Figure 5 is a flowchart of a corn disease index simulation and a disease spot spatial distribution visualization method provided by the application.

[0021] Figure 6 is a schematic diagram of a disease severity of a single corn plant changing with a disease period provided by the application.

[0022] Figure 7 is a structural schematic diagram of a corn disease index simulation and a disease spot spatial distribution visualization device provided by the application.

[0023] Figure 8 is a structural schematic diagram of an electronic device provided by the application. DETAILED DESCRIPTION

[0024] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0025] The coupling corn model mentioned in the following embodiments is a general corn 4D model driven by cumulative growth length day, which is constructed by integrating a heat-driven crop growth model and a plant functional structure algorithm; the compartment model (Susceptible-Exposed-Infectious-Removed, SEIR) is a tool for analyzing the dynamic flow of plant diseases; the remote sensing radiation transfer model (Radiative Transfer Models, RTMs) is used to simulate and analyze the propagation process of electromagnetic radiation in different media such as atmosphere, surface and vegetation.

[0026] The following embodiments will be described below in combination with Figures 1-8 The corn disease index simulation and disease spot spatial distribution visualization method and device of the present application are described.

[0027] Figure 1is one of the process schematic diagrams of the corn disease index simulation and disease spot spatial distribution visualization method provided by the present application, as shown in Figure 1 The method comprises the following steps: Step 100, constructing a disease transmission dynamics model based on the interaction between the corn host population and the pathogen vector population; the parameters of the disease transmission dynamics model include disease transmission parameters; Specifically, in order to realize dynamic simulation of disease severity in the disease development process and three-dimensional visualization of disease spot distribution, the present application extends the performance of the coupled corn model by integrating the disease transmission dynamics model considering the interaction between the corn host population and the disease vector population. The disease transmission dynamics model provided by the present application takes hourly temperature and relative humidity and other environmental factors as driving variables to simulate the disease development process under corn growth and environmental stress conditions, and combines with the corn canopy function structure module and the biochemical component vertical profile module of the coupled corn model to provide corn population disease index simulation results and disease spot spatial distribution conditions. The content of the disease transmission dynamics model includes: disease index prediction based on disease transmission dynamics, environmental response mechanism (temperature factor, humidity factor and growth period factor) of the disease transmission dynamics model, and biochemical component algorithm and disease spot spatial distribution visualization varying with disease severity. The schematic diagram of the coupled corn model integrated with the disease transmission dynamics model is shown in Figure 2 .

[0028] In the disease transmission dynamics model, the contact rate between different populations and the conversion rate between different subpopulations of the same population are the key parameters of the disease transmission dynamics model, i.e. the disease transmission parameters in the present embodiment.

[0029] Step 200, analyzing the environmental factor response of the disease transmission parameters to obtain the environmental driving corn disease index simulation results; The disease transmission process is significantly affected by environmental factors such as temperature factor, relative humidity factor and crop growth period factor, and there are differences in the optimal temperature, relative humidity and growth period of disease transmission between different populations at different disease occurrence periods. Since the influence of external environmental factors on the contact rate or conversion rate cannot be ignored, the present application aims to quantify the effect of environmental factors on the disease development process, and introduces the temperature response function, humidity response function and growth length day response function of the disease transmission parameters. The transmission rate parameters (i.e. disease transmission parameters) of the disease development process are adjusted by using the temperature response function, humidity response function and growth length day response function to realize the response of the disease transmission dynamics model to environmental factors.

[0030] The present invention uses cumulative growing degree days as the driving factor to achieve an effective combination of the corn coupling model and the disease transmission dynamics model. The disease transmission dynamics model can describe the interactions among corn, the environment, and the vector and the dynamic changes in the number of their subpopulations, thereby obtaining the simulation results of the environment-driven corn disease index.

[0031] Step 300: Determine a visualization result of the spatial distribution of corn lesions based on the corn disease index simulation result.

[0032] Simulations of environmentally driven corn disease indices still fail to quantitatively describe the severity of corn diseases and lack a visual output of the spatial distribution of lesions. To address this, the present invention establishes a rule for how the expansion area of ​​lesions on individual corn plants changes with disease severity and counts the number of newly infected corn plants daily, thereby simulating the dynamic changes in disease severity across a corn population. Furthermore, based on this rule, the spatial distribution of corn lesions changes with disease severity, enabling dynamic visualization of the spatial distribution of lesions across a corn population.

