CN116402177B - Method and system for predicting occurrence degree of athetis lepigone - Google Patents
Method and system for predicting occurrence degree of athetis lepigone Download PDFInfo
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- 241000013228 Athetis lepigone Species 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 25
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 23
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- 238000009825 accumulation Methods 0.000 claims description 3
- 240000008042 Zea mays Species 0.000 abstract description 13
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 abstract description 13
- 235000002017 Zea mays subsp mays Nutrition 0.000 abstract description 13
- 235000005822 corn Nutrition 0.000 abstract description 13
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- 241000238631 Hexapoda Species 0.000 description 10
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- 235000013601 eggs Nutrition 0.000 description 2
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- 235000015099 wheat brans Nutrition 0.000 description 2
- 241001057636 Dracaena deremensis Species 0.000 description 1
- 208000028804 PERCHING syndrome Diseases 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000012447 hatching Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
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- 238000011160 research Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000003971 tillage Methods 0.000 description 1
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Abstract
The invention relates to a method and a system for predicting the occurrence degree of athetis lepigone, belonging to the technical field of pest control. The method comprises the following steps: acquiring meteorological data, constructing a generation-1 adult occurrence prediction model, and predicting generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when a model prediction triggering condition is met; constructing a generation 2 larva occurrence medium-term prediction model and a generation 2 larva occurrence short-term prediction model based on the predicted generation 1 adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing; and predicting the occurrence amount of the 2-generation larva in the full period according to the 2-generation larva occurrence amount medium-term prediction model and the 2-generation larva occurrence amount short-term prediction model, and outputting the occurrence degree level of the athetis lepigone. The method improves the efficiency, timeliness, convenience and accuracy of prediction, and is beneficial to reducing the loss of corn yield caused by pests.
Description
Technical Field
The invention belongs to the technical field of pest control, and particularly relates to a method and a system for predicting the occurrence degree of athetis lepigone.
Background
Athetis lepigone is a main pest of summer corn, and larvae are mainly used for damaging corn seedlings by boring. The athetis lepigone eggs are mostly scattered on field wheat straws, the hatched larvae are hidden under the broken wheat straws around corn seedlings, or the bases of 3-5 leaf seedlings of corn are killed by perching in soil gaps of 2-5 cm and boring, and the corn 5 leaves bite off aerial roots on corn fields to cause lodging, the damaged corn plants of the corn fields fall to the east and west, even seedling and ridge shortage occurs, and large-area blank fields appear in the corn fields. Serious land areas are damaged and even the seeds need to be destroyed.
According to the research, the wheat bran and wheat straw coverage area is larger than that without wheat straw and wheat bran coverage, and the sowing time is later than the sowing time. The athetis lepigone is not a pest which seriously happens every year, and the occurrence period is different from other pests, so that the prediction of the occurrence degree in the same year has very important guiding significance for deciding whether to control or not and adding more cost for controlling.
At present, the prediction of the occurrence degree of the main pest generation of the athetis lepigone, namely 2 generation larvae, is determined by combining the experience of plant protection staff based on the field statistical data of the plant protection staff in each county; or based on the imago data transmitted by the monitoring equipment, the occurrence of the second generation of larvae is predicted after the occurrence of the first generation of imago is determined, namely, the prediction of the occurrence of the larvae is performed. The existing prediction method has the following defects:
(1) A large amount of staff investigation data is needed to be used as support, and the manpower investment is large;
(2) Investigation of data when larvae occur, information lag for timely preparation of mechanical equipment and supplies;
(3) The larva occurrence degree is calculated only according to the occurrence quantity of adults, so that the accuracy is difficult to ensure;
(4) The prediction accuracy of the occurrence degree of 2-generation larvae of athetis lepigone is large in difference of the levels of different personnel according to the experience judgment of plant protection staff.
