A rice rice blast occurrence and prevalence prediction method, a prediction device, and an electronic device
By screening the pathogenic factors and actual occurrence index of rice blast, and using R language to establish a discriminant function, the problem of inaccurate prediction of rice blast in existing technologies has been solved, and efficient guidance for disease control has been achieved.
Patent Information
- Application Number
- CN202211309791.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-10-25
- Publication Date
- 2026-01-20
- Estimated Expiration
- 2042-10-25
AI Technical Summary
Existing methods for predicting rice blast disease have low accuracy and cannot provide timely and effective guidance for integrated disease control.
By acquiring data on the pathogenic factors and actual occurrence index of rice blast in different regions for at least five years, the stepdisc and discrim procedures in R language are used to screen out influencing factors and establish a discriminant function to achieve accurate prediction of rice blast occurrence and spread.
It improves the accuracy of predicting the occurrence and spread of rice blast, and can guide integrated disease control in a timely and effective manner.
Smart Images
Figure CN115497568B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of rice blast prediction, and in particular to a rice blast occurrence and prevalence prediction method, a prediction device and an electronic device. BACKGROUND
[0002] Rice blast caused by Magnaporthe oryzae (anamorph: Pyricularia oryzae) is one of the most devastating diseases of rice. Due to the complex factors of Magnaporthe oryzae occurrence and prevalence, the prevalence degree fluctuates greatly year by year. Therefore, developing accurate annual prevalence prediction of rice blast and timely and effective guidance of disease comprehensive prevention are still problems to be solved in current rice production.
[0003] Discriminant analysis method is a method of classifying prediction objects according to a number of factors, and a mathematical model for qualitative prediction can be established through analysis. In recent years, discriminant analysis method has been more and more applied in the field of plant protection. Scholars select different indexes to establish crop disease and pest prevalence grade discriminant function and apply it to crop disease and pest prevalence prediction. Zhang Jiaqi et al. selected the main meteorological factors affecting the occurrence and prevalence of corn stalk rot, and established a two-stage prediction model of corn stalk rot in Henan Province by discriminant analysis method. Liang Zhongzhong et al. applied discriminant analysis to long-term prediction of wheat scab occurrence. Wang Qingwen et al. used wheat stripe rust disease, variety and meteorological data to establish a discriminant function to predict the prevalence of wheat stripe rust in Hanzhong City by stepwise discriminant analysis method. In the aspect of rice blast, Tang Qiyi et al. screened 8 meteorological factors for long-term prediction of rice blast occurrence in late rice; Yin Shicai et al. used nitrogen fertilizer application amount, variety, rainfall and leaf blast field rate to establish a discriminant function to predict the occurrence value of Luliang rice blast panicle blast. However, the existing rice blast prediction methods have low accuracy and still cannot timely and effectively guide disease comprehensive prevention. SUMMARY
[0004] The technical problem to be solved by the present application is to overcome the defects of the prior art that the rice blast prediction method has low accuracy and still cannot timely and effectively guide disease comprehensive prevention.
[0005] Therefore, the present application provides a rice blast occurrence and prevalence prediction method, a prediction device and an electronic device.
[0006] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows:
[0007] A rice blast occurrence and prevalence prediction method, comprising the following steps:
[0008] Obtaining data of pathogenic factors and actual occurrence index of rice blast in different regions for at least five years;
[0009] The pathogenic factors include the annual average rainfall days, the annual average rainfall, the annual average temperature, the average virulence of Magnaporthe oryzae in the last year, and the frequency of avirulence genes of Magnaporthe oryzae in the last year;
[0010] According to the data of the pathogenic factors and the actual occurrence index of rice blast in at least five years, the influence factors of the actual occurrence index of rice blast are screened by a stepdisc process;
[0011] According to the data of the influence factors, a discriminant function is established by a discrim process;
[0012] The data of the influence factors in the current year are input into the discriminant function to obtain the predicted occurrence index of rice blast in the current year.
[0013] Further, the screening of the influence factors of the actual occurrence index of rice blast according to the data of the pathogenic factors and the actual occurrence index of rice blast in at least five years by the stepdisc process comprises:
[0014] The data of the pathogenic factors and the actual occurrence index of rice blast in at least five years are input, and the regional characteristics of the actual occurrence index of rice blast are determined by running the ggplot2 package in R language;
[0015] The data of the pathogenic factors and the actual occurrence index of rice blast in at least five years are input, and the influence factors of the actual occurrence index of rice blast are screened by running the FactoMineR package and the factoextra package in R language.
