CN114298418A - Wheat scab epidemic meteorological suitability degree grade prediction method and computer system - Google Patents
Wheat scab epidemic meteorological suitability degree grade prediction method and computer system Download PDFInfo
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Abstract
The invention discloses a prediction method of wheat scab epidemic meteorological suitability degree grade, which mainly comprises the steps of determining meteorological disease promotion indexes of wheat in each growth stage through historical meteorological data analysis of a wheat scab epidemic sample, and further establishing a prediction model of the wheat scab epidemic meteorological suitability degree grade, wherein the prediction model of the wheat scab epidemic meteorological suitability degree grade comprises a comprehensive scab index sub-model; and inputting future weather forecast data in the wheat field area range into a wheat scab epidemic weather suitability grade prediction model for prediction to obtain the future wheat scab epidemic weather suitability grade. The method is based on the comprehensive gibberellic disease index submodel and combines a refined lattice weather forecast product to realize real-time rolling prediction of the meteorological suitability level of the gibberellic disease in each place. According to the prediction result, the condition of controlling the meteorological phenomena is suitable for the disease occurrence area, the disease occurrence area and the disease occurrence time period in time, the optimal time and the optimal dosage of the pesticide are grasped, the disease defense timeliness of the wheat scab is improved, meanwhile, the harm of chemical pesticides is reduced, and the ecological benefit of wheat production is improved.
Description
Technical Field
The invention belongs to the technical field of agricultural hazard prediction, and particularly relates to a wheat scab epidemic meteorological suitability grade prediction method.
Background
Wheat scab is an epidemic disease caused by various fusarium, and the germs of the fusarium infect and invade from wheat floral organs to cause necrosis of conducting tissues, so that the grouting process is stopped, and the yield and the quality of wheat are influenced. In recent years, under the influence of climate change, gibberellic disease in major wheat producing areas has the characteristics of increased retransmission frequency and larger difference between years, and the wheat can be reduced by 20-50% in epidemic years, even the wheat is completely harvested, and the major hidden danger of influencing the high quality and yield of the wheat is formed.
The prevalence of wheat scab is closely related to meteorological conditions and has different effects at each growth stage. The influence of meteorological conditions on wheat scab can be divided into 3 stages: stage 1, before wheat heading, the formation and accumulation of ascospores and ascospores of gibberellic disease are mainly influenced; stage 2, in the heading-flowering period, the spreading and infection of ascospores are influenced; stage 3, after flowering, affects the extent of disease development. The disease occurrence can be prevented and controlled in advance by carrying out prediction on the epidemic grade of the gibberellic disease by utilizing a meteorological factor refined prediction result.
At present, the wheat scab epidemic meteorological suitability degree grade prediction research mainly utilizes longer scale meteorological factors and disease observation data in the early stage of heading and flowering, and adopts a statistical method to establish a regression model so as to predict the epidemic trend of wheat scab; or establishing an index to carry out gibberellic disease grade prediction based on the influence of the heading and flowering weather on the diseases. In the early-stage research, when the epidemic grade of the disease is predicted, only the influence of the spicing and blossoming weather on the disease is considered, the influence analysis of weather conditions before and after the critical period of the disease is less, statistical and deep learning methods are mostly adopted, the continuous influence of the weather conditions on the disease development is not considered, the mechanicalness is lacked, the day-by-day rolling prediction on the disease occurrence degree cannot be carried out, and the method has certain hysteresis on the mastering of the disease occurrence and development condition.
Disclosure of Invention
The invention provides a method for predicting the wheat scab epidemic meteorological suitability degree grade, which can accurately predict wheat scab epidemic meteorological suitability degree in real time aiming at the problems in the prior art.
