Ahmadvand, M. (2009). Modeling the effect of water table fluctuations on sugarcane yield using artificial neural network and fuzzy logic (Case Study: Mirza Koochak-Khan Agro-Industry Co.). Master of Science Thesis. Shahid Chamran University of Ahvaz. (In Farsi).
AmirEntezari, K. (2008). Review of new methods for training neural networks using artificial intelligence algorithms. Master of Science Thesis. University of Tabriz. (In Farsi).
Bagheri, A. and Sohrabi, N. (2018). Predicting yield of rainfed and irrigated barley (Hordeum vulgare L.) in Kermanshah by Artificial Neural Network approach (Case study Kermanshah, Iran). Journal of Agroecology. 10(2): 516-528. (In Farsi).
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan, pp: 39-43.
Esfandiarpour-Boroujeni, I., Karimi, E., Shirani, H., Esmaeilizadeh, M. and Mosleh, Z. (2019). Yield prediction of apricot using a hybrid particle swarm optimization- imperialist competitive algorithm- support vector regression (PSO-ICA-SVR) method. Scientia Horticulturae, 257, 1-12.
Golabi, M., Karami, B. and Albaji, M. (2013). Sensitivity analysis of sugarcane yield using artificial neural networks. 4th National Conference on Irrigation and Drainage Network Management. Faculty of Water Sciences Engineering. Shahid Chamran University of Ahvaz, pp: 1917-1924. (In Farsi).
Heydarnejadi, S.Z. (2016). Effects of climate change on net irrigation requirement and yield of sugarcane in south of Ahvaz (Case study: Amir Kabir Agro-Industry Co.). Master of Science Thesis. Shahid Chamran University of Ahvaz. (In Farsi).
Hosseini, M.T., Siosemarde, A., Fathi, P. and Siosemarde, M. (2007). Application of artificial neural network and multiple regression for estimating dry farming wheat yield in Ghorveh region, Kurdistan province. Agricultural research: water, soil and plant in agriculture, 7(1), 41-54. (In Farsi).
Hosseini, M., Movahedi-Naeini, S.A., Dehghani, A.A. and Khaledian, Y. (2016). Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods. Soil and Tillage Research, 157, 32-42.
Jeong, S., Hasegawa, S., Shimoyama, K. and Obayashi, S. (2009). Development and investigation of efficient GA/PSO hybrid algorithm applicable to real-world design optimization, IEEE Computational Intelligence Magazine, pp. 36–44.
Kia, S.M. (2011). Neural Networks in MATLAB. Kian Publication. Tehran, 232 pages. (In Farsi).
Keynia, F. and Heydari, A. (2014). The combination of Particle Swarm Optimization algorithm and artificial neural network to forecast wind power. 4th Annual Clean Energy Conference (ACEC2014). Kerman. (in Farsi).
Kumar, M., Raghuwanshi, N.S., Wallender, W.W. and Pruitt, W.O. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4), 224-233.
Menhaj, M.B. (2008). Fundamentals of neural networks (computational intelligence). Volume 1, 8th Edition. Publishing Center, Amirkabir University of Technology (Tehran Polytechnic). 715 pages. (in Farsi).
Pourmohammadali, B., Hosseinifard, S.J., Salehi, M.H., Shirani, H. and Esfandiarpour Boroujeni, I. (2019). Effects of soil properties, water quality and management practices on pistachio yield in Rafsanjan region, southeast of Iran. Agricultural Water Management, 213, 894-902.
Rezaei, A.R. and Ranjbaran, S. (2009). Functional training of genetic algorithm in MATLAB software (2nd ed.), Farhang-e-Matin Publication. Tehran. 144 pages. (In Farsi)
Rodriguez, J. D., A. Perez, and J. A. Lozano. (2010). A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceeding of the international joint conference on artificial intelligence, 32: 569-575.
Settles, M. and Soule, T. (2005). Breeding swarms: A GA/PSO hybrid. the Genetic and Evolutionary Computation Conference (GECCO-2005): 161–168.
Thanuja, V., Venkateswarlu, B. and Anjaneyulu, G. S. G. N. (2011). Applications of Data Mining in Customer Relationship Management. Journal of Computer and Mathematical Sciences, 2(3): 423-433.
Yang, X.S. (2008). Nature Inspired Metaheuristic Algorithm. 2nd Edition, Luniver Press, UK. 128 pages.