Dynamic prediction method of tea green leafhopper pest based on meteorological and insect monitoring data

Abstract

The present invention discloses a method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data. The method comprises the following steps: Step 1, obtaining data and constructing a model data set. Step 2, calculating the pest pressure index. The pest pressure index includes a pressure value and a degree value. The pressure value is a comprehensive pressure value of the occurrence of tea green leafhopper pests at the current time and the future pest development trend; the degree value is the risk level of the occurrence of tea green leafhopper pests at the current time and the future pest development trend. Step 3, according to the size of the pressure value and the degree value, the risk of pest outbreak in the measured area at present and in the next few days is evaluated. The present invention is based on the historical green leafhopper pest pressure in the measured area, combined with meteorological data and insect population density monitoring data, to obtain the risk of tea green leafhopper pest occurrence on the current day and in the next few days in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological disaster prediction, and specifically relates to a business-based insect pest prediction, which uses time-continuous meteorological information data and real-time monitoring data of insect conditions as input to establish a meteorological prediction model for tea green leafhopper pests, and realizes a prediction method for dynamic early warning of the development trend of tea green leafhopper pests. Background Art

[0002] Tea pests and diseases seriously limit the quality and yield of tea in my country. The tea green leafhopper is one of the most common and difficult to control pests in my country's tea gardens, posing a serious threat to the development of the tea industry. Effective early warning of the tea green leafhopper is of great significance for guiding the scientific and green prevention and control of pests. The occurrence and development process of the tea green leafhopper is significantly affected by meteorological factors. At present, other methods for predicting the tea green leafhopper are mostly based on expert experience and simple statistical analysis. They can only make an overall prediction of the annual occurrence of pests. It is a "static" prediction and cannot give dynamic prediction results on the occurrence and development trend of pests. It is usually unable to effectively reflect the impact of the changing process of meteorological factors on the development trend of pests. Therefore, the versatility of the model has certain limitations, which greatly limits the guiding role of the prevention and control of the tea green leafhopper.

[0003] This method combines the process dynamic data of meteorology and insect quantity monitoring to construct a model that can realize the dynamic prediction of the development trend of tea green leafhopper pests, overcomes the problems of traditional pest prediction dominated by experience, poor versatility and poor timeliness, and provides technical support for the scientific prevention and control guidance of tea green leafhopper. Summary of the invention

[0004] The purpose of the present invention is to provide a method for dynamically predicting tea green leafhopper pests based on meteorological and insect monitoring data for the commercial production of the tea industry.

[0005] The specific steps of the present invention are as follows:

[0006] Step 1: Get data and build a model dataset

[0007] 1-1. Obtain the occurrence pressure value of green leafhopper pests in different months in the measured area

[0008] According to the occurrence of green leafhopper pests in the measured area from March to July in previous years, the occurrence pressure value mPress in different months is determined t ; t=3,4,5,6,7,8,9,10,11. Occurrence pressure value mPress t The value range is 0 to 100.

[0009] 1-2. Calculate the historical daily average temperature averTemp within the preset time before the current date n, historical daily cumulative rainfall averRain n , the forecast daily average temperature foreTemp within the preset time after the current date n , forecast daily cumulative rainfall foreRain n .

[0010] 1-3. Real-time detection of the density data of the green leafhopper in the measured area C n .

[0011] Step 2: Calculate the pest pressure index.

[0012] Pest pressure index includes pressure value n and degree n Among them, the pressure value porecast n is the comprehensive pressure value of the current occurrence of tea green leafhopper pests and the future pest development trend, and its expression is shown in formula (3.1); degree value degreed n It is the risk level of the current occurrence of tea green leafhopper pests and future pest development trends, and its expression is shown in formula (3.2).