[0016] Further, the establishment of the discriminant function according to the data of the influence factors by the discrim process comprises:
[0017] The data of the influence factors of the actual occurrence index of rice blast are input, and the discriminant function is established by running the MASS package in R language.
[0018] Further, if the actual occurrence index of rice blast has regional characteristics, discriminant functions are respectively established for different regions;
[0019] If the actual occurrence index of rice blast does not have regional characteristics, discriminant functions do not need to be respectively established for different regions.
[0020] Further, the method for predicting the occurrence and prevalence of rice blast in rice comprises:
[0021] Data of pathogenic factors of rice blast and the actual occurrence index of rice blast in rice in at least three regions are obtained.
[0022] Further, the rice blast occurrence and prevalence prediction method comprises the following steps.
[0023] Data of pathogenic factors and actual occurrence indexes of rice blast in at least three regions in the past 14 years are acquired.
[0024] The application further provides a rice blast occurrence and prevalence prediction device, comprising the following steps.
[0025] An acquisition module is configured to acquire data of pathogenic factors and actual occurrence indexes of rice blast in different regions in at least five years, wherein the pathogenic factors in each year include annual average rainfall days, annual average rainfall, annual average temperature, average pathogenicity of Magnaporthe grisea in the previous year, and frequency of avirulence genes of Magnaporthe grisea in the previous year.
[0026] An impact factor screening module is configured to screen impact factors of the actual occurrence indexes of rice blast by a stepdisc process according to the data of the pathogenic factors and the actual occurrence indexes of rice blast in at least five years.
[0027] A discriminant function establishing module is configured to establish a discriminant function by a discrim process according to the data of the impact factors.
[0028] A prediction module is configured to input the data of the impact factors in the current year into the discriminant function to obtain a predicted occurrence index of rice blast in the current year.
[0029] The application further provides an electronic device, comprising the following steps.
[0030] A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the rice blast occurrence and prevalence prediction method.
[0031] The application further provides a computer readable storage medium storing computer instructions for making the computer perform the rice blast occurrence and prevalence prediction method.
[0032] The application has the following advantages.
[0033] 1. In the application, data of pathogenic factors and actual occurrence indexes of rice blast in at least five years are acquired, impact factors of the actual occurrence indexes of rice blast are screened by a stepdisc process, a discriminant function is established by a discrim process, and the occurrence and prevalence of rice blast are finally predicted. The selection of pathogenic factor data improves the accuracy of the prediction of the occurrence and prevalence of rice blast, and can effectively guide the comprehensive prevention and control of the disease.
[0034] 2. The application is based on the rice blast pathogenic factor and the rice blast actual occurrence index to establish a prediction model of the rice blast pathogenic factor and the rice blast predicted occurrence index, and the rice blast predicted occurrence index can be simply and quickly predicted by collecting the data of the pathogenic factor and the rice blast actual occurrence index. BRIEF DESCRIPTION OF DRAWINGS
[0035] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.
[0036] Figure 1 It is a flowchart of the rice blast occurrence and epidemic prediction method in Example 1.
[0037] Figure 2 It is a graph of the rice blast actual occurrence index in different regions in Example 2 of the present application.
[0038] Figure 3 It is a graph of the influence of different influence factors on the rice blast actual occurrence index in Example 2 of the present application.
[0039] Figure 4 It is a structural schematic diagram of the rice blast occurrence and epidemic prediction device in Example 3 of the present application.
[0040] Figure 5 It is a structural schematic diagram of the electronic device in Example 4 of the present application. DETAILED DESCRIPTION
[0041] The following examples are provided to better further understand the present application, and do not limit the best embodiments, and do not constitute a limitation on the content and protection scope of the present application. Any person under the inspiration of the present application or the combination of the present application with other prior art features can obtain any product same or similar to the present application, which falls within the protection scope of the present application.
[0042] If the specific experimental steps or conditions are not indicated in the examples, the operation or conditions can be carried out according to the conventional experimental steps described in the literature in the art. If the reagents or instruments are not indicated by the manufacturer, they are conventional reagent products that can be obtained by market purchase.
[0043] In the present application, the average pathogenicity of rice blast fungus (%) is that 30 rice blast single gene lines resistant to rice blast are inoculated with rice blast single spore strains isolated from different rice areas, and the percentage of susceptible varieties is taken as the evaluation index of the pathogenicity of rice blast fungus, and the average pathogenicity of rice blast fungus in the year is calculated.
