CN107229991B - Distribution prediction method for pest plant rate in corn borer region - Google Patents
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Abstract
The invention provides a distribution prediction method of pest-affected plant rate in a corn borer area, which comprises the steps of obtaining key elements for predicting the regional distribution of the corn borers in a small area through aerial shooting of an unmanned aerial vehicle and manual field investigation, obtaining the source base number of the corn borers and the pest-affected plant rate of the corn borers through the manual field investigation, and constructing a correlation function of the regional distribution of the corn borers; and correcting the spatial distribution prediction model of the corn borers by utilizing the actually measured plant pest ratio data of the corn borers according to the time axis of the hatching and development of the corn borers. And performing spatial interpolation on the pest-suffered plant rate by using the optimal semi-variation function model obtained by fitting the pest-suffered plant rate to obtain a spatial distribution grade map of the pest-suffered plant rate. The invention solves the defects that the existing analysis method ignores the spatial correlation and can not predict the corn borer in a small area.
Description
Technical Field
The invention relates to the technical field of pest control, in particular to a distribution prediction method of pest plant rates in a corn borer area.
Background
The corn borers are main pests of corn, can cause the corn to reduce the yield by 5 to 10 percent in the light-age period, and can cause the corn to reduce the yield by more than 30 percent in the big-age period. The existing research on the corn borer is basically reserved in county-level or larger areas, the requirements of releasing trichogramma and variably spraying the medicament in small areas (such as field blocks) according to needs cannot be met, and more serious medicament waste and environmental pollution are caused. The research and application in large scale and small area range should first clarify the spatiotemporal distribution characteristics of corn borer. The requirement of accurate prevention and treatment is increasingly urgent under the aim of realizing the double reduction of fertilizers and drugs in 2020 proposed in China.
In the aspect of the research on the spatiotemporal diffusion of the corn borer, the sampling, prediction and prevention are generally carried out on a large area at present, and the function description is difficult. The method utilizes the geostatistics half-variation function to research the diffusion characteristics of the European corn borer larvae in a small area, and reveals the spatial diffusion and distribution rule of the larvae. But the research on the aspects of quickly reflecting the space distribution in a small area to provide reference for variable control and the like is less through the investigation of the plant rate of the pests.
Disclosure of Invention
The present invention provides a method for predicting the distribution of pest infestation rates in a region of corn borer that overcomes or at least partially solves the above problems.
According to one aspect of the invention, a method for predicting distribution of pest damage rates in a corn borer region is provided, which comprises the following steps:
s1, acquiring the pest-affected plant rate of each sampling point in the corn borer area based on the corrected spatial distribution prediction model of the corn borers, and acquiring the spatial variation coefficient of the pest-affected plant rate based on the standard deviation and the average value of the pest-affected plant rate of each sampling point;
s2, when the spatial variation coefficient of the pest-suffered plant rate is larger than a first threshold value and the pest-suffered plant rate of each sampling point accords with normal distribution, fitting errors of the pest-suffered plant rate based on a plurality of semi-variation function models, and selecting an optimal semi-variation function model; and
and S3, obtaining the distribution condition of the pest plant rate of the corn borer region by using a common Kriging interpolation method based on the optimal semi-variation function model.
The invention utilizes the actually measured plant rate data of the corn borers to correct the spatial distribution prediction model of the corn borers, predicts the plant rate of the corn borers at a sampling point based on the corrected spatial distribution prediction model of the corn borers, judges whether spatial interpolation is necessary or not based on the variation condition of the plant rate of the corn borers, further guides the subsequent pesticide spraying mode, obtains the optimal semi-variation function model through the plant rate of the corn borers, more accurately speculates the spatial diffusion characteristic of the corn borers, obtains the plant rate of the corn borers in the whole area through the common Kerrikin interpolation method, and solves the defects that the existing analysis method ignores the spatial correlation and can not predict the corn borers in a small area.
Drawings
Fig. 1 is a schematic flow chart of a method for predicting the distribution of pest damage rates in a region of ostrinia nubilalis according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The invention provides a distribution prediction method for the rate of plants damaged by insect pests in a corn borer region in a small region, aiming at overcoming the problems that in the prior art, the general prediction is mainly carried out on a large-scale corn borer region through the influence factors such as climatic conditions, the annual occurrence degree and the like, the spatial correlation is ignored, and the error of the prediction result is large.
