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مقایسه پراکنش فضایی کرم میوه گوجه‌فرنگی،Helicoverpa armigera ، با روش‌های زمین آمار و شبکه فازی-عصبی | ||
| آفات و بیماری های گیاهی | ||
| دوره 92، شماره 1 - شماره پیاپی 118، 1403، صفحه 57-66 اصل مقاله (628.52 K) | ||
| نوع مقاله: حشره شناسی کشاورزی | ||
| شناسه دیجیتال (DOI): 10.22092/jaep.2024.365217.1512 | ||
| نویسندگان | ||
| روناک محمدی1؛ عباسعلی زمانی* 2؛ حسنعلی واحدی3؛ حمیدرضا پوریان3؛ بهرام تفقدی نیا4 | ||
| 1گروه گیاهپزشکی، دانشگاه رازی کرمانشاه | ||
| 21- گروه گیاهپزشکی، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه | ||
| 3گروه گیاهپزشکی، دانشگاه رازی | ||
| 4سازمان پژوهش‌های علمی و صنعتی ایران | ||
| چکیده | ||
| با وجود روش‌های آماری قوی و پیدایش شبکه‌های فازی- عصبی، مدل‌های پیشبینی کننده پراکنش موجودات بهسرعت در علم اکولوژی توسعه پیدا کرده است. با توجه به دشواری نمونه برداری، معمولاً در این گونه مطالعات تعداد نمونه کافی وجود ندارد. لذا در این پژوهش به مقایسه روش زمین آمار و شبکه ی فازی-عصبی جهت تخمین پراکندگی کرم میوه گوجه فرنگی، Helicoverpa armigera (Hubner)(Lep., Noctuidae)، در مزرعه گوجه فرنگی شهر کرمانشاه پرداخته شد. بدین منظور، مختصات طول و عرض جغرافیایی نقاط نمونه برداری در سطح مزرعه مشخص و بهعنوان ورودی‌های هر دو روش تعریف شد. خروجی هر روش نیز، تعداد این آفت در آن نقاط بود. در بخش زمین‌آمار، از روش کریجینگ معمولی و در بخش شبکه فازی-عصبی مصنوعی، از تابع فعال سازی سیگموئیدی استفاده شد. مقایسه نتایج زمین‌آمار و شبکه فازی – عصبی، بیانگر توانایی بالای شبکه فازی - عصبی در مقایسه با روش زمین‌آمار بود، بهطوری که ضریب تبیین برای شبکه فازی - عصبی و زمین آمار بهترتیب 9/0 و 6/0 بهدست آمد. در مجموع میتوان نتیجه گرفت که روش شبکه فازی- عصبی با تلفیق دو عامل طول و عرض جغرافیایی و تراکم جمعیت آفت، قادر به پیشبینی تراکم کرم میوه گوجه فرنگی با دقت بسیار مناسب است. | ||
| کلیدواژه‌ها | ||
| شبکه فازی- عصبی (ANFIS)؛ کریجینگ؛ Helicoverpa armigera | ||
| عنوان مقاله [English] | ||
| Comparison of Spatial distribution of tomato fruit borer, Helicoverpa armigera using geostatistics and fuzzy-neural network methods | ||
| نویسندگان [English] | ||
| Ronak Mohammadi1؛ Abbas Ali Zamani2؛ Hassanali Vahedi3؛ Hamid-Reza Pourian3؛ Bahram Tafaghodinia4 | ||
| 1Dep. of Plant Protection, Razi University | ||
| 2Department of Plant Protection, Faculty of Agriculture, Razi University, Kermanshah | ||
| 3Department of Plant Protection, Razi University | ||
| 4Iranian Research Organization for Science and Technology | ||
| چکیده [English] | ||
| Despite the use of robust statistical methods and fuzzy-neural networks, models that predict the distribution of organisms have seen rapid development in the field of ecology. However, due to the challenges associated with sampling, these studies often lack sufficient samples. In this research, we compared geostatistics and fuzzy-neural networks to estimate the distribution of the tomato fruit worm in a tomato farm in Kermanshah city. For this purpose, the length and width coordinates of the sampling points at the field level were identified and used as inputs for both methods. The output of each method was the count of this pest at those locations. In the geostatistics approach, we employed the normal Kriging method, while in the fuzzy-artificial neural network approach, we used the sigmoid activation function. A comparison of the results from geostatistics and the fuzzy-neural network demonstrated the superior performance of the fuzzy-neural network. The coefficient of determination for the fuzzy-neural network and geostatistics was 0.9 and 0.6, respectively. In conclusion, the fuzzy-neural network method, by integrating latitude and longitude factors, was able to predict the density of the tomato fruit worm with high accuracy. | ||
| کلیدواژه‌ها [English] | ||
| Fuzzy-Neural Network (ANFIS), Kriging, Helicoverpa armigera | ||
| مراجع | ||
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