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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 | ||
| مراجع | ||
|
Anonymous (2018) Agricultural Statistics Letters, Horticultural Products. Vol. 1, 95 pp. Office of Statistics and Information Technology, Ministry of Jahad-Agriculture, Tehran, Iran.
Bagheri, M. R. (2017) Some tritrophic level interactions between three host plant species, the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) and its two natural enemies. PhD Thesis, University of Mohaghegh Ardabili, Iran. [In Persian].
Baldin, E. L., Silva, J. P. & Panniti, L. E. (2012) Resistance of melon cultivars to Bemisia tabaci biotype B. Horticultura Brasileira 30(4), 600-606.
Butler, G. D. Jr & Henneberry, T. J. (1984) Bemisia tabaci: Effect of cotton leaf pubescence on abundance. Southwestern Entomologist 9, 91-94.
Butler, G. D. Jr & Wilson, F. (1984) Activity of adult whiteflies (Homoptera: Aleyrodidae) within plantings of different cotton strains and cultivars as determined by sticky-trap catches. Journal of Economic Entomology 77, 1137-1140.
Butler, G. D. Jr, Henneberry, T. J. & Wilson, F. D. (1986) Bemisia tabaci (Homoptera: Aleyrodidae) on cotton: Adult activity and cultivar oviposition preference. Journal of Economic Entomology 79, 350-354.
Campos, O. R., Crocomo, W. B. & Labinas, A. M. (2003) Comparative biology of the whitefly Trialeurodes vaporariorum (West.) (Hemiptera - Homoptera: Aleyrodidae) on soybean and bean cultivars. Neotropical Entomology 32, 133-138.
Cetintas, R. & McAuslane, H. (2009) Effectiveness of parasitoids of Bemisia tabaci (Hemiptera: Aleyrodidae) on cotton cultivars differing in leaf morphology. Florida Entomologist 92(4), 538-547.
Chi, H. (2019) TWOSEX - MSChart: A computer program for the age-stage, two-sex life table analysis. Available from: http://140.120.197.173/Ecology (Accessed 12 April 2019).
Choudhury, S. K. & Bartarya, G. (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. International Journal of Machine Tools and Manufacture 43, 747-753.
Costa, H. S., Brown, J. K. & Byrne, D. N. (2008) Host plant-selection by the whitefly, Bemisia tabaci (Gennadius), (Homoptera: Aleyrodidae) under greenhouse conditions. Journal of Applied Entomology 112, 146-152.
da Silva Oliveira, C. E., Hoffmann, L. V., Toscano, L. C., Queiroz, M. S., Zoz, T. & Witt, T. W. (2020) Resistance of cotton genotypes to silverleaf whitefly (Bemisia tabaci [GENNADIUS] Biotype B). International Journal of Tropical Insect Science 41(2):1697-707, https://doi.org/10.1007/s42690-020-00373-8.
Freeman, J. & Skapura, D. (2005) Neural networks: algorithms, applications, and programming techniques. 401 pp. Addison-Wesley Publishing.
Gerling, D. & Mayer, R. T. (1996) Bemisia: taxonomy, biology, damage, control and management. 702 pp. Intercept Publishing.
Ghahraman, A. (2003) Basic botany: explanation and morphology of vegetative organs in large groups of plant world. 540 pp. Tehran University Press. [In Persian].
Goldberg, D. (1999) Genetic algorithms in search optimization and machine learning. 1th ed. 570 pp. Addison-Wesley Longman Publishing Company.
Gotep, J. (2011) Glycosides fraction extracted from fruit pulp of Cucumis metuliferus E. Meyer has antihyperglycemic effect in rats with alloxaninduced diabetes. Journal of Natural Pharmaceuticals 2, 48-51.
Guerfel, M., Baccouri, O., Boujnah, D., Chaïbi, W. & Zarrouk, M. (2009) Impacts of water stress on gas exchange, water relations, chlorophyll content and leaf structure in the two main Tunisian olive (Olea europaea L.) cultivars. Scientia Horticulturae 119, 257-263.
