A method, storage medium and device for predicting corn southern rust disease index

Abstract

The present application provides a method, storage medium and device for predicting the disease index of southern corn rust, and the method comprises: predicting the seasonal epidemic progress of multiple piles of rust spores that cause southern rust, obtaining a growth index equation of multiple piles of rust spores, and the growth index equation of multiple piles of rust spores is used to illustrate the influence of temperature and rainfall on the growth index of multiple piles of rust spores; obtaining a corn southern rust fitness index formula according to the growth index equation of multiple piles of rust spores, and the corn southern rust fitness index formula is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area and the fitness of corn southern rust in the area when the temperature and rainfall are different. Index; from the beginning of the corn inoculation season, assuming that the number of multiple piles of handle rust spores in the air remains unchanged, diffusion events will occur when the wind speed exceeds 1 meter / second. As the cumulative diffusion events increase, the number of multiple piles of handle rust spores in the air changes with the wind speed and follows the negative exponential decrease condition, fitting the data set of wind speed and multiple piles of handle rust spore growth index in the corn inoculation season in historical years, obtaining the diffusion index of the multiple piles of handle rust spore growth index, and expressing the diffusion index with an exponentially decreasing function equation; according to the condition that rainfall events will cause the deposition of multiple piles of handle rust spores, fitting the multiple piles of handle rust spore concentrations and daily average temperatures in historical years to obtain a disease index prediction model. Through the scheme of this application, the potential distribution range of southern corn rust can be predicted, the possible time and outbreak period of southern corn rust can be obtained, and technical support can be provided for precise application of pesticides.

Description

Technical Field

[0001] The present application relates to the technical field of early warning and monitoring of crop airborne diseases, and in particular to a method, storage medium and device for predicting a disease index of southern corn rust. Background Art

[0002] Traditional approaches to predicting and managing airborne diseases rely on historical data and farmers' experience, which often leads to reactive rather than proactive strategies. To assess the impact of climatic conditions on the dynamics of airborne disease epidemics and understand the regional distribution and potential changes in disease occurrence, appropriate modeling techniques must be used. Summary of the invention

[0003] In order to solve the above problems in the prior art, the present invention provides a method, storage medium and device for predicting the disease index of southern corn rust based on climate and environmental changes.

[0004] In the first aspect, the technical solution of the present application provides a method for predicting the disease index of southern corn rust, comprising:

[0005] Predicting the seasonal epidemic progress of multiple piles of rust spores that cause southern corn rust, and obtaining a multiple pile rust spore growth index equation, wherein the multiple pile rust spore growth index equation is used to illustrate the influence of temperature and rainfall on the multiple pile rust spore growth index;

[0006] A formula for the adaptability index of southern corn rust is obtained according to the growth index equation of multiple piles of rust spores, wherein the formula for the adaptability index of southern corn rust is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area and the adaptability index of southern corn rust in the area when the temperature and rainfall in the area are different;

[0007] According to the condition that the number of multiple piles of stem rust spores in the air remains unchanged from the beginning of the corn inoculation season, diffusion events will occur when the wind speed exceeds 1 m / s, and as the cumulative diffusion events increase, the number of multiple piles of stem rust spores in the air changes with the wind speed and follows a negative exponential decrease, the data sets of wind speed and multiple piles of stem rust spore growth index in the corn inoculation season in historical years are fitted to obtain the diffusion index of the multiple piles of stem rust spore growth index, and the diffusion index is expressed as an exponentially decreasing function equation;

[0008] According to the condition that rainfall events will lead to the deposition of multiple piles of Puccinia spores, the disease index prediction model was obtained by fitting the concentration of multiple piles of Puccinia spores and the daily average temperature in historical years.

[0009] The method for predicting the disease index of southern corn rust described in some schemes predicts the seasonal epidemic progress of multiple piles of rust spores that cause southern rust, and obtains the multiple piles of rust spore growth index equation, which is used to illustrate the influence of temperature and rainfall on the epidemic dynamics of multiple piles of rust spores, including:

[0010] The equation for the growth index of multiple piles of Puccinia spores is as follows:

[0011] PPGI_w=TI_w×LMI_w;

[0012] Among them, PPGI_w represents the weekly epidemic dynamics of polypsilosis spores, TI_w represents the weekly temperature index, which describes the response of polypsilosis to the daily temperature cycle and varies between 0 and 1; LMI_w represents the leaf surface moisture index, which describes the response of the plant leaf surface to rainfall.

