CN104036129A - Tea empoasca vitis gothe forecasting expert knowledge base and construction method for same - Google Patents
Tea empoasca vitis gothe forecasting expert knowledge base and construction method for same Download PDFInfo
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- CN104036129A CN104036129A CN201410249944.2A CN201410249944A CN104036129A CN 104036129 A CN104036129 A CN 104036129A CN 201410249944 A CN201410249944 A CN 201410249944A CN 104036129 A CN104036129 A CN 104036129A
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- eye leafhopper
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Abstract
The invention provides a tea empoasca vitis gothe forecasting expert knowledge base. The tea empoasca vitis gothe forecasting expert knowledge base is characterized in that the number (X1) of overwintering tea empoasca vitis gothe remaining in a field of a tea garden in January, the sum (X2) of the lowest air temperature of each of December in the last year to February and the average air temperature (X3) of February are selected as influencing factors. A method for predicting the number of tea empoasca vitis gothe at a first occurrence peak has the characteristics of high speed, accuracy and intuitiveness for the mastering of first peak occurrence dynamics of tea empoasca vitis gothe, and tea plant insect pest prediction and forecasting methods are enriched; the tea empoasca vitis gothe forecasting expert knowledge base is significant for guiding the comprehensive prevention and control of tea empoasca vitis gothe, and is also well used for researches on the prediction of occurrence of other crop insect pests as reference.
Description
Technical field
The present invention relates to expert knowledge library and build, be used in particular for observing and predicting the insect pest of tea false eye leafhopper.
Background technology
False eye leafhopper (Empoasca vitis Gothe) is that Ge Cha district of China distributes the most extensively, harm is serious, affects a class keypoint control insect of tea yield and quality.Year generation is many, generation overlap, year generation is different with area, most at 9-15 for left and right.In tea district, the middle and lower reach of Yangtze River of China, normal summer in time, autumn tea loss reach 10%～15%, and heavy famine year tealeaves lose up to more than 50%.
In view of the harmfulness of false eye leafhopper to tea place, how it is is accurately observed and predicted, just seem extremely important for the control of this worm.Quantity research shows greatly, and temperature, rainfall amount and rainfall number of days are the main weather factor that affects false eye leafhopper field insect population growth and decline in this region.The suitableeest ten days samming that this worm grows is 15～26 DEG C, time rain and moderate rainfall when fine, favourable its breeding when relative humidity 80% left and right; Insect population first contain send out peak period sooner or later with 2,3 month temperature significant correlations then, the second peak period sooner or later, is obviously subject to the impact of the equal relative humidity of 7,8 monthly gentleness.Research method comprises models such as setting up fuzzy comprehensive evaluation method, leafhopper growth, time series autoregression, simplifies the prediction insect population peak of occurrence such as stepping statistic law, weighting crosstab or occur dynamically.At present, not yet the expert knowledge library of report structure false eye leafhopper predicts that dynamic research occurs for it.Expert knowledge library predicted method is the regularity of occurrence and development according to disease and pest, by inquiry with collect the aspect data such as relevant worm amount, meteorologic factor and kind and cultivation management, contrast with historical summary, after comprehensive analysis, according to state and the variation tendency of main predictor, a situation arises to estimate disease and pest.
Summary of the invention
The object of the invention is to build the tea false eye leafhopper expert knowledge library that a kind of predictablity rate is high, be particularly useful for predicting the tea false eye leafhopper first peak of occurrence extent of injury.
The object of the invention is to realize by following measures:
Tea false eye leafhopper observes and predicts expert knowledge library, it is characterized in that: choose field, tea place in the March tea false eye leafhopper remaining mu insect amount (X that survives the winter
1), Dec～February last year monthly lowest temperature sum (X
2), 2 monthly mean temperature (X
3) as influence factor.There is to it effect developing in biological characteristics, environment that the present invention has considered tea false eye leafhopper, such as multiple meteorological factors (as the lowest temperature in February in Dec last year～then and sum thereof, January～April samming and sum, the quantity of precipitation in February～April and sum thereof, the monthly average sunshine time in March～April and sum, the relative humidity in March～April and sum thereof, 22 meteorologic factors such as warm rain coefficient and sum thereof in March～April), etc. multiple factors, the present invention can effectively improve the accuracy of forecast.
