Prediction of Crop Yield Using Fuzzy-Neural System

  • Bindu GargEmail author
  • Tanya Sah
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Sustaining the burgeoning population is one of the major concerns of the twenty-first century. In one of its report FAO has clearly mentioned that as more developing countries enter into the developed phase, the purchasing power of the people will increase and there will be a constant increase in the food demand. To suffice the growing needs it is necessary to keep up with the demands. Addressing this situation a lot of research has been conducted in the past towards developing a robust time series forecasting algorithm. We in our research observed that due to the precarious nature of the crop yield Fuzzy time series has been particularly successful in predicting the crop production. In this chapter we propose a method to predict crop yield using fuzzy logic and artificial neural network and established the results by implementing it on rice yield dataset.


Fuzzy logic Rice yield forecasting Neural network Back propagation 


  1. 1.
    C. Musvoto, K. Nortze, B. de Wet, B.K. Mahumani, A. Nahman, Imperatives for an agricultural green economy in South Africa. S. Afr. J. Sci. 111 (2015)CrossRefGoogle Scholar
  2. 2.
    L.A. Zadeh, The concept of linguistic variable and its application to approximate reasoning-I. Inform. Sci. 8, 199–249 (1975)MathSciNetCrossRefGoogle Scholar
  3. 3.
    L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC-3, 1 (1973)MathSciNetCrossRefGoogle Scholar
  4. 4.
    L.A. Zadeh, Fuzzy sets. Inform. Contr. 8, 338–353 (1965)CrossRefGoogle Scholar
  5. 5.
    Q. Song, B.S. Chissom, Fuzzy time series and its models. Fuzzy Set. Syst. 54, 269–277 (1993)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Q. Song, B.S. Chissom, Forecasting enrollments with fuzzy time series: Part II. Fuzzy Set. Syst. 62, 1–8 (1994)CrossRefGoogle Scholar
  7. 7.
    U. Yolcu, E. Egrioglu, R.V.R. Uslu, M.A. Basaran, C.H. Aladag, A new approach for determining the length of intervals for fuzzy time series. Appl. Soft Comput. 9, 647–651 (2009)CrossRefGoogle Scholar
  8. 8.
    K. Huarng, Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Set. Syst. 12, 387–394 (2001)MathSciNetCrossRefGoogle Scholar
  9. 9.
    K. Huarng, Heuristic models of fuzzy time series for forecasting. Fuzzy Set. Syst. 123, 369–386 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    J.R. Hwang, S.M. Chen, C.H. Lee, Handling forecasting problems using fuzzy time series. Fuzzy Set. Syst. 100, 217,228 (1998)Google Scholar
  11. 11.
    L.W. Lee, L.W. Wang, S.M. Chen, Handling forecasting problems based on two-factors high-order time series. IEEE Trans. Fuzzy Syst. 14, 468–477 (2006)CrossRefGoogle Scholar
  12. 12.
    S.M. Chen, Forecasting enrollments based on high order fuzzy time series. Int. J. Cybernetics Syst. 33, 1–16 (2002)CrossRefGoogle Scholar
  13. 13.
    C.H.L. Lee, A. Lin, W.S. Chen, Pattern discovery of fuzzy time series for financial prediction. IEEE Trans. Knowledge Data Eng. 18, 613–625 (2006)CrossRefGoogle Scholar
  14. 14.
    T.A. Jilani, S.M.A. Burney, C. Ardil, Multivariate high order fuzzy time series forecasting for car road accidents. Int. J. Comput. Intell. 4, 15–20 (2007)Google Scholar
  15. 15.
    B. Garg, S. Aggarwal, J. Sokhal, Crop yield forecasting using fuzzy logic and regression model. Comput. Electrical Eng. 67, 383–403 (2017). CrossRefGoogle Scholar
  16. 16.
    S.M. Chen, Forecasting enrollments based on fuzzy time series. Fuzzy Set. Syst. 81, 311–319 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Engineering DepartmentBharati Vidyapeeth (Deemed to be University) College of EngineeringPuneIndia
  2. 2.Tanya Sah Senior Software Engineer Globallogic India Pvt. LtdNoidaIndia

Personalised recommendations