In conclusion, the existing technology for predicting the occurrence degree of 2-generation larvae of athetis lepigone has the defects in efficiency, convenience and accuracy.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art, and provides a method and a system for predicting the occurrence degree of athetis lepigone, which are used for predicting the occurrence amount of adults of generation 1 by acquiring meteorological data, constructing a prediction model, predicting the occurrence amount of larvae of generation 2 according to the occurrence amount of adults of generation 1, and finally outputting the occurrence degree level of larvae of generation 2, thereby improving the efficiency, timeliness, convenience and accuracy of prediction and being beneficial to reducing the loss of corn yield caused by pests.
According to one aspect of the present invention, there is provided a method for predicting the extent of occurrence of athetis lepigone, the method comprising the steps of:
s1: acquiring meteorological data, constructing a generation-1 adult occurrence prediction model, and predicting generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when a model prediction triggering condition is met;
s2: constructing a generation 2 larva occurrence medium-term prediction model and a generation 2 larva occurrence short-term prediction model based on the predicted generation 1 adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing;
s3: and predicting the occurrence amount of the 2-generation larva in the full period according to the 2-generation larva occurrence amount medium-term prediction model and the 2-generation larva occurrence amount short-term prediction model, and outputting the occurrence degree level of the athetis lepigone.
Preferably, the acquiring meteorological data includes:
monitoring weather data is obtained from weather stations in the predicted area, the weather data including historical weather data and predicted weather data.
Preferably, the predicting the generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when the model prediction triggering condition is satisfied includes:
triggering a generation-1 adult occurrence prediction model based on the daily captured pest number started on a predetermined date and the daily pest number growth rate, wherein the generation-1 adult occurrence prediction model is as follows:
D 0 ＝0.32*x1-4.934*x2+2.237*x3-116.969*x4
wherein x1 represents a 15-day future accumulated temperature, x2 represents a 15-day future rainfall, x3 represents a 5-month average air temperature, and x4 represents a 5-month average rainfall.
Preferably, the method comprises:
correcting the generation-1 adult occurrence prediction model according to future 15 weather forecast data to obtain a corrected generation-1 adult occurrence prediction model:
D 1 ＝X*(0.32*x1-4.934*x2+2.237*x3-116.969*x4)
wherein D is 1 For the number of adult full period of 1 generation, x=k T *K H ，K T : represents a temperature stress factor, K, affected by sustained high temperature H : represents a humidity stress factor affected by sustained low humidity.
Preferably, said constructing a 2-generation larva occurrence medium-term prediction model based on the predicted 1-generation adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing comprises:
calculating the occurrence amount of the 2 generation larva full period according to the mid-term prediction model of the occurrence amount of the 2 generation larva:
E 0 ＝D 1 *(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein y1 represents the 15-day future accumulated temperature, y2 represents the 15-day future rainfall, y3 represents the 6-month average air temperature, and y4 represents the 6-month average rainfall.
Preferably, the method comprises:
correcting the 2-generation larva occurrence medium-term prediction model according to future 15 weather image forecast data to obtain a corrected 2-generation larva occurrence medium-term prediction model:
E 2 ＝Z*E 1 ，E 1 ＝Y*E 0 ，Y＝K T *K H *K P ，Z＝K TL
when E is 2 When the preset threshold is reached, reminding a user to take measures in time for prevention and treatment; wherein K is P : the rainfall stress factor influenced by the continuous rainfall is represented, Z is a coefficient corresponding to cultivation measures, K TL : representing a measure factor of cultivation affected by the proportion of the stubble processing area.
Preferably, said constructing a 2-generation larva occurrence short-term prediction model based on said predicted 1-generation adult occurrence prediction value, said meteorological data and remotely monitored local farming measure data comprises:
calculating the occurrence amount of the 2 generation larva full period according to the short-term prediction model of the occurrence amount of the 2 generation larva:
E 3 ＝D 2 *Y*Z*(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein D is 2 Represents the full-period moth amount of the generation 1 adults, which is the average daily captured adult amount of continuous 5 days meeting the preset condition before triggering the short-term prediction model of the occurrence amount of the generation 2 larvae.