[0044] The frequency of avirulence genes of Magnaporthe grisea (%) is: according to the results of inoculating 30 rice blast monogenic lines with isolated rice blast single strains, the presence or absence of corresponding avirulence genes in Magnaporthe grisea is deduced according to the "gene-for-gene" principle of rice blast resistance; the frequency of each avirulence gene in all strains is counted, and the average frequency of all avirulence genes in Magnaporthe grisea in the year is calculated.
[0045] The determination standard of rice blast occurrence index is shown in Table 1:
[0046] Table 1
[0047] Rice blast occurrence index Occurrence degree description Corresponding occurrence rate (P) 1 Light occurrence P≤1％ 2 Slightly light occurrence 1％<P≤5％ 3 Medium occurrence 5％<P≤10％ 4 Slightly heavy occurrence 10％<P≤15％ 5 Heavy occurrence P>15％
[0048] Note: P = [rice blast occurrence area (10,000 mu) / total rice planting area (10,000 mu)] x 100%.
[0049] Example 1
[0050] The present embodiment provides a rice blast occurrence and prevalence prediction method, Figure 1 The flowchart of the rice blast occurrence and prevalence prediction method in Example 1 of the present application is shown in Figure 1 As shown, the prediction method comprises the following steps:
[0051] S1: Obtain data of pathogenic factors and actual occurrence index of rice blast in different regions for at least five years; the pathogenic factors in each year include the number of annual average rainfall days in the current year, the annual average rainfall in the current year, the annual average temperature in the current year, the average virulence of Magnaporthe grisea in the previous year, and the frequency of avirulence genes of Magnaporthe grisea in the previous year.
[0052] S2: According to the data of pathogenic factors and actual occurrence index of rice blast for at least five years, the influence factors of actual occurrence index of rice blast are screened out by stepdisc process.
[0053] Specifically, S201: input the data of pathogenic factors and actual occurrence index of rice blast for at least five years, and run ggplot2 package in R language to determine the regional characteristics of actual occurrence index of rice blast.
[0054] S202: input the data of pathogenic factors and actual occurrence index of rice blast for at least five years, and run FactoMineR package and factoextra package in R language to screen out influence factors related to actual occurrence index of rice blast.
[0055] S3: According to the data of the influence factors, a discriminant function is established by the discrimin process.
[0056] Specifically, the data of the influencing factors of the actual occurrence index of rice blast is input, and a discriminant function is established by running the MASS package in R language.
[0057] Specifically, if the actual occurrence index of rice blast has regional characteristics, discriminant functions are respectively established for different regions.
[0058] If the actual occurrence index of rice blast does not have regional characteristics, discriminant functions do not need to be respectively established for different regions.
[0059] S4: The data of the influencing factors of the current year is input into the discriminant function to obtain the predicted occurrence index of rice blast in the current year.
[0060] Embodiment 2
[0061] The embodiment provides a rice blast occurrence and prevalence prediction method, and the prediction method is consistent with that in embodiment 1, and specifically:
[0062] The data of pathogenic factors and the actual occurrence index of rice blast in Jiangxi Province (A), Jiujiang City (B) and Shanggao County (C) from 2007 to 2020 is obtained, and the data is shown in Table 2.
[0063] The data of the pathogenic factors and the actual occurrence index of rice blast obtained in Table 2 is input, and ggplot2 package is run to obtain Figure 2 , Figure 2 The figure of the actual occurrence index of rice blast in different regions in embodiment 2 of the application is obtained by Figure 2 It can be known that the actual occurrence index of rice blast in Jiangxi Province (A), Jiujiang City (B) and Shanggao County (C) does not have regional characteristics.
[0064] The data of the pathogenic factors and the actual occurrence index of rice blast obtained in Table 2 is input, and FactoMineR package and factoextra package are run to obtain Figure 3 , Figure 3 The figure of the influence of different influencing factors on the actual occurrence index of rice blast in embodiment 2 of the application is obtained by Figure 3 It can be known that the annual average rainfall days, the annual average rainfall in the current year, the annual average temperature in the current year, the average pathogenicity of rice blast in the last year and the frequency of avirulence genes of rice blast in the last year all have extremely strong correlation with the actual occurrence index of rice blast, and all are influencing factors of the actual occurrence index of rice blast.
[0065] The data of the pathogenic factors and the actual occurrence index of rice blast obtained in Table 2 is input, and the LDA function in the MASS package of R4.1.3 software is run to obtain the discriminant function of the occurrence and prevalence of rice blast in Jiangxi Province, and the discriminant function is shown in Table 3.
[0066] The data of the influence factors in any year in Table 2 is input into the discriminant function to obtain the predicted occurrence index of rice blast in the year.
