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Tea empoasca vitis gothe forecasting expert knowledge base and construction method for same

A technology of false-eye small green leafhopper and expert knowledge base, which is applied in the field of expert knowledge base construction to achieve the effect of enriching forecasting methods

Inactive Publication Date: 2014-09-10
CHONGQING ACAD OF AGRI SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there is no report on the construction of an expert knowledge base for the false-eyed green leafhopper to predict its occurrence dynamics

Method used

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  • Tea empoasca vitis gothe forecasting expert knowledge base and construction method for same
  • Tea empoasca vitis gothe forecasting expert knowledge base and construction method for same
  • Tea empoasca vitis gothe forecasting expert knowledge base and construction method for same

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Embodiment Construction

[0022] The present invention will be described in further detail below in conjunction with examples and specific implementation methods.

[0023] The present invention selects the influence factor X of forecasting the first occurrence peak of false-eyed green leafhopper 1 、X 2 、X 3 Rather than being a significant correlation factor, the remaining mu insect population of Leafhopper with false eyes in March X 1 (R=0.798*), the sum of the monthly minimum temperature from December to February X 2 (R=-0.723*), average temperature in February X 3 (R=0.691*).

[0024] Select the number of remaining mu insect population (X 1 ), the sum of the monthly minimum temperature from December to February of the previous year (X 2 ), the average temperature in February (X 3 ) three main factors, to build a nationwide expert knowledge database for forecasting and forecasting of Leafhopper, and apply it to the prediction of the first peak occurrence of Leafhopper.

[0025] 1. Setting of c...

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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 [0001] The invention relates to the construction of an expert knowledge base, and is especially used for forecasting and reporting the pest damage of the tea leafhopper. Background technique [0002] Empoasca vitis Gothe is one of the most widely distributed and serious pests in tea areas in my country, which affects the yield and quality of tea leaves. The annual algebra is many, and the generations overlap. The annual algebra varies with regions, and most of them are about 9-15 generations. In the tea areas in the middle and lower reaches of the Yangtze River in my country, the loss of tea in summer and autumn in normal years is 10% to 15%, while the loss of tea in severe disaster years is as high as more than 50%. [0003] In view of the harmfulness of the false-eyed green leafhopper to tea gardens, how to accurately forecast it is very important for the prevention and control of this insect. A large number of studies have shown that temperature, rainfal...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 王晓庆徐一茗李品武彭萍
Owner CHONGQING ACAD OF AGRI SCI
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