In order to realize the purpose, the invention provides a method for predicting the wheat scab epidemic meteorological suitability level, which mainly comprises the following steps:
step 1: determining the meteorological disease promotion indexes of the wheat at each growth stage through the meteorological data analysis of the historical wheat scab epidemic, and further establishing a wheat scab epidemic meteorological suitability level prediction model;
step 2: inputting future weather forecast data in the wheat field area range into a wheat scab epidemic weather suitability grade prediction model for prediction to obtain a future wheat scab epidemic weather suitability grade;
the construction method of the wheat scab epidemic meteorological suitability grade prediction model comprises the following steps:
step 11: dividing the growth stage of wheat;
step 12: analyzing and determining the weather disease promotion indexes of each growth stage according to historical weather data of wheat scab epidemic samples; wherein the weather disease promotion indexes comprise daily average air temperature for promoting diseases and relative humidity for promoting diseases;
step 13: according to the formula:
respectively calculating the proper quantity D of the epidemic meteorological conditions of each monitoring day in each growth stage of the wheati,j: wherein i is a monitoring day number; j is the number of the growth stage of wheat, Ti,jRepresents the average air temperature of the wheat on the ith monitoring day of the jth growth stage; RH (relative humidity)i,jIndicating the relative humidity of the wheat on the ith monitoring day of the jth growth stage; t iso,jIs small in representationMean daily temperature of disease promotion in jth growth stage of wheat; RH (relative humidity)o,jIndicating the relative disease-promoting humidity of the jth growth stage of the wheat; di,jIndicating the appropriate amount of gibberellic disease epidemic meteorological conditions on the ith monitoring day of the jth growth stage of the wheat;
step 14: historical meteorological data and formula combined with wheat scab epidemic samples
Constructing a comprehensive gibberellic disease index Z sub-model; wherein n is1Representing the total monitoring day of the booting-heading stage of the wheat; n is2Represents the total monitoring day of the heading-flowering stage of wheat; n is3Represents the total monitored days of the flowering-maturity stage of wheat;
and
respectively accumulating proper amount of disease epidemic meteorological conditions at each growth stage of the wheat; alpha, beta and gamma are weight coefficients of the meteorological factors in three different growth stages influencing the final disease occurrence;
step 15: respectively calculating corresponding comprehensive gibberellic disease indexes Z according to the meteorological data of each wheat scab epidemic sample; then, the final ear disease rate of each wheat scab epidemic sample is combined to divide the grade of the scab epidemic meteorological suitability; obtaining a comprehensive gibberellic disease index range corresponding to each disease degree; and completing the construction of a wheat scab epidemic meteorological suitability degree grade prediction model.
Further, the method for inputting the future weather forecast data into the established wheat scab epidemic weather suitability grade prediction model for prediction in the step 2 comprises the following steps:
step 21: determining a growth stage according to the growth state of the wheat;
step 22: according to the formula:
predicting the appropriate amount of future annual gibberellic disease epidemic meteorological conditions;
step 23: calculating a future comprehensive gibberellic disease index Z according to the comprehensive gibberellic disease index Z sub-model;
step 24: and obtaining the epidemic degree of the gibberellic disease on the corresponding day according to the comprehensive gibberellic disease index Z obtained in the step 23.
Further, the growth stage of wheat is divided into three stages which are respectively: booting-heading, heading-flowering, flowering-mammary maturation. Therefore, the deviation caused by judging the occurrence of the diseases by using a single index in the whole disease monitoring period can be effectively avoided.
Further, the average disease promotion temperature is the average value of the daily average temperature of the sample with the final ear disease rate of more than 1% in each growth stage; the relative humidity of the disease promotion is the mean value of the relative humidity of the sample with the final ear disease rate of > 1% in each breeding stage.
Further, the method for determining the weight coefficients of the three different growth stages of which the meteorological factors influence the final disease occurrence comprises the following steps: continuously adjusting the values of alpha, beta and gamma, wherein the value ranges of the alpha, the beta and the gamma are 0-1, and the constraint condition is that the value ranges of the alpha, the beta and the gamma are 1.0; setting the adjustment step length of the parameters alpha, beta and gamma to be 0.01; and inputting each wheat scab epidemic sample into a comprehensive scab index Z submodel to respectively calculate a comprehensive scab index Z until the correlation coefficient of the comprehensive scab index Z and the final ear disease rate of the corresponding wheat scab epidemic sample reaches the maximum, and obtaining the optimal values of alpha, beta and gamma. The influence difference of meteorological factors at different growth stages on the diseases and the disease harmful effect accumulation are considered, and the dynamic process of continuous occurrence and development of the diseases is better reflected.
Further, the wheat field is refined into grid points, and the future weather forecast of each grid point is obtained respectively. The result of prediction is more accurate. Further, the grid points are squares of 3km × 3 km.