[0013] pressure n =mPress t +T(averTemp n )+R(averRain n )+FT(foreTemp n )+FR(foreRain n )+k*C n (3.1)

[0014]

[0015] Among them, mPress t represents the occurrence pressure value of the month corresponding to the current time, and k is the insect population density data C of the tea garden monitored in real time n The proportionality factor. 1 ,x 2 ,x 3 They represent three tea green leafhopper pest pressure thresholds that increase in sequence. n )、R(averRain n )、FT(foreTemp n )、FR(foreRain n) are the historical temperature adjustment parameter, historical rainfall adjustment parameter, forecast temperature adjustment parameter, and forecast rainfall adjustment parameter, and their expressions are shown in equations (3.3), (3.4), (3.5), and (3.6), respectively.

[0016]

[0017]

[0018]

[0019]

[0020] Among them, G 1 ,G 2 ,G 3 The historical temperature adjustment parameters T(averTemp n ) three alternative values; G 1 and G 3 are all positive numbers, and G 1 >G 3 ; G 2 Is a negative number. 1 ,FG 2 ,FG 3 are respectively the forecast temperature adjustment parameter FT(foreTemp n ) three alternative values; FG 1 and FG 3 are all positive numbers, and FG 1 >FG 3 ; FG 2 is a negative number. 1 ,H 2 ,H 3 are the historical rainfall adjustment parameters R(averRain n ) three alternative values; H 1 and H 3 are all positive numbers, and H 1 >H 3 ;H 2 FH is a negative number. 1 ,FH 2 ,FH 3 They are respectively the forecast rainfall adjustment parameter FR (foreRain n ) three alternative values; FH 1 and FH 3 are all positive numbers, and FH 1 >FH 3 ; FH 2 A negative number. ST 1 、ST 2are the lower and upper limits of the suitable temperature range for the growth of tea green leafhoppers; T max 、T min are the upper and lower limits of the temperature range in which the tea green leafhopper can survive. 1 , SR 2 are the lower and upper limits of the daily cumulative rainfall range suitable for the growth of tea green leafhoppers; R max , R min They respectively represent the upper and lower limits of the survivable rainfall range for the tea green leafhopper.

[0021] Step 3: According to the pressure value n and degree n The size of the pest is used to assess the risk of pest outbreaks in the monitored area currently and in the next few days.

[0022] Preferably, the pest pressure index is updated once a day from March 1 to November 30 of a year to achieve real-time assessment of the risk of pest outbreak.

[0023] As a preferred option, the pressure values ​​mPress in different months t The values ​​were obtained by distributing questionnaires and conducting interviews with tea plantation growers and managers in the surveyed areas.

[0024] Preferably, the preset duration in step 1-2 is 15 days.

[0025] As a preference, ST 1 The value of ST is 18℃~22℃; 2 The value of T is 25℃~30℃; max The value is 35℃~40℃; T min The value is 0℃~10℃; SR 1 The value is 5mm~10mm; SR 2 The value is 20mm~30mm; R max The value is 30mm~40mm; R min The value ranges from 0mm to 5mm.

[0026] As a preference, ST 1 The value of ST is 20℃; 2 The value of T is 28℃; max The value is 38℃; T min The value is 5℃; SR 1 The value is 8mm; SR 2 The value is 25mm; R max The value is 35mm; R min The value is 3mm.

[0027] As a preference, x1 =30,x 2 =50,x 3 =70,G 1 =FG 1 =3,G 2 =FG 2 =-3,G 3 =FG 3 =1,H 1 =FH 1 =3,H 2 =FH 2 =-3,H 3 =FH 3 =1,k=2.

[0028] As a preference, the degree value n When the value of is 0, 1, 2, and 3, it means no pest occurrence, slight pest risk, moderate pest risk, and severe pest risk, respectively.

[0029] The beneficial effects of the present invention are:

[0030] 1. The present invention is based on the historical pest pressure of tea green leafhoppers in the measured area, combined with meteorological data and insect population density monitoring data, to predict the occurrence and development risk of tea green leafhopper pests in continuous phases, and can provide timely and reliable technical support for scientific pest prevention and control guidance with high timeliness.

[0031] 2. This method is simple in form, which greatly alleviates the complex training and calculation process of current pest and disease prediction. The model has strong versatility and provides an efficient data analysis method for the commercial development of pest and disease prediction systems in specific scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a graph showing changes in the pressure value of tea green leafhopper pests in one year in an embodiment of the present invention;

[0033] Figure 2 This is a graph showing changes in the degree of occurrence of tea green leafhopper pests in one year in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described below in conjunction with the accompanying drawings.