Hare, J. D. & Elle, E. (2002) Variable impact of diverse insect herbivores on dimorphic Datura wrightii. Ecology 83, 2711-2720.
Hasanuzzaman, A. T. M., Islam, M. N., Zhang, Y., Zhang, C. Y. & Liu, T. X. (2016) Leaf morphological characters can be a factor for intra-varietal preference of whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) among eggplant varieties. PloS One 11(4), p.e0153880.
Hassoun, M. H. (1995) Fundamentals of Artificial Neural Networks. 501 pp. The MIT Press, Cambridge, US.
Heykin, S. (1999) Neural networks: a comprehensive foundation. 2ed. 125pp. Oxford University press.
Inbar, M. & Gerling, D. (2008) Plant-mediated interactions between whiteflies, herbivores, and natural enemies. Annual Review of Entomology 53, 431-448.
Jimam, N. S., Wannang, N. N., Omale, S. & Gotom, B. (2010) Evaluation of the hypoglycemic activity of Cucumis metuliferus (Cucurbitaceae) fruit pulp extract in normoglycemic alloxaninduced hyperglycemic rats. Journal of Young Pharmacists 2(4), 384-387.
Kaastra, I. & Boyd, M. (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10, 215-236.
Kaul, M., Hill, R. L. & Walthall, C. (2005) Artificial neural networks for corn and soybean yield prediction. Agricultural Systems 85, 1-18.
Khezri, S. S. (2003) Dictionary of medicinal plants (fruits and vegetables). 572 p. Khezir Publication, Sanandaj, Iran. [In Persian].
Kim, K. (2006) Artificial neural network with evolutionary instance selection for financial forecasting. Expert systems with application 30(3), 519-526.
Kumar, D. N., Srinivasa, R. K. & Ashok, B. (2006) Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. Journal of Irrigation Drainage Engineering 132, 123-129.
Lohn J. D., Kraus, W. & Haith, G. (2002) Comparing a coevolutionary genetic algorithm for multiobjective optimization. In Proceedings of the 2002 IEEE Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1157-1162). IEEE.
Manzano, M. R. & van Lenteren, J. C. (2009) Life history parameters of Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) at different environmental conditions on two bean cultivars. Neotropical Entomology 38(4), 452-458.
Mirzamohammadzadeh, S., Iranipour, S., Lotfalizadeh, H. & Jafarloo, M. (2015) Biological parameters of Trialeurodes vaporariorum (Hem.: Aleyrodidae) in four greenhouse cucumber cultivars. Letter from the Iranian Entomological Association 34(4), 53-67.
Miyazaki, J., Stiller, W. N. & Wilson, L. J. (2013) Identification of host plant resistance to silverleaf whitefly in cotton: implications for breeding. Field Crops Research 154, 145-152.
Mound, L. & Halsey, S. (1978) Whitefly of the world: A systematic catalogue of the Aleyrodidae (Homoptera) with host plant and natural enemy data. 340 pp. John Wiley and Sons, UK.
Paredis, J. (1995) The symbiotic evolution of solutions and their representations. pp. 359-365. In: Eshelman, L. (ed), Proceedings of the sixth international conference on genetic algorithms. San Mateo, CA: Morgan Kaufmann.
Prabhaker, N., Toscano, N. C. & Henneberry, T. J. (1998) Evaluation of insecticide rotations and mixtures as resistance management strategies for Bemisia argentifolii (Homoptera: Aleyrodidae). Journal of Economic Entomology 91(4), 820-826.
Prado, J. C., Peñaflor, M. F. G. V., Cia, E., Vieira, S. S., Silva, K. I., Carlini-Garcia, L. A. & Lourenção, A. L. (2016) Resistance of cotton genotypes with different leaf colour and trichome density to Bemisia tabaci biotype B. Journal of Applied Entomology 140 (6), 405-413.
Provvidenti, R. & Robinson, R. W. (1977) Inheritance of resistance to watermelon mosaic virus 1 in Cucumis metuliferus. Journal of Heredity 68, 56-57.
