Abstract
Predicting the effects of climate change on crop yields requires a model and its parameters, how crops respond to weather. Predictions from different models often disagree with the climatic variables and its impact. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Climate change is really concern to the entire world. Its direct impact on the crop growth and yield is very important to understand. In the present study Fuzzy logic crop yield model was developed by considering different climate change variables. Temperature, Rainfall, evaporation, humidity parameters are considered for the crop yield model. Model is being developed by considering the 15-year crop yield data and same period for the climatic variables. Triangular membership function is being adopted in the fuzzy model. In this study a fuzzy rule-based system (FRBS) using the Takagi Sugeno-Kang approach has been used for the developing the crop yield model. Model is validated by coefficient of correlation, and found that, there is more than 0.9 coefficient of correlation between observed and evaluated yield.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Jain, R.C., Mehta, S.C.: Yield forecast based on weather variables and agricultural inputs on agro-climatic zone basis. Ind. J. Agri. Sci. 71(7), 487–490 (2001)
Huda, A.K.S., Ghildyal, B.P., Tomar, V.S., Jain, R.C.: Contribution of climate variables in predicting rice yield. Agric. Met. 15, 71–86 (1975)
Chowdhury, A., Sarkar, M.B.: Estimation of rice yield through weather factors in a dry sub-humid region. Mausam 32(4), 393–396 (1981)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence. Prentice Hall, NJ, USA (1997). ISBN 0-13-261066-3
Loukas, Y.L.: Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J. Med. Chem. 44(17), 2772–2783 (2001)
Jang, J.-S.R.: ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)
Kim, B., Park, J.H., Kim, B.S.: Fuzzy logic model of Langmuir probe discharge data. Comput. Chem. 26(6), 573–581 (2002)
Nayak, P.C., Sudheer, K.P., Rangan, D.M., Ramasastri, K.S.: A neuro-fuzzy computing technique for modelling hydrological time series. J. Hydrology 291, 52–66 (2004)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)
Zadeh, L.A.: Fuzzy Sets. Inf. Control 8, 338–353 (1965)
Kokate, K.D., Sananse, S.L., Kadam, J.R.: Pre-harvest forecasting of rice yield in Konkan region of Maharashtra. J. Maha. Agric. Univ. 25(3), 289–293 (2000)
Jones, A.J.: New tools in non-linear modeling and prediction. CMS 1(2), 109–149 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Borse, K., Agnihotri, P.G. (2019). Prediction of Crop Yields Based on Fuzzy Rule-Based System (FRBS) Using the Takagi Sugeno-Kang Approach. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-00979-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-00979-3_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00978-6
Online ISBN: 978-3-030-00979-3
eBook Packages: EngineeringEngineering (R0)