[0067] The average pathogenicity of Magnaporthe grisea and the gene frequency of Magnaporthe grisea in Table 2 are provided by the Plant Protection Institute of Jiangxi Academy of Agricultural Sciences; the annual average rainfall days (Raining_days), the annual average rainfall (Rainfall) and the annual average temperature (Temperature) in the year are provided by the Jiangxi Meteorological Bureau, the Jiujiang Meteorological Bureau and the Shanggao Meteorological Bureau respectively; the actual occurrence index of rice blast: the occurrence index of rice blast in Jiangxi Province from 2007 to 2020 is provided by the Jiangxi Plant Protection and Inspection Institute.
[0068] Table 2
[0069]
[0070]
[0071] Table 3
[0072]
[0073] Note: The predicted occurrence index of rice blast = LD1*85.22% + LD2*11.13% + LD2*3.65%
[0074] Example 3
[0075] Corresponding to Examples 1-2, the present embodiment 3 provides a prediction device for predicting the occurrence and prevalence of rice blast, Figure 4 The structure diagram of the prediction device for predicting the occurrence and prevalence of rice blast in the present embodiment 3 is shown in the figure, Figure 4 According to the present embodiment 3, the prediction device for predicting the occurrence and prevalence of rice blast includes an acquisition module 41, an influence factor screening module 42, a discriminant function establishing module 43 and a prediction module 44. The acquisition module 41 is used to acquire the data of pathogenic factors and the actual occurrence index of rice blast in different regions for at least five years; the pathogenic factors in each year include the annual average rainfall days, the annual average rainfall, the annual average temperature in the year, the average pathogenicity of Magnaporthe grisea in the previous year and the gene frequency of Magnaporthe grisea in the previous year. The influence factor screening module 42 is used to screen the influence factors of the actual occurrence index of rice blast by the stepdisc process according to the data of the pathogenic factors and the actual occurrence index of rice blast for at least five years; the discriminant function establishing module 43 is used to establish a discriminant function by the discrim process according to the data of the influence factors; and the prediction module 44 is used to input the data of the influence factors in the year into the discriminant function to obtain the predicted occurrence index of rice blast in the year.
[0076] Embodiment 4
[0077] Corresponding to Embodiments 1-2, Embodiment 4 of the present application provides an electronic device, Figure 5 A schematic diagram of the structure of the electronic device in Embodiment 4 of the present application is shown in FIG. 4. Figure 5 As shown, the electronic device in Embodiment 4 of the present application includes a memory 52 and a processor 51, which are in communication connection with each other, the memory 52 stores computer instructions, and the processor 51 executes the computer instructions to perform the rice rice blast occurrence and prevalence prediction method described above.
[0078] The processor 51 can be a central processing unit (CPU). The processor 51 can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, or combinations thereof.
[0079] The memory 52 is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs, non-transitory computer executable programs and modules. The processor 51 executes various functions and data processing of the processor 51 by running the non-transitory software programs, instructions and modules stored in the memory 52, that is, implements the rice rice blast occurrence and prevalence prediction method in Embodiments 1-2 described above.
[0080] The memory 52 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created by the processor and the like. In addition, the memory 52 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. The memory 52 can optionally include a memory disposed remotely with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0081] Embodiment 5
[0082] Corresponding to the embodiments 1-2, the embodiment 5 of the present application provides a computer readable storage medium, which stores computer instructions for causing the computer to execute the rice rice blast occurrence and prevalence prediction method of the embodiments 1-2.
[0083] Comparative example 1
[0084] In the prior art “Tang Qiyi, et al. Late rice blast BP neural network zoning prediction. Plant Protection, 2002, 29(3): 205-209”, the correlation analysis method was used to analyze the relationship between the occurrence of late rice blast in 19 counties of Zhejiang Province from 1988 to 1999 and the relevant environmental factors, and the four elements of meteorological factors, i.e., ten-day mean temperature, relative humidity, rainfall and rainy days, were selected as prediction factors for long-term prediction of the occurrence of late rice blast. According to the correlation between each prediction factor and the occurrence of rice blast, the 19 points (counties) were divided into four ecological zones by using the adjacent two-dimensional graph theory clustering analysis method, and a model was established in each ecological zone by using the neural network technology, and fitting and trial prediction were performed. The neural network model was applied to the trial prediction of rice blast from 1997 to 1999, and the success rates of the three years were 78.95%, 84.21% and 78.95%, respectively.
