مقایسه عملکرد شبکه عصبی مصنوعی بهینه شده با الگوریتم رقابت استعماری و جهش قورباغه مخلوط شده برای پیش‌بینی الگوی توزیع کفشدوزک هفت‌نقطه‌ای Coccinella septempunctata در مزرعه یونجه شهرستان زرقان

نوع مقاله : حشره شناسی کشاورزی

نویسندگان

1 دانشجوی دکترا رشته حشره شناسی کشاورزی دانشگاه رازی کرمانشاه

2 عضو هیت علمی دانشگاه شیراز

10.22092/jaep.2022.356397.1418

چکیده

امروزه تشریح الگوهای پراکندگی حشرات با استفاده از روش­های درونیابی و تخمین تراکم مورد توجه بسیاری از محققین قرار گرفته است. این پژوهش به منظور پیش بینی و ترسیم نقشه توزیع مکانی کفشدوزک هفت­نقطه­ای با استفاده از شبکه‌های عصبی پرسپترون چندلایه (MLP) ترکیب شده با الگوریتم رقابت استعماری، جهش قورباغه در سطح مزرعه انجام شد. داده از صد نقطه سطح یک مزرعه یونجه شهرستان زرقان در سال 1398 به دست آمده است. برای ارزیابی شبکه‌های عصبی مورد استفاده و  مقایسه عملکرد آن‌ها در پیش بینی توزیع این گونه از پارامترهای آماری مانند توزیع آماری، مقایسه میانگین و ضریب تبیین بین مقادیر پیش بینی شده مکانی توسط شبکه عصبی و مقادیر واقعی آن‌ها استفاده شد. نتایج نشان داد که در فازهای آموزش و آزمایش، بین مقادیر توزیع آماری و میانگین مجموعه داده‌های واقعی و پیش بینی شده مکانی این گونه توسط شبکه عصبی ترکیب شده با الگوریتم جهش قورباقه مخلوط شده ، تفاوت معنی­داری وجود نداشت و الگوریتم جهش قورباقه مخلوط شده دقت بالاتری در تشخیص توزیع داشت. بر اساس نقشه‌های ترسیمی ما ا توزیع این گونه کفشدوزک تجمعی است.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of optimized artificial neural network performance with imperialist competitive algorithm and shuffled frog leaping algorithm to predict the distribution pattern of Coccinella septempunctata (Col: Coccinellidae ) in alfalfa fields of zarghan

نویسندگان [English]

  • Ronak Mohamaddi 1
  • Maryam Al osfoor 2
1 Phd student of entomology, colleg of agriculture in Razi university
2 Shiraz university
چکیده [English]

Nowadays, many researchers have paid attention to explaining the patterns of insect dispersion using interpolation and density estimation methods in order to investigate the possibility of proper integrated management with the location of pests. This study was conducted to predict and map the spatial distribution of the Coccinella septempunctata using multilayer perceptron neural networks (MLPs) combined with the imperialist competitive algorithm and shuffled frog leaping algorithm at the field level. Data have been obtained through100 samples taking from the surface of a hay field of zarghan area in 2019. To evaluate the neural networks used and compare their performance, statistical parameters such as statistical distribution, mean comparison and coefficient of explanation between the spatially predicted values by the neural network and their actual values were used to predict the distribution of this species. The results showed that in the training and experimental phases, there was no significant difference between the values of statistical distribution and the mean of real and predicted spatial data sets of this species combined by neural network with shuffled frog leaping algorithm. Shuffled frog leaping algorithm was more accurate in detecting the distribution. Our map showed that pest distribution was patchy
 

کلیدواژه‌ها [English]

  • Artificial neural network
  • Coccinella septempunctata
  • metaheuristic algorithm
  • spatial distribution
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