عنوان مقاله [English]
Today, with the advance statistical techniques and neural networks, predictive models of distribution were rapidly developed in ecology. Due to the difficulty of sampling, there are usually not enough samples in such studies. Therefore, in order to predict and mapping the distribution of Coccinella
septempunctata used the combination of the Kriging method with multilevel perceptron neural
networks (MLP) combined with genetic algorithm at the farm level. Population data of pest was
obtained in 2014 by sampling in 221 fixed points in the alfalfa farm of Bajgah. The data was interpolated by ordinary Kriging method with spherical variogram, which had the best performance, and introduced as a neural network input. To evaluate the ability combined geostatistics with optimized artificial neural network by genetic to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and between predicted values and actual values. Results indicating that there was non-significant difference between statistical parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Coccinella septempunctata density. Our map showed that pest distribution was patchy.
Ansari pour, A. & Shakarami, J. (2011). Study of ladybirds (Col.: Coccinellidae) in Khorramabad district and the first report of Hyperaspis quadrimaculata (Redtenbacher 1844) for Iranian fauna. Life Science Journal 8, 488-495.
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 & Manufacture 10, 747–753.
De Alves, M.C., Da Silva, F.M., Moraes, J.C., Pozza, E.A., De Oliveira, M.S., Souza, J.C.S & Alves, L.S. (2011).Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem. Precision Agriculture 12, 18–31.
Freeman, J. & Sakura, D. (2005). Neural Networks: Algorithms, Applications, and
Programming Techniques. 1thed.372pp. Addison-Wesley, Berlin.
Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B. & Viau, A. A. (2003).Classification of hyper spectral data by decision trees and artificial neural
networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture 39, 67–93.
Gressie, N. (1993). Statistics for spatial data. 1thed. 550 pp. John Wiley, New York.
Goldberg, D. (1999). Genetic algorithms in search optimization and machine learning.2thed.320pp. Addison-Wesley Longman Publishing Company. Boston.
Habashi, H., Hosseini, M., Mohammadi, J. & Rahmani, R. (2007).Geostatistic applied in forest soil studding process. Journal of Agricultural Science and natural Resources 14, 1–10. [In Persian with English summary]
Hagen, J.S. (1962). Biology and ecology of predaceous Coccinllidae. Annual Review of
Entomology 7, 289-326.
Hasani Pak, A. (2007).Geostatistics. 3thed. 538 pp. University of Tehran Press. [In Persian]
Heykin S. (1999). Neural network, a comprehensive foundation. 1thed. 572 pp. John Wiley & Sons, New York.
Honek, A. & Martinkova, Z. (2005). Long term changes in abundance of Coccinella
septempunctata L. (Coleoptera: Coccinellidae) in the Czech Republic. European
Journal of Entomology 102, 443-448.
Hodek, I. (1973). Biology of Coccinellidae. Academia publishing house of the
czechoslosvak, Academy of Sciences Pragus, 260pp.
Isman, M. (1999). Pesticides based on plant essential oils. Pesticide. Crop Protection 18, 603–608.
Irmak, A., Jones, J. W., Batchelor, W. D., Irmak, S., Boote, k. J. & Paz, J. (2006).
Artificial neural network model as a data analysis tool in precision farming.
Transactions of the American Society of Agricultural and Biological Engineers 49, 2027-2037.
Katherine, A.R. (2001). Geostatistic using SAS software. Owen analytic inc. Deep. River, CT., 6 pp.
Kianpour, R., Fathipour, Y., Kamali, K. & Naseri, B. (2010). Bionomics of Aphis
gossypii (Homoptera: Aphididae) and its predators Coccinella septempunctata and
Hippodamia variegata (Coleoptera: Coccinellidae) in natural conditions. Journal of
Agricultural Science and Technology 12, 1-11.
Kim, K. (2006). Artificial Neural Network with evolutionary instance selection for financial forcasting. Expert systems with application 30, 519–526.
Kumar, D. N., R. K, Srinivasa and B. Ashok. (2006). Optimal reservoir operation for
irrigation of multiple crops using genetic algorithms. Journal of Irrigation Drainage
Engineering 132, 123-129.
Castera, I. & Boyd, M. (1996). Designing an artificial neural network for forecasting
financial and economic time series. Neurocomputing 12, 13–19.
Liebhold, A.M., Zhang, X., Hohn, M.E., Elkinton, J.S., Ticehurst, M., Benzon, C.L. & Campbell, R.W. (1991).Geostatistical analysis of Gypsy moth (Lepidoptera: Lymantridae) egg mass population. Environmental Entomology 20, 1407–1417.
Lohn J.D., W. Kraus, G. Haith. (2002). Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization. Proceedings of the 2002 IEEE Congress on
Evolutionary Computation, pp. 1157-1162.
