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Evaluation of artificial neural network MLP optimized with genetic algorithm in estimating and predicting R0 and rm of greenhouse whitefly Trialeurodes vaporariorum (Hemiptera: Aleyrodoidae) according to some characteristics of host plants under greenhouse conditions | ||
| Journal of Entomological Society of Iran | ||
| دوره 41، شماره 1 - شماره پیاپی 85، شهریور 2021، صفحه 55-72 اصل مقاله (1.22 M) | ||
| نوع مقاله: Paper, Persian | ||
| شناسه دیجیتال (DOI): 10.22117/jesi.2021.354385.1415 | ||
| نویسندگان | ||
| S. Naeim Amini1؛ A. Golizadeh* 2؛ B. Tafaghodinia3؛ J. Razmjou1؛ H. Abbasipour4؛ A. Shaabaninejad5 | ||
| 1Department of Plant Protection, Faculty of Agriculture and Natural Resources, University of Mo-haghegh Ardabili, Ardabil, Iran | ||
| 2Department of Plant Protection, Faculty of Agriculture and Natural Resources, University of Mo-haghegh Ardabili | ||
| 3Iranian Research Organization for Science and Technology (IROST), Tehran, Iran | ||
| 4Department of Plant Protection, Faculty of Agriculture, Shahed University, Tehran, Iran | ||
| 5Department of Plant Protection, Faculty of Agriculture, University of Razi, Kermanshah, Iran. | ||
| چکیده | ||
| Regarding the importance of greenhouse productions and high activity of pests including Trialeurodes vaporariorum under greenhouse conditions, the management of this pest requires ecological studies with a new approach. Therefore, due to the influence of the host plant characteristics on biological performance of greenhouse whitefly, the current research was performed to predict and estimate the values of its population growth parameters including net reproduction rate (R0) and intrinsic rate of population increase (rm). Estimation was based on some morphological features of the host plants using a MLP artificial neural network. The network was optimized with a genetic algorithm. The R0 and rm values of T. vaporariurum were calculated on two host plants, Cucumis sativus L. and Cucumis metuliferus May. Moreover, density and length of the leaf trichomes, density and area of leaf stomata cell of the lower leaf surface and the amount of leaf chlorophyll of each host plant was measured. The MLP neural network with optimal algorithm was designed. In order to evaluate the MLP neural network the T-test, F-test and Kolmogorov-Smirnov test were used to compare mean, variance, and statistical distribution, respectively. The obtained coefficient of determination (R2 = 0.9621) and probability level (P > 0.773) of statistical tests indicated high accuracy and capability and high generalizability of the MLP neural network for estimating R0 and rm of greenhouse whitefly. | ||
| کلیدواژه‌ها | ||
| Trialeurodes vaporariorum؛ artificial neural network؛ genetic algorithm؛ host plants morphological characteristics؛ population growth parameters | ||
| عنوان مقاله [English] | ||
| ارزیابی شبکه عصبی مصنوعی MLP بهینه شده با الگوریتم ژنتیک در تخمین و پیش بینی R0 و rm سفیدبالک گلخانه Trialeurodes vaporariorum (Hemiptera: Aleyrodoidae) با توجه به برخی ویژگی های گیاهان میزبان در شرایط گلخانه | ||
| نویسندگان [English] | ||
| سکینه نعیم امینی1؛ علی گلی زاده2؛ بهرام تفقدی نیا3؛ جبراییل رزمجو1؛ حبیب عباسی پور4؛ علیرضا شعبانی نژاد5 | ||
| 1گروه گیاه پزشکی، دانشکدة کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 2گروه گیاه پزشکی، دانشکدة کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 3پژوهشکدة کشاورزی، سازمان پژوهش‌های علمی و صنعتی، تهران، ایران | ||
| 4گروه گیاه پزشکی، دانشکدة کشاورزی، دانشگاه شاهد، تهران، ایران | ||
| 5گروه گیاه پزشکی، دانشکدة کشاورزی، دانشگاه رازی کرمانشاه، کرمانشاه، ایران. | ||
| چکیده [English] | ||
| با توجه به اهمیت تولید محصولات گلخانه ای و فعالیت بالای آفات از جمله سفید بالک Trialeurodes vaporariorum در گلخانه ها، مدیریت این آفت ایجاب می کند تا مطالعات بوم‌شناختی با رویکردی جدید صورت گیرد. بنابراین، با توجه به تأثیرپذیری عملکرد زیستی سفیدبالک گلخانه از ویژگی های گیاه میزبان، پژوهش حاضر به منظور پیش بینی و تخمین مقادیر پراسنجه های رشد جمعیت شامل نرخ خالص تولید مثل (R0)و نرخ ذاتی افزایش جمعیت (rm) آفت، با توجه به برخی ویژگی های گیاهان میزبان و با استفاده از شبکه عصبی مصنوعیMLP بهینه شده با الگوریتم ژنتیک انجام شد. مقادیر نرخ خالص تولید مثل و نرخ ذاتی افزایش جمعیت آفت روی دو میزبان خیار، Cucumis sativus و کیوانو، Cucumis metuliferus محاسبه شد. همچنین تراکم و طول تریکوم های برگ، تراکم و مساحت سلولهای روزنه سطح زیرین برگ و مقدار سبزینه برگ هر یک از گیاهان میزبان اندازه گیری شد. شبکه عصبی مصنوعیMLP بهینه شده با الگوریتم ژنتیک طراحی شد و برای اطمینان از یادگیری شبکه عصبی آموزش دیده، آزمون های t، F و کولموگروف–اسمیرنوف به ترتیب برای مقایسة میانگین، واریانس و توزیع آماری مورد استفاده قرار گرفتند. مقادیر ضرایب تبیین 9621/0 = R2) و سطح احتمال معنی‌داری (773/0 P >) برای آزمونهای آماری بیانگر دقت و توانمندی بالا و قدرت تعمیم پذیری شبکه عصبی مصنوعی MLP در تخمین R0 و rm مربوط به سفیدبالک گلخانه بود. | ||
| کلیدواژه‌ها [English] | ||
| سفیدبالک گلخانه, شبکه عصبی مصنوعی, الگوریتم ژنتیک, گیاه میزبان, پراسنجه‌های رشد جمعیت | ||
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