The present invention also provides a computer readable medium storing software comprising instructions executable by one or more computers, the instructions causing the one or more computers to perform operations comprising the flow of the method for predicting a wheat scab epidemic weather suitability level as described above.
The present invention also provides a computer system comprising:
one or more processors;
a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising a flow of a wheat scab epidemic weather suitability rating prediction method as previously described.
The working principle is as follows: according to the occurrence climate characteristics of scab diseases in the past year, three growth stages of booting-heading, heading-flowering and flowering-maturity in wheat growth are combined to determine a disease promotion index, and from disease promotion meteorological conditions of scab, influence differences of meteorological factors on the diseases and disease damage effect accumulation of different growth stages are considered, so that a comprehensive scab index sub-model is constructed; and predicting the wheat scab epidemic meteorological suitability grade by combining the comprehensive scab index submodel with meteorological prediction data.
Has the advantages that: compared with the prior art, the method disclosed by the invention realizes real-time rolling prediction of the meteorological suitability level of the gibberellic disease in each place based on the comprehensive gibberellic disease index and by combining with a refined lattice weather forecast product. According to the prediction result, the condition of controlling the meteorological phenomena is suitable for the disease occurrence area, the disease occurrence area and the disease occurrence time period in time, the optimal time and the optimal dosage of the pesticide are grasped, the disease defense timeliness is improved, the chemical pesticide harm is reduced, and the wheat production ecological benefit is improved; meanwhile, the invention effectively avoids the deviation caused by judging the occurrence of the diseases by using a single index in the whole disease monitoring period, and greatly improves the accuracy of prediction. Furthermore, considering that the occurrence and development of the head blight disease are a gradually aggravating process, the comprehensive head blight index submodel constructed by the invention is expressed in a weighted summation mode of accumulating appropriate amount of head blight epidemic meteorological conditions at each growth stage of the wheat, and can better reflect the trend of gradual rise of the head disease rate and the change of the disease grade.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b): the embodiment discloses a method for predicting the wheat scab epidemic meteorological suitability degree grade, which mainly comprises the following steps:
step 1: a wheat scab epidemic meteorological appropriateness grade prediction model is established through meteorological data of historical wheat scab epidemic. The method specifically comprises the following steps:
step 11: and (5) dividing the growth stage. According to the influence of meteorological conditions on the occurrence of gibberella zeae in different stages, the growth stage of wheat is divided into 3 stages of booting-ear extraction, ear extraction-flowering and flowering-milk maturity.
Step 12: determining the weather disease promotion indexes in each growth stage. And determining the wheat scab epidemic sample according to the wheat growth period and the final scab rate observation result of the wheat at different sites and in different years by using the meteorological historical data. Respectively calculating the mean value of daily average temperature and the mean value of relative humidity of each growth stage of the wheat scab epidemic sample, analyzing the climate background when the scab occurs, and determining the meteorological index value T suitable for the scab occurrence in each growth stageo,j、RHo,j. Wherein, To,jThe mean temperature of the disease promotion day of the jth growth stage of the wheat is represented; RH (relative humidity)o,jIndicating the relative disease-promoting moisture of the j growth stage of wheat. In meteorological data, one by oneThe daily air temperature values include daily average air temperature, daily maximum air temperature, daily minimum air temperature, and the like. The daily average air temperature is commonly calculated by daily maximum and minimum air temperature averages, 4 time averages (i.e., 02-, 08-, 14-, and 20-hour averages), and 24-hour averages. The average daily average air temperature is the average daily average air temperature of all days in a certain period. When the plant protection department carries out systemic investigation of the ear disease rate, the ear disease rate is observed every day from the beginning of heading, the ear disease rate is investigated every 3 days after the ear disease begins to appear, the ear disease rate is generally in the wheat wax ripening stage when the condition of the disease is stable, and the ear disease rate investigated at this time is the final ear disease rate.
