Persian Journal of Acarology

Evaluation of geostatistical methodand hybridArtificial Neural Network with imperialist competitive algorithmfor predicting distribution pattern of Tetranychusurticae(Acari: Tetranychidae) in cucumber field of Behbahan, Iran

Authors

Alireza Shabaninejad 1
Bahram Tafaghodinia 2
Nooshin Zandi-Sohani 3

1 Departmentof Plant Protection, Faculty of Agriculture, Shahrood University, Shahrood, Iran;

2 Departmentof Plant Production and Sustainable Agriculture, Iranian Research Organization for Science and Technology, Tehran, Iran;

3 Department of Plant Protection, Faculty of Agriculture, Ramin Agricluture and Natural Resources University of Khuzestan, Ahvaz, Iran;

https://doi.org/10.22073/pja.v6i4.30295
Abstract
In this study, the statistical methods and artificial neural network (ANN) were used to estimate the spatial distribution of Tetranychus urticae in cucumber field of Behbahan, Iran. Pest density assessments were performed following a 10 × 10 m2 grid pattern on the field and a total of 100 sampling units on field. In both methods latitude and longitude information were used as input data and output of each methods showed number of pest. In Geostatistics methods ordinary kriging, and ANN with imperialist competitive algorithm were evaluated. Comparison of ANN and geostatistical showed that ANN capability is more than ordinary kriging method so that the ANN predicts distribution of this pest dispersion with 0.98 coefficient of determination and 0.0038 mean squares errors lower than the Geostatistical methods. In general, it can be concluded that the ANN with imperialist competitive algorithm approach with combining latitude and longitude can forecast pest density with sufficient accuracy. Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field.

Keywords

Algorithm
kriging
pest dispersion
statistical methods
variogram
Persian Journal of Acarology
Volume 6, Issue 4
Autumn 2017
Pages 315-328

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