According to another aspect of the present invention, there is also provided a system for predicting the occurrence of athetis lepigone, the system comprising:
the first construction module is used for acquiring meteorological data, constructing a generation 1 adult occurrence prediction model, and predicting generation 1 adult occurrence according to the generation 1 adult occurrence prediction model when a model prediction triggering condition is met;
the second construction module is used for constructing a medium-term prediction model of the occurrence amount of 2-generation larvae and a short-term prediction model of the occurrence amount of 2-generation larvae based on the predicted occurrence amount prediction value of the 1-generation adults, the meteorological data and the local farming measure data monitored by remote sensing;
and the prediction module is used for predicting the occurrence amount of the 2-generation larva in the full period according to the 2-generation larva occurrence amount medium-term prediction model and the 2-generation larva occurrence amount short-term prediction model and outputting the occurrence degree level of the athetis lepigone.
Preferably, the predicting the generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when the model prediction triggering condition is satisfied includes:
triggering a generation-1 adult occurrence prediction model based on the daily captured pest number started on a predetermined date and the daily pest number growth rate, wherein the generation-1 adult occurrence prediction model is as follows:
D 0 ＝0.32*x1-4.934*x2+2.237*x3-116.969*x4
wherein x1 represents a 15-day future accumulated temperature, x2 represents a 15-day future rainfall, x3 represents a 5-month average air temperature, and x4 represents a 5-month average rainfall.
Preferably, said constructing a 2-generation larva occurrence medium-term prediction model based on the predicted 1-generation adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing comprises:
calculating the occurrence amount of the 2 generation larva full period according to the mid-term prediction model of the occurrence amount of the 2 generation larva:
E 0 ＝D 1 *(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein y1 represents the 15-day future accumulated temperature, y2 represents the 15-day future rainfall, y3 represents the 6-month average air temperature, and y4 represents the 6-month average rainfall.
The beneficial effects are that: according to the method, meteorological data are acquired, a prediction model is constructed, the occurrence amount of the adults of the generation 1 is predicted, the occurrence amount of the larvae of the generation 2 is predicted according to the occurrence amount of the adults of the generation 1, and finally the occurrence degree level of the larvae of the generation 2 is output, so that the efficiency, timeliness, convenience and accuracy of prediction are improved, and the loss of corn yield caused by pests is reduced.
Features and advantages of the present invention will become apparent by reference to the following drawings and detailed description of embodiments of the invention.
Drawings
FIG. 1 is a flowchart of a method for predicting the extent of occurrence of athetis lepigone;
FIG. 2 is a temperature stress factor K T Schematic diagram of influence degree affected by continuous high temperature;
FIG. 3 is a humidity stress factor K H Schematic diagram of the degree of influence of sustained low humidity;
FIG. 4 is a rainfall stress factor K p Schematic diagram of the influence degree of continuous rainfall;
FIG. 5 is a tillage measure factor K TL Schematic diagram of the degree of influence of the wheat stubble treatment area proportion;
fig. 6 is a schematic diagram of a system for predicting the occurrence degree of athetis lepigone.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Fig. 1 is a flowchart of a method for predicting the occurrence degree of athetis lepigone. As shown in fig. 1, the present embodiment provides a method for predicting the occurrence degree of athetis lepigone, which includes the following steps:
s1: and acquiring meteorological data, constructing a generation-1 adult occurrence prediction model, and predicting generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when the model prediction triggering condition is met.
Preferably, the acquiring meteorological data includes:
monitoring weather data is obtained from weather stations in the predicted area, the weather data including historical weather data and predicted weather data.
Specifically, the small weather station is used for monitoring weather indexes locally, including but not limited to indexes such as average temperature, minimum temperature, maximum temperature, rainfall and the like, and data are simultaneously transmitted to a system for storage. Starting from 5 months, the system can output the generation quantity of the 1-generation adult athetis lepigone through a prediction model according to the number or daily growth rate of the adults monitored by the insect condition monitoring equipment, and 3-4 months historical meteorological data and predicted meteorological data stored in a meteorological station.
