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ارزیابی روش‌های زمینآمار و شبکه عصبی مصنوعی در تعیین پراکنش مکانی کنه تارتن دولکه‌ای (Acari: Tetranychidae) Tetranychus urticae در مزرعه خیار شهرستان رامهرمز | ||
| آفات و بیماری های گیاهی | ||
| مقاله 3، دوره 85، شماره 1 - شماره پیاپی 104، شهریور 1396، صفحه 21-30 اصل مقاله (248.45 K) | ||
| نوع مقاله: حشره شناسی کشاورزی | ||
| شناسه دیجیتال (DOI): 10.22092/jaep.2017.107121.1110 | ||
| نویسندگان | ||
| علیرضا شعبانی نژاد1؛ بهرام تفقدی نیا* 2 | ||
| 1دانش‌آموخته کارشناسی ارشد حشره شناسی، دانشکده کشاورزی، دانشگاه شاهرود، شاهرود | ||
| 2استادیار گروه گیاه پزشکی سازمان پژوهش‌های علمی و صنعتی ایران، تهران | ||
| چکیده | ||
| پژوهش حاضر با هدف پیشبینی تراکم کنه تارتن دولکه‌ای با روش‌های زمینآمار و شبکه‌ی عصبی مصنوعی در مرزعه خیار استان خوزستان شهرستان رامهرمز انجام شد. بدین منظور مختصات طول و عرض ۱۰۰ نقطه با فاصله ۱۰متر، در سطح مزرعه مشخص و به عنوان 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 عصبی مصنوعی، ساختار پرسپترون سه لایه با الگوریتم پس انتشار خطا، مورد ارزیابی قرار گرفت. مقایسه نتایج زمینآمار و شبکه عصبی مصنوعی بیانگر توانایی بالای شبکه عصبی در مقایسه با روش زمینآمار بود، به طوری که به ترتیب شبکه عصبی مصنوعی و زمین آمار با ضریب تبیین 0.891 ، 0.601 و مجموع مربعات باقیمانده 0.14، 0.071 نسبت به زمینآمار خطای کمتری داشت. در مجموع میتوان چنین نتیجه گرفت که روش شبکه عصبی مصنوعی با تلفیق دو عامل طول و عرض جغرافیایی، قادر به پیشبینی تراکم آفت با دقت مناسب بود. | ||
| کلیدواژه‌ها | ||
| تغییر نما؛ شبکه عصبی مصنوعی؛ کنه تارتن دولکه‌ای؛ کریجینگ | ||
| عنوان مقاله [English] | ||
| Evaluation of the Geostatistical and Artificial Neural Network Methods to estimate the Spatial Distribution of Tetranychus urticae (Acari: Tetranychidae) in Ramhormoz Cucumber fields | ||
| نویسندگان [English] | ||
| A. R. SHABANINEJAD1؛ B. TAFAGHODINIA2 | ||
| چکیده [English] | ||
| In this study, the geostatistical and artificial neural network methods were used to estimate the spatial distribution of Tetranychus urticae in Ramhormoz Cucumber fields. For this purpose, latitude and longitude of 100 points with 10 meters distance of each point were defined as inputs and output of each method was number of these pests on those points. Ordinary kriging, and perceptron with propagation algorithm were evaluated in geostatistical and artificial neural network method, respectively. In neural network a hidden layer and three-layer were considered as input. Results of the aforementioned two methods showed that artificial neural network capability is more than kriging method. So that, the artificial neural network predicts distribution of this pest with 0.891 coefficient of determination and 0.14 residual sums of squares. While in the geostatistical methods coefficient of determination and residual sums of squares were 0.601 and 0.071, respectively. So it can be concluded that the Artificial Neural Network approach with combining latitude and longitude can forecast pest density with sufficient accuracy. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Neural Network, Kriging, Tetranychus urticae, Variogram | ||
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