[0033] This example analyzes the response mechanism of the environmental factor response function of the disease transmission parameters in the disease transmission dynamics model to obtain the simulation results of the environment-driven corn disease index, thereby improving and perfecting the performance of the coupled corn model. By analyzing the response mechanism of the spatial distribution of corn lesions to the severity of the disease, a visual simulation result of the dynamic change of the spatial distribution of corn lesions with the severity of the disease is achieved.

[0034] In one embodiment, the method for simulating corn disease index and visualizing lesion spatial distribution provided by the embodiment of the present invention may further include: Step 110: constructing an environmental pathogen population; Step 120: Obtain disease transmission parameters of disease-related populations; the disease-related populations include corn host populations, pathogen vector populations, and environmental pathogen populations; the disease transmission parameters include intra-population conversion rate, inter-population contact rate, and population entry and exit rate; Step 130: construct a disease propagation dynamics model based on the disease propagation parameters.

[0035] like Figure 3 As shown in Figure 2, the disease transmission dynamics model covers the maize host population, pathogen vector population, and pathogen population. Among them, the corn host population can be subdivided into corn host susceptible subpopulations , corn host exposed subpopulation , maize host-infected subpopulation and maize host removal subpopulations Pathogen vector populations include pathogen vector susceptible subpopulations , pathogen-vector-infected subpopulations and pathogen vector removal subpopulation The disease transmission dynamic model describes the susceptible subpopulation of corn hosts to the exposed subpopulation of corn hosts at a conversion rate comprising contributions from the pathogen population in the environment , the infected subpopulation of corn hosts , and the infected subpopulation of pathogen vectors .

[0036] The exposed subpopulation of corn hosts at an infection progress rate converts to the infected subpopulation of corn hosts , and the infected subpopulation of corn hosts has a mortality rate of the corn infection mortality rate . The infected subpopulation of corn hosts and the pathogen population in the environment recruit new pathogens from the infected corn and the infected vectors in the environment at a contact rate and a contact rate of the other plants (non-corn) carrying the pathogen with the corn , respectively, and transmit the pathogen to the susceptible subpopulation of pathogen vectors . The susceptible subpopulation of pathogen vectors recruit new susceptible vectors at a recruitment rate of the susceptible vector population , and die naturally at a natural mortality rate of the vector population , and convert to the infected subpopulation of pathogen vectors at a conversion rate . The pathogen in the environment recruits new pathogens from the infected corn and the infected vectors at a contact rate proportional to the contact rate of the infected corn with the nutrient environment and the contact rate of the virus vector with the plant environment , and dies naturally at a natural mortality rate of the vector in the environment . The secondary infection number of a diseased corn in the susceptible subpopulation of corn is defined as the basic reproduction number .

[0037] The embodiment obtains the disease transmission parameters for constructing the disease transmission dynamic model by analyzing the interaction between the host and the vector.

[0038] In an embodiment, the corn disease index simulation and lesion spatial distribution visualization method provided by the embodiment of the present application can further include: ​Step 131, determining model construction conditions; the model construction conditions include a first condition and a second condition; the first condition is that each corn individual has the same probability of being infected; the second condition is that the natural mortality rate of each subpopulation in the corn host population is the same; Step 132, obtaining the population base of the disease-related population; Step 133, constructing a disease transmission dynamics model based on the model construction conditions, the population base, and population change information; the population change information is determined based on the disease transmission parameters.

[0039] Specifically, the susceptible corn plant population and the number of pathogens (bacteria or viruses) in the environment , the infected corn population and the infected medium The relationship diagram of the infection force interaction of the population. The solid line in the figure represents the conversion rate of different categories in the same species, and the dashed line represents the interaction between different categories or different or the same species.

[0040] The construction conditions of the disease transmission dynamics model are as follows: 1. Infection from the infected corn population to the plant environment and from the plant environment to the susceptible corn population is transmitted through vectors and non-biological factors (such as wind, people, rain, and birds); 2. The probability of infection of each susceptible corn individual is equal; 3. Part of the susceptible corn individuals are exposed to the pathogen environment, but can resist the infection of the pathogen throughout the growing season; 4. Once the vector becomes a carrier of the pathogen, it will accompany it throughout the growing period; 5. The natural mortality rate of each subpopulation of the corn host population is the same.