[0013] The method for predicting the disease index of southern corn rust described in some schemes determines TI_w in the following way:

[0014] The development stage of multiple piles of handle rust spores is expressed as the area under the daily temperature curve; the temperature range most suitable for the survival of multiple piles of handle rust spores is divided into the lowest temperature threshold AUDTC1, the lowest suitable temperature AUDTC2, the highest suitable temperature AUDTC3 and the highest temperature threshold AUDTC4; TI_w is calculated based on the value of the effect of the optimal temperature on the growth rate of multiple piles of handle rust spores obtained by the shaded area S1 under the AUDTC1 to AUDTC4 curve and the shaded area S2 under the AUDTC1 and AUDTC2 curves and the value of the growth rate of multiple piles of handle rust spores reduced by high temperature obtained by the highest temperature and key parameters;

[0015] when:

[0016]

[0017] otherwise:

[0018]

[0019] Some of the methods for predicting the southern rust disease index for corn described in the scheme determine the LMI in the following way:

[0020] According to the condition that the rainfall remains constant within 24 hours, the minimum moisture threshold LSM1, the minimum suitable moisture threshold LSM2, the maximum suitable moisture threshold LSM3 and the maximum moisture threshold LSM4 are derived based on the moisture on the leaf surface;

[0021] The leaf surface moisture index LMI_w of the current week is calculated based on the leaf surface moisture index LSM of the previous week. (i-1)and this week's rainfall evapotranspiration R i Calculation shows: LMI_w=(LSM (i-1) +R i -0.8) / 100; when LSM2≤R i When LSM3 is less than LMI, LMI=1, and the spore quantity increases to the maximum; when R i ≤LSM1 or R i When ≥LSM4, LMI=0 and the spore quantity growth is zero.

[0022] The method for predicting the disease index of southern corn rust described in some schemes, the formula for the adaptability index of southern corn rust is obtained according to the growth index equation of multiple piles of rust spores, and the formula for the adaptability index of southern corn rust is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area when the temperature and rainfall are different, and the adaptability index of southern corn rust in the area:

[0023] The formula for the adaptability index is:

[0024] SCREI = PPGI_a × PPSI_a;

[0025]

[0026]

[0027] Among them, TCS is the cold stress index related to temperature, THS is the heat stress index related to temperature, RWS is the wet stress index related to rainfall, and RDS is the dry stress index related to rainfall.

[0028] In some embodiments of the method for predicting the southern corn rust disease index, in which the diffusion index (disPPGI) is represented by an exponentially decreasing function equation, the exponentially decreasing function equation is represented as:

[0029] GI=1.662×exp( -1.315×WS );

[0030] Wherein, WS represents wind speed.

[0031] In some schemes, the method for predicting the disease index of southern corn rust is described, according to the condition that a rainfall event will cause multiple piles of stem rust spores to be deposited, and the concentration of multiple piles of stem rust spores and the daily average temperature in historical years are fitted to obtain the disease index prediction model:

[0032] The disease index prediction model is represented by the disease index fitting function equation:

[0033] SCRDI=70.94+90×(PPGI_w-disPPGI)-2.18×T average

[0034] Among them, T average Indicates the average daily temperature.

[0035] In a second aspect, the technical solution of the present application provides a computer-readable storage medium, in which program information is stored. After a computer reads the program information, the computer executes the method for predicting the southern rust disease index of corn described in any one of the first aspects.

[0036] In a third aspect, the technical solution of the present application provides an electronic device, comprising at least one processor and at least one memory, wherein at least one of the memories stores program information, and after reading the program information, at least one of the processors executes the method for predicting the southern rust disease index of corn described in any one of the first aspects.

[0037] In a fourth aspect, the technical solution of the present application provides a computer program product, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the steps of the method for predicting the southern rust disease index of corn described in any one of the first aspects are implemented.

