Modeling the population changes of sunn pest with environmental variables using artificial neural network and comparison with the linear regression model in Chadegan County

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Plant Protection, College of Agriculture, Razi University, Kermanshah, Iran

2 Assistant Professor, Department of Plant Protection, College of Agriculture, Razi University, Kermanshah, Iran

3 Associate Professor, Department of Plant Protection, College of Agriculture, Razi University, Kermanshah, Iran

4 Assistant Professor, Department of Biosystem Mechanic Enginnering, College of Agriculture, Razi University, Kermanshah, Iran

Abstract

This study aimed to predict population fluctuation of sunn pest in the field using artificial neural network and multiple linear regression was performed. The data on population fluctuation of Sunn pest in years 2015 and 2016 on a farm with an area of one hectare in the city Chadegan was obtained. In this model of the variables sampling date, the average temperature, average relative humidity, wind speed, wind direction, rainfall as the input variables and population changes mother Sunn pest was used as the outcome variable. The network was used of type Multilayer Perceptron with back propagation algorithm and was learning method Levenberg Markvart. Results showed between these two models, artificial neural network with coefficient of determination 0.96 better than regression with coefficient of determination 0.40 population density of mother Sunn pest was predicted. After sensitivity analysis model for easier and factors more effective extraction, four factors: the number of days of the year, temperature, humidity and wind speed were selected. Neural network model was trained again using the four factor model and a model with 11 hidden layer gave the best result. The coefficient of determination testing stepe was 0.97 that was showed high accuracy relative to the multiple linear regression model with the coefficient of determination 0.43.

Keywords

References

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Iranian Journal of Plant Protection Science
Volume 48, Issue 2 - Serial Number 2
February 2018
Pages 307-315

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History

  • Receive Date: 03 April 2017
  • Revise Date: 11 November 2017
  • Accept Date: 18 December 2017

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