[0035] The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data comprises the following steps:

[0036] Step 1: Model effective use time

[0037] The tea leafhoppers hibernate as adults under tea bushes or on weeds in tea gardens. When the temperature gradually rises in March and the new shoots of tea trees begin to grow, the overwintering adults begin to move and lay eggs one after another, and the population of tea leafhoppers in the tea garden begins to grow. In December, the temperature begins to drop and the rainfall decreases, which is not suitable for the growth and development of tea leafhoppers, and the insect population also remains at a low level. Therefore, in order to fully and effectively utilize the early warning model and reduce losses, the model only runs from March 1 to November 30. The model will automatically stop running when it is not in effective operation time.

[0038] Step 2: Get data and build a model dataset

[0039] 2.1 Obtaining the occurrence pressure value data of green leafhopper pests in different months

[0040] Research and investigation have shown that the occurrence, development, epidemic, and outbreak of any pest is a progressive process of damage over time, which often takes a certain amount of time to complete. The occurrence and development time of tea green leafhopper pests each year also follows a regular pattern. This embodiment surveys the occurrence and development time of tea green leafhopper pests by sending electronic questionnaires and conducting in-depth interviews with tea garden growers and managers in Jinhua City, Zhejiang Province, and obtains the occurrence pressure value mPress of tea green leafhopper pests in different months. t . Occurrence pressure value mPress t The value range is 0 to 100.

[0041] In this embodiment, the occurrence pressure value mPress of the green leafhopper pest in different months i as follows:

[0042]

[0043] Among them, mPress t is the pressure value in month t. t=3,4,5,6,7,8,9,10,11.

[0044] 2.2 Obtaining meteorological data

[0045] The occurrence of tea green leafhopper pests is closely related to temperature and rainfall. Suitable temperature and rainfall will promote the growth of tea green leafhoppers, and vice versa. Through meteorological monitoring of temperature and rainfall, the occurrence and future development trend of tea green leafhopper pests can be predicted and reported, providing a scientific basis for timely prevention and control of the pest.

[0046] This example obtains temperature and rainfall meteorological data of the experimental tea garden area in Jinhua City, Zhejiang Province from the National Meteorological Information Center. In order to reflect the impact of meteorological factors on the occurrence and development trend of insect pests in different periods and control random fluctuations of data, the historical daily average temperature averTemp within a preset time period before the current date (the nth day of the year) is calculated. n , historical daily cumulative rainfall averRain n , the forecast daily average temperature foreTemp within the preset time after the current date n , forecast daily cumulative rainfall foreRain n , the calculation process is as follows;

[0047]

[0048]

[0049]

[0050]

[0051] The preset duration in this embodiment is 15 days; j , R j , T′ j , R′ j They are the actual average temperature, actual cumulative rainfall, forecast average temperature, and forecast cumulative rainfall on the jth day of the year respectively.

[0052] 2.3 Obtaining real-time monitoring of tea garden insect population density data

[0053] At present, pest monitoring technology is increasingly developed, and information such as tea garden insect population density can be obtained in real time through monitoring instruments. The tea garden insect population density reflects the current pest situation and the changing trend of pests in the future. This example conducts a field survey on pests in an experimental tea garden in Jinhua City, Zhejiang Province, and obtains the tea garden insect population density data C on the nth day of real-time monitoring by the tea garden monitoring instrument. n (The unit is head / square meter. Under normal circumstances, the value is in the single digit. In abnormal circumstances such as insect disasters, the value will be greater than ten and there is no upper limit). The prediction model will combine this data to predict the future development trend of tea green leafhopper pests.