[0038] The above technical solution has the following beneficial effects:

[0039] The above scheme provided by the present application comprises the following methods: predicting the seasonal epidemic progress of multiple piles of rust spores that cause southern rust, obtaining a multiple pile rust spore growth index equation, the multiple pile rust spore growth index equation is used to illustrate the influence of temperature and rainfall on the multiple pile rust spore growth index; obtaining a corn southern rust fitness index formula based on the multiple pile rust spore growth index equation, the corn southern rust fitness index formula is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area when the temperature and rainfall are different, and the corn southern rust fitness index in the area; according to the corn At the beginning of the inoculation season, the number of multiple piles of rust spores in the air remains unchanged, and diffusion events will occur when the wind speed exceeds 1 meter / second. As the cumulative diffusion events increase, the number of multiple piles of rust spores in the air follows the condition of negative exponential decrease with the change of wind speed. The data set of wind speed and multiple piles of rust spore growth index in the corn inoculation season in historical years is fitted to obtain the diffusion index of the multiple piles of rust spore growth index, and the diffusion index is expressed as an exponentially decreasing function equation; according to the conditions that rainfall events will cause the deposition of multiple piles of rust spores, the concentration of multiple piles of rust spores and the daily average temperature in historical years are fitted to obtain the disease index prediction model. Through the scheme of this application, the potential distribution range of southern corn rust can be predicted, the possible time and outbreak period of southern corn rust can be obtained, and technical support can be provided for precise application of pesticides. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for predicting a disease index of southern corn rust in one embodiment of the present application;

[0041] Figure 2 A schematic diagram of the hardware connection relationship of electronic equipment for executing a method for predicting a disease index of southern corn rust. DETAILED DESCRIPTION

[0042] The specific implementation of the present application is further described below with reference to the accompanying drawings.

[0043] It is easy to understand that according to the technical solution of the present application, without changing the essential spirit of the present application, a variety of structural modes and implementation modes that can be replaced by those skilled in the art can be replaced with each other. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present application, and should not be regarded as the entirety of the present application or as a limitation or restriction on the technical solution of the application.

[0044] The directional terms such as up, down, left, right, front, back, front, back, top, bottom, etc. mentioned or may be mentioned in this specification are defined relative to the structures shown in the drawings. They are relative concepts and may change accordingly according to different positions and different usage conditions. Therefore, these or other directional terms should not be interpreted as restrictive terms.

[0045] The present application embodiment provides a method for predicting the disease index of southern corn rust. Figure 1 As shown, including:

[0046] S10: Predicting the seasonal epidemic progress of multiple piles of Puccinia spores that cause southern corn rust, and obtaining a multiple pile of Puccinia spore growth index equation, wherein the multiple pile of Puccinia spore growth index equation is used to illustrate the influence of temperature and rainfall on the multiple pile of Puccinia spore growth index.

[0047] S20: obtaining a corn southern rust adaptability index formula according to the multiple piles of rust spore growth index equations, wherein the corn southern rust adaptability index formula is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area and the corn southern rust adaptability index in the area when the temperature and rainfall are different;

[0048] S30: According to the condition that the number of multiple piles of handle rust spores in the air remains unchanged from the beginning of the corn inoculation season, diffusion events will occur when the wind speed exceeds 1 m / s, and as the cumulative diffusion events increase, the number of multiple piles of handle rust spores in the air changes with the wind speed and follows a negative exponential decrease, fitting the data sets of wind speed and multiple piles of handle rust spore growth index in the corn inoculation season in historical years to obtain the diffusion index of the multiple piles of handle rust spore growth index, and expressing the diffusion index with an exponentially decreasing function equation;

[0049] S40: Based on the condition that rainfall events will lead to the deposition of multiple piles of Puccinia spores, the concentrations of multiple piles of Puccinia spores and the daily average temperature in historical years are fitted to obtain a disease index prediction model.

[0050] The above method flow is described in detail below with reference to specific examples.

[0051] (S10) predicting the seasonal epidemic progress of multiple piles of Puccinia spores that cause southern corn rust, and obtaining a multiple pile of Puccinia spore growth index equation, wherein the multiple pile of Puccinia spore growth index equation is used to illustrate the influence of temperature and rainfall on the multiple pile of Puccinia spore growth index.