Latifian, M. & Soleymannejadian, E. (2009). Study of the Lesser moth Batrachedra
amydraula (Lep.: Batrachedridae) distribution based on geostatistical models in Khuzestan province. Journal of Entomological Research 1, 43–55.[In Persian with
Makarian, H., Rashed Mohassel, M. H., Bannayan, M. & Nassiri, M. (2007).Soil seed bank and seedling populations of Hordeum murinum and Cardaria draba in saffron fields. Agriculture Ecosystems and Environment 120, 307- 312.
Obrycki, J.J. & Kring, T. J. (1998). Pradaceus Coccinellidae in biological control. Annual Review of Entomology 43, 295-321.
Paredis, J. (1995). The symbiotic evolution of solutions and their representations. Pages 359 365 of: Eshelman, L. (ed), Proceedings of the sixth international conference on genetic algorithms. San Mateo, CA: Morgan Kaufmann.
Ribes-Dasi, M., Almacellas, J., Sió, J., Torà, R., Planas, S. & Avilla, J. (2005).the use of Geostatistics and GIS to optimise pest control practices in precision farming systems. Information and Technology for Sustainable Fruit and Vegetable Production 10,583–590.
Story, M. & Congalton, R.G. (1994).Accuracy assessment: A user’s perspective: L.K.
Fenester maleer. Remote sensing thematic assessment. American society for photogr
ammetry and remote sensing 12, 257–259.
ShafieeNasab, B., Shakarami, J., Mohiseni, A. & Jafari, S.H. (2015).Geostatistical
characteristics of the spatial distribution of the infestation pods by the pod borer,
Heliothis viriplaca Huf. (Lep: Noctuidae) in rain-fed chickpea (Cicer arietinum L.) fields in Delfan (Lorestan province). Plant Pests Research 5, 49–59. [In Persian with English summary]
Sciarretta, A., Trematerra, P. & Baumgärtner, J. (2001). Geostatistical analysis of Cydia funebrana (Lepidoptera: Tortricidae) pheromone trap catches at two spatial scales. American Entomologist 47, 174–184.
Shabani nejad, A. R. & Tafaghodinia, B. (2016). Evaluation of the ability of LVQ4
artificial neural network model to predict the spatial distribution pattern of Tuta
absoluta in the tomato field in Ramhormoz. Journal of Entomological society of Iran 36, 195-204. [In Persian with English summary]
Shabani nejad, A. & Tafaghodiniya, B. (2017). Automatic clustering of data from sampling and evaluationg of neuro-fuzzy network tofor estimateinge the distribution of Bemisia. tabaci (Hem.: Aleyrodidae). Journal of Entomological society of Iran 37, 91-105. [In Persian with English summary]
Shabani nejad, A. R. & Tafaghodinia, B. (2016). Evaluation of Geostatistical Methods and Artificial Neural Network for Estimating the Spatial Distribution of Tetranychus urticae (Acari: Tetranychidae)in Cucumber field Ramhormoz. Journal of Applied
Entomology and Phytopathology 85, 22-30. [In Persian with English summary].
Shabani nejad, A. R., Tafaghodinia, B. & Zandi- Sohani, N. (2016). Hybrid neural
network with genetic algorithms for predicting distribution pattern of Tetranychus
urticae T. in cucumbers field of Rāmhormoz. Persian journal of Acarology 8, 240-252.
Shu-Heng, C. (2002). Genetic Algorithm and Genetic Programming in Computational
Finance. 1thed. 389 pp. Springer Kluwer Acadamic Publications.
Vellido, A., Liboa, P. J. G. & Vaughan, J. (2010). Neural Networks in Business: a Survey of Applications. Expert Systems with Application 19, 12-24.
Shanker, M., Hu, M. Y., Hung, M. S. (1996). Effect of data standardization on neural
networks training. Omega, 24, 385-397.
Seraj A. A. (2011). Principle of Plant pest control. 7745pp. Shahid chamran Press.
Wright, R.J., Devries, T.A., Young, L.J., Jarvi, K.J. & Seymout, R.C. (2002)
Geostatistical analysis of smallscale distribution of European corn borer (Lepidoptera: Crambidae) larvae and damage in whorl stage corn. Environmental Entomology 31, 160–167.
Young-S.P., Ja-Myung, K., Buom-Young, L., Yeong-Jin, L. & YooShin, K. (2000). Use of an artificial neural network to predict population dynamics of the forest–pest pine needle gall midge (Diptera: Cecidomyiida).Environmental Entomology, 29, 1208–1215
Zhang, W. J., Zhong, X. Q. & Liu, G. H. (2008).Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stochastic Environmental. Research and Risk Assessment 22, 207–216.
Zhang, Y. F. & Fu, J. Y.H. (1998).A neural network approach for early cost estimation of packaging products. Computers & Industrial Engineering, 34, 433-50.