Analysis of Deep Neural Network for Forecasting of the Turmeric yield in Telangana

Authors

  • Md. Atheeq Sultan Ghori  Research Scholar, Research and development centre, Bharathiar University, Coimbatore, Tamilnadu, India
  • M. Balakrishnan  Principal Scientist, National Academy of Agriculture research Management (NAARM), ICAR Rajendra Nagar, Hyderabad, Telangana, India

Keywords:

Deep Learning.

Abstract

Artificial Neural Network is the mostly used machine learning method for forecasting, in this paper work was carried out on a new technique by introducing Deep neural network concept by increasing the number of hidden layers (two layers) in the existing levenberg Marqardt algorithm and is applied on the data set of the turmeric crop for the forecasting of the accurate yield .to test the algorithm by introducing the two hidden layers the concept of Deep learning was introduced for the forecasting problem to try a new method instead of traditional linear methods and non linear ANN methods.

References

  1. W. Guo, H. Xue, “An incorporative statistic and neural approach for crop yield modelling and forecasting”, Neural Commuting and Applications, 21, pp. 109-117, 2012.
  2. W. Guo, H. Xue, “Crop yield forecasting using artificial neural networks: A comparision between spatial and temporal models”, Mathematical Problems in Engineering, pp. 1-7, 2014.
  3. R. Medar, V. Rajpurohit, “A survey on data mining techniques for crop yield prediction”, International Journal of Advance Research in Computer Science and Management Studies, 2(9), pp. 59-64, 2014.
  4. S. Bejo, S. Mustaffha, W. Ismail, “Application of artificial neural network in preedicting crop yield: A review”, Journal of Food Science and Engineering, 4, pp. 1-9, 2014.
  5. S. Dahikar, S. Rode, “Agricultural crop yield prediction using artificial neural network approach”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2(1), pp. 683-686, 2014.
  6. G. Yengoh, J. Ardo, “Crop yield gaps in Cameroon”, AMBIO, Springer, 43, pp. 175-190, 2014.
  7. D. Ramesh, B. Vardhan, “Analysis of crop yield prediction using data mining techniques”, International Journal of Research in Engineering and Technology, 4(1), pp. 47-473, 2015.
  8. J. Abello, P.M. Pardalos, M. Resende, Handbook of massive data sets, Kluwer, New York, 2002.
  9. W. Klosgen, J.M. Zytkow, Handbook of data mining and knowledge discovery,Oxford University Press, 2002.
  10. P.M. Pardalos, L.V. Boginski, A. Vazacopoulos, Data mining in biomedicine, Springer, New York, 2007.
  11. P.M. Pardalos, P. Hansen, Data mining and mathematical programming, American Mathematical Society, USA, 2008.
  12. R. Rupnik, M. Kukar, M. Krisper, “Integrating data mining and decision support through data mining based decision support system”, Journal of computer information systems, 47(3), pp.89-104, 2007.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-09-30

Issue

Volume 3, Issue 7, September-October-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Md. Atheeq Sultan Ghori, M. Balakrishnan, " Analysis of Deep Neural Network for Forecasting of the Turmeric yield in Telangana , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.90-93, September-October-2018.