[0085] Test example 1
[0086] The occurrence indexes of rice blast in Jiangxi Province, Jiujiang City and Shanggao County from 2007 to 2020 were predicted according to the prediction method of embodiment 2, and the number of the same predicted occurrence indexes of rice blast and the actual occurrence indexes of rice blast was counted, and the statistical results are shown in Table 4. According to Table 4, when the data of the pathogenic factors of rice blast in the three regions for 14 years and the actual occurrence indexes of rice blast are obtained, the accuracy rate of the predicted occurrence indexes of rice blast predicted by the prediction method of the present application is as high as 88.9%.
[0087] Table 4
[0088]
[0089]
[0090] According to the prediction results of comparative example 1 and test example 1, the accuracy rate of the prediction of the occurrence and prevalence of rice blast by the prediction method of the present application is as high as 88.09%, which is significantly higher than the prediction accuracy rates of 78.95%, 84.21% and 78.95% of rice blast in the three years of 1997-1999 disclosed in comparative example 1.
[0091] Obviously, the above embodiments are merely example for clearly illustrating but not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments need not and can not be enumerated. The obvious changes or variations derived from the above description are still within the protection scope of the present application.
Claims
1. A method for predicting the occurrence and prevalence of rice blast in rice, characterized by, The method comprises the following steps: obtaining data of pathogenic factors and actual occurrence indexes of rice blast in different regions for at least five years; the pathogenic factors of each year include annual average rainfall days, annual average rainfall, annual average temperature, average virulence of Magnaporthe oryzae in the previous year, and frequency of avirulence genes of Magnaporthe oryzae in the previous year; screening impact factors of the actual occurrence indexes of rice blast from the data of the pathogenic factors and the actual occurrence indexes of rice blast for at least five years by a stepdisc process; establishing a discriminant function by a discrim process according to the data of the impact factors; inputting the data of the impact factors of the current year into the discriminant function to obtain a predicted occurrence index of rice blast in the current year; if the actual occurrence indexes of rice blast have regional characteristics, discriminant functions are respectively established for different regions; if the actual occurrence indexes of rice blast do not have regional characteristics, discriminant functions do not need to be respectively established for different regions; the step of screening the impact factors of the actual occurrence indexes of rice blast from the data of the pathogenic factors and the actual occurrence indexes of rice blast for at least five years by the stepdisc process comprises: inputting the data of the pathogenic factors and the actual occurrence indexes of rice blast for at least five years into an R language ggplot2 package to determine regional characteristics of the actual occurrence indexes of rice blast; inputting the data of the pathogenic factors and the actual occurrence indexes of rice blast for at least five years into an R language FactoMineR package and a factoextra package to screen the impact factors of the actual occurrence indexes of rice blast.
2. The method according to claim 1, wherein, the step of establishing the discriminant function by the discrim process according to the data of the impact factors comprises: inputting the data of the impact factors of the actual occurrence indexes of rice blast into an R language MASS package to establish the discriminant function.
3. The method according to claim 1, wherein the method is characterized by, The method comprises the following steps: obtaining data of pathogenic factors and actual occurrence indexes of rice blast in at least three regions.
4. The method according to claim 3, wherein the method is characterized by, The method comprises the following steps: obtaining data of pathogenic factors and actual occurrence indexes of rice blast in at least three regions for fourteen years.
5. A predictive device for predicting the occurrence and spread of rice blast, characterized in that, The method comprises the following steps: an obtaining module is configured to obtain data of pathogenic factors and actual occurrence indexes of rice blast in different regions for at least five years; the pathogenic factors of each year include annual average rainfall days, annual average rainfall, annual average temperature, average virulence of Magnaporthe oryzae in the previous year, and frequency of avirulence genes of Magnaporthe oryzae in the previous year; an impact factor screening module is configured to screen impact factors of the actual occurrence indexes of rice blast from the data of the pathogenic factors and the actual occurrence indexes of rice blast for at least five years by a stepdisc process; specifically, the data of the pathogenic factors and the actual occurrence indexes of rice blast for at least five years are inputted into an R language ggplot2 package to determine regional characteristics of the actual occurrence indexes of rice blast. Inputting data of the pathogenic factors and the actual occurrence index of rice blast in at least five years, running FactoMineR package and factoextra package in R language to screen out the influence factor of the actual occurrence index of rice blast; The discriminant function establishment module is configured to establish a discriminant function through a disc process according to data of the influence factor; The prediction module is configured to input data of the influence factor of the current year into the discriminant function to obtain a predicted occurrence index of rice blast in the current year. If the actual occurrence index of rice blast has regional characteristics, discriminant functions are respectively established for different regions; if the actual occurrence index of rice blast does not have regional characteristics, discriminant functions do not need to be respectively established for different regions.
6. An electronic device, comprising: The method comprises: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method of any one of claims 1-4.
7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1-4.