Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm
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 Optimizatio...
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| Format: | Article |
| Language: | English |
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Entomological Society of Iran
2020-11-01
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| Series: | نامه انجمن حشرهشناسی ایران |
| Subjects: | |
| Online Access: | https://jesi.areeo.ac.ir/article_123148_798969f272daaa6c313714cd93c6343f.pdf |
| _version_ | 1797973755573567488 |
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| author | M. Aleosfoor K. Minaei Faezeh Bagheri |
| author_facet | M. Aleosfoor K. Minaei Faezeh Bagheri |
| author_sort | M. Aleosfoor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T04:09:16Z |
| format | Article |
| id | doaj.art-0f1a4ad99e5246ba9f9a9d0085e16793 |
| institution | Directory Open Access Journal |
| issn | 0259-9996 2783-3968 |
| language | English |
| last_indexed | 2024-04-11T04:09:16Z |
| publishDate | 2020-11-01 |
| publisher | Entomological Society of Iran |
| record_format | Article |
| series | نامه انجمن حشرهشناسی ایران |
| spelling | doaj.art-0f1a4ad99e5246ba9f9a9d0085e167932023-01-01T10:03:33ZengEntomological Society of Iranنامه انجمن حشرهشناسی ایران0259-99962783-39682020-11-0140329530810.22117/jesi.2020.127759.1366123148Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithmM. Aleosfoor0K. Minaei1Faezeh Bagheri2Department of Plant Protection, College of Agriculture, Shiraz University, Shiraz, Iran.Department of Plant Protection, College of Agriculture, Shiraz University, Shiraz, Iran.Department of Plant Protection, College of Agriculture, Shiraz University, Shiraz, Iran.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.https://jesi.areeo.ac.ir/article_123148_798969f272daaa6c313714cd93c6343f.pdfneural networkspatial distributionbird cherry-oat aphid |
| spellingShingle | M. Aleosfoor K. Minaei Faezeh Bagheri Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm نامه انجمن حشرهشناسی ایران neural network spatial distribution bird cherry-oat aphid |
| title | Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm |
| title_full | Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm |
| title_fullStr | Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm |
| title_full_unstemmed | Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm |
| title_short | Predicting distribution pattern of Rhopalosiphum padi (Hemiptera: Aphididae) by hybrid neural network using particle swarm optimization algorithm |
| title_sort | predicting distribution pattern of rhopalosiphum padi hemiptera aphididae by hybrid neural network using particle swarm optimization algorithm |
| topic | neural network spatial distribution bird cherry-oat aphid |
| url | https://jesi.areeo.ac.ir/article_123148_798969f272daaa6c313714cd93c6343f.pdf |
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