Application of artificial neural network models in estimating nectarine crop yield under two-sided furrow irrigation

Document Type : Research/Original/Regular Article

Authors

1 Ph.D. Candidate Water Structures/ Department of Water Engineering, University of Tabriz, Tabriz, Iran

2 Associate Professor/Agricultural Engineering Research Institute (AERI), Agricultural Research Education, and Extension Organization (AREEO), Karaj, Iran

3 M.Sc. Student,/Department of Water Engineering, Faculty of Agriculture, University of Urmia, Urmia, Iran

Abstract

Introduction
Due to the lack of rainfall, Iran is one of the arid countries in the world where most irrigation systems are done as surface irrigation. Due to the high costs of pressurized irrigation systems, improvement and modification of surface irrigation methods such as land leveling, the correct choice of irrigation method, proper design and thus increase efficiency is significant. If surface irrigation is properly designed and implemented, it is one of the most suitable methods for farmers due to the lack of complex equipment and devices. Researchers use artificial neural networks to simulate and estimate parameters such as weekly evaporation rate, daily evaporation, water capacity, and permeability coefficient have been used. 
Materials and Methods
Perceptrons are arranged in layers, with the first layer taking in inputs and the last layer producing outputs. The middle layers have no connection with the external world and hence are called hidden layers. Each perceptron in one layer is connected to every perceptron on the next layer. Hence information is constantly "fed forward" from one layer to the next. There is no connection among perceptrons in the same layer. 
Radial basis function (RBF) networks have three layers: an input layer, a hidden layer with a non-linear RBF activation function, and a linear output layer. The input can be modeled as a vector of real numbers. The output of the network is then a scalar function of the input vector, and is given by where is the number of neurons in the hidden layer, is the center vector for neuron, and is the weight of neuron functions in the linear output neuron. Functions that depend only on the distance from a center vector are radially symmetric about that vector. 
Results and Discussion
The best results were calculated using the average savings in the treatment section compared to the observed section, 31.7%. It also shows water consumption in the treatment section and the control is calculated as 5793 and 6566.9 m3/ha, respectively, which indicates an 11.8% decrease in water consumption reduction (733.9 m3/ ha) of the treatment compared to the control. According to the obtained results and after comparing the results of RBF and GFF networks, RBF networks (function with radial base) with parameters of different irrigation levels as input were recognized as the best network. The R2 is equal to 0.92 and the square root of the RMS is equal to 0.035.
Conclusion
It can be stated that the method of two-sided furrow irrigation, in addition to reducing water consumption, increased crop yield. Also, there was the highest water loss in the first irrigation. The average efficiency of water application efficiency in the treatment and control sections was calculated to be 2.24 and 1.52 kg/m3, respectively, with the majority of losses being deep penetration. The RBF model had better results in predicting than the GFF neural network model. RBF neural networks with the parameter of different irrigation levels as input were recognized as the best network.

Keywords

References

Akbarpour, A., Khorashadizadeh, A., Shahidi, A., & Ghochanian, A. (2013). Evaluation of artificial neural network model in estimation of Saffron crop performance based on climate parameters. Saffron Research Journal, 1(1), 27-35.
Alvarez, A. (2009). Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European Journal of Agronomy, 30, 70-77. 
Bagheri, S., Gheisari, M., Aubi, Sh. A., & Lavaie, N. (2012). Prediction of forage maize yield using artificial neural networks. Journal of Plant Production Research (JOPPR), 19(4), 77-94.
Bariklo, A., Alamdari P., Moravaj K., & Servati, M. (2017). Prediction of irrigated wheat yield by using hybrid algorithm methods of artificial neural networks and genetic algorithm. Journal of Water and Soil, 31 (3), 715-726.
Esmaielzadeh-KordKheili, S. (2012). Estimition of rice yield using statistical methods, artificial neural network and multi-regression methods in Giullan. M.Sc. Thesis, Vali-Asr University, Rafsanjan (in Persian).
FAO. (1992). CROPWAT a computer program for irrigation planning and management, by M. Smith. FAO Irrigation and Drainage Paper No. 46. Rome.
Fathi Tarshizi, M. (2012). Field evaluation of surface irrigation models under irrigation with saline water (Case study of furrow irrigation). M.Sc. Thesis, Ferdowsi University of Mashhad, Mashhad (in Persian).
Gershenfeld, N. A., & Gershenfeld, N. (1999). The nature of mathematical modeling. Cambridge university press.
Ghorbani, M. A., Shahabboddin, Sh., Zare Haghi, D., Azani, A., Bonakdari, H., & Ebtehaj, I. (2017). Application of Firefly algorithm-based support vector machines for prediction of filed capacity and permanent wilting point. Soil and Tillage Research, 172, 32-38.
Kaul, M., Hill, R.L., & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85, 1-18. 
Landeras, G., Ortiz-Barredo, A., & López, J. (2009). Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. Irrigation and Drainage Engineering, ASCE, 135, 323-334.
Merdun, H., Çınar, Ö., Meral, R., & Apan, M. (2009). Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research, 90, 108-116.
Nourani, V., & Babakhani, A. (2012). Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modeling. Journal of Computing in Civil Engineering, 27(2), 183-195.
Piri, J., Amin, S., Moghaddamnia, A., Keshavarz, A., Han, D., & Remesan, R. (2009). Daily pan evaporation modeling in a hot and dry climate. Journal of Hydrologic Engineering, ASCE, 15(8), 803-811.
Rahmani, E., Liaghat, A., & Khalili, A. (2008). Estimating barley yield in eastern azerbaijan using drought indices and climatic parameters by artificial neural network (ANN). Iranian Journal of Soil and Water Research, 39(1), 47-56 (in Persain).
Smith, B.A., Hoogenboom, G., & McClendon, R.W. (2009). Artificial neural networks for automated year-round temperature prediction. Computers and Electronics in Agriculture, 68(1), 52-61.  
Seiler, R.A., & Kogan, F. (1998). AVHRR-based vegetation and temperature condition indices for drought detection in Argentiana. Advances in Space Research, 21(3), 481-484.
Taghizadeh Mehrjerdi, R., Seyedjalali, S.A., & Sarmadian, F. (2016). Prediction of corn spatial yield by soil digital mapping in Gotend region (Khuzestan Province, Iran). Journal of plant production, 19 (4), 70-96.
Talebi, H. (2018). Calibration and improvement of furrow irrigation performance indices (case study: Moghan Agriculture, Industry and Animal Husbandry). M.Sc. Thesis,  Mohaghegh Ardabili University, Ardabil (in Persian).
Zareh-Abyaneh, H. (2012). Evaluation of Artificial Neural Network and Geostatistical Methods in Estimating the Spatial Distribution of Irrigated and Dry Wheat Yield (Case Study: Khorasan Razavi). Physical Geography Research Quarterly, 44 (4), 23-42 (in Persian).  
Water and Soil Management and Modelling
Volume 1, Issue 2
June 2021
Pages 47-59

Files

History

  • Receive Date: 20 June 2021
  • Revise Date: 02 August 2021
  • Accept Date: 03 August 2021

Share

How to cite

Statistics