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مقایسۀ عملکرد الگوریتم‌های مختلف یادگیری شبکۀ عصبی در پیش‌بینی الگوی توزیع سفید‌ بالک پنبه Bemisia tabaci در خیارکاری‌های بهبهان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی سابق کارشناسی ارشد حشره‌شناسی، دانشکدۀ کشاورزی، دانشگاه شاهرود، شاهرود

2 استادیار، گروه گیاه‌پزشکی سازمان پژوهش‌های علمی و صنعتی ایران، تهران

چکیده

امروزه تشریح الگوهای پراکندگی حشرات با استفاده از روش­های درون‌یابی و برآورد تراکم به‌منظور بررسی امکان مدیریت و کنترل متناسب با مکان آن‌ها مورد توجه بسیاری از محققان قرار گرفته است. این پژوهش به‌منظور ارزیابی قابلیت الگوریتم­های مختلف شبکۀ عصبی پرسپترون چندلایه‌ای (MLP) در درون‌یابی و برآورد جمعیت سفید­ بالک پنبه در نقاط نمونه­برداری نشده و نیز ترسیم نقشۀ پراکنش آن انجام شد. برای ارزیابی قابلیت الگوریتم­های مختلف شبکۀ عصبی MLP از میانگین مربعات خطا و ضریب تبیین استفاده شد و برای ارزیابی شبکه با الگوریتم مطلوب از مقایسۀ فراسنجه (پارامتر)­هایی مانند میانگین، واریانس، توزیع آماری و نیز ضریب تبیین رابطۀ خطی رگرسیونی بین مقادیر پیش‌بینی‌شده توسط شبکۀ عصبی با الگوریتم یادگیری مطلوب و مقادیر واقعی آن‌ها استفاده شد. نتایج نشان از عملکرد مطلوب شبکۀ عصبی با الگوریتم لونبرگ- مارکوات و نرخ یادگیری 26/0، عامل مومنتوم 75/0 و شمار یازده نرون در لایۀ میانی و همچنین نبود تفاوت معنی­داری بین مقادیر ویژگی­های آماری (میانگین، واریانس) و توزیع آماری مجموعۀ داده‌های پیش­بینی‌شدۀ تراکم آفت و میزان واقعی آن بود. به عبارتی شبکۀ عصبی مصنوعی با الگوریتم لونبرگ- مارکوات به‌خوبی توانست مدل داده­های تراکم سفید­ بالک پنبه را بیاموزد. نقشۀ به‌دست‌آمده از درون‌یابی نشان داد، این آفت توزیع تجمعی داشته و لذا امکان کنترل مناسب با مکان آن در مزرعۀ مورد بررسی وجود دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Comparing the performance of different learning neural network algorithms to predict distribution pattern of Bemisia tabaci in cucumber fields of Behbahan

نویسندگان [English]

  • Alireza Shabaninejad 1
  • Bahram Tafaghodiniya 2
1 Former M. Sc. Student of Entomology, Faculty of Agriculture, Shahrood University, Shahrood, Iran
2 Assistant Professor of Entomology, Iranian Research Organization for Science and Technology, Tehran, Iran
چکیده [English]

Today, describing the distribution patterns of insects using interpolation and estimation methods in order to explore the possibility of proportional control where they has gained the attention of many researchers. This study was performed to evaluate the MLP neural network algorithms and interpolation of population estimates of B. tabaci in areas not sampled and mapped its distribution. Information density of this pest was obtained by sampling in cucumber field of Behbahan. For evaluating ability of different neural network algorithms we used, mean square error and coefficient of determination and to evaluat the network with optimal algorithm we utilized a comparisson of parameters such as mean, variance, statistical distribution and the determination coefficients of linear regression between predicted values by the neural network and actual values. Results showed optimum performance of neural network white Levenberg-Marquardt algorithms was in Learning rate 0.26, Momentum Factor 0.75 and 11 neuron in hidden layer and no significant difference between the values of statistical characteristics (mean, variance) and differences in the statistical distribution of predict and the actual pest density. In other words, an artificial neural network with Levenberg-Marquardt could well learn whitefly density data model. Map of interpolation showed that the pest had cumulative distribution and proved possibility of site-specific pest control on this field.

کلیدواژه‌ها [English]

  • B. tabaci
  • interpolation
  • neural network
  • Spatial distribution
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