The history data used in this embodiment is: in 2000-2015, observation data of the final ear disease rate and occurrence area of 8 wheat scab disease observation sites are obtained, namely 128 wheat scab epidemic samples; and in year 2000-2015, correspondingly observing the wheat growth period observation data and daily meteorological observation data of the sites, wherein the daily meteorological observation data mainly comprise daily average air temperature and relative humidity. Selecting a sample with the final ear disease rate of more than 1%, and respectively counting the mean values of daily average air temperature and relative humidity of all days in the bearing stages of 3 wheat bearing of pregermination-heading, heading-flowering and flowering-maturity to obtain the gibberellic disease promotion indexes of the wheat in different bearing stages as shown in table 1.
TABLE 1 wheat growth stages gibberellic disease promotion index
Step 13: respectively calculating the appropriate amount of the epidemic gibberellic disease meteorological conditions of each monitoring day in each birth stage sample according to a formula:
wherein i is a monitoring day number; j is the number of the growth stage of the wheat, namely j is 1 to represent the booting stage and the heading stage of the wheat, j is 2 to represent the heading stage and the flowering stage of the wheat, and j is 3 to represent the flowering stage and the milk stage of the wheat; t isi,jRepresents the average air temperature on the ith monitoring day of the jth growth stage of the wheat; RH (relative humidity)i,jRepresents the relative humidity on the ith monitoring day of the jth growth stage of wheat; di,jIndicating the appropriate amount of gibberellic disease epidemic on the ith monitoring day of the jth growth stage of wheat.
Step 14: and constructing a comprehensive gibberellic disease index Z model. Combining the germination, infection, latent breeding and symptom development processes of disease germs, and constructing a single-point comprehensive gibberellic disease index Z as follows:
wherein n is1Representing the total monitoring day of the booting-heading stage of the wheat; n is2Represents the total monitoring day of the heading-flowering stage of wheat; n is3Represents the total monitored days of the flowering-maturity stage of wheat;
and
respectively accumulating proper amount of disease epidemic meteorological conditions at each growth stage of the wheat; alpha, beta and gamma are weight coefficients of the meteorological factors in three different growth stages influencing the final disease occurrence, and are determined by utilizing an optimization technology.
In the embodiment, according to the meteorological historical data of the sample with the final ear disease rate of more than 1%, the optimal values of alpha, beta and gamma are determined; wherein the value ranges of alpha, beta and gamma are 0-1, and the constraint condition is that alpha + beta + gamma is 1.0; setting the adjustment step length of the parameters alpha, beta and gamma to be 0.01; and respectively inputting the meteorological historical data sequences of the wheat scab epidemic samples with the final scab rate of more than 1% into a comprehensive scab index Z model, and obtaining the optimal values of alpha, beta and gamma when the correlation coefficients of the comprehensive scab index Z and the corresponding final scab rate reach the maximum by adjusting the adjustment step lengths of parameters alpha, beta and gamma. Thereby completing the construction of the comprehensive gibberellic disease index Z model.
Step 15: and (4) grading the prevalence degree of the gibberellic disease. Respectively calculating the comprehensive gibberellic disease index Z of each wheat scab epidemic sample according to the comprehensive gibberellic disease index Z model constructed in the step 14 by utilizing historical meteorological observation and paired disease observation data, and pairing the comprehensive gibberellic disease index Z of the obtained wheat scab epidemic sample with the corresponding final head rate of scab; and dividing the grade of the incidence degree of the head blight according to the range of the final head blight rate of the head blight, and obtaining the value ranges of the comprehensive head blight indexes Z corresponding to different grades of the incidence degree of the head blight by combining the relationship between the final head blight rate of the head blight and the corresponding comprehensive head blight index Z.
In this embodiment, the final head rate of scab of each site per year, that is, the head rate of 128 historical samples in total, is sorted from small to large; the comprehensive head blight indexes Z of each site every year are combined with the corresponding head blight final disease rates to be sequenced, namely the comprehensive head blight indexes Z calculated by 128 historical samples are sequenced according to the final head disease rates of the corresponding samples; and (3) dividing the grade of the incidence degree of the head blight disease of the root according to the range of the final head blight rate of the head scab, and combining the relationship between the final head blight rate of the head scab and the corresponding comprehensive head scab index Z to obtain the value ranges of the comprehensive head scab indexes corresponding to different head scab incidence degree grades. Thereby completing the construction of the wheat scab epidemic meteorological suitability degree grade prediction model.