[0041] The algorithm (differential equation) for establishing the disease transmission dynamics model considering the interaction between the host population and the medium population is as follows:

[0042] ; (1) ; (2) ; (3) ; (4) ; (5) ; (6) ; (7) ; (8) ; (9) ; (10) (11) wherein, is the number of susceptible subpopulation of corn host increased per day; is the number of exposed subpopulation of corn host increased per day; is the number of infected subpopulation of corn host increased per day; is the number of removed subpopulation of corn host increased per day. is the number of susceptible subpopulation of pathogen vector increased per day; is the number of infected subpopulation of pathogen vector increased per day; is the number of removed subpopulation of pathogen vector increased per day. , , , , and are all greater than or equal to 0. is the total number of corn plant population; is the total number of pathogen vector population; is the carrying capacity of each vector; is the contact rate of virus to susceptible vector population in the environment; is the contact rate of virus-carrying vector to ; is the contact rate of infectious corn to susceptible corn population; is the contribution of infected corn to virus growth; is the contribution of infected plant to virus growth in the environment; is the natural mortality rate of corn population; is the infection rate of infected corn and virus combined effect population in the environment.

[0043] The embodiment constructs a disease transmission dynamics model by algorithm analysis on the process of interaction between host population and vector population.

[0044] In one embodiment, the corn disease index simulation and lesion spatial distribution visualization method provided by the embodiment of the present application can further include: Step 210, determining an environmental response function based on the influence information of the environmental factors on the disease transmission parameters; the environmental response function includes a temperature response function, a humidity response function and a day length response function; Step 220, adjusting the disease transmission parameters in the corn disease development process through the environmental response function to obtain an environmental-driven corn disease index simulation result.

[0045] Specifically, the temperature factor is a key factor affecting the development process of corn diseases, and the present application constructs a temperature response function through an asymmetric normal distribution curve, as shown in Figure 4As shown, Figure 4 Figure a shows that when the optimum temperature is the same, the standard deviation Effect of response range and sensitivity. Figure 4 in The optimum temperature for disease transmission is at this temperature. The impact on contact rate or conversion rate is 1. Figure 4 in and is the standard deviation of the curve on both sides of the optimum temperature for disease transmission, indicating that on the left side of the optimum temperature for disease transmission, as the temperature increases, increases; on the right side of the optimum temperature for disease spread, as the temperature decreases, Increase. Figure 4 in and The bigger, The larger the temperature response range, the lower the sensitivity to temperature changes. > reflects Higher sensitivity to high temperatures, extreme high temperatures The inhibitory effect of is stronger than that of extreme low temperature, as shown in Formula 12.

[0046] ; (12) Figure 4 Figure d in the figure uses the example of northern corn leaf blight to illustrate the temperature response function for the conversion rate between different maize subpopulations and the contact rate between maize, the environment, and the vector. When the pathogen of northern corn leaf blight, Helminthosporium convexum, enters the susceptible maize population from the environment-vector, the optimal temperature for spore germination and infection is 23°C, the optimal temperature for lesion expansion and spread between maize seeds is 25°C, and the optimal temperature for lesion sporulation and spread from maize to the environment-vector is 24°C. The spread patterns of different diseases are similar to those of northern corn leaf blight.

[0047] Corn diseases are usually high temperature and high humidity diseases. Therefore, the present invention constructs a relative humidity response function through an S-shaped growth curve. Figure 4 Figure b shows that the standard deviation of the optimal relative humidity is the same. Effect of response range and sensitivity. Indicates the relative humidity when the disease transmission rate reaches the inflection point. The impact on contact rate or conversion rate is 0.5. To control the speed of curve change, The larger the value, the lower the relative humidity. The effect of high relative humidity is more significant. The smaller the impact, the The wider the range of relative humidity response, the lower the sensitivity to relative humidity changes. Figure 4 Figure e in the figure uses the example of corn leaf blight to show the relative humidity response function of the conversion rate between different corn subpopulations and the contact rate between corn, the environment, and the vector. When the pathogen of corn leaf blight, Helminthosporium convexum, comes into contact with susceptible corn populations from the environment-vector, the optimal relative humidity for spore germination and infection, lesion expansion, and lesion sporulation is greater than 90%. There are variations in the range of response to relative humidity, as shown in Equation 13.