Rasband, W. S. (2018) ImageJ. US National Institutes of Health, Bethesda, Maryland, USA, URL: https://imagej.nih.gov/ij/. Accessed 5 Dec 2018
Rehman, H., Bukero, A., Lanjar, A. G. & Bashir, L. (2020) Investigation of varietal characteristics of tomato plants for determining the diverse preferences of Bemisia tabaci (Aleyrodidea: Hemiptera). Gesunde Pflanzen 72, 163-170.
RStudio Team. (2020) RStudio: Integrated development for R. RStudio, PBC, Boston, MA. Available from: http://www.rstudio.com/ (Accessed 2 March 2020).
Shabaninejad, A. & Tafaghodinia, B. (2017a) Evaluation of the geostatistical and artificial neural network methods to estimate the spatial distribution of Tetranychus urticae (Acari: Tetranychidae) in Ramhormoz cucumber fields. Journal of Applied Entomology and Pathology 85 (1), 21-29.
Shabaninejad, A. & Tafaghodiniya, B. (2016) Evaluation of LVQ4 artificial neural network model for predicting spatial distribution pattern of Tuta absoluta in Ramhormoz, Iran. Journal of Entomological Society of Iran 36(3), 195-204.
Shabaninejad, A. & Tafaghodiniya, B. (2017b) Automatic clustering of data from sampling and evaluating of neuro–fuzzy network to for estimating the distribution of Bemisia. tabaci (Hem, Aleyrodidae). Journal of Entomolological Society of Iran 37, 91-105.
Singh, D., Jaglan, R. S. & Singh, R. (2002) Leaf morphological characteristics of brinjal in relation to whitefly incidence. Haryana Journal of Horticultural Sciences 31, 289-291.
Sorensen, J. T., Gill, R. T., Dowell, R. V. & Garrison, R. W. (1990) The introduction of Siphoninus phillyreae (Haliday) (Homoptera: Aleyrodidae) into North America: niche competition, evolution of host plant acceptance and prediction of its potential range in the Nearctic. Pan-Pacific Entomologist 66(1), 43- 54.
Southwood, T. & Henderson, A. (2009) Ecological methods. Blackwell Science Ltd, Oxford, UK.
Thomas, J. D. & Sycara, K. (2002) GP and the predictive power of internet message traffic. pp 80-102 in Shu-Heng, C. (Ed.) Genetic algorithm and genetic programming in computational finance. 510 pp. Kluwer Acadamic Publications, NewYork, USA.
Torrecilla, J. S., Otero, L. & Sanz, P. D. (2004) A neural network approach for thermal/pressure food processing. Journal of Food Engineering 62, 89-95.
Usman, J. G., Sodipo, O. A., Kwaghe, A. & Sandabe, U. K. (2015) Uses of Cucumis metuliferus: a review. Cancer Biology 5, 24-34.
Vakil-Baghmisheh, M. T. & Pavešic, N. (2003) Premature clustering phenomenon and new training algorithms for LVQ. Pattern Recognition 36, 1901-1921.
van Lenteren, J. E. & Woets, J.V. (1988) Biological and integrated pest control in greenhouses. Annual Review of Entomology 33(1), 239-269.
Vellido, A., Liboa, P. J. G. & Vaughan, J. (2010) Neural networks in Business: a survey of applications. Expert Systems with Application 17, 51-70.
Vondohlen, C. D. & Moran, N. A. (2002) Molecular phylogeny of the Homoptera– a paraphyletic taxon. Journal of Molecular Evolution 41, 211-223.
Wilson, F. D., Flint, H. M., Stapp, B. R. & Parks, N. J. (1993) Evaluation of cultivars, germplasm lines, and species of Gossypium for resistance to biotype-B of sweet-potato whitefly (Homoptera: Aleyrodidae). Journal of Economic Entomology 86, 1857-1862.
Yuxin M., Mulla, D. J. & Pierre, C. R. (2006) Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture 7, 117-135.
Zhang, Y. F. & Fuh, J. Y. H. (1998) A neural network approach for early cost estimation of packaging products. Computers and Industrial Engineering 34, 433-50.
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