<p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p>
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...
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| Format: | Article |
| Language: | English |
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Acarological Society of Iran
2017-10-01
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| Series: | Persian Journal of Acarology |
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| Online Access: | https://www.biotaxa.org/pja/article/view/30295 |
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| author | Alireza Shabaninejad Bahram Tafaghodinia Nooshin Zandi-Sohani |
| author_facet | Alireza Shabaninejad Bahram Tafaghodinia Nooshin Zandi-Sohani |
| author_sort | Alireza Shabaninejad |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-b4705434b54546a281515cd0d8df7264 |
| institution | OA Journals |
| issn | 2251-8169 |
| language | English |
| publishDate | 2017-10-01 |
| publisher | Acarological Society of Iran |
| record_format | Article |
| series | Persian Journal of Acarology |
| spelling | doaj-art-b4705434b54546a281515cd0d8df72642025-08-20T01:57:04ZengAcarological Society of IranPersian Journal of Acarology2251-81692017-10-016410.22073/pja.v6i4.30295<p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p>Alireza ShabaninejadBahram TafaghodiniaNooshin Zandi-Sohani 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. https://www.biotaxa.org/pja/article/view/30295Algorithmkrigingpest dispersionstatistical methodsvariogram |
| spellingShingle | Alireza Shabaninejad Bahram Tafaghodinia Nooshin Zandi-Sohani <p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> Persian Journal of Acarology Algorithm kriging pest dispersion statistical methods variogram |
| title | <p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> |
| title_full | <p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> |
| title_fullStr | <p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> |
| title_full_unstemmed | <p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> |
| title_short | <p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> |
| title_sort | p strong evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of em tetranychus em em urticae em acari tetranychidae in cucumber field of behbahan iran strong p |
| topic | Algorithm kriging pest dispersion statistical methods variogram |
| url | https://www.biotaxa.org/pja/article/view/30295 |
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