Fig. 1 shows a schematic flow chart of a distribution prediction method according to an embodiment of the present invention, and as can be seen, the prediction method includes:
s1, acquiring the pest-affected plant rate of each sampling point in the corn borer area based on the corrected spatial distribution prediction model of the corn borers, and acquiring the spatial variation coefficient of the pest-affected plant rate based on the standard deviation and the average value of the pest-affected plant rate of each sampling point;
s2, when the spatial variation coefficient of the pest-suffered plant rate is larger than a first threshold value and the pest-suffered plant rate of each sampling point accords with normal distribution, fitting errors of the pest-suffered plant rate based on a plurality of semi-variation function models, and selecting an optimal semi-variation function model; and
and S3, obtaining the distribution condition of the pest plant rate of the corn borer region by using a common Kriging interpolation method based on the optimal semi-variation function model.
The invention utilizes the actually measured plant rate data of the corn borers to correct the spatial distribution prediction model of the corn borers, predicts the plant rate of the corn borers at a sampling point based on the corrected spatial distribution prediction model of the corn borers, judges whether spatial interpolation is necessary or not based on the variation condition of the plant rate of the corn borers, further guides the subsequent pesticide spraying mode, obtains the optimal semi-variation function model through the plant rate of the corn borers, more accurately speculates the spatial diffusion characteristic of the corn borers, obtains the plant rate of the corn borers in the whole area through the common Kerrikin interpolation method, and solves the defects that the existing analysis method ignores the spatial correlation and can not predict the corn borers in a small area.
The method for acquiring the pest-receiving plant rate of each sampling point in the step S1 of the invention can obviously be acquired by manual collection in spring, and although the distribution of the pest-receiving plant rate in the corn borer area can be finally acquired, since the distribution is predicted in spring and is actually late, the pest-receiving plant rate in the corn borer area can be predicted before the corn borer adults breed in spring, which is another urgent problem to be solved.
The development and harm of the corn borers have the development rule of the corn borers. Before the corn is mature, the larvae of the corn borers enter the straws, 70-80% of the corn borers live through the winter in the straw stack, and about 20% of the corn borers live through the winter at the root parts of the corn. Therefore, the corn borers in the corn stack become the main insect source of the corn borers of the second generation. 95% of the migration distance of the first generation of the corn borers is within the range of 4km, and 80% of the migration distance of the corn borers is within the range of 1 km; 98% of the migration distance of the second generation corn borers is within the range of 2 km. Therefore, the corn borers are not long-distance migratory insects, which is the basic basis for predicting the regional distribution of the corn borers.
Factors influencing the diffusion and development of the corn borer are many and mainly related to the quantity of insect sources and the diffusion distance. The corn borer has a spatial spreading line for larvae and adults and is dominated by the large-scale spreading of adults. The adults need to ovulate and develop into larvae, and the larvae destroy crops. Because the migration radius of the larva is extremely small and can be ignored, the space diffusion characteristic of the adult can be estimated according to the plant rate of the insect pests. On the contrary, the spatial distribution variation of the pest-suffered plant rate in the area can be estimated by utilizing the survey data of the pest-suffered plant rate.
The current prediction and forecast of the corn borers are mainly carried out in a large range, and probabilistic prediction is carried out according to influence factors such as climatic conditions, annual occurrence degree and the like. Accurate prediction for small areas is a challenging task with higher difficulty, because the corn borers mainly diffuse through the space flight of adults, and damage the corn through laying eggs and developing into larvae, a certain time is needed to pass between the corn borers, and external factors can generate necessary interference on the laying eggs and the development of the larvae. That is, the larval survey data (pest population) does not truly reflect the actual situation of adult spread.
In one embodiment, step S1 specifically includes: the step S1 of obtaining the pest-infected plant rate of each sampling point in the corn borer area comprises the following steps:
s1.1, establishing a correlation function of an insect source base number and an insect pest receiving rate based on a diffusion distance matrix of the corn borers, a corrected corn borer spatial distribution prediction model and an insect source base number matrix; and
s1.2, obtaining the pest-suffered plant rate based on the correlation function and the collected pest source base number.