Above-mentioned influence factor is divided into respectively three grades, respectively: 1. X
1: critical value is 550/mu and 1100/mu, and 2 critical values are by X
1be divided into three codomain sections, IF1:0≤X
1≤ 550, IF2:550 < X
1≤ 1100, IF3:X
1> 1100; 2. X
2: critical value is 0 DEG C and 6 DEG C, and 2 critical values are by X
2be divided into 3 codomain sections, IF4:X
2≤ 0, IF5:0 < X
2≤ 6, IF6:X
2> 6.3. X
3, critical value is 5 DEG C and 10 DEG C, 2 critical values are by X
3be divided into 3 codomain sections, IF7:X
3≤ 5, IF8:5 < X
3≤ 10, IF9:X
3> 10.
Above-mentioned tea false eye leafhopper observes and predicts expert knowledge library, being divided into of the insect pest grade of tea false eye leafhopper: (1) light level: 12 of <; (2) middle rank: 12～24; (3) 24 of heavy duty: >.
Each section of above-mentioned different affecting factors constitutes criterion combination, sets according to each criterion combination the probability that each grade of insect pest occurs, and each section of above-mentioned 3 influence factors forms 27 conditional combinations altogether.Above-mentioned tea false eye leafhopper observes and predicts expert knowledge library, according to X
1, X
2, X
3observe and predict the probable value of the insect pest grade of false eye leafhopper, as shown in table 1.
Table 1
Above-mentioned tea false eye leafhopper observes and predicts the construction method of expert knowledge library, comprises following step mule:
(1) set tea false eye leafhopper first peak make a difference factor and critical value thereof;
(2) hazard rating of division tea false eye leafhopper insect pest;
(3) form the criterion of influence factor, and set tea false eye leafhopper hazard rating probability of happening;
(4), according to tea false eye leafhopper hazard rating probability of happening, verify predicting the outcome of expert knowledge library.
Beneficial effect
1. the present invention occurs dynamically grasping the first peak of this worm for the method for predicting tea false eye leafhopper the first peak of occurrence insect population quantity, have fast, accurately, feature intuitively, enriched Pests of Tea-Plants prediction methods.Integrated control to tea false eye leafhopper has directive significance.The present invention is also for the generation forecasting research of other crop pests provides good reference function.
2. the organize models of this knowledge base is the function and structure relevant tea false eye leafhopper knowledge based on " tea industrial technology system disease and pest expert system " and building, by forming a kind of network model between the criterion of knowledge description, feature critical value, generation and plague grade.
3. the present invention is for observing and predicting tea false eye leafhopper occurrence probability and insect pest grade in advance, more than rate of accuracy reached to 78.6%.
Embodiment
Below in conjunction with embodiment and embodiment, the present invention is further detailed explanation.
The influence factor X of forecast false eye leafhopper the first peak of occurrence value that the present invention chooses
1, X
2, X
3be conspicuousness correlative factor with it, false eye leafhopper remaining mu in March insect amount X
1(R=0.798*), Dec to February monthly lowest temperature sum X
2(R=-0.723*), 2 monthly mean temperature X
3(R=0.691*).
Choose this worm of field, tea place in March remaining mu insect amount (X that survives the winter
1), Dec～February last year monthly lowest temperature sum (X
2), 2 monthly mean temperature (X
3) three principal elements, build and be suitable for nationwide false eye leafhopper and observe and predict expertise database, and be applied to the prediction of false eye leafhopper first peak occurrence degree.
1, the setting of influence factor critical value
According to equal difference stage method, 3 influence factors are divided into respectively to three grades (table 2), respectively: 1. X
1: March field mu insect amount; Its critical value is 550/mu and 1100/mu, and 2 critical values can be divided into three codomain sections, IF1:0≤X
1≤ 550, IF2:550 < X
1≤ 1100, IF3:X
1> 1100.2. X
2: Dec last year to February is lowest temperature sum monthly; Its critical value is 0 DEG C and 6 DEG C, and these 2 critical values can be divided into temperature 3 codomain sections, IF4:X
2≤ 0, IF5:0 < X
2≤ 6, IF6:X
2> 6.3. X
3: February temperature on average, its critical value is 5 DEG C and 10 DEG C, these 2 critical values can be divided into temperature 3 codomain sections, IF7:X
3≤ 5, IF8:5 < X
3≤ 10, IF9:X
3> 10.