Preferably, the predicting the generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when the model prediction triggering condition is satisfied includes:
triggering a generation-1 adult occurrence prediction model based on the daily captured pest number started on a predetermined date and the daily pest number growth rate, wherein the generation-1 adult occurrence prediction model is as follows:
D 0 ＝0.32*x1-4.934*x2+2.237*x3-116.969*x4
wherein x1 represents a 15-day future accumulated temperature, x2 represents a 15-day future rainfall, x3 represents a 5-month average air temperature, and x4 represents a 5-month average rainfall.
Specifically, according to the occurrence rule of 1 generation adults investigated for many years, a condition based on a monitoring device triggering prediction model is constructed. The pest situation monitoring device captures the number of pests daily from 1 day of 6 months and the daily growth rate of the number of pests, and triggers a generation-1 adult occurrence prediction model.
Examples: setting that if the capturing amount on the same day is more than or equal to 4 or the capturing amount on the same day is less than 4, the quantity increasing rate of the insect is more than 50% than the previous day (the insect amount on the same day-the insect amount on the previous day)/the insect amount on the previous day ]
One of the above conditions is met for 2 consecutive days, i.e. the predictive model can be triggered. Triggering and early warning the capturing amount on the same day.
When none of the above conditions is satisfied, it means that the local generation amount of athetis lepigone is 0 and does not occur.
According to the generation 1 adult occurrence rule of years of investigation and the analysis of the historical meteorological data for years, the following 4 meteorological parameters are obtained, and the correlation of the generation 1 adult occurrence amount is obvious, and the method comprises the following steps: 15 days of heat accumulation (x 1) in the future, 15 days of rainfall x2 in the future, 5 months of average air temperature and average precipitation x3,4 (simultaneously calling historical and forecast meteorological data), and 4 parameters in total.
Algorithm: d (D) 0 ＝0.32*x1-4.934*x2+2.237*x3-116.969*x4
And 1, retrieving 5 month weather data stored in a weather station, combining weather prediction data, and calculating by using the model to obtain a D value, wherein D is the occurrence amount of 1 generation adults in the region reaching the full period after triggering and early warning.
Preferably, the method comprises:
correcting the generation-1 adult occurrence prediction model according to future 15 weather forecast data to obtain a corrected generation-1 adult occurrence prediction model:
D 1 ＝X*(0.32*x1-4.934*x2+2.237*x3-116.969*x4)
wherein D is 1 For the number of adult full period of 1 generation, x=k T *K H ，K T : represents a temperature stress factor, K, affected by sustained high temperature H : represents a humidity stress factor affected by sustained low humidity.
Specifically, when the future 15 weather forecast data satisfies one of the following conditions, the D value should be multiplied by a coefficient X, x=k T *K H ；
K T : the temperature stress factor is influenced by continuous high temperature, and the specific influence degree is shown in figure 2;
K H : humidity stress factors are affected by sustained low humidity to the extent shown in figure 3.
S2: and constructing a medium-term prediction model of the occurrence amount of the 2-generation larvae and a short-term prediction model of the occurrence amount of the 2-generation larvae based on the predicted occurrence amount prediction value of the 1-generation adults, the meteorological data and the local farming measure data monitored by remote sensing.
Preferably, said constructing a 2-generation larva occurrence medium-term prediction model based on the predicted 1-generation adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing comprises:
calculating the occurrence amount of the 2 generation larva full period according to the mid-term prediction model of the occurrence amount of the 2 generation larva:
E 0 ＝D 1 *(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein y1 represents the 15-day future accumulated temperature, y2 represents the 15-day future rainfall, y3 represents the 6-month average air temperature, and y4 represents the 6-month average rainfall.
Specifically, the resulting D 1 The number of the full-period of the 1 generation adults is calculated based on the number of the full-period of the 2 generation larvae. The occurrence amount of 2-generation larvae is not only influenced by the number of 1-generation adults, the spawning amount of the adults, the egg hatching rate and cultivation measures, but also by the temperature and humidity. According to historical survey data, a prediction model of the number of adults of generation 1, meteorological factors and the occurrence amount of larvae of generation 2 (hundred strains of insects) is built.