In one embodiment, said step S1.1 comprises:
substituting each element in the diffusion distance matrix into the corrected spatial distribution prediction model of the ostrinia nubilalis to obtain a diffusion weight matrix;
multiplying the diffusion weight matrix with the insect source base number matrix to obtain a pest-affected plant rate fitting matrix; and
performing regression analysis on the pest-suffered plant rate fitting matrix and the pest-suffered plant rate matrix to obtain a correlation function of the pest source base number and the pest-suffered plant rate;
wherein, the elements in the diffusion distance matrix are the diffusion distances from each pest-receiving plant rate sampling point to each pest source base number sampling point;
elements in the insect source base number matrix are the insect source base number of each insect source base number sampling point; and
and the element in the pest plant rate matrix is the pest plant rate of each pest plant rate sampling point.
The insect pest receiving rate is predicted based on the insect source base number, and the method is easy to understand, because most of generation corn borer larvae are developed through overwintering larvae in the straw stacks, in one embodiment, grid division is carried out on the corn borer source region to obtain a plurality of grid points, the straw stacks in the grid points are randomly sampled, 20 corn straws are randomly selected at each grid point, the insect source base number, also called the insect content of the corn borers at each grid point is calculated, and the value is between 0 and 1.
Because the diffusion distance is the key weight of the insect source quantity fitting insect pest receiving rate, the distribution density of the corn borer adults is in a rapid descending trend along with the increase of the diffusion distance. Specifically, within the range of 2km, the descending speed is high; then, the descending speed is slowed down; and when the range exceeds 4km, the range is almost negligible. Therefore, the invention obtains a formula (1) as a relational expression of the diffusion weight coefficient and the diffusion distance through comprehensive comparison, namely an original corn borer spatial distribution prediction model.
k＝-0.071d+0.167 (1)
Wherein d is an element in the diffusion distance matrix, the unit is km, d is less than 2.3, and k is a diffusion weight coefficient.
And forming a distance matrix D based on the space distance D from each pest-receiving plant rate sampling point to each pest source base number sampling point.
Wherein d [ (x)m,ym)(xn,yn)]And the space distance from the mth overwintering period sampling point to the nth worm source base number sampling point is represented and obtained based on GNSS coordinates.
Substituting each element in the distance matrix D into an original formula for predicting the spatial distribution of the ostrinia nubilalis to obtain a weight matrix K, wherein obviously, the weight matrix K is also a matrix with m rows and n columns.
Obtaining a hundred-stalk insect-content matrix X based on the hundred-stalk insect content of each grid point, wherein the matrix X is a matrix with 1 row and n columns, and n is equal to the number of the insect source base number sampling points:
X＝[a1a2…an]T
wherein, anRepresenting the content of the petiole at the sampling point of the nth worm source base.
And multiplying the weight matrix K by the insect-content-in-hundreds-rods matrix X to obtain a fitting matrix X'.
Obtaining a pest-receiving plant rate matrix Y based on the pest-receiving plant rate of each overwintering period sampling point, wherein the matrix Y is a matrix with m rows and 1 column, and m is equal to the number of the pest-receiving plant rate sampling points:
Y＝[b1b2…bm]
wherein, bmThe pest rates of the m-th pest rate sampling points are shown.
And performing regression analysis on the matrix X' and the matrix Y to obtain an expression for expressing the quantitative relationship between the insect source base number and the insect pest strain rate matrix. For example, Y ═ 0.11X' + 13.06.
And when the pest-receiving plant rate of the pest-receiving plant rate sampling points after overwintering is predicted based on the original spatial distribution prediction model of the ostrinia nubilalis, correcting the original spatial distribution prediction model of the ostrinia nubilalis according to the correlation between the actually acquired pest-receiving plant rate after overwintering and the predicted pest-receiving plant rate, and obtaining the corrected spatial distribution prediction model of the ostrinia nubilalis as shown in a formula (2).
k＝-0.011d3+0.083d2-0.22d+0.22 (2)
The-0.011, 0.083 and 0.22 in the formula (2) are obtained by the applicant through an infinite number of hard deductions, and the existence of the values establishes a basis for the accuracy of the correlation function of the insect source base and the insect pest ratio.
