Hybrid Artificial Neural Network with Meta-heuristic Algorithms for Predicting Sugarcane Yield

Document Type : Research Paper


1 Ph.D. Candidate of Agricultural Mechanization, Biosystems Engineering Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Associate Professor, Biosystems Engineering Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Professor, Biosystems Engineering Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

4 Assistant Professor, Computer Engineering Department, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran


In this study, to predict sugarcane yield, an Artificial Neural Network based on meta-heuristic algorithms is used as an efficient method to estimate crop yield based on actual data. In order to predict sugarcane yield, effective parameters such as plant characteristics, electrical conductivity of soil and water, maximum temperature and average hours of sunshine during the growing season and on a time scale of seven years were used. Accordingly, four hybrid models were used to build neural networks which including of artificial neural network based on Back Propagation (BP) algorithm, combining neural network with Genetic Algorithm (GA), combining neural network with Particle Swarm Optimization (PSO) algorithm and finally a new approach as combining neural network with GA-PSO. The results show that the neural network performance can be improved using meta-heuristic algorithms and can be increased significantly the prediction power. The Mean Square Error (MSE) and correlation coefficient (R) in the hybrid method of neural network with GA-PSO were obtained 0.00057 and 0.91457 respectively on the test data, which show the superiority of this method to other patterns. In addition, cross validation test of the proposed model by K-Nearest Neighbors showed the accuracy of training and test data to predict sugarcane yield has been equal to 98.5% and 95.5%, respectively.


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.