[0048] ; (13) ; (14) The optimal growth periods corresponding to different transmission pathways of corn diseases also vary. The present invention describes the growth degree-day response function through an asymmetric normal distribution curve. Figure 4 Figure c shows the relationship between the growth period and the standard deviation. Effect of response range and sensitivity. 、 and are the cumulative growing degree days corresponding to the peak values ​​of the three curves. The impact on the contact rate or conversion rate reaches a maximum value of 1. The optimal growth period corresponding to different transmission pathways can be determined by the leaf stage (Growing Degree-Days (GDD), i∈[10, 20]) when leaf i expands to 100% of the potential maximum leaf area. and To control the parameters of the change speed on the left and right sides of the curve, when combined with the corn coupling model, and It is determined by the life of the blade. The larger the value, the more important the growth period in the early stage of growth is. The more significant the impact, The larger the value, the more important the growth period is. The more significant the impact, The wider the range of responses to the reproductive period, the lower the sensitivity to changes in the reproductive period. Figure 5 Figure f in Figure 1 shows the growth degree-day response function for the conversion rate between different maize subpopulations and the contact rate between maize, the environment, and the vector, using the example of northern leaf blight. When the pathogen of northern leaf blight, Helicoverpa convexum, enters the susceptible maize population from the environment-vector, the optimal growth periods for spore germination and infection, lesion expansion, and lesion sporulation are defined as the 12-leaf stage, the 15-leaf stage, and the 18-leaf stage, respectively. are defined as the lifespans of the 12th, 15th, and 18th blades, respectively, as shown in Equation 14.

[0049] ; (15) ; (16) ; (17) ; (18) ; (19) ; (20) ; (21) The propagation rate parameters of the disease development process are adjusted by using a temperature response function, a humidity response function and a growth length day response function to realize the response of the model to environmental factors. The influence of the response function on the propagation path parameter from the diseased corn to the susceptible corn is represented by formula 15; the influence on the propagation path parameter from the diseased corn to the environment (pathogen and pathogen medium) is represented by formula 16 to 17; the influence on the propagation path parameter from the environment (pathogen and pathogen medium) to the susceptible corn is represented by formula 18; and the influence on the propagation path parameter from the pathogen in the environment to the pathogen medium is represented by formula 19 to 21.

[0050] The embodiment quantifies the effect of environmental factors on the disease development process, analyzes the environmental factor response of the disease propagation parameter, and obtains the simulation result of the corn disease index driven by the environment.

[0051] Figure 5 is a second flowchart of the corn disease index simulation and lesion spatial distribution visualization method provided by the application, as shown in Figure 6 The method can further include the following steps. Step 310, constructing a corn disease model of the corn disease degree changing with time; Step 320, constructing a lesion expansion distribution rule; the lesion expansion distribution rule is a rule of the lesion expansion area and the lesion spatial distribution changing with the corn disease degree; Step 330, determining a visualization result of the corn lesion spatial distribution based on the corn disease model and the lesion expansion distribution rule.

[0052] Specifically, the application establishes a rule of the lesion expansion area of a single corn changing with the disease severity, and counts the daily newly added diseased corns, thereby simulating the dynamic change of the disease severity of the corn population. Meanwhile, according to the rule of the lesion spatial distribution of the corn changing with the disease severity, the dynamic visualization of the lesion spatial distribution of the corn population is realized. The visualization process mainly includes the following steps: constructing a mathematical model of the disease severity of a single corn changing with the disease cycle; counting the daily newly added diseased corns , calculate the severity of the disease in the group; formulate rules for the expansion area of ​​lesions on individual corn plants and the spatial distribution of lesions as the severity of the disease changes, and realize the dynamic visualization of the spatial distribution of lesions in corn groups.

[0053] ;(twenty two) Mathematical model of the disease cycle of a single corn plant. In a complete disease cycle of a single corn plant, it goes through four stages: spore germination and infection, latent growth and manifestation, lesion spread and lesion sporulation, as shown in Formula 22. The severity of disease in a single corn plant is established by the S-shaped curve ( ) changes with the number of days of pathogen infection, that is, the corn disease model in this embodiment. A schematic diagram of the change in the severity of a single corn disease with the disease cycle is drawn, as shown in FIG. Figure 7 When corn plants are infected by pathogens for the second disease cycle, the disease severity reaches the inflection point, at which the disease severity is 0.5; when corn plants are infected by pathogens for more than one disease cycle, the disease severity reaches 1. To control the speed of curve change, The larger the value, the slower the lesion spreads.

