Advertisement

Crop Prediction Models—A Review

  • Supreeth S. Avadhani
  • Aashrith B. Arun
  • Varun Govinda
  • Juyin Shafaq Imtiaz Inamdar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)

Abstract

Crop yield is directly dependent on climatic and weather conditions. A lot of research has been done studying the dependency of weather on crop yield. Crop prediction models have proven to be successful in increasing the crop yield. Soil parameters and atmospheric parameters are used by the models to predict the suitable crop. Parameters such as type of soil, pH, phosphate, potassium, organic carbon, sulphur, manganese, copper, iron, depth, temperature, rainfall, humidity have shown to influence the yield of crop. In this paper, we review the research conducted by several researchers in this direction with a logical conclusion.

Keywords

Artificial neural network (ANN) Weather Crop prediction models 

Notes

Acknowledgements

The authors are grateful to Dr. Anitha C, Assistant Professor, Department of Computer Science & Engineering, The National Institute of Engineering, Mysuru for her guidance and helpful comments. The authors also would like to thank the HOD, Department of CSE and the Principal for their continuous support and encouragement.

References

  1. 1.
    Dahikar, S.S., Rode, S.V.: Agricultural crop yield production using artificial neural networks. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 2(1), 683–686 (2014)Google Scholar
  2. 2.
    Shahane, S.K., Tawale, P.V.: Prediction on crop cultivation. In: Int. J. Adv. Res. Comput. Sci. Electron. Eng. (IJARCSEE) 5(10) (2016)Google Scholar
  3. 3.
    Honawad, S.K., Chinchali, S.S., Pawar, K., Deshpande, P.: Soil classification and suitable crop prediction. In: National Conference on Advances in Computational Biology, Communication, and Data Analytics, pp. 25–29 (2017)Google Scholar
  4. 4.
    Hiremath, P.S., Shivashankar, S.: Wavelet based features for texture classification. GVIP J. 6(3) (2006)Google Scholar
  5. 5.
    Ramana Reddy, B.V., Suresh, A., Radhika Mani, M., Vijaya Kumar, V.: Classification of textures based on features extracted from preprocessing images on random windows. Int. J. Adv. Sci. Technol. 9 (2009)Google Scholar
  6. 6.
    Kanjana Devi, P., Shenbagavadivu, S.: Enhanced crop yield prediction and soil data analysis using data mining. Int. J. Modern Comput. Sci. 4(6) (2016)Google Scholar
  7. 7.
    Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T.: The DSSAT cropping system model. Eur. J. Agron. 18(3), 235–265 (2003)Google Scholar
  8. 8.
    Fernando, M.T.N., Zubair, L., Peiris, T.S.G., Ranasinghe, C.S., Ratnasiri, J.: Economic value of climate variability impacts on coconut production in Sri Lanka. In: AIACC Working Papers, Working Paper No. 45 (2007)Google Scholar
  9. 9.
    Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., Barker, B.: Wheat yield forecasting for Punjab province from vegetation index time series and historic crop statistics. Remote Sens. 6 (2014)Google Scholar
  10. 10.
    Ji, B., Sun, Y., Yang, S., Wan, J.: Artificial neural networks for rice yield prediction in mountainous regions. J. Agric. Sci. 145, 249–261 (2007)Google Scholar
  11. 11.
    Gholap, J., Ingole, A., Gohil, J., Gargade, S., Attar, V.: Soil data analysis using classification techniques and soil attribute prediction. Int. J. Comput. Sci. Issues 9(3) (2012)Google Scholar
  12. 12.
    Kushwaha, A.K., Bhattachrya, S.: Crop yield prediction using agro algorithm in Hadoop. Int. J. Comput. Inf. Technol. Secur. 5(2) (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Supreeth S. Avadhani
    • 1
  • Aashrith B. Arun
    • 1
  • Varun Govinda
    • 1
  • Juyin Shafaq Imtiaz Inamdar
    • 1
  1. 1.The National Institute of EngineeringMysuruIndia

Personalised recommendations