Therefore, the pest-receiving plant rate after the wintering period is estimated according to the collected pest source base number of the wintering period, the estimated pest-receiving plant rate data is compared with the actual pest-receiving plant rate, if the correlation number is larger than a second threshold value, the accuracy of the correlation function is higher, and the pest-receiving plant rate of the sampling point does not need to be obtained manually. In addition, due to the fact that the pest-suffered plant rate data at the future time can be obtained in the wintering period, a plan can be made for pesticide release in advance, and the prediction value is greatly improved.
Before obtaining the corrected spatial distribution prediction model of the corn borers, the method further comprises the following steps: acquiring an image of a corn borer area, and extracting spatial distribution of a straw stack and a corn field in the image through visual interpretation.
Visual interpretation is a kind of remote sensing image interpretation, also called visual interpretation, or visual interpretation, and is the reverse process of remote sensing imaging. The method refers to a process of acquiring information of a specific target ground object on a remote sensing image by a professional through direct observation or by means of an auxiliary interpreter.
Randomly sampling a corn field, randomly extracting 100 corns from each sampling point, and recording the pest-damaged plant rate of each sampling point and the GNSS coordinates of the sampling points, wherein the value of the pest-damaged plant rate is between 0 and 1.
The GNSS is generally referred to as Global Navigation Satellite System (Global Navigation Satellite System) and generally refers to all Satellite Navigation systems, including Global, regional, and enhanced Satellite Navigation systems, such as GPS in the united states, Glonass in russia, Galileo in europe, beidou Satellite Navigation System in china, and related augmentation systems.
In one embodiment, standard deviation and average value of pest receiving rate are calculated based on pest receiving rate of all sampling points, and ratio of the standard deviation and the average value of pest receiving rate is used as Coefficient of Variation (CV). Defining the first threshold as 10%, if CV < 10%, there is slight variation in the explanatory variable; if 10% < CV < 100%, the explanatory variable has a moderate degree of variation; if CV > 100%, the explanatory variable has intensity variation.
The degree of variation is referred to as the degree of variation. The high and low of the coefficient of variation determines that the kriging interpolation is not necessary, and if the coefficient of variation is low, the difference of the rates of the plants suffered from the insect pests in the area is proved to be avoided, the interpolation is not necessary. The large variation coefficient indicates that the difference of plant rates of the pests in the area is large, and the spatial interpolation is necessary, namely, the pesticide is sprayed in a variable mode, otherwise, the uniform pesticide spraying mode can be directly adopted.
In the aspect of regional diffusion research of corn borers, spatial correlation is ignored in the traditional statistical analysis method, geostatistics have superiority due to the fact that correlation among samples is considered, and geostatistics are a method for researching spatial variation and spatial structure of natural phenomena on the basis of a regional variable theory with spatial distribution characteristics. The geostatistics uses a semivariance function as a main tool and is widely applied to the fields of soil, agriculture, meteorology, ecology and the like.
When the variation coefficient of the pest-receiving plant rate is moderate or has intensity variation, the invention further calculates that the pest-receiving plant rate of each sampling point conforms to normal distribution, and selects an optimal semi-variation function model when conforming to the normal distribution, so as to fit the spatial distribution of the pest-receiving plant rate of the corn borer by utilizing a Krigin interpolation method, thereby providing theoretical basis and feasible method for the prevention and treatment of the corn borer according to small areas.
In one embodiment, the pest-receiving plant rate value of a sampling point is used as an input item, error results are fitted by utilizing three semi-variable function models of a sphere, an index and a gauss respectively, and the optimal semi-variable function model of the spatial distribution of the pest-receiving plant rate is obtained based on values of ME (mean predicted error), MSE (mean standard deviation), RMSE (root mean square error) and ASE (mean Kriging standard deviation) and RMSSE (mean estimated root mean square standard deviation) obtained by the 3 semi-variable function models.
As the values of ME, MSE, RMSE and ASE are closer to 0 and the values of RMSSE are closer to 1, the calculation error of the model is smaller, and therefore, through comprehensive analysis, the 0-order exponential model is the optimal semi-variation function model of the spatial distribution of the rates of the plants suffering from insect pests.
