Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm

Document Type : Paper, Persian

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Department of Plant Protection, College of Agriculture, Shiraz University, Shiraz, Iran.

Abstract

Nowadays, with the advent of powerful statistical techniques and neural networks, predictive models of distribution have been rapidly developed in ecology. This study was carried out to model distribution of aphid, Rhopalosiphum padi,using MLP neural networks combined with Particle Swarm Optimization in wheat fields of Badjgah area, Fars province. Population data of the pest was obtained by sampling at 100 locations across wheat fields during 2013. For evaluation the capability of neural networks used in dispersal prediction, statistical comparison of parameters such as mean, variance, statistical distribution of spatial predicted values by neural network and their actual values, were conducted. Results showed that there were not significant differences between variance, mean and statistical distribution of actual and predicted values in training and test phases of neural network combined Particle Swarm Optimization algorithm. Our map showed a patchy pest distribution offers large potential for using site-specific pest control on this field.

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