The division of the disease occurrence degree refers to the technical Specification for wheat scab prediction, and the disease occurrence degree is divided into three grades of mild occurrence, moderate occurrence and severe occurrence according to the final ear disease rate threshold corresponding to 3 grades of disease occurrence degree, wherein the corresponding final ear Disease Rate (DR) ranges are that DR is less than or equal to 10%, DR is less than or equal to 40% within 10% and DR is more than 40%.
The specific division result according to the employed history data in this embodiment is shown in table 2.
TABLE 2 gibberellic disease epidemic weather suitability rating
Step 2: inputting the weather forecast data of the wheat field for several days in the future into the established wheat scab epidemic weather suitability grade prediction model for prediction to obtain the wheat scab epidemic weather suitability grade for several days in the future; the method specifically comprises the following steps:
step 21: determining a growth stage according to the growth state of the wheat;
step 22: refining the wheat field into grid points, respectively obtaining the future weather forecast of each grid point, and obtaining the future weather forecast of each grid point according to a formula
j＝1,2,3,Ti,j＞To,j,RHi,j＞RHo,jPredicting the appropriate amount of future daily epidemic meteorological conditions; the grid points for thinning the wheat field in this example are squares of 3km × 3 km.
Step 23: calculating the comprehensive gibberellic disease index Z of the next days according to the comprehensive gibberellic disease index Z sub-model;
step 24: and obtaining the corresponding gibberellic disease epidemic meteorological suitability grade according to the comprehensive gibberellic disease index Z obtained in the step 23 for several days in the future. Thereby completing the prediction of the degree of suitability of the future gibberellic disease epidemic meteorological phenomena for several days. And realizing refined weather suitability degree rolling prediction of disease occurrence in the region.
In the actual forecasting, once the wheat enters the booting stage, the disease occurrence condition is monitored and forecasted, the actual condition and the forecast result of the meteorological elements such as daily average air temperature, relative humidity and the like required by the pathogenic day are extracted and judged according to the meteorological element observation actual condition and the weather forecast product, and the comprehensive gibberellic disease index value is calculated, so that the meteorological suitability level and the possible popularity degree of the gibberellic disease are dynamically monitored until the milk stage is monitored.
Dynamic monitoring, namely updating the forecast time day by day, and judging the meteorological condition suitability degree and the occurrence grade of the future scab by continuously accumulating and analyzing the pathogenic meteorological conditions starting from the monitoring period; meanwhile, by continuously and alternately utilizing the latest forecast products and actual observation data, the forecast accuracy of the weather forecast product forecast aging in a period of 7 days can be improved day by day.
The method for predicting the wheat scab epidemic weather suitability grade provided by the invention is used for calculating the 2016 scab epidemic weather suitability grade.
In 2016, head blight has been generally predominant to be prevalent. Most of the diseases in Huainan are very popular, and the diseases in Huabei are moderate. A refined forecast product with the spatial resolution of 0.03 degrees is formed by finely integrating a 'Rayleigh' RIOF (refined Integration Optimal forecast) of an Optimal forecast system by the Jiangsu provincial weather bureau.
According to the dynamic prediction result of the annual suitability grade of head blight in 2016, 5, month and 20 days:
the meteorological conditions in the last 5 months are very suitable for the rapid development and aggravation of wheat scab. According to the results of the classification of the epidemic meteorological suitability of the gibberellic disease in the table 2, the comprehensive gibberellic disease index of the western region between Jiang Sunan and Jiang Huai in 27 days after 5 months is more than 9, the meteorological suitability grade is 3 grade, and the gibberellic disease of wheat is severe.
According to the survey of plant protection stations: disease spikes are found in the early 5 months, in Sunan and along the river; in ten days in the middle of the month 5, the disease ears are generally checked in each wheat area of the whole province, and the disease ear rate is 10-15%; in the last ten days of 5 months, the wheat area in the south of Huaihe river of China in the filling period is subjected to a plurality of rainfall processes, which is very beneficial to the spreading of the gibberellic disease. The disease condition is fast to rise, the ear disease rate is monitored and investigated to 50% -65% by a field fixing and seedling fixing system in 30 days after 5 months, and the ear disease rate of gibberellic disease exceeds 70% in systematic observation fields and test field control areas in Sunan and Yangtze river regions.