In one embodiment, the selected optimal semi-variogram model is subjected to ARCMAP software to obtain fitting parameters of the model: a range value, a lump value, and a base value. The base value represents the total variability within the region, i.e., the plateau value of the half-variogram that occurs after the distance increases to some extent, and when the value of the variogram exceeds the base value, i.e., the half-variogram does not change with the sample separation distance, there is no spatial correlation. When looking at the model of the semi-variant function, it is found that the model exhibits a horizontal state at the characteristic distance. The distance that the model first assumes a horizontal state is called the range. Sample positions separated by a distance greater than the range are spatially auto-correlated, while sample positions whose distance results from the range are not spatially auto-correlated. Theoretically, at zero pitch (step size ═ 0), the value of the half-variogram is 0, but at very small pitches, the half-variogram usually exhibits the block-gold effect, i.e., a value greater than 0. For example, if the semi-variogram model has an intercept of 2 on the y-axis, the block gold is 2.
In one embodiment, a distribution diagram of pest-affected plant rate of the whole corn borer area can be obtained by a semi-variation function model with fitting parameters and a Poweringin interpolation method. Based on the distribution map, a spraying prescription map or a natural enemy release map can be further generated, and the variable spraying of the pesticide and the release of the natural enemies of the corn borers can be realized according to the requirements.
In one embodiment, the prediction method further comprises:
grading the pest-receiving plant rate, and displaying the corresponding grade of the interpolated pest-receiving plant rate distribution situation on the image of the region of the ostrinia nubilalis to obtain a pest-receiving plant rate grade distribution map.
In one embodiment, the prediction method further comprises:
and obtaining a spraying metering or throwing amount of the natural enemies of the corn borers based on the pest-receiving plant rate grade distribution map, and displaying the spraying metering or throwing amount of the natural enemies of the corn borers on an image of the region of the corn borers to obtain a spraying prescription map or a throwing map of the natural enemies.
Compared with uniform control, variable control can effectively save the medicament and reduce pollution. The variable control dose can be calculated by the following formula:
wherein, the total dosage is/kg; i is the emergence grade of the corn borers; a. theiFarmland area/ha of grade i; sigmaiDosage/kg-ha of grade i-1。
By verification, the pesticide is sprayed on farmlands with the same area, and under the condition of the same effect, the pesticide spraying amount specified by combining the level of the pest-receiving rate is 12.7 percent less than the pesticide spraying amount for uniform control.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A distribution prediction method for pest plant damage rate in a corn borer region is characterized by comprising the following steps:
s1, acquiring the pest-receiving plant rate of each sampling point in the corn borer area after the wintering period based on the insect source base number of each sampling point in the wintering period and the corrected spatial distribution prediction model of the corn borers, and acquiring the spatial variation coefficient of the pest-receiving plant rate based on the standard deviation and the average value of the pest-receiving plant rate of each sampling point;
s2, when the spatial variation coefficient of the pest-receiving plant rate is larger than a first threshold value and the pest-receiving plant rate of each sampling point accords with normal distribution, fitting errors of the pest-receiving plant rate based on a plurality of semi-variation function models, and selecting an optimal semi-variation function model; and
s3, based on the optimal semi-variation function model, obtaining the distribution condition of the pest plant rates of the corn borer region by using a common Krigin interpolation method;
the step S1 of obtaining the pest-infected plant rate of each sampling point in the corn borer area comprises the following steps:
s1.1, establishing a correlation function of an insect source base number and an insect pest receiving rate based on a diffusion distance matrix of the corn borers, a corrected corn borer spatial distribution prediction model and an insect source base number matrix; and
s1.2, acquiring the pest-damaged plant rate based on the correlation function and the acquired pest source base number;
wherein step S1.1 comprises:
substituting each element in the diffusion distance matrix into the corrected spatial distribution prediction model of the ostrinia nubilalis to obtain a diffusion weight matrix;
multiplying the diffusion weight matrix with the insect source base number matrix to obtain a pest-affected plant rate fitting matrix; and
performing regression analysis on the pest-suffered plant rate fitting matrix and the pest-suffered plant rate matrix to obtain a correlation function of the pest source base number and the pest-suffered plant rate;
wherein, the elements in the diffusion distance matrix are the diffusion distances from each pest-receiving plant rate sampling point to each pest source base number sampling point;
elements in the insect source base number matrix are the insect source base number of each insect source base number sampling point; and
the element in the pest-affected plant rate matrix is the pest-affected plant rate of each pest-affected plant rate sampling point;
the step S1.2 comprises:
acquiring an insect source base number and a GNSS coordinate of an insect source base number sampling point in an overwintering period, and acquiring the GNSS coordinate of a pest-suffered plant rate sampling point after the overwintering period; and
substituting the GNSS coordinates of the pest-receiving plant rate sampling points, and the pest-source base numbers and the GNSS coordinates of the pest-source base number sampling points into the correlation function to obtain the pest-receiving plant rate of the pest-receiving plant rate sampling points; the GNSS coordinates are global navigation satellite system coordinates.