[0054] Step 3: Build a disease prediction model

[0055] The disease prediction model is used to calculate the occurrence pressure value mPress of green leafhopper pests during the effective operation time. t , historical daily average temperature averTemp n , historical daily cumulative rainfall averRain n , forecast daily average temperature foreTempn , forecast daily cumulative rainfall foreRain n and real-time monitoring of tea garden insect population density data C n , and the pest pressure index is predicted for the occurrence and development trend of tea green leafhopper pests. The pest pressure index includes pressure value n and degree n Among them, the pressure value porecast n The comprehensive pressure value indicating the occurrence of tea green leafhopper pests on the nth day of the year and the future pest development trend; degree value n It indicates the comprehensive degree value of the occurrence of tea green leafhopper pests on the nth day of the year and the future pest development trend (the prediction results are 0, 1, 2, and 3, representing no pest occurrence, mild pest, moderate pest, and severe pest, respectively).

[0056] Pressure value at any time n The degree value is given by adding and coupling the parameter values ​​at the corresponding time phase, and its expression is shown in formula (3.1); n According to the pressure value n The numerical value of is determined, and its expression is shown in formula (3.2).

[0057] pressure n =mPress t +T(averTemp n )+R(averRain n )+FT(foreTemp n )+FR(foreRain n )+k*C n (3.1)

[0058]

[0059] Among them, porecast n It represents the comprehensive pressure value of the occurrence of tea green leafhopper pests on the nth day of the year and the future pest development trend; degreed n Indicates the comprehensive degree value of the occurrence of tea green leafhopper pests on the nth day of the year and the future pest development trend (the prediction results are 0, 1, 2, and 3, representing no pest occurrence, mild pest, moderate pest, and severe pest, respectively); mPress t represents the occurrence pressure value in month t, where month t is the month of day n; k is the insect population density data C in the tea garden monitored in real time n The proportionality factor.

[0060] x1 ,x 2 ,x 3 They represent three tea green leafhopper pest pressure thresholds that increase in sequence. n )、R(averRain n )、FT(foreTemp n )、FR(foreRain n ) are the historical temperature adjustment parameter, historical rainfall adjustment parameter, forecast temperature adjustment parameter, and forecast rainfall adjustment parameter, and their expressions are shown in equations (3.3), (3.4), (3.5), and (3.6), respectively.

[0061]

[0062]

[0063]

[0064]

[0065] Among them, averTemp n Indicates the historical daily average temperature on the nth day of the year;averRain n Indicates the historical daily cumulative rainfall on the nth day of the year; foreTemp n Indicates the forecast daily average temperature for the nth day of the year, foreRain n Indicates the cumulative rainfall on the forecast day of the nth day of the year. 1 ,G 2 ,G 3 The historical temperature adjustment parameters T(averTemp n ) three alternative values; G 1 and G 3 are all positive numbers, and G 1 >G 3 ; G 2 Is a negative number. 1 ,FG 2 ,FG 3 are respectively the forecast temperature adjustment parameter FT(foreTemp n ) three alternative values; FG 1 and FG 3 are all positive numbers, and FG 1 >FG 3 ; FG 2 Different alternative values ​​indicate the influence of different temperature ranges on the occurrence and future development trend of tea green leafhopper pests.

[0066] H1 ,H 2 ,H 3 are the historical rainfall adjustment parameters R(averRain n ) three alternative values; H 1 and H 3 are all positive numbers, and H 1 >H 3 ;H 2 FH is a negative number. 1 ,FH 2 ,FH 3 They are respectively the forecast rainfall adjustment parameter FR (foreRain n ) three alternative values; FH 1 and FH 3 are all positive numbers, and FH 1 >FH 3 ; FH 2 Different alternative values ​​indicate the influence of different daily cumulative rainfall ranges on the occurrence and future development trend of tea green leafhopper pests.

[0067] ST 1 、ST 2 are the lower and upper limits of the suitable temperature range for the growth of tea green leafhopper; ST 1 ~ST 2 The temperature is conducive to the growth, development and reproduction of tea green leafhoppers, and promotes the occurrence and development trend of insect pests; max It indicates the upper limit of the temperature range in which the tea green leafhopper can survive. When the ambient temperature is higher than this temperature, the growth and development of the tea green leafhopper will be inhibited, which will limit the future development trend of the pest. min It represents the lower limit of the temperature range in which the tea green leafhopper can survive. When the ambient temperature is lower than this temperature, the growth and development of the tea green leafhopper will be inhibited, which will limit the future development trend of the pest.

