Shahed University

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 green

Sakineh Naeim Amini | Jabraeil Razmjou | Alireza Shabaninejad | Ali Golizadeh | Habib Abbasipour | Bahram Tafaghodinia

URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=158807
Date :  2021/05/22
Publish in :    نامه انجمن حشره شناسي ايران=Journal of Entomological Society of Iran
DOI :  https://doi.org/10.22117/JESI.2021.354385.1415
Link :  https://jesi.areeo.ac.ir/article_124721.html
Keywords :Trialeurodes vaporariorum, artificial neural network, genetic algorithm, host plants morphological characteristics, population growth parameters

Abstract :
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 (R 2 = 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


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