Advertisement

Crop Yield Forecasting Using Neural Networks

  • Mukesh Meena
  • Pramod Kumar Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

The crop production forecasting has become an important issue, now, as it is a key factor for our economy and sustainable development on account of increased demand of the food grains with growing population. It helps farmers and government to develop a better post-harvest management at local / regional / national level, e.g., transportation, storage, distribution. Additionally, it helps farmers to plan next year’s crop and government to plan import/export strategies. This work is based on the yield forecasting of the pearl millet (bajra) in the Jaipur region of Rajasthan, India. The proposed method uses a back propagation artificial neural network to forecast current yield of the crop with respect to the environmental factors using time series data. The obtained results are encouraging and much better in comparison to a recent fuzzy time series based methods for forecasting.

Keywords

Crop yield forecasting Pearl millet Time series Correlation analysis Neural network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Metzger, E., Nuss, W.A.: The Relationship between Total Cloud Lightning Behavior and Radar-Derived Thunderstorm Structure. Weather and Forecasting 28(1), 237–253 (2013)CrossRefGoogle Scholar
  2. 2.
    Rubia, A., Sanchis-Marco, L.: On downside risk predictability through liquidity and trading activity: A dynamic quantile approach. International Journal of Forecasting 29(1), 202–219 (2013)CrossRefGoogle Scholar
  3. 3.
    Jain, A., Singh, P.K., Singh, K.A.: Short Term Load Forecasting Using Fuzzy Inference and Ant Colony Optimization. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 626–636. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Chatterjee, A., Venigalla, M.M.: Travel Demand Forecasting for Urban Transportation Planning. In: Kutz, M. (ed.) Handbook of Transportation Engineering, vol. 34, ch. 7, pp. 7.1–7.34. McGraw-Hill (2004)Google Scholar
  5. 5.
    Davis, D.F., Mentzer, J.T.: Organizational factors in sales forecasting management. International Journal of Forecasting 23(3), 475–495 (2007)CrossRefGoogle Scholar
  6. 6.
    Lee, W.W., Henschel, H., Madnick, S.E.: A Framework for Technology Forecasting and Visualization. MIT Sloan Research Paper No. 4757-09 (2009), http://ssrn.com/abstract=1478054 or http://dx.doi.org/10.2139/ssrn.1478054
  7. 7.
    Kumar, S., Kumar, N.: Fuzzy Time Series based Method for Wheat production Forecasting. International Journal of Computer Applications 44(12), 5–10 (2012)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Kumar, N., Ahuja, S., Kumar, V., Kumar, A.: Fuzzy time series forecasting of wheat production. International Journal on Computer Science and Engineering 02(03), 635–640 (2010)Google Scholar
  10. 10.
    Crone, S.F., Dhawan, R.: Forecasting Seasonal Time Series with Neural Networks: A Sensitivity Analysis of Architecture Parameters. In: International Joint Conference on Neural Networks, pp. 2099–2104 (2007)Google Scholar
  11. 11.
    Sivanandam, S.N., Sumathi, S.: Introduction to Neural Networks Using MATLAB 6.0. Tata McGraw Hill (2005)Google Scholar
  12. 12.
    Kandil, N., Khorasani, K., Patel, R.V., Sood, V.K.: Optimum Learning Rate for Backpropagation Neural Networks. In: Canadian Conference on Electrical and Computer Engineering (CCECE/CCGEI 1993), pp. 465–468 (1993)Google Scholar
  13. 13.
  14. 14.
  15. 15.
  16. 16.

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mukesh Meena
    • 1
  • Pramod Kumar Singh
    • 1
  1. 1.Computational Intelligence and Data Mining Research LabABV-Indian Institute of Information Technology and Management GwaliorIndia

Personalised recommendations