[0054] ;(twenty three) ;(twenty four) Calculation of disease severity in corn populations. The calculation of disease severity in corn populations is based on the number of newly infected corn plants per day and the disease severity of each corn plant. Assume that the pathogen begins to infect the susceptible corn subpopulation when the area of ​​the 10th leaf reaches 50% of the potential maximum leaf area, and the initially infected corn subpopulation is in the latent and symptomatic state of the pathogen at this time. By counting the number of newly infected corn plants per day, the disease severity of corn populations is calculated. The disease severity of the corn population is calculated using the corresponding pathogen infection days and individual corn disease severity. Equations 23 and 24 describe the calculation of disease severity for the corn population and infected subpopulation. When infected corn enters the lesion sporulation stage, new pathogens are added to the disease transmission dynamics model.

[0055] in, and are the severity of disease in the maize population and the susceptible subpopulation on the i-th day after being infected by the pathogen; The number of times a single corn plant is infected by the pathogen severity of the disease at the time of the weather; is the initial number of maize infected subpopulations; and are the number of corn population and infected subpopulation on the i-th day of pathogen infection, respectively; The number of days for a single corn plant to be infected by a pathogen; The number of days for a disease cycle. The value of the corn large spot disease cycle can be 24; The value of the corn large spot disease cycle can be 2.

[0056] The spatial distribution of the disease spot can be visualized. To realize the dynamic visualization of the disease severity of the corn population to the spatial distribution of the disease spot, the present application divides the leaves of the whole corn plant into four groups according to the growth characteristics of the corn. At the same time, according to the disease occurrence law of the corn, the rule of the spatial distribution of the disease spot of the corn disease varying with the disease severity is formulated. Assuming that 80% of the sum of the maximum potential areas of the upper, middle and lower leaf groups is the upper limit of the disease spot expansion area, and all the disease spots of the same corn plant develop at the same time. The coupled corn model performs three-dimensional visualization on the corn scene by classifying and coloring the triangular facets. Considering the complexity of the scene and the calculation efficiency of three-dimensional radiation transmission, each leaf is divided into 19 triangular facets. By calculating the disease severity of the corn population, combining the rule of the spatial distribution of the disease spot varying with the disease severity, the proportion of the disease spot area to the leaf area in each layer (leaf group) is calculated, and then the proportion of the number of diseased triangular facets in each layer is converted, so as to realize the dynamic visualization of the spatial distribution of the disease spot of the corn.

[0057] The present embodiment determines the visualization result of the spatial distribution of the disease spot of the corn through the corn disease model and the disease spot expansion distribution rule.

[0058] In one embodiment, the corn disease index simulation and disease spot spatial distribution visualization method provided by the present embodiment can further include: Step 400, acquiring measured corn disease data and a target disease degree; the target disease degree is a corn disease degree without environmental driving; Step 500, determining the simulation reliability of the disease transmission dynamics model based on the measured corn disease data, the target disease degree and the corn disease index simulation result.

[0059] Specifically, the environmental factor response of the disease transmission parameter is an important content of the parameter optimization of the disease transmission dynamics module. The present application quantitatively analyzes the difference between the disease transmission process under the ideal disease condition and the constraint disease condition considering the environmental factor response function. At the same time, taking the measured corn large spot disease data set as the benchmark, the reliability of the disease transmission dynamics model in simulating the corn disease severity is tested. The influence of the environmental factor on the corn disease transmission is obvious. Under the ideal disease condition, without considering the influence of the environmental factor response function on the disease transmission parameter, the group disease severity value of the corn population (for example, 0.86) is much larger than the disease severity value under the constraint disease condition considering the environmental factor response function (for example, 0.08).

[0060] The embodiment takes a corn large spot disease data set as a benchmark to verify reliability of the disease spread dynamics model in simulating corn disease severity.

[0061] The corn disease index simulation and disease spot spatial distribution visualization device provided by the application is described below, and the corn disease index simulation and disease spot spatial distribution visualization device described below can be correspondingly referred to the corn disease index simulation and disease spot spatial distribution visualization method described above.

