Evaluation of LVQ4 artificial neural network model for Predicting spatial distribution pattern of Tuta absoluta in Ramhormoz, Iran

Authors

Member of Scientific Board, Head of plant production Dep.

Abstract

In this research, a Learning Vector Quantization (LVQ) neural network model was developed to predict the spatial distribution of Tuta absoluta in tomato fields of the city of Ramhormoz, Iran. Pest density was assessed through 10 m × 10 m grid pattern on the field with a total of 100 sampling units. Some statistical tests, such as means comparison, variance and statistical distribution were performed between the sampling point data and the estimated pest values in order to evaluate the performance of prediction of pest distribution. In training and test phase, there was no significant difference in average, variance, statistical distribution and coefficient of determination at 95% confidence level. The results suggest that LVQ neural network can learn pest density model precisely and trained LVQ neural network high capability (88%) of predicting pest density for non-sampled points. The LVQNN successfully predicted and mapped the spatial distribution of Tuta absoluta whose aggregation distribution implied the possibility of using site-specific pest control in the field.
 

Keywords

References

Azadeh, A., Ghaderi, S. F. & Sohrabkhani, S. (2006) Forecasting electrical consumption by integration of Neural Network, time series and ANOVA. Applied Mathematics and Computation 186, 1753-1761.
Baniameri, V. & Cheraghian, A. (2011) The current status of Tuta absoluta in Iran and initial control strategies. EPPO/IOBC/FAO/NEPPO Joint International Symposium on management of Tuta absoluta (tomato borer, Lepidoptera.:Gelechiidae) in collaboration with the IRAC and IBMA. Agadir, Morocco, p. 20.
Cardina, J., & Doohan, D. J. (2008) Weed biologyand precision farming. Site-specific management guideline.www.ppi-far.org/ssmg.
Dille, J. A., Milner, M., Groeteke, J. J., Mortensen, D. A. & Williams, M. M. (2003) How good is your weed map? A comparison of spatial interpolators. Weed Science 51: 44 – 55.
Drummond, S. T., Sudduth, K. A., Joshi, A., Birrell, S. J. & Kitchen, N. R. (2003) Statistical and neural methods for site-specific yield prediction. Transactions of the American Society of Agricultural and Biological Engineers 46, 5–14.
Filippi, A. M. & Jensen, J. R. (2006) Fuzzy learning vector quantization for hyper spectral coastal vegetation classification. Remote Sensing Environment 100,512–530
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.
Gonzalez-Cabrera, J., Molla, O., Monton, H., & Urbaneja, A. (2011) Efficacy of Bacillus thuringiensis (Berliner) in controlling the tomato borer, Tuta absoluta (Meyrick) (Lepidoptera:Gelechiidae). International Organization of Biological Control (IOBC) 56, 71- 80.
Garzia, T. G., Siscaro, G., Biondi, A. & Zappala, L. (2011) Distribution and damage of Tuta absoluta, an exotic invasive pest from South America.  International symposium on management of Tuta absoluta (Tomato borer) Proceeding. Agadir, Morocco,16-18
Gotway, C. A., Ferguson, R. B., Hergert, G. W. & Peterson, T. A. (1996) Comparison of kriging and inverse distance methods for mapping soil parameters. Soil Science Society America Journal 60, 1237-1247.
Heykin, S. (1999) Neural Networks A Comprehensive Foundation. 2thed. 125pp. Oxford University press.
Kaul, M., Hill, R. L. & Walthall, C. (2005) Artificial neural networks for corn and soybean yield prediction. Agriculture system 85, 1-18.
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, 99-106.
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 in weed science. Ferdowsi University of Mashhad. 193pp.
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.
Seraj A. A. (2011) Principle of Plant pest control. 7745pp. Shahid chamran Press.
Searcy, S. W. (2008) Precision farming: a new approach to crop management. Texas agricultural extension service. Txprecag.tamu.edu/content/pub/pf-ncm.pdf.
Torrecilla, J. S., Otero, L. & Sanz, P. D. (2004) A neural network approach for thermal/pressure food processing. Food Engineer 62,89-95.
Vakil-Baghmisheh, M. T. & Pavešic, N. (2003) Premature clustering phenomenon and new training algorithms for LVQ. Pattern recognition 36, 1901-1921.
Vakil-Baghmisheh, M. T. & Pavešic, N. (2003) A Fast simplified fuzzy ARTMAP network. Neural Processing Letters 17, 273-301.
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, 336-348.
Williams, M. M., Gerhards, R. & Mortensen, D. A. (1999) Spatio temporal outcomes of site-specific weed management in maize. pp. 897-906 in J. V. Stafford (Eds) Precision Agriculture. 99 pp. Sheffield, Great Britain: Society of Chemical Industry.
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.
Young-S. P., Ja-Myung, 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 29,1208-1215.
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.
Journal of Entomological Society of Iran
Volume 36, Issue 3 - Serial Number 66
December 2016
Pages 195-204

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