Meteorological parameters with highest correlation to 2 generation larva occurrence: 15 days of accumulated temperature (y 1) in the future, 15 days of rainfall y2 in the future, and average air temperature and average rainfall y3 and average rainfall y4 in 6 months (simultaneously calling historical and forecast meteorological data), wherein the total number of the parameters is 4;
from D 1 Calculating the occurrence quantity E of 2 generation larva in full period 0 The algorithm of (1) is as follows:
E 0 ＝D 1 *(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
preferably, the method comprises:
correcting the 2-generation larva occurrence medium-term prediction model according to future 15 weather image forecast data to obtain a corrected 2-generation larva occurrence medium-term prediction model:
E 2 ＝Z*E 1 ，E 1 ＝Y*E 0 ，Y＝K T *K H *K P ，Z＝K TL
when E is 2 When the preset threshold is reached, reminding a user to take measures in time for prevention and treatment; wherein K is P : the rainfall stress factor influenced by the continuous rainfall is represented, Z is a coefficient corresponding to cultivation measures, K TL : representing a measure factor of cultivation affected by the proportion of the stubble processing area.
Specifically, when the future 15 weather forecast data satisfies one of the following conditions when entering the low-age of 2-generation larvae, D 1 The value should be multiplied by a certain coefficient Y, since the risk of larvae damaging corn is reduced when this condition is met.
Y＝K T *K H *K P Wherein K is P : the rainfall stress factor is affected by continuous rainfall, and the influence degree is shown in figure 4.
E 1 ＝Y*E 0 Z is a coefficient corresponding to the cultivation measure, Z=K TL ，K TL : the cultivation measure factor is affected by the proportion of the wheat stubble processing area, and the specific influence degree is shown in figure 5.
E 2 ＝Z*E 1 After E2 is obtained, when E2 reaches 20 or more, the system reminds the user to take measures in time for prevention and treatment.
Preferably, said constructing a 2-generation larva occurrence short-term prediction model based on said predicted 1-generation adult occurrence prediction value, said meteorological data and remotely monitored local farming measure data comprises:
calculating the occurrence amount of the 2 generation larva full period according to the short-term prediction model of the occurrence amount of the 2 generation larva:
E 3 ＝D 2 *Y*Z*(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein D is 2 Represents the full-period moth amount of the generation 1 adults, which is the average daily captured adult amount of continuous 5 days meeting the preset condition before triggering the short-term prediction model of the occurrence amount of the generation 2 larvae.
Specifically, when the daily adult capturing amount of the insect condition monitoring device meets the following conditions, namely the end stage of the 1-generation adult flourishing:
setting if the capturing amount on the same day is less than or equal to 3 or the capturing amount on the same day is more than 3, the quantity reduction rate is more than 50 percent compared with the previous day (the previous day insect quantity-the current day insect quantity)/the previous day insect quantity ]
One of the conditions is met for 3 consecutive days, namely the short-term prediction model of the 2-generation larva occurrence amount can be triggered, and the medium-term prediction model of the 2-generation larva occurrence amount is deactivated.
1 generation adult full period moth quantity D 2 : the day of the trigger model was taken as the day of 50% of the sum of the number of adults captured each day during the short-term predictive model of the number of larvae produced at generation 2, and the average daily number of adults captured for a total of 5 days, before and after the day.
From D 2 Calculating the occurrence quantity E of 2 generation larva in full period 3 The algorithm of (1) is as follows:
E 3 ＝D 2 *Y*Z*(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
and obtaining a prediction method of the 2-generation larva occurrence amount of the athetis lepigone according to the algorithm of the prediction model, and packaging the method into a prediction system.
S3: and predicting the occurrence amount of the 2-generation larva in the full period according to the 2-generation larva occurrence amount medium-term prediction model and the 2-generation larva occurrence amount short-term prediction model, and outputting the occurrence degree level of the athetis lepigone.
According to the embodiment, the meteorological data are acquired, the prediction model is constructed, the generation 1 adult occurrence is predicted, the generation 2 larva full-period occurrence is predicted according to the generation 1 adult occurrence, and finally the generation 2 larva occurrence degree level is output, so that the efficiency, timeliness, convenience and accuracy of prediction are improved, and the loss of the corn yield caused by pests is reduced.