Table 2 critical value setting table
The division of 2, insect pest grade
Be divided into altogether Three Estate with false eye leafhopper field blinds insect population note:
(1) gently occur: 12 of <;
(2) in, occur: 12-24 head;
(3) retransmit life: 24 of >.
3, criterion forms with insect pest grade probability of happening and sets and build expert knowledge library
According to the institutional framework of knowledge base network model, each section of different affecting factors constitutes criterion combination, can set according to each criterion combination the probability (statistics by expertise and test figure is specified) that each grade of insect pest occurs.Each section of above-mentioned 3 influence factors forms 27 conditional combinations altogether.As: mu worm amount≤550 of surviving the winter, Dec last year to February monthly lowest temperature sum lower than 0 DEG C, February temperature on average≤5 DEG C, so, the condition that 3 influence factors combine is IF1 & IF4 & IF7, and the probability of false eye leafhopper occurrence degree is light by 63%, medium generation 20%, retransmit life 17%, predict the outcome as light occur (maximal values in 3 probability).It is as shown in table 3 that the tea false eye leafhopper building observes and predicts expert knowledge library.
Table 3
Remarks: set probability that each grade of insect pest occurs by expert rule of thumb or the statistics of test figure specify.
4, the result
2009～2013 years false eye leafhopper field investigation data that arrange according to 15 main product tea districts, the whole nation that 17 monitoring points provide and meteorological data, application expertise database authentication false eye leafhopper the 1st peak value, part monitoring point is due to the disappearance of meteorological data or leafhopper data, and the data that enter checking have 42.Predictablity rate reaches 78.6%.The results are shown in Table 4.
2009～2013 years, tea false eye leafhopper expert knowledge library was about 78.6% to 15, whole nation main product tea district leafhopper occurrence tendency and the checking coincidence rate that predicts the outcome.(in table 4, √ represents that the insect pest grade predicting the outcome with reality generation conforms to)
Claims (5)
1. tea false eye leafhopper observes and predicts expert knowledge library, it is characterized in that: choose field, tea place in the March tea false eye leafhopper remaining insect amount (X that survives the winter
1), Dec last year～February lowest temperature sum (X
2), 2 monthly mean temperature (X
3) as influence factor.
2. tea false eye leafhopper as claimed in claim 1 observes and predicts expert knowledge library, and described influence factor is divided into respectively three grades: 1. X
1critical value is 550/mu and 1100/mu, and 2 critical values are by X
1be divided into three codomain sections, IF1:0≤X
1≤ 550, IF2:550 < X
1≤ 1100, IF3:X
1> 1100; 2. X
2critical value is 0 DEG C and 6 DEG C, and 2 critical values are by X
2be divided into 3 codomain sections, IF4:X
2≤ 0, IF5:0 < X
2≤ 6, IF6:X
2> 6; 3. X
3critical value is 5 DEG C and 10 DEG C, and 2 critical values are by X
3be divided into 3 codomain sections, IF7:X
3≤ 5, IF8:5 < X
3≤ 10, IF9:X
3> 10.
3. tea false eye leafhopper as claimed in claim 1 observes and predicts expert knowledge library, being divided into of the insect pest grade of tea false eye leafhopper: (1) light level: 12 of <; (2) middle rank: 12～24; (3) 24 of heavy duty: >.
4. tea false eye leafhopper as claimed in claim 1 observes and predicts expert knowledge library, according to X
1, X
2, X
3observe and predict the insect pest grade probability of happening of false eye leafhopper, as shown in table 1;
Table 1
5. the tea false eye leafhopper as described in as arbitrary in claim 1～4 observes and predicts the construction method of expert knowledge library, comprises the following steps:
(1) set tea false eye leafhopper first peak make a difference factor and critical value thereof;
(2) hazard rating of division tea false eye leafhopper insect pest;
(3) form the criterion of influence factor, and set tea false eye leafhopper hazard rating probability of happening;
(4), according to tea false eye leafhopper hazard rating probability of happening, verify predicting the outcome of expert knowledge library.
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Cited By (2)
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| CN104794537A (en) * | 2015-04-17 | 2015-07-22 | 中国农业科学院柑桔研究所 | Method for building prediction models for unaspis yanonensis kuwana emergence periods of mandarins |
| CN112949917A (en) * | 2021-02-20 | 2021-06-11 | 廖廓 | Tea leafhopper insect pest early warning method and system based on meteorological data |
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