Iranian Journal of Plant Protection Science

Iranian Journal of Plant Protection Science

Comparing the performance of different learning neural network algorithms to predict distribution pattern of Bemisia tabaci in cucumber fields of Behbahan

Document Type : Research Paper

Authors
Alireza Shabaninejad 1
Bahram Tafaghodiniya 2
1 Former M. Sc. Student of Entomology, Faculty of Agriculture, Shahrood University, Shahrood, Iran
2 Assistant Professor of Entomology, Iranian Research Organization for Science and Technology, Tehran, Iran
10.22059/ijpps.2017.215628.1006736
Abstract
Today, describing the distribution patterns of insects using interpolation and estimation methods in order to explore the possibility of proportional control where they has gained the attention of many researchers. This study was performed to evaluate the MLP neural network algorithms and interpolation of population estimates of B. tabaci in areas not sampled and mapped its distribution. Information density of this pest was obtained by sampling in cucumber field of Behbahan. For evaluating ability of different neural network algorithms we used, mean square error and coefficient of determination and to evaluat the network with optimal algorithm we utilized a comparisson of parameters such as mean, variance, statistical distribution and the determination coefficients of linear regression between predicted values by the neural network and actual values. Results showed optimum performance of neural network white Levenberg-Marquardt algorithms was in Learning rate 0.26, Momentum Factor 0.75 and 11 neuron in hidden layer and no significant difference between the values of statistical characteristics (mean, variance) and differences in the statistical distribution of predict and the actual pest density. In other words, an artificial neural network with Levenberg-Marquardt could well learn whitefly density data model. Map of interpolation showed that the pest had cumulative distribution and proved possibility of site-specific pest control on this field.
Keywords
B. tabaci
interpolation
neural network
Spatial distribution
  1. Baniameri, V. & Cheraghian, A. (2011). The current status of Tuta absoluta in Iran and initial control strategies. In: Proceedings of 1st International Symposium on management of Tuta absoluta in collaboration with the IRAC and IBMA, 5-7 May., Agadir University, Egypt, pp. 19-20.
  2. Dille, J. A., Milner, M., Groeteke, J. J., Mortensen, D. A. & Williams, I. M. (2003). How good is your weed map? A comparison of spatial interpolators. Weed Science, 51(9), 44-55.
  3. Filippi, A. M. & Jensen, J. R. (2006). Fuzzy learning vector quantization for hyper spectral coastal vegetation classification. Remote Sensing Environment, 100(26), 512-530.
  4. 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(12), 67-93.
  5. Gonzalez- Cabrera, J., Molla, O., Monton, H. & Urbaneja A. (2011). Efficenacy of Bacillus thuringiensis in controlling the Tuta absoluta (Lepidoptera: Gelechiidae). International Organization of Biological Control, 56(9), 71-80.
  6. Garzia, T. G., Siscaro, G., Biondi, A. & Zappala, L. (2011). Distribution and damage of Tuta absoluta, an exotic invasive pest from South America. In: Proceedings of 1st International symposium on management of Tuta absoluta. 5-7 Nov, Agadir University, Egypt, pp. 12-13.
  7. Gutierrez, P. A., Lopez-Granados, F., Pena-Barragan, J. M., Jurado-Exposito, M., Gomez- Casero, M. T. & Hervas-Martinez, C. (2008). Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data. Computers and Electronics in Agriculture, 60(13), 122-132.
  8. Gerling, D. (1990). Whiteflies: their bionomics, pest status and management. (1st ed.).Oxford University Press.
  9. Heykin, S. (1999). Neyral Networks A Comprehensive Foundation. (1st  ed.). Cambridge University Press.
  10. Hagan, M. T., Demuth, H. B. & Beale, H. (1996). Neural network design. (2nd ed.). Boston University Press.
  11. Hudaib, M. & Cooke, T. E. (2005). The Impact of Managing Director Changes and Financial Distress on Audit Qualification and Auditor Switching. Journal of Business Finance & Accounting, 32(9), 1703-1739.
  12. Lee, D. S., Joen, C. O., Park, J. M. & Chang, K. S. (2002). Hybrid neural network modeling of a full-scale industrial wastewater treatment plant. Biotechnology and Bioengineering, 78(12), 670-682.
  13. Liu, Z. Y., Wu, H. F. & Huang, J. F. (2010). Application of neural networks to discriminate fungal infection levels in rice panicles using hyper spectral reflectance and principal components analysis. Computers and Electronics in Agriculture, 72(14), 99-106.
  14. Makarian, H. (2008). Investigation of spatial and temporal dynamic of weed seed bank and seedling populations and its effect on saffron (Crocus sativus L.) leaf dry weight under different weed management conditions. Ph.D. Thesis. Faculty of Agriculture Ferdowsi University of Mashhad, Iran.
  15. 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(7), 307-312.
  16. Aninomous. (2011). Agricultural statistics, Department of Planning and Economy. The office of Statistics and Information Technology, Tehran.
  17. Seraj, A. A. (2011). Principle of Plant pest control. (1th ed.). Shahid chamran University Publication.
  18. Shishehbor, P. (2001). White fly. (1thed.). Shahid Chamran University Publication.
  19. Shafie, A., Mazoghi, H., Shehata, A. & Taha, M. (2011). Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. International Journal of the Physical Sciences, 6(2), 1306-1316.
  20. Shabni nejad, A. R. & Tafaghodinia, B. (2017). 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 Entomolological Society of Iran, 36(13), 195-204.
  21. Shabni nejad, A. R., Tafaghodinia, B. & Zandi Sohani, N. (2017). Hybrid neural network With genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari.: Tetranychidae) in cucumbers field of Ramhormoz. Persian Journal of Acarology, 6(5), 53-62.
  22. Vakil-Baghmisheh, M. T. & Pavešic, N.(2003). Premature clustering phenomenon and new training algorithms for LVQ. Pattern Recognition,36(5), 1901-1921.
  23. Wang, Y. M. & Elhag, T. M. S. (2007). A comparison of neural network, evidential reasoning and multiple regression analysis in modeling bridge risks. Expert Systems with Applications, 32(5), 336-348.
  24. Williams, M. M., Gerhards, R. & Mortensen, D. A. (1999). Spatiotemporal outcomes of site-specific weed management in maize. Computers and Electronics in Agriculture, 53(14), 105-116.
  25. Young, S. P., JaMyung, K., Buom Young, L., Yeong, J. & 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(6), 1208-1215.
  26. Yang, N. W., Li AL Wan, F. H., Liu, W. X. & Johnsom, D. (2010). Effects of on essential oils on immature and adult sweet potato whitefly, Bemisia tabaci biotype B. Crop Protection, 29(3), 1200-1207.
  27. Zhang, Y. F. & Fuh, J. Y. H. (1998). A neural network approach for early cost estimation of packaging Products. Computers and Industrial Engineering, 34(3), 433-50.
  28. 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(6), 207-216.
  29. Zhang, Y. F. & Fuh, J. Y. H.(1998). A neural network approach for early cost estimation of packaging products. Computers and Industrial Engineering, 34(7), 433-50.

  • Receive Date 15 October 2016
  • Revise Date 31 July 2017
  • Accept Date 01 August 2017