[0052] This step is based on the first assumption: it is assumed that the germination of polypile rust spores is closely related to the eco-physiological parameters temperature index TI and leaf surface moisture LSM. By simulating temperature and rainfall, the optimal time window for the response of the potential spore amount of polypile rust spores in the air to climate is revealed. The weekly polypile rust growth index PPGI has a fine time scale, which can analyze the effects of temperature and rainfall on the prevalence dynamics of polypile rust spores in the air and the date of the first possible appearance. The weekly prevalence dynamics of polypile rust spores are represented by the polypile rust spore growth index PPGI_w, and the polypile rust spore growth index equation is as follows:

[0053] PPGI_w=TI_w×LMI_w

[0054] Among them, the weekly temperature index TI_w describes the response of polypile rust to the daily temperature cycle, which varies between 0 and 1. The spore development stage of polypile rust is represented by the area under the daily temperature curve AUDTC in the model. The temperature range most suitable for the survival of polypile rust is divided into four key parameters, namely the minimum temperature threshold AUDTC1, the minimum suitable temperature AUDTC2, the maximum suitable temperature AUDTC3, and the maximum temperature threshold AUDTC4. TI_w is calculated based on the value of the effect of the optimal temperature on the growth rate obtained by the shaded area S1 under the AUDTC1 to AUDTC4 curve and the shaded area S2 under the AUDTC1 and AUDTC2 curves, and the value of the growth rate reduced by high temperature obtained by the maximum temperature and key parameters. The specific conditions are as follows:

[0055] when:

[0056]

[0057] otherwise:

[0058]

[0059] In addition, the leaf surface moisture index LMI describes the response of the plant leaf surface to rainfall, and the leaf surface moisture is the dominant factor determining the germination of polypile rust. Assuming that the rainfall remains constant within 24 hours, the four parameters derived based on the leaf surface moisture have the same form as the temperature parameter, namely, the minimum moisture threshold LSM1, the minimum suitable moisture threshold LSM2, the maximum suitable moisture threshold LSM3 and the maximum moisture threshold LSM4. The weekly leaf surface moisture index LMI_w is calculated based on the leaf surface moisture stored in the previous week and the rainfall and evapotranspiration R of this week. i Calculated according to the Tetens equation (Tetens, 1930): LMI_w = (LSM (i-1) +R i -0.8) / 100; when LSM2≤R i When LSM3 is less than LMI, LMI=1, and the spore quantity increases to the maximum. i ≤LSM1 or ≥LSM4, LMI=0, and the spore quantity growth is zero.

[0060] (S20) A formula for a maize southern rust adaptability index is obtained based on a growth index equation for multiple piles of Puccinia spores, wherein the formula for a maize southern rust adaptability index is used to illustrate the adaptability of multiple piles of Puccinia spores to survive in a region when the temperature and rainfall in the region are different, as well as a maize southern rust adaptability index in the region.

[0061] Southern corn rust Ecoclimatic Index (SCREI), the SCREI value helps to gain a deeper understanding of the adaptability of climate change in different regions to the occurrence of southern corn rust, and is used to describe the changing trend of the potential distribution range of the disease. Therefore, this step is based on the second assumption: when adverse weather conditions affect the potential amount of multiple piles of handle rust spores, especially when related to temperature and rainfall, the effective stress value will accumulate. The temperature stress value and water stress value are evaluated using long-term weather data, combined with the multiple piles of handle rust spore growth index PPGI and the multiple piles of handle rust spores temperature and humidity stress index PPSI. Combining these parameters, the adaptability of multiple piles of handle rust spores to survive in a certain place is predicted. The formula for the adaptability index of the disease in the occurrence area simulated by this scheme is:

[0062] SCREI = PPGI_a × PPSI_a;

[0063]

[0064]