[0068] SR 1 , SR 2 are the lower and upper limits of the daily cumulative rainfall range suitable for the growth of tea green leafhoppers; SR 1 ~SR 2 The daily cumulative rainfall is beneficial to the growth, development and reproduction of tea green leafhoppers, and promotes the occurrence and development trend of insect pests; max It represents the upper limit of the survivable rainfall range of the tea green leafhopper. When the daily cumulative rainfall exceeds this amount, the growth and development of the tea green leafhopper will be inhibited, which will limit the future development trend of the pest. min It represents the lower limit of the survivable rainfall range of the tea green leafhopper. When the daily accumulated rainfall is lower than this amount, the growth and development of the tea green leafhopper will be inhibited, which will limit the future development trend of the pest.

[0069] In this embodiment, the values ​​of the parameters are as follows:

[0070] ST 1 ,ST 2 ,T max ,T min ,SR 1 ,SR 2 ,R max ,R min The temperature and rainfall threshold parameters related to the growth and development of tea green leafhoppers are determined by experimental research data on the relationship between the growth and development process of tea green leafhoppers and temperature and rainfall. 1 The value of ST is 18℃~22℃, preferably 20℃; 2 The value of T is 25℃~30℃, preferably 28℃; max The value is 35℃~40℃, preferably 38℃; T min The value is 0℃~10℃, preferably 5℃; SR 1 The value is 5mm to 10mm, preferably 8mm; SR 2 The value is 20mm~30mm, preferably 25mm; R max The value is 30mm~40mm, preferably 35mm; R min The value is 0mm to 5mm, preferably 3mm.

[0071] For x1,x2,x3,G 1 ,G 2 ,G 3 ,FG 1 ,FG 2 ,FG 3 ,H 1 ,H 2 ,H 3 ,FH 1 ,FH 2 ,FH 3 , k parameters, and use the parameter optimization method to substitute the parameter combination into the model for calculation. In this embodiment, x is set 1 =30,x 2 =50,x 3 =70,G 1 =FG 1 =3,G 2 =FG 2 =-3,G 3 =FG 3 =1,H 1 =FH 1 =3,H 2 =FH 2 =-3,H 3 =FH3 =1,k=2.

[0072] The effect of temperature and precipitation on tea green leafhopper can be considered as a threshold effect. When the temperature and precipitation are below a certain threshold or above a certain threshold, it is considered that the environmental conditions are unfavorable to the occurrence and development trend of tea green leafhopper pests and play an inhibitory role. When the temperature and precipitation are within a certain temperature and precipitation range, it is conducive to the growth, development and reproduction of tea green leafhoppers, and promotes the occurrence and development trend of pests. Therefore, T (averTemp n )、R(averRain n )、FT(foreTemp n )、FR(foreRain n ) to reflect the impact of temperature and precipitation on insect pests.

[0073] Step 4: Calculate the pressure value of the pest pressure index based on the new data every day from March to November n and degree n ; According to the pressure valuepressure n and degree n Real-time assessment of the risk of pest outbreaks now and in the next few days.

[0074] In this embodiment, the data of the experimental tea garden in Jinhua City, Zhejiang Province in 2020 was tested, and the pressure value pressure n and degree n The changes in Figure 1 and 2 The prediction results are consistent with the occurrence trend of tea green leafhoppers in local tea gardens, that is, there are two peak periods of tea green leafhoppers in a year, in May and September respectively; the pest pressure value and peak time of the second peak period are lower than those of the first peak month.

