Evaluation of Adaptive Neuro-Fuzzy Inference System Models in Estimating Saffron Yield Using Meteorological Data

Authors
N. Nekoei 1
M. A. Bedani 2
A. Khashei Siuki 3
1 Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
2 Saffron Research Group, Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
3 Water Engineering Department, Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
Abstract
Saffron is one of the most valuable agricultural and medicinal plants of the world and has a special place in Iran's export of products. Presently, Iran is the world's largest producer and exporter of saffron and more than 93/7% of the world production belongs to Iran. However, despite the long history of saffron cultivation and its value-added in comparison to many of the other crops in the country, a lower share of new technologies is assigned to it, and its production is mainly based on local knowledge. This study aimed to develop and evaluate the performance of Adaptive Neuro-Fuzzy Inference System model (ANFIS) in calculating the yield of saffron using meteorological data from 20 synoptic stations in the province, including evapotranspiration, temperature (maximum, minimum), the mean relative humidity, and rainfall. To this end, by using software Wingamma, data and parameters were analyzed and the best combinations of inputs to the model were determined. In order to assess the models, statistical parameters of correlation coefficient, the mean absolute error, and mean square error were used to predict the performance of the plant. ANFIS model was most effective when the data of total minimum temperature, precipitation, evapotranspiration, and relative humidity of autumn were used as independent variables for forecasting yield (R2= 0.5627, RMSE= 2.051 kg ha-1, and MAE = 1.7274 kg ha-1) .

Keywords

Subjects

1. Adams, R. M. 2000. Climate Variability and Climate Change: Implications for Agriculture. IRI Proceedings, Oregon State University, USA.
2. Behdani, M. A., Jami-Alahmadi, M. and Akbarpour, A. 2010. Research Project: Category Ecological Approach to Optimize the Production of Saffron in Southern Khorasan. Birjand University, 107 PP. [in Persian].
3. Dehghani, M., Seifib, A. and Riahi-Madva, H. 2019. Novel Forecasting Models for Immediate-Short-Term to Long-Term Influent Flow Prediction by Combining ANFIS and Grey Wolf Optimization. J. Hydrol., 576: 698-725.
4. Doğan, E. 2008. Reference Evapotranspiration Estimation Using Adaptive Neuro-Fuzzy Inference System. J. Irrig. Drain. Eng. ASCE, 58: 617-628.
5. Jones, A., Evans, D., Margetts, S. J. and Durrant, P. 2002. The GAMMATEST. Idea Group Publishing, Pennsylvanya, U. S. A.
6. Jang, J. -S. R. 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetic, 23(3): 665-685.
7. Jia Bign, C. 2004. Prediction of Daily Reference Evapotranspiration Using Adaptive Neuro-Fuzzy Inference System. Trans Chin. Soc. Agric. Eng., 20: 4. 13-16.
8. Joorabyan, M. and Hooshmand, R. A. 2002. Fuzzy Logic and Neural Networks (Ed): Kartalpolos. Printing 1, Martyr Chamran University Press, Iran, 300 PP.
9. Hashemi Najafi, F., Palangi, G. A. and Azbarmi, R. 2007. Estimation of Reference Evapotranspiration Using Adaptive Nero-Fuzzy Inference System. The 9th National Seminar of Irrigation and Reduction of Evaporation, Kerman.
10. Hosseini, M., Mollafilabi. A., Nassiri, M., 2008. Spatial and Temporal Patterns in Saffron (Crocus sativus L.) Yield of Khorasan province and Their Relationship with Long Term Weather Variation. J. Agric. Rec., 6(1):79-86. (In Persian with English summary)
11. Karamooz, M., Tabesh, M., Nazif, S. and Moridi, A. 2005. Prediction of Pressure in Water Distribution Systems Using Artificial Neural Networks and Fuzzy Inference. J. Water Wastewater, 53(1):14-3.
12. Kholghi, M. and Hosseini, S. M. 2009. Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging. Environ Model Assess, 14:729–737.
13. Khashei-Siuki, A., Kouchakzadeh, M. and Ghahraman, B. 2011. Predicting Dryland Wheat Yield from Meteorological DataUsing Expert System, Khorasan Province, Iran. J. Agr. Sci. Tech., 13: 627-640.
14. Koozegaran, S., Mousavi Baygi, M., Sanaeinejad, S. H. and Behdani, M. A. 2011. Study of the Minimum, Average and Maximum Temperature in South Khorasan to Identify Relevant Areas for Saffron Cultivation using GIS. J. Water Soil, 25(4): 892-904.
15. Kisi, O and Oztork, O. 2007. Adaptive Neurofuzzy Computing Technique for evapotranspiration Estimation. J. Irrig. Drain. Eng., 133(4).
16. Leffingwell, J. 2008. Saffron. Leffingwell Rep., 2(5): 1-6.
17. Lee, E. S. 2000. Neuro-Fuzzy Estimation in Spatial Statistics. J. Math. Anal. Appl., 249: 221– 231.
18. Menhaj, M. B. 1998. Foundations of Neural Networks. First Edition, Evolution Center Professor Farsi.
19. Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S. and Han, D. 2009. Evaporation Estimation Using Artificial Neural Networks and Adaptive Neurofuzzy. Inference System Techniques. J. Adv. Water Resour., 32: 88-97.
20. Nayak, P. C., Sudheer, K. P., Rangan, D. M. and Ramasastri, K. S. 2004. Aneuro-fuzzy Computing Technique for Modeling Hydrological Time Series. J. Hydrol. (Amsterdam), 291: 52– 66.
21. Nekouei, N., Behdani, M. A. and Khashei-Siuki, A. 2014. Predicting Saffron Yield from Meteorological Data Using Expert System, Razavi and South Khorasan Provinces, Iran. J. Saffron Res., 2(1): 13-31.
22. Riahi Modavar, H., Khashei-Siuki, A. and Seifi, A. 2017. Accuracy and Uncertainty Analysis of Artificial Neural Network in Predicting Saffron Yield in the South Khorasan Province Based on Meteorological Data. Saffron Agron. Technol., 5(3): 255-271.
23. Sharrif-Moghaddasi, M. 2010. Saffron Chemicals and Medicine Usage. J. Med. Plants Res., 4(6): 427-430.
24. Seifi, A. and Riahi-Modavar, H., 2020. Estimating Daily Reference Evapotranspiration Using Hybrid Gamma Test-Least Square Support Vector Machine, Gamma Test-ANN, and Gamma Test-ANFIS Models in an Arid Area of Iran. J. Water Clim. Change, 11(1): 217–240.
25. Taher Hosseini, M. T., Siosemarde, A., Fathi, P. and Siosemarde, M. 2007. Application of Artificial Neural Network (ANN) and Multiple Regression for Estimating the Performance of Dry Farming Wheat Yield in Ghorveh Region, Kurdistan Province. J. Agr. Res.: Water Soil Plant Agr., 7(1): 41-54. [in Persian with English Summary]
26. Zare Abyaneh, H., Bayat Varkeshi, M., Maroofi, S. and Amiri Chayjan, R. 2010. Evaluation of Neural Systems in Reducing the Parameters of Reference Evapotranspiration Estimates. J. Soil Water (Agric. Sci. Technol.), 24(2): 305-297.
Journal of Agricultural Science and Technology
Volume 23, Issue 1
January and February 2021
Pages 221-234