Example 2
Fig. 6 is a schematic diagram of a system for predicting the occurrence degree of athetis lepigone. As shown in fig. 6, the present embodiment provides a system for predicting the occurrence degree of athetis lepigone, the system comprising:
the first construction module 601 is configured to acquire meteorological data, construct a generation 1 adult occurrence prediction model, and predict generation 1 adult occurrence according to the generation 1 adult occurrence prediction model when a model prediction triggering condition is satisfied;
a second construction module 602, configured to construct a mid-term prediction model of 2 generation larva occurrence and a short-term prediction model of 2 generation larva occurrence based on the predicted 1 generation adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing;
and the prediction module 603 is used for predicting the occurrence amount of the 2-generation larva in the full period according to the 2-generation larva occurrence amount middle-term prediction model and the 2-generation larva occurrence amount short-term prediction model, and outputting the occurrence degree level of the athetis lepigone.
Preferably, the predicting the generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when the model prediction triggering condition is satisfied includes:
triggering a generation-1 adult occurrence prediction model based on the daily captured pest number started on a predetermined date and the daily pest number growth rate, wherein the generation-1 adult occurrence prediction model is as follows:
D 0 ＝0.32*x1-4.934*x2+2.237*x3-116.969*x4
wherein x1 represents a 15-day future accumulated temperature, x2 represents a 15-day future rainfall, x3 represents a 5-month average air temperature, and x4 represents a 5-month average rainfall.
Preferably, said constructing a 2-generation larva occurrence medium-term prediction model based on the predicted 1-generation adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing comprises:
calculating the occurrence amount of the 2 generation larva full period according to the mid-term prediction model of the occurrence amount of the 2 generation larva:
E 0 ＝D 1 *(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein y1 represents the 15-day future accumulated temperature, y2 represents the 15-day future rainfall, y3 represents the 6-month average air temperature, and y4 represents the 6-month average rainfall.
The implementation process of the functions implemented by each module in this embodiment 2 is the same as the implementation process of each step in embodiment 1, and will not be described here again.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the specification and drawings of the present invention or direct/indirect application in other related technical fields are included in the scope of the present invention.
Claims (2)
1. A method for predicting the extent of occurrence of athetis lepigone, comprising the steps of:
s1: acquiring meteorological data, constructing a generation-1 adult occurrence prediction model, and predicting generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when a model prediction triggering condition is met;
s2: constructing a generation 2 larva occurrence medium-term prediction model and a generation 2 larva occurrence short-term prediction model based on the predicted generation 1 adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing;
s3: predicting the occurrence amount of the 2 generation larva in the full period according to the 2 generation larva occurrence amount medium-term prediction model and the 2 generation larva occurrence amount short-term prediction model, outputting the occurrence degree level of the athetis lepigone,
the predicting the generation-1 adult occurrence according to the generation-1 adult occurrence prediction model when the model prediction triggering condition is satisfied comprises:
triggering a generation-1 adult occurrence prediction model based on the daily captured pest number started on a predetermined date and the daily pest number growth rate, wherein the generation-1 adult occurrence prediction model is as follows:
D 0 ＝0.32*x1-4.934*x2+2.237*x3-116.969*x4
wherein x1 represents the accumulation temperature of 15 days in the future, x2 represents the rainfall of 15 days in the future, x3 represents the average air temperature of 5 months, x4 represents the average rainfall of 5 months,
and correcting the generation-1 adult occurrence prediction model according to future 15 weather forecast data to obtain a corrected generation-1 adult occurrence prediction model:
D 1 ＝X*(0.32*x1-4.934*x2+2.237*x3-116.969*x4)
wherein D is 1 For the number of adult full period of 1 generation, x=k T *K H ，K T Represents a temperature stress factor, K, affected by sustained high temperature H Represents a humidity stress factor affected by sustained low humidity,