[0065] Among them, PPSI is considered by weather factors, including rainfall and temperature. Each factor is divided into four grades, forming four combinations respectively. The factors related to moisture refer to "wet" and "dry" on the leaf surface. The temperature levels are marked as "hot" and "cold". Therefore, PPSI consists of four stress indices: cold stress (TCS) and heat stress (THS) related to temperature, and wet stress (RWS) and dry stress (RDS) related to rainfall. These factors jointly affect the dynamics of Puccinia polysora spores. The thresholds used in this study were set according to the reference values reported by Ramirez-Cabral (2017). In this formula, if T min <TTCS(=0), then TCS is calculated as (TTCS - T min ) × 0.0001; otherwise, TCS = 0. If T max > TTHS(=34), then THS is calculated as (T max - TTHS) × 0.0002; otherwise, THS = 0. Similarly, if LSM < TRDS(=0.1), then the calculation formula for RDS is (TRDS - LSM) × 0.001; otherwise, RDS = 0. If LSM > TRWS(=0.1), then the estimation formula for RWS is (LSM - TRWS) × 0.01; otherwise, RWS = 0.

[0066] (S30) From the beginning of the maize inoculation season, the number of Puccinia polysora spores in the air remains constant. A diffusion event occurs when the wind speed exceeds 1 m / s. As the cumulative diffusion events increase, the number of Puccinia polysora spores in the air follows a negative exponential decrease with the change of wind speed. Fit the dataset of wind speed and Puccinia polysora spore growth index in the maize inoculation season of historical years to obtain the diffusion index of the Puccinia polysora spore growth index, and represent the diffusion index with an exponential decay function equation; According to the condition that rainfall events will cause the deposition of Puccinia polysora spores, fit the spore concentration and daily average temperature of historical years to obtain a disease index prediction model.

[0067] In this step, the third hypothesis is proposed that there are multiple piles of stem rust spores in the corn planting area from the beginning of the corn growing season. During the airborne transmission process, affected by the wind speed WS and wind direction WD under extreme weather conditions, the diffusion and propagation distance affect the local presence of some multiple piles of stem rust spores or only some spores infect the host. In this step, it is assumed that at the beginning of the inoculation season, the number of multiple piles of stem rust spores in the air remains unchanged, and a diffusion event will occur as long as the wind speed exceeds 1 m / s. As the cumulative diffusion events increase, the number of multiple piles of stem rust spores in the air follows a negative exponential decrease with the change in wind speed, that is, an increase or decrease in PPGI occurs in the current area. By fitting the WS and PPGI data sets of multiple occurrence seasons, the diffusion index disPPGI of PPGI is obtained and expressed as an exponentially decreasing function equation:

[0068] disPPGI=1.662×exp (-1.315×WS) ;

[0069] In addition, it is assumed that when each rainfall event (R ≥ 1 mm / d) occurs, a deposition event will occur in the multi-pile rust spores. The infection amount of the multi-pile rust spores is calculated from the first wet time when R ≥ 1. If R = 0, the infection amount of the multi-pile rust spores = 0, which is recorded as the termination of infection. The disease index prediction model was constructed after refitting based on the existing multi-pile rust spore concentration and daily average temperature forecast. The obtained SCRDI fitting function equation is as follows:

[0070] SCRDI=70.94+90×(PPGI_w-disPPGI)-2.18×T average .

[0071] The input parameters required in the model can be determined as follows: The meteorological data of 16 cities in Shandong Province from 2007 to 2022 are from the National Oceanic and Atmospheric Administration (NOAA) Ocean Integrated Surface Database and China Meteorological Data Network. The input parameter data sets include daily average temperature, maximum temperature and minimum temperature, precipitation, 2-meter and 10-meter wind speeds, and 10-meter wind direction. The weekly time series values ​​of the main climate variables from 2007 to 2022 were resampled using Python (3.9.13), and the weekly maximum temperature and precipitation data were extracted as the model training set.

[0072] The outputs of the model include (1) weekly simulations to simulate the growth dynamics of multiple stem rust. The annual PPGI values ​​are calculated to delineate the first appearance date of multiple stem rust and its growth dynamics throughout the year. (2) The SCREI is used to assess the climate suitability of southern corn rust and the potential distribution areas for high levels of southern corn rust. The comparison results are represented on the map by SCREI, ranging from 0 (unsuitable) to 100 (completely suitable). (3) The disease index prediction value SCRDI is used to assess the incidence of southern corn rust under environmental changes for real-time monitoring and analysis.

