Skip to main content

Application of Soft Computing in Crop Management

  • Conference paper
  • First Online:
Intelligent Engineering Informatics

Abstract

Indian agriculture is overwhelmed by numerous complications; some of them are usual, and some others are artificial like small and fragmented land-holdings, seeds, manures, crop selection, crop planning, fertilizers and biocides, irrigation, lack of mechanization, soil erosion, agricultural marketing, inadequate storage facilities, and so on. With the progression of different and specific outfits for the viability test of crop management are essential for providing reliable data observing to the performance of crop management. Valuable practical data can be collected by utilizing fuzzy logic-based scheme, in contrast with the intrinsic objectivity for collecting the data in gradual progression without any flaw. By dint of subject expertise and with the knowledge of scientific derivation, the approach should inspire to every corners of the country and management of cropping schemes. This paper analyzes the application of soft computing techniques in crop management in the field of farming and organic engineering is manifested. Upcoming progress and implementation using soft computing in the arena of farming and organic work to be think about.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rani, A.S.: The Impact of Data Analytics in Crop Management based on Weather Conditions (2017)

    Google Scholar 

  2. Kumari, P.L., Reddy, G.K., Krisna, T.G.: Optimum allocation of agriculture land to the vegetable crops under uncertain profits using fuzzy multiobjective linear programming. IOSR J. Agric. Vet. Sci. 7(12), 19–28

    Article  Google Scholar 

  3. Ingole, K., et al.: Crop prediction and detection using fuzzy logic in Matlab. Int. J. Adv. Eng. Technol. 6(5), p. 2006 (2013)

    Google Scholar 

  4. Huang, Y., et al.: Development of soft computing and applications in agricultural and biological engineering. Comput. Electron. Agric. 71(2), 107–127 (2010)

    Article  Google Scholar 

  5. Regulwar, D.G., Gurav, J.B.: Fuzzy approach based management model for irrigation planning. J. Water Resour. Prot. 2(06), p. 545 