Amini, M., Afyuni, M., Fathianpour, N., Khademi, H & Fluhler, H. (2005) Continuous soil pollution mapping using fuzzy logic and spatial interpolation. Geoderma 124, 223- 233.
Afrous, A., Hosseini, S.M. & Goudarzi, Sh. (2007) Assesment of the Ordinary Kriging and NeuroFuzzy appraoches in interpolation of the groundwater level. Journal of Groundwater13, 978-984.
Ahmad, A., Dey, L. (2007) A k-Mean Clustering Algorithm for Mixed Numeric and Categorical Data. Data &Knowledge Engineering 63, 503- 527.
Anonymous. (2011) Agricultural statistics, Department of Planning and Economy, The office of Statistics and Information Technology, Tehran
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.
Filippi, A. M. & Jensen, J. R. (2006)Fuzzy learning vector quantization for hyper spectral coastal vegetation classification. Remote Sensing Environment 100, 512–530.
Goldberg, D. (1999) Genetic algorithms in search optimization and machine learning. 1th ed. 570 pp. Addison-Wesley Longman Publishing Company.
Garzia, T. G., Siscaro, G., Biondi, A. & Zappala, L. (2011)Distribution and damage of Tuta absoluta, an exotic invasive pest from South America. In: International symposium on management of Tuta absoluta (Tomato borer) Proceeding. Agadir, Morocco, November, 16-18.
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.
Gerling, D. (1990). Whiteflies: their bionomics, pest status and management. 2th. 562pp. Oxford University Press.
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.
Jang, J.S.R. (1993) Anfis: adaptive-network-based fuzzy inference systems. Journal of IEEE Transactions on System, Management and Cybernetics 23, 665–685.
Jang, J. S. R., Sun, C.T. & Mizutani, E. (1997) Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligance Upper Saddle River. 4th ed. 560 pp. New Jersey University Press.
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.
Kianpour, R., Fathipour, Y & Kamali, K. (2002).Population Fluctuation and spatial distribution patterns b. Tabasi on eggplants in varamin. Journal of Plant protection77, 71-94.
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.
Mertens, M. & Huwe B. (2002) Fun-Balance: a fuzzy balance approach for the calculation of nitrate leaching with incorporation of data imprecision. Geoderma 109, 269 287.
Mousavi, S. F. & Amiri, M. J. (2012)Modelling nitrate concentration of groundwater using adaptive neural based fuzzy inference system. Journal of Soil and water Research 7, 73-83. [In Persian with English summary]
Murphy, K. (2012)Machine learning a probabilistic perspective. 2th ed. 875 pp. MIT Press.
Nario, L. S., Oliver-Verel, J., & Stashenko, E. E. (2010). Repellent activity of essential oils. A review. Bioresource Technology 101, 372-378.
Nugraha, H.S. (2011) Integration of stream sediment geochemical and airborne gamma-ray data for surficial lithological mapping using clustering methods, Master of Science Thesis, Twente University, Netherland, Supervisor: E.J.M. Caranza.
Paasche, H. & Eberle, D. (2010) automated integration of large geophysical data using three partitioning cluster algorithms: A Comparison. 11th SAGA Biennial Technical Meeting and Exhibition Swaziland, pp. 286-291.
Shrestha, R.R, Bardossy, A., Rode, M. (2007). A hybrid deterministic fuzzy rule based model for catchment scale nitrate dynamics. Journal of Hydrology 342, 143 156.
Seraj A. A. (2011)Principle of Plant pest control. 1th ed. 7745pp. Shahid chamran Press. [In Persian]
Shishehbor, P. (2001). White fly. 1th ed. 750pp. Shahid chamran Press. [In Persian]
Tonhasca, A., Palumbo, J.C. & Byrne, D.N. (1994).Distribution pattern of bemisia tabaci in cantaloupe fildes in Arizona. Enviromental Entomology 23, 949 – 954.
Velmurugan, T. (2010) Performance evaluation of K_Means and Fuzzy K_Means clustering algorithms for statistical distributions of input data points. European Journal of Scientific Research 46, 320-330
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., K, Ja-Myung, L, Buom-Young, L, Yeong-Jin & YooShin, K. (2010) 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, Y. F. & Fu, J. Y.H. (1998)A neural network approach for early cost estimation of packaging products. Computers & Industrial Engineering, 34, 433-50.
Zandi sohani, N., Shishehbor, P. & Kocheli, F. (2012) Seasonal changes and spatial distribution of Bemisia tabaci on cucumbe in ahvaz. Plant Protection 35, 73-85. [In Persian with English summary]
Naranjo, S.E. & Flint, H.M. (1994) spatial distribution bemisia tabaci in cotton and development of fixed- precision sequential plant. Enviromental Entomology 23, 245-266.
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.