[0073] Application and verification of the disease index prediction model:

[0074] The model was validated and applied using meteorological data and field observations collected from 66 locations in 2023, which served as a test set for the model. This comprehensive procedure included validation and improvement of the model by integrating field observations. The main objective was to assess the consistency between the potential distribution of southern rust occurrence and the distribution of actual observed values. In addition, the prediction error compared to actual observations was minimized by quantifying the disease index within the prediction range. The relationship between the model output and the actual southern rust prevalence of corn was evaluated.

[0075] (1) Survey location

[0076] Shandong Province is located in eastern China and is an important area in the Huang-Huai-Hai region, with a maize planting area of ​​approximately 57.41 million mu in 2023. The diverse environmental conditions in the region have an important impact on the prevalence and spread of southern rust of maize, which is usually observed in the south and southwest. Historically, southern rust of maize has been an occasional disease of maize before 1998. However, local outbreaks of the disease occurred in 2007, 2008, 2015, 2017 and 2021, and after the national outbreak in 2015, sporadic outbreaks occurred every year, accompanied by an increasing trend in incidence. This application evaluated maize leaves infected with multiple piles of stem rust fungi in 66 maize-growing areas in 16 prefecture-level cities in Shandong Province. All evaluation sites were geolocated using a handheld global positioning system (GPS). All field management followed an integrated pest management (IPM) plan, including control measures and irrigation.

[0077] (2) Disease assessment

[0078] In 2023, an assessment was conducted during the grain filling stage of corn, using a five-point survey method to assess the number of diseased leaves and the severity of each diseased leaf. Ten plants were evaluated at each location, starting with the fourth leaf at the base of the corn and counting from bottom to top, with a total of 100 leaves surveyed at each location. The number of infected leaves and their corresponding severity were recorded visually. The classification of disease severity is based on the percentage of leaf area covered by multiple piles of rust spores, and is classified as follows 0, 1%, 5%, 10%, 20%, 40%, 60%, 80% and 100%. The disease index (DI) (0-100) is used as a measurement standard. The disease index obtained at each assessment point is a reliable indicator to describe the extent of southern rust on corn. Specifically:

[0079]

[0080]

[0081] Disease index (DI) = incidence × average severity × 100;

[0082] In this scheme, the SCREI value can be considered as a possible predictor of the occurrence of southern rust of corn during 2007-2022 by plotting the receiver operating characteristic (ROC) curve of the classification performance. The ROC curve shows the true positive proportion (TPP or sensitivity) and the false negative proportion (FNP or 1-specificity) at different SCREI cut-off values, representing the trade-off between sensitivity and specificity. The accuracy of the model is evaluated by the area under the cross-validation ROC curve AUC, and an AUC value close to 1 means that the model can predict the probability of high-level occurrence of southern rust of corn. The overall accuracy is calculated based on its 95% confidence interval. The larger the AUC value (>0.7), the better the performance in distinguishing between occurrence (=1) and non-occurrence (=0). The optimal cut-off value is determined by calculating the square of the distance between the upper left corner of the ROC space and any point on the curve, indicating the highest ratio between accurately classified occurrence cases and total occurrence cases. The model was validated using the southern rust of corn occurrence data during 2007-2022 using the Bayesian algorithm. SCREI values ​​were classified according to the model predictions of occurrence and non-occurrence using the optimal cutoff value derived from the ROC curve. The model predictions (P+ or P-) were compared with the observed values ​​(O+ or O-) to form a 2×2 table. In this case, PO- means that when SCREI < optimal cutoff value, the model predicted no infestation, but the disease was observed to be non-occurring; P+O+ means that when SCREI > optimal cutoff value, the model predicted infestation, but the disease was observed to be occurring. P+O- means that when SCREI > optimal cutoff value, the model predicted infestation, but the disease was observed to be non-occurring. P-O+ means that when SCREI < optimal cutoff value, the model predicted no infestation, but the disease was observed to be occurring. The consistency of the spatial distribution of SCREI, SCRDI and observed DI was compared using the inverse distance weighted interpolation analysis method of ArcGIS, and its values ​​were classified into a 0-100 level. In addition, the correlation between the independent variables "SCREI, SCRDI" (continuous independent variables X) and the variable "DI" (continuous dependent variable Y) was evaluated. To compare the accuracy and predictive ability of the models, the K-nearest neighbor algorithm (KNN) and the linear regression algorithm (LR) were used. The KNN algorithm was used to classify the SCREI to distinguish between high levels (=2) and low levels (=1) of southern rust incidence in corn. The LR algorithm was used to determine the predictive ability of the SCRDI in predicting DI. The evaluation of the goodness of fit relied on the coefficient of determination (R 2 ), accuracy, precision, and F1 score, which illustrate the degree of agreement between the simulated and observed datasets. Statistical significance (P < 0.05) is considered a high reliability indicator for evaluating the correspondence between the predicted and observed datasets.