Claims

1. A method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data, characterized in that: The following steps are involved: Step 1: Obtain data and build a model dataset; 1-1. According to the occurrence of green leafhopper pests in the measured area from March to July in previous years, determine the occurrence pressure value mPress in different months t ; t=3,4,5,6,7,8,9,10,11; pressure value mPress t The value range of is 0 to 100; 1-2. Calculate the historical daily average temperature averTemp within the preset time before the current date n , historical daily cumulative rainfall averRain n , the forecast daily average temperature foreTemp within the preset time after the current date n , forecast daily cumulative rainfall foreRain n ; 1-3. Real-time detection of the density data of the green leafhopper in the measured area C n ; Step 2: Calculate the pest pressure index; Pest pressure index includes pressure value n and degree n ; Among them, the pressure value porecast n is the comprehensive pressure value of the current occurrence of tea green leafhopper pests and the future pest development trend, and its expression is shown in formula (3.1); degree value degreed n is the current occurrence of tea green leafhopper pests and the risk level of future pest development trends, and its expression is shown in formula (3.2); pressure n =mPress t +T(averTemp n )+R(averRain n )+FT(foreTemp n )+FR(foreRain n )+k*C n (3.1) Among them, mPress t represents the occurrence pressure value of the month corresponding to the current time, and k is the insect population density data C of the tea garden monitored in real time n The proportional coefficient; x1, x2, x3 represent the three tea green leafhopper pest pressure thresholds that increase in sequence; T (averTemp n )、R(averRain n )、FT(foreTemp n )、FR(foreRain n ) are the historical temperature adjustment parameter, historical rainfall adjustment parameter, forecast temperature adjustment parameter, and forecast rainfall adjustment parameter, respectively, and their expressions are shown in equations (3.3), (3.4), (3.5), and (3.6); Among them, G1, G2, G3 are the historical temperature adjustment parameters T (averTemp n ) three alternative values; G1 and G3 are both positive numbers, and G1>G3; G2 is a negative number; FG1, FG2, and FG3 are the forecast temperature adjustment parameters FT (foreTemp n ) three alternative values; FG1 and FG3 are both positive, and FG1>FG3; FG2 is negative; H1, H2, H3 are the historical rainfall adjustment parameters R (averRain n ) three alternative values; H1 and H3 are both positive numbers, and H1>H3; H2 is a negative number; FH1, FH2, and FH3 are the forecast rainfall adjustment parameters FR (foreRain n ) three alternative values; FH1 and FH3 are both positive numbers, and FH1>FH3; FH2 is a negative number; ST1 and ST2 are the lower and upper limits of the suitable temperature range for the growth of tea green leafhoppers, respectively; T max , T min are the upper and lower limits of the temperature range in which the tea green leafhopper can survive; SR1 and SR2 are the lower and upper limits of the daily cumulative rainfall range suitable for the growth of the tea green leafhopper; R max , R min They represent the upper and lower limits of the survivable rainfall range of the tea green leafhopper respectively; Step 3: According to the pressure value n and degree n The size of the pest is used to assess the risk of pest outbreaks in the monitored area currently and in the next few days.

2. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: The pest pressure index is updated daily from March 1 to November 30 of each year to enable real-time assessment of the risk of pest outbreaks.

3. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: The pressure value mPress in different months t The values ​​were obtained by distributing questionnaires and conducting interviews with tea plantation growers and managers in the surveyed areas.

4. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: The preset duration in steps 1-2 is 15 days.

5. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: The value of ST1 is 18℃~22℃; the value of ST2 is 25℃~30℃; T max The value is 35℃~40℃; T min The value is 0℃~10℃; SR1 is 5mm~10mm; SR2 is 20mm~30mm; R max The value is 30mm~40mm; R min The value ranges from 0mm to 5mm.

6. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: The value of ST1 is 20℃; the value of ST2 is 28℃; T max The value is 38℃; T min The value is 5℃; SR1 is 8mm; SR2 is 25mm; R max The value is 35mm; R min The value is 3mm.

7. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: x1=30, x2=50, x3=70, G1=FG1=3, G2=FG2=-3, G3=FG3=1, H1=FH1=3, H2=FH2=-3, H3=FH3=1, k=2.

8. The method for dynamic prediction of tea green leafhopper pests based on meteorological and insect monitoring data according to claim 1, characterized in that: degree n When the value of is 0, 1, 2, and 3, it means no pest occurrence, slight pest risk, moderate pest risk, and severe pest risk, respectively.

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