خوشه‌بندی خودکار داده‌های حاصل از نمونه‌برداری و ارزیابی شبکه‌ فازی- عصبی جهت تخمین پراکندگی سفیدبالک پنبه (Hem.:Aleyrodidae) Bemisia tabaci

نوع مقاله: مقاله کامل، فارسی

نویسندگان

1 عضو هیات علمی - مدیر گروه تولیدات گیاهی

2 دانش آموخته حشره شناسی از دانشگاه صنعتی شاهرود

چکیده

پژوهش حاضر با هدف پیش­بینی تراکم سفید­بالک پنبه با روش شبکه­ی فازی- عصبی مصنوعی در شهرستان بهبهان انجام گرفت. داده‌های مربوط به تراکم جمعیت سفید بالک پنبه از طریق نمونه­برداری بر روی یک شبکه علامت‌گذاری شده مربعی با ابعاد ۱۰×۱۰ متر و در مجموع از ۱۰۰ نقطه از سطح مزرعه به دست آمد. مختصات طول و عرض نقاط علامت­گذاری شده سطح مزرعه به عنوان ورودی­ شبکه فازی- عصبی تعریف شد. خروجی نیز تعداد این آفت در آن نقاط بود. برای بررسی میزان حساسیت این روش به سطوح مختلف این آفت پس از جمع­آوری نمونه­ها از روش خوشه­بندی اتوماتیک برای تعیین تعداد خوشه­ها، و از شاخص دیویس و بولدین به عنوان معیار ارزیابی استفاده شد. به منظور جست و‌‌ جوی فضای جواب خوشه­بندی از الگوریتم ژنتیک استفاده شد. نتایج خوشه­بندی بر اساس شاخص دیویس و بولدین (0.46) نشان داد که داده­ها باید به سه خوشه تقسیم شود. نتایج نشان داد که در فازهای آموزش و آزمایش بین مقادیر ویژگی‌های آماری واریانس، توزیع آماری و میانگین مجموعه داده‌های واقعی و پیش‌بینی شده مکانی آفت توسط شبکه فازی - عصبی، تفاوت معنی­داری وجود نداشت. نقشه‌های ترسیم شده نشان داد که پراکندگی این آفت به صورت تجمعی است و امکان کنترل متناسب با مکان را در مزرعه مورد مطالعه دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Automatic clustering of data from sampling and evaluating of neuro-fuzzy network forestimatinge the distribution of Bemisia. tabaci (Hem.:Aleyrodidae)

نویسندگان [English]

  • Bahram Tafaghodinia 1
  • Alireza Shabani Nejad 2
1 Member of Scientific Board, Head of plant production Dep.
2 Graduated Student of Entomology, Faculty of Agriculture, Shahrood University, Shahrood
چکیده [English]

In this study, Neuro Fuzzy network was used to estimate the spatial distribution of Bemisia tabaci in a cucumber field in Behbahan. Pest density assessments were performed  based on a 10 m × 10 m grid pattern pattern and a total of 100 sampling units in. In this method  latitude and longitude information was used the input data and output of method showed the number of pest. To determine the sensitivity of this method to different levels of the pest after collecting samples, automatic clustering method was used to determine the number of clusters Davies and Bouldin index was used to evaluae criterion. In order to finding the answer, Clustering Search Space Genetic Algorithm was used.Davies and Bouldin index (0.46) showed that the data should be divided into three clusters. Results indicated average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Bemisia tabaci density were not significantly different.Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field. 

کلیدواژه‌ها [English]

  • Automatic clustering
  • Bemisia tabaci
  • Genetic algorithm
  • Neuro Fuzzy network

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