[0083] The above-mentioned embodiment of the present invention provides a prediction model for the disease index of southern corn rust based on climate and environmental changes. The model integrates various weather variables that affect the spread of airborne diseases, and includes three main parts: (i) tracking the dynamics of seasonal spore spread; (ii) determining changes in the potential distribution range of the disease; and (iii) estimating the disease index. The model provided by the embodiment of the present invention fits the prediction factors reflecting the changing trend of airborne diseases through ecological and physiological parameters, and establishes an airborne disease prediction model to quickly and accurately predict the seasonal epidemic dynamics of airborne disease pathogens, the characteristics and distribution of seasonal outbreaks of diseases, and the prediction results, which effectively improves the accuracy of airborne disease monitoring prediction, provides a new tool for the development of early monitoring and early warning systems, and provides support for guiding the comprehensive monitoring and precise prevention and control strategies of crop airborne diseases.

[0084] The present application also provides an electronic device, such as Figure 2As shown, the electronic device includes at least one processor 201 and at least one memory 202, at least one of the memories 202 stores program information, and at least one of the processors 201 reads the program information and executes the method for predicting the disease index of southern rust of corn described in any one of the above method embodiments. The device may also include: an input device 203 and an output device 204. The processor 201, the memory 202, the input device 203 and the output device 204 can be connected in communication. The memory 202, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The processor 201 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 202, that is, the method for predicting the disease index of southern rust of corn provided by any of the above schemes is implemented. The memory 202 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the method for predicting the disease index of southern rust of corn, etc. In addition, the memory 202 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 202 may optionally include a memory remotely arranged relative to the processor 201, and these remote memories may be connected to a device for executing the method for predicting the disease index of southern corn rust 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 a combination thereof. The input device 203 may receive input user clicks, and generate signal inputs related to user settings and function controls of the method for predicting the disease index of southern corn rust. The output device 204 may include a display device such as a display screen. When the one or more modules are stored in the memory 202, when the one or more processors 201 are run, the method for predicting the disease index of southern corn rust in any of the above-mentioned method embodiments is executed.

[0085] The embodiment of the present application also provides a computer-readable storage medium, in which program information is stored. After the computer reads the program information, the method for predicting the southern corn rust disease index described in the above method embodiment is executed.

[0086] The embodiment of the present application also provides a computer program product, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the steps of the method for predicting the southern corn rust disease index described in the method embodiment are implemented.

[0087] As needed, the above technical solutions can be combined to achieve the best technical effect.

[0088] The above are only the principles and preferred embodiments of the present application. It should be noted that, for ordinary technicians in this field, on the basis of the principles of the present application, several other modifications can be made, which should also be considered as the protection scope of the present application.