[0062] Please refer to Figure 8 The application also provides a corn disease index simulation and disease spot spatial distribution visualization device, comprising: The disease spread dynamics model construction module 701 is configured to construct a disease spread dynamics model based on the interaction between the corn host population and the pathogen vector population, wherein the parameters of the disease spread dynamics model include disease spread parameters. The corn disease index simulation module 702 is configured to analyze the response of the disease spread parameters to environmental factors to obtain an environmental-driven corn disease index simulation result. The disease spot spatial distribution visualization module 703 is configured to determine a visualization result of the corn disease spot spatial distribution based on the corn disease index simulation result.

[0063] Optionally, the disease spread dynamics model construction module comprises: The environmental pathogen population construction unit is configured to construct an environmental pathogen population. The disease spread parameter acquisition unit is configured to acquire disease spread parameters of disease-related populations, wherein the disease-related populations include the corn host population, the pathogen vector population, and the environmental pathogen population, and the disease spread parameters include intra-population conversion rate, inter-population contact rate, and population entry and exit rate. The disease spread dynamics model construction unit is configured to construct a disease spread dynamics model based on the disease spread parameters.

[0064] Optionally, the disease spread dynamics model construction unit comprises: The model construction condition determination unit is configured to determine model construction conditions, wherein the model construction conditions include a first condition and a second condition, the first condition is that the probability of infection of each corn individual is the same, and the second condition is that the natural mortality rate of each subpopulation in the corn host population is the same. The population base acquisition unit is configured to acquire population bases of the disease-related populations. The model construction unit is configured to construct a disease spread dynamics model based on the model construction conditions, the population bases, and population change information, wherein the population change information is determined based on the disease spread parameters.

[0065] Optionally, the environmental factors comprise a temperature factor, a humidity factor and a day length factor; and the corn disease index simulation module comprises: an environmental response function determination unit configured to determine an environmental response function based on the influence information of the environmental factors on the disease transmission parameters; the environmental response function comprises a temperature response function, a humidity response function and a day length response function; a corn disease index simulation unit configured to adjust the disease transmission parameters in the corn disease development process by using the environmental response function, and obtain an environmental-driven corn disease index simulation result.

[0066] Optionally, the lesion spatial distribution visualization module comprises: a corn disease model construction unit configured to construct a corn disease model of the corn disease degree changing over time; a lesion expansion distribution rule construction unit configured to construct a lesion expansion distribution rule; the lesion expansion distribution rule is a rule of the corn lesion expansion area and the lesion spatial distribution changing with the corn disease degree; a lesion spatial distribution visualization unit configured to determine a visualization result of the corn lesion spatial distribution based on the corn disease model and the lesion expansion distribution rule.

[0067] Optionally, the corn disease index simulation and lesion spatial distribution visualization device further comprises: an acquisition module configured to acquire measured corn disease data and a target disease degree; the target disease degree is a corn disease degree without environmental driving; a simulation reliability determination module configured to determine a simulation reliability of the disease transmission dynamics model based on the measured corn disease data, the target disease degree and the corn disease index simulation result.

[0068] Figure 8 An example of an entity structure diagram of an electronic device is shown in FIG. 1. ​As shown, the electronic device can include a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 complete mutual communication through the communications bus 840. The processor 810 can invoke a logic instruction in the memory 830 to execute the corn disease index simulation and lesion spatial distribution visualization method, which includes: based on the interaction between the corn host population and the pathogen vector population, a disease transmission dynamics model is constructed; the parameters of the disease transmission dynamics model include disease transmission parameters; the response of the environmental factors to the disease transmission parameters is analyzed to obtain an environmental-driven corn disease index simulation result; and based on the corn disease index simulation result, a visualization result of the spatial distribution of corn lesions is determined.

[0069] In addition, the logic instruction in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0070] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, and the computer can execute the corn disease index simulation and lesion spatial distribution visualization method provided by the above-mentioned methods, which includes: based on the interaction between the corn host population and the pathogen vector population, a disease transmission dynamics model is constructed; the parameters of the disease transmission dynamics model include disease transmission parameters; the response of the environmental factors to the disease transmission parameters is analyzed to obtain an environmental-driven corn disease index simulation result; and based on the corn disease index simulation result, a visualization result of the spatial distribution of corn lesions is determined.

