Skip to main content
SpringerLink
Log in

Application of artificial neural networks in yield prediction of potato crop

  • Biophysics
  • Published:
Russian Agricultural Sciences Aims and scope

Abstract

Forecasting of crop yield is helpful in food management and growth of a nation, which has specially agriculture based economy. In the last few decades, Artificial Neural Networks have been used successfully in different fields of agricultural remote sensing especially in crop type classification and crop area estimation. The present work employed two types of Artificial Neural Networks i.e., a Generalized Regression Neural Network (GRNN) and a Radial Basis Function Neural Network (RBFNN) to predict the yield of potato crops, which have been sown differently (flat and rough). Crop parameters like leaf area index, biomass and plant height were used as input data, while the yield of potato fields as output dataset to train and test the Neural Networks. Both GRNN and RBNN predicted potato crop yield accurately. However based on quick learning capability and lower spread constant (0.5), the GRNN was found a better predictor than RBFNN. Furthermore, the rough surface field was found more productive than flat field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Frei, U., Sporri, S., Stebler, O., and Holecz, F., Rice field mapping in Sri Lanka using ERS SAR data, Earth Observ Q., 1999, vol. 63, pp. 30–35.

    Google Scholar 

  2. Lam-Dao, N., Le-Toan, T., and Floury, N., The use of SAR Data for rice crop monitoring—a case study of Mekong River Delta-Victnam, 26th Asian Conference on Remote Sensing, Na Noi, 2005.

    Google Scholar 

  3. Choudhary, I. and Chakraborty, M., SAR signature investigation of rice crop using Radarsat data, Int. J. Remote Sens., 2006, vol. 27, pp. 519–534.

    Article  Google Scholar 

  4. Yang, C.C., Prasher, S.O., Landry, J.A., Ramaswary, H.S., and Ditommaso, A., Application of artificial neural network in image recognition, classification of crop and weed, Can. Agric. Eng., 2000, vol. 42, pp. 147–152.

    Google Scholar 

  5. Benedetti, R. and Rossini, P., On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia romagna, Remote Sens. Environ., 1993, vol. 45, pp. 311–326.

    Article  Google Scholar 

  6. Burks, T.F., Shearer, S.A., Sobalic, C., and Fulton, J.P., Combine yield monitor test facility development, Appl. Engr. Agric., 2003, vol. 19, pp. 5–12.

    Google Scholar 

  7. Stathakis, D., Savin, I., and Negre, T., Neuro-fuzzy modeling for crop yields prediction, ISPRS Commission VII Mid-Term Symposium on Remote Sensing: From Pixels to Processes, Enschede, 2006, vol. 34, pp. 358–361.

    Google Scholar 

  8. Savin, I.Yu., Stathakis, D., Negre, T., and Isaev, V.A., Prediction of crop yields with the use of neural networks, Russ. Agric. Sci., 2007, vol. 33, pp. 361–363.

    Article  Google Scholar 

  9. Nakano, K., Application of neural networks to the color grading of apples, Comput. Electron. Agric., 1997, vol. 18, pp. 105–116.

    Article  Google Scholar 

  10. Kimes, D.S., Holben, B.N., Nickeson, J.E., and Mackee, A., Extracting forest age in a Pacific North West forest from thematic mapper and topographic data, Remote Sens. Environ., 1996, vol. 56, pp. 133–140.

    Article  Google Scholar 

  11. Hyakin, S., Neural Network—A Comprehensive Foundation, Singapore: Pearson Education, 2003, 2nd ed.

    Google Scholar 

  12. Powell, M.J.D., Radial basis function approximation to polynomials, in Numerical Analysis Proceedings, Jordan Hill, Ed., Oxford: Dundee, 1987.

    Google Scholar 

  13. Chen, S., Cowan, C.F.N., and Grant, P.M., Orthogonal least squares learning algorithm for radial basis function networks, IEEE Trans. Neural Networks, 1991, vol. 2, pp. 302–309.

    Article  CAS  PubMed  Google Scholar 

  14. Kim, B., Kim, S., and Kim, K., Modeling of plasma etching using a generalized regression neural network, Vacuum, 2003, vol. 71, pp. 497–503.

    Article  CAS  Google Scholar 

  15. Specht, D.F., A general regression neural networks, IEEE Trans. Neural Network, 1991, vol. 2, no. 6, pp. 568–576.

    Article  CAS  Google Scholar 

  16. Specht, D.F., Probabilistic Neural Networks and General Regression Neural Network. Handbook, McGraw Hill Inc., 1995, chap. 3.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Carpet Technology (I.I.C.T.), Bhadohi, 221401, India

    Abhishek Pandey & Anu Mishra

Authors
  1. Abhishek Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anu Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Pandey.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, A., Mishra, A. Application of artificial neural networks in yield prediction of potato crop. Russ. Agricult. Sci. 43, 266–272 (2017). https://doi.org/10.3103/S1068367417030028

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1068367417030028

Keywords

Search

Navigation