Claims

1. A method for predicting the disease index of southern corn rust, characterized in that: include: Predicting the seasonal epidemic progress of multiple piles of rust spores that cause southern rust, and obtaining a multiple pile rust spore growth index equation, the multiple pile rust spore growth index equation is used to illustrate the effects of temperature and rainfall on the growth dynamics of multiple pile rust spores; A formula for the adaptability index of southern corn rust is obtained according to the growth index equation of multiple piles of rust spores, wherein the formula for the adaptability index of southern corn rust is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area and the adaptability index of southern corn rust in the area when the temperature and rainfall in the area are different; According to the condition that the number of multiple piles of stem rust spores in the air remains unchanged from the beginning of the corn inoculation season, diffusion events will occur when the wind speed exceeds 1 m / s, and as the cumulative diffusion events increase, the number of multiple piles of stem rust spores in the air changes with the wind speed and follows a negative exponential decrease, the data sets of wind speed and multiple piles of stem rust spore growth index in the corn inoculation season in historical years are fitted to obtain the diffusion index of the multiple piles of stem rust spore growth index, and the diffusion index is expressed as an exponentially decreasing function equation; According to the condition that rainfall events will lead to the deposition of multiple piles of Puccinia spores, the disease index prediction model was obtained by fitting the concentration of multiple piles of Puccinia spores and the daily average temperature in historical years. The method of predicting the seasonal epidemic progress of multiple piles of rust spores that cause southern rust disease obtains a multiple pile rust spore growth index equation, which is used to illustrate the influence of temperature and rainfall on the growth dynamics of multiple piles of rust spores, including: The equation for the growth index of multiple piles of Puccinia spores is as follows: ； in, PP GI_w represents the weekly epidemic dynamics of Puccinia multipilosa spores, TI_w represents the weekly temperature index, describing the response of Puccinia multipilosa to the daily temperature cycle, varying between 0 and 1; LMI_w represents the leaf surface wetness index of the current week, describing the response of the plant leaf surface to rainfall; TI_w is determined as follows: The development stage of multiple piles of handle rust spores is expressed as the area under the daily temperature curve; the temperature range most suitable for the survival of multiple piles of handle rust spores is divided into the lowest temperature threshold AUDTC1, the lowest suitable temperature AUDTC2, the highest suitable temperature AUDTC3 and the highest temperature threshold AUDTC4; TI_w is calculated based on the value of the effect of the optimal temperature on the growth rate of multiple piles of handle rust spores obtained by the shaded area S1 under the AUDTC1 to AUDTC4 curve and the shaded area S2 under the AUDTC1 and AUDTC2 curves and the value of the growth rate of multiple piles of handle rust spores reduced by high temperature obtained by the highest temperature and key parameters; T max Indicates the maximum temperature; when: otherwise: LMI_w is determined as follows: According to the condition that the rainfall remains constant within 24 hours, the minimum moisture threshold LSM1, the minimum suitable moisture threshold LSM2, the maximum suitable moisture threshold LSM3 and the maximum moisture threshold LSM4 are derived based on the moisture on the leaf surface; The leaf surface moisture index LMI_w of the current week is calculated based on the leaf surface moisture index LSM of the previous week. (i-1) and this week's rainfall evapotranspiration R i Calculation: LMI_w=(LSM (i-1) +R i -0.8) / 100; when LSM2≤R i When LSM3 is less than LSM3, LMI_w = 1, and the spore quantity increases to the maximum; when R i ≤LSM1 or R i When ≥LSM4, LMI_w=0, the spore quantity growth is zero; The formula for the adaptability index of southern corn rust is obtained according to the growth index equation of multiple piles of rust spores. The formula for the adaptability index of southern corn rust is used to illustrate the adaptability of multiple piles of rust spores to survive in a certain area when the temperature and rainfall are different, and the adaptability index of southern corn rust in the area: The formula for the adaptability index is: Among them, TCS is the cold stress index related to temperature, THS is the heat stress index related to temperature, RWS is the wet stress index related to rainfall, and RDS is the dry stress index related to rainfall; In expressing the diffusion index by an exponentially decreasing function equation, the exponentially decreasing function equation is expressed as: ； in, represents the diffusion index; WS represents the wind speed; According to the condition that rainfall events will lead to the deposition of multiple piles of Puccinia spores, the concentration of multiple piles of Puccinia spores and the daily average temperature in historical years are fitted to obtain the disease index prediction model: The disease index prediction model is represented by the disease index fitting function equation: Among them, T average Indicates the average daily temperature.

2. A computer-readable storage medium, characterized in that: The storage medium stores program information, and the computer executes the method for predicting the disease index of southern corn rust according to claim 1 after reading the program information.

3. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory, at least one of the memories stores program information, and at least one of the processors reads the program information and executes the method for predicting the southern corn rust disease index according to claim 1.

4. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for predicting the disease index of southern corn rust described in claim 1 are implemented.

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