[0071] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the method for corn disease index simulation and disease spot spatial distribution visualization provided by any of the above methods, the method comprising: constructing a disease transmission dynamics model based on the interaction between a corn host population and a pathogen vector population; parameters of the disease transmission dynamics model include disease transmission parameters; analyzing the response of the disease transmission parameters to environmental factors to obtain an environmental-driven corn disease index simulation result; and determining a visualization result of corn disease spot spatial distribution based on the corn disease index simulation result.

[0072] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0073] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in terms of contribution to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0074] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for simulating corn disease index and visualizing lesion spatial distribution, characterized in that: include: Based on the interaction between the corn host population and the pathogen vector population, a disease transmission dynamics model is constructed; the parameters of the disease transmission dynamics model include disease transmission parameters; Analyzing the environmental factor responses of the disease transmission parameters to obtain the simulation results of the environment-driven corn disease index; Based on the corn disease index simulation results, a visualization result of the spatial distribution of corn lesions is determined.

2. The method for simulating corn disease index and visualizing lesion spatial distribution according to claim 1, characterized in that: The disease transmission dynamics model constructed based on the interaction between the corn host population and the pathogen vector population includes: constructing environmental pathogen populations; Obtaining disease transmission parameters of disease-related populations; the disease-related populations include corn host populations, pathogen vector populations, and the environmental pathogen populations; the disease transmission parameters include intra-population conversion rate, inter-population contact rate, and population entry and exit rate; A disease propagation dynamics model is constructed based on the disease propagation parameters.

3. The method for simulating corn disease index and visualizing lesion spatial distribution according to claim 2, characterized in that: The constructing of the disease propagation dynamics model based on the disease propagation parameters includes: Determining model construction conditions; the model construction conditions include a first condition and a second condition; the first condition is that the probability of each corn individual being infected is the same; the second condition is that the natural mortality rate of each subpopulation in the corn host population is the same; Obtaining the population base of the disease-related population; A disease propagation dynamics model is constructed based on the model construction conditions, the population base and the population change information; the population change information is determined based on the disease propagation parameters.

4. The method for simulating corn disease index and visualizing lesion spatial distribution according to claim 1, characterized in that: Environmental factors include temperature factors, humidity factors, and growing degree-day factors. The environmental factor responses of the disease transmission parameters are analyzed to obtain the simulation results of the environment-driven corn disease index, including: Determining an environmental response function based on the information on the impact of the environmental factors on the disease propagation parameters; the environmental response function includes a temperature response function, a humidity response function, and a growing degree-day response function; The disease propagation parameters in the development process of corn diseases are adjusted by the environmental response function to obtain the simulation results of the corn disease index driven by the environment.

5. The method for simulating corn disease index and visualizing lesion spatial distribution according to claim 1, characterized in that: Determining the visualization result of the spatial distribution of corn lesions based on the corn disease index simulation result includes: Construct a corn disease model in which the severity of corn diseases changes over time; Constructing a lesion expansion distribution rule; the lesion expansion distribution rule is a rule that the expansion area and spatial distribution of corn lesions change with the severity of the corn disease; Based on the corn disease model and the lesion expansion distribution rule, a visualization result of the spatial distribution of corn lesions is determined.

6. The method for simulating corn disease index and visualizing lesion spatial distribution according to claim 1, characterized in that: Determining the visualization result of the spatial distribution of corn lesions based on the corn disease index simulation result, then comprising: Obtaining measured corn disease data and a target disease severity; the target disease severity is the corn disease severity without environmental factors; The simulation reliability of the disease transmission dynamics model is determined based on the measured corn disease data, the target disease degree and the corn disease index simulation result.

7. A device for simulating corn disease index and visualizing lesion spatial distribution, characterized in that: include: A disease transmission dynamics model construction module is used to construct a disease transmission dynamics model based on the interaction between the corn host population and the pathogen vector population; the parameters of the disease transmission dynamics model include disease transmission parameters; A corn disease index simulation module is used to analyze the environmental factor response of the disease transmission parameters and obtain an environment-driven corn disease index simulation result; The lesion spatial distribution visualization module is used to determine the visualization result of the corn lesion spatial distribution based on the corn disease index simulation result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for simulating corn disease index and visualizing lesion spatial distribution as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for simulating corn disease index and visualizing lesion spatial distribution as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for simulating corn disease index and visualizing lesion spatial distribution as claimed in any one of claims 1 to 6 is implemented.

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