2. The method for predicting the distribution of pest infestation rates in an area of ostrinia nubilalis as claimed in claim 1, wherein said step S1 is preceded by the steps of:
and S0, carrying out empirical value speculation according to the migration rule of the ostrinia nubilalis to obtain an original ostrinia nubilalis spatial distribution prediction model, predicting the insect pest rate of an insect pest rate sampling point after overwintering based on the original ostrinia nubilalis spatial distribution prediction model, correcting the original ostrinia nubilalis spatial distribution prediction model according to the correlation degree of the insect pest rate actually collected after overwintering and the predicted insect pest rate, and obtaining the corrected ostrinia nubilalis spatial distribution prediction model.
3. The method for predicting the distribution of pest infestation rates in an area of ostrinia nubilalis as claimed in claim 2, wherein said step S0 is preceded by the steps of:
acquiring an image of a corn borer area, and extracting spatial distribution of a straw stack and a corn field in the image through visual interpretation;
taking the straw stacks as a corn borer source area and carrying out grid division on the corn borer source area to obtain a plurality of grid points; and
and randomly taking part of the grid points as the insect source base number sampling points, and randomly selecting the sites in the corn field as the insect pest receiving plant rate sampling points.
4. The method for predicting the distribution of pest-affected plant rates in the ostrinia nubilalis area according to claim 2, wherein the correcting the original ostrinia nubilalis spatial distribution prediction model according to the correlation degree between the actually collected ostrinia nubilalis pest-affected plant rate after overwintering and the predicted pest-affected plant rate specifically comprises:
collecting the insect pest rate of the corn borers at insect pest rate sampling points after overwintering;
and calculating a correlation coefficient between the predicted pest-suffered plant rate and the actually collected pest-suffered plant rate, and if the correlation coefficient is smaller than a second threshold, adjusting the spatial distribution prediction model of the ostrinia nubilalis until the correlation coefficient is larger than the second threshold.
5. The method for predicting the distribution of pest infestation rates in a region of ostrinia nubilalis as claimed in claim 1, wherein said diffusion weight coefficient is expressed by:
k= - 0.011d3+ 0.083d2- 0.22d + 0.22
where d is an element in the diffusion distance matrix and k is a diffusion weight coefficient.
6. The method for predicting the distribution of pest infestation rates in a region of ostrinia nubilalis as claimed in claim 1, wherein said step S2 comprises:
s2.1, setting a first threshold value of the variation coefficient, and outputting an error result by respectively utilizing a spherical, exponential and Gaussian semi-variation function model by taking a value of a plant suffering from the insect pest of a sampling point as an input item when the variation coefficient is larger than the first threshold value; and
s2.2, fitting error results of pest rates based on the three semi-variable function models to obtain an optimal semi-variable function model;
wherein the error result comprises an average prediction error, an average standard deviation, a root mean square error, an average kriging standard deviation and a consistency estimation root mean square standard deviation.
7. The method of predicting the distribution of pest exposure rates in an area of corn borer according to claim 6, further comprising:
and S4, grading the pest-suffered plant rate, and displaying the grade corresponding to the interpolated pest-suffered plant rate on the image of the region of the ostrinia nubilalis to obtain a pest-suffered plant rate grade distribution map.
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