the construction of the medium-term prediction model of the occurrence amount of 2-generation larvae based on the predicted occurrence amount prediction value of the 1-generation adults, the meteorological data and the local farming measure data monitored by remote sensing comprises the following steps:
calculating the occurrence amount of the 2 generation larva full period according to the mid-term prediction model of the occurrence amount of the 2 generation larva:
E 0 ＝D 1 *(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein y1 represents the 15-day accumulation temperature in the future, y2 represents the 15-day rainfall in the future, y3 represents the average air temperature of 6 months, y4 represents the average rainfall of 6 months,
and correcting the 2-generation larva occurrence medium-term prediction model according to future 15 weather image forecast data to obtain a corrected 2-generation larva occurrence medium-term prediction model:
E 2 ＝Z*E 1 ，E 1 ＝Y*E 0 ，Y＝K T *K H *K P ，Z＝K TL
when E is 2 When the preset threshold is reached, reminding a user to take measures in time for prevention and treatment; wherein K is P The rainfall stress factor influenced by the continuous rainfall is represented, Z is a coefficient corresponding to cultivation measures, K TL Represents a cultivation measure factor affected by the proportion of the wheat stubble processing area,
the constructing a 2-generation larva occurrence short-term prediction model based on the predicted 1-generation adult occurrence prediction value, the meteorological data and the local farming measure data monitored by remote sensing comprises the following steps:
calculating the occurrence amount of the 2 generation larva full period according to the short-term prediction model of the occurrence amount of the 2 generation larva:
E 3 ＝D 2 *Y*Z*(-6.816*y1-0.054y2+12.218*y3+3.477*y4)
wherein D is 2 Represents the full-period moth amount of the 1 generation adults, which is the average daily captured adult amount of continuous 5 days meeting preset conditions before triggering a short-term prediction model of the occurrence amount of the 2 generation larvae,
the acquiring meteorological data includes:
monitoring weather data is obtained from weather stations in the predicted area, the weather data including historical weather data and predicted weather data.
2. A system for predicting the extent of occurrence of athetis lepigone based on the prediction method of claim 1, comprising:
the first construction module is used for acquiring meteorological data, constructing a generation 1 adult occurrence prediction model, and predicting generation 1 adult occurrence according to the generation 1 adult occurrence prediction model when a model prediction triggering condition is met;
the second construction module is used for constructing a medium-term prediction model of the occurrence amount of 2-generation larvae and a short-term prediction model of the occurrence amount of 2-generation larvae based on the predicted occurrence amount prediction value of the 1-generation adults, the meteorological data and the local farming measure data monitored by remote sensing;
and the prediction module is used for predicting the occurrence amount of the 2-generation larva in the full period according to the 2-generation larva occurrence amount medium-term prediction model and the 2-generation larva occurrence amount short-term prediction model and outputting the occurrence degree level of the athetis lepigone.
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| CN103583282A (en) * | 2013-10-18 | 2014-02-19 | 固镇县万佳生态养殖场 | Prevention method for athetis lepigone moschler in Yi County |
| CN109034443A (en) * | 2018-06-05 | 2018-12-18 | 中国气象局乌鲁木齐沙漠气象研究所 | A method of the phase occurs for prediction bollworm |
| CN113689034A (en) * | 2021-08-19 | 2021-11-23 | 云南省气候中心(云南省生态气象和卫星遥感中心) | Method for comprehensively predicting growth suitability of spodoptera frugiperda |
| CN114511161A (en) * | 2022-04-21 | 2022-05-17 | 中化现代农业有限公司 | Method, device, equipment and storage medium for predicting opposita lepigone control due to right-time period |
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| CN109034443A (en) * | 2018-06-05 | 2018-12-18 | 中国气象局乌鲁木齐沙漠气象研究所 | A method of the phase occurs for prediction bollworm |
| CN113689034A (en) * | 2021-08-19 | 2021-11-23 | 云南省气候中心(云南省生态气象和卫星遥感中心) | Method for comprehensively predicting growth suitability of spodoptera frugiperda |
| CN114511161A (en) * | 2022-04-21 | 2022-05-17 | 中化现代农业有限公司 | Method, device, equipment and storage medium for predicting opposita lepigone control due to right-time period |
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