Therefore, the degree of suitability of the gibberellic disease determined by the method provided by the invention is basically consistent with the actual occurrence condition of the wheat scab.
The present invention also provides a computer readable medium storing software comprising instructions executable by one or more computers, the instructions causing the one or more computers to perform operations comprising the flow of the method for predicting a wheat scab epidemic weather suitability level as described above.
The present invention also provides a computer system comprising: one or more processors; a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising the flow of the wheat scab epidemic weather suitability rating prediction method as previously described.
It should be understood that the foregoing examples of the method for predicting the degree of suitability of wheat scab for epidemic weather may be embodied in any computer system having data storage and data processing, and the foregoing computer system may be at least one electronic processing system or electronic device including a processor and a memory, such as a PC computer, whether a personal PC computer, a commercial PC computer, or a graphic processing PC computer, a server-level PC computer. These PC computers realize wired and/or wireless data transmission by having a data interface and/or a network interface.
In other embodiments, the computer system may also be a server, especially a cloud server, having data storage, processing, and network communication functions.
An exemplary computer system typically includes at least one processor, memory, and a network interface connected by a system bus. The network interface is used to communicate with other devices/systems.
The processor is used to provide the calculation and control of the system.
The memory includes non-volatile memory and a cache.
The non-volatile memory, which typically has mass storage capability, may store an operating system and a computer program that may include instructions that are operable, when executed by the one or more processors, to enable the one or more processors to perform the processes of the wheat scab epidemic weather suitability rating prediction method of the aforementioned embodiments of the present invention.
In a desirable or reasonable implementation, the computer system, whether a PC device or a server, may include more or less components than those shown, or may be combined, or different components such as different hardware and software may be used, or may be deployed in different manners.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (9)
1. A wheat scab epidemic meteorological suitability degree grade prediction method is characterized by comprising the following steps: the method mainly comprises the following steps:
step 1: determining the meteorological disease promotion indexes of the wheat at each growth stage through the meteorological data analysis of the historical wheat scab epidemic, and further establishing a wheat scab epidemic meteorological suitability level prediction model;
step 2: inputting future weather forecast data in the wheat field area range into a wheat scab epidemic weather suitability grade prediction model for prediction to obtain a future wheat scab epidemic weather suitability grade;
the construction method of the wheat scab epidemic meteorological suitability grade prediction model comprises the following steps:
step 11: dividing the growth stage of wheat;
step 12: analyzing and determining the weather disease promotion indexes of each growth stage according to historical weather data of wheat scab epidemic samples; wherein the weather disease promotion indexes comprise daily average air temperature for promoting diseases and relative humidity for promoting diseases;
step 13: according to the formula:
respectively calculating the proper quantity D of the epidemic meteorological conditions of each monitoring day in each growth stage of the wheati,j: wherein i is a monitoring day number; j is the number of the growth stage of wheat, Ti,jRepresents the average air temperature of the wheat on the ith monitoring day of the jth growth stage; RH (relative humidity)i,jIndicating the relative humidity of the wheat on the ith monitoring day of the jth growth stage; t iso,jRepresents the jth of wheatMean daily temperature of the fertile phase; RH (relative humidity)o,jIndicating the relative disease-promoting humidity of the jth growth stage of the wheat; di,jIndicating the appropriate amount of gibberellic disease epidemic meteorological conditions on the ith monitoring day of the jth growth stage of the wheat;
step 14: historical meteorological data and formula combined with wheat scab epidemic samples
Constructing a comprehensive gibberellic disease index Z sub-model; wherein n is1Representing the total monitoring day of the booting-heading stage of the wheat; n is2Represents the total monitoring day of the heading-flowering stage of wheat; n is3Represents the total monitored days of the flowering-maturity stage of wheat;
and
respectively accumulating proper amount of gibberellic disease epidemic meteorological conditions in each growth stage of the wheat; alpha, beta and gamma are weight coefficients of the meteorological factors in three different growth stages influencing the final disease occurrence;
step 15: respectively calculating corresponding comprehensive gibberellic disease indexes Z according to the meteorological data of each wheat scab epidemic sample; then, the final ear disease rate of each wheat scab epidemic sample is combined to divide the grade of the scab epidemic meteorological suitability; obtaining a comprehensive gibberellic disease index range corresponding to each disease degree; and completing the construction of a wheat scab epidemic meteorological suitability degree grade prediction model.
2. The method for predicting the wheat scab epidemic meteorological suitability degree grade according to claim 1, characterized in that: the method for inputting the future weather forecast data into the established wheat scab epidemic weather suitability degree prediction model for prediction in the step 2 comprises the following steps:
step 21: determining a growth stage according to the growth state of the wheat;
step 22: according to the formula:
predicting the appropriate amount of future annual gibberellic disease epidemic meteorological conditions;
step 23: calculating a future comprehensive gibberellic disease index Z according to the comprehensive gibberellic disease index Z sub-model;
step 24: and obtaining the epidemic degree of the gibberellic disease on the corresponding day according to the comprehensive gibberellic disease index Z obtained in the step 23.
3. The method for predicting the wheat scab epidemic meteorological suitability degree grade according to claim 1, characterized in that: the growth stage of wheat is divided into three stages which are respectively: booting-heading, heading-flowering, flowering-mammary maturation.
4. The method for predicting the wheat scab epidemic meteorological suitability degree grade according to claim 1, characterized in that: the average disease promotion temperature is the average value of the daily average temperature of the sample with the final ear disease rate of more than 1% in each growth stage; the relative humidity of the disease promotion is the mean value of the relative humidity of the sample with the final ear disease rate of > 1% in each breeding stage.
5. The method for predicting the wheat scab epidemic meteorological suitability degree grade according to claim 1, characterized in that: the method for determining the weight coefficients of the three different growth stage meteorological factors influencing the final disease occurrence comprises the following steps: continuously adjusting the values of alpha, beta and gamma, wherein the value ranges of the alpha, the beta and the gamma are 0-1, and the constraint condition is that the value ranges of the alpha, the beta and the gamma are 1.0; setting the adjustment step length of the parameters alpha, beta and gamma to be 0.01; and inputting each wheat scab epidemic sample into a comprehensive scab index Z submodel to respectively calculate a comprehensive scab index Z until the correlation coefficient of the comprehensive scab index Z and the final ear disease rate of the corresponding wheat scab epidemic sample reaches the maximum, and obtaining the optimal values of alpha, beta and gamma.
6. The method for predicting the wheat scab epidemic meteorological suitability degree grade according to claim 1, characterized in that: and refining the wheat field into grid points, and respectively acquiring future weather forecasts on each grid point.
7. The method for predicting the wheat scab epidemic meteorological suitability degree grade according to claim 6, characterized in that: the grid points are squares of 3km x 3 km.
8. A computer-readable medium storing software, the software comprising instructions executable by one or more computers, the instructions by such execution causing the one or more computers to perform operations comprising the flow of the method of predicting a wheat scab epidemic weather suitability level according to any one of claims 1-7.
9. A computer system, comprising:
one or more processors;
a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising the flow of the wheat scab epidemic weather suitability rating prediction method of any one of claims 1-7.
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| CN115166866A (en) * | 2022-07-01 | 2022-10-11 | 广东省韶关市气象局 | Citrus disease and insect pest occurrence forecasting method and system based on lattice point meteorological data |
| CN115166866B (en) * | 2022-07-01 | 2024-03-15 | 广东省韶关市气象局 | Citrus pest occurrence forecasting method and system based on lattice meteorological data |
| CN116028834A (en) * | 2023-02-28 | 2023-04-28 | 中国农业科学院植物保护研究所 | Wheat scab prediction method based on XGBoost algorithm |
| CN116028834B (en) * | 2023-02-28 | 2023-12-12 | 中国农业科学院植物保护研究所 | Wheat scab prediction method based on XGBoost algorithm |
| CN116523149A (en) * | 2023-07-04 | 2023-08-01 | 中化现代农业有限公司 | Method and device for predicting appropriate period for preventing and controlling tiny pests, electronic equipment and storage medium |
| CN116523149B (en) * | 2023-07-04 | 2023-10-31 | 中化现代农业有限公司 | Method and device for predicting appropriate period for preventing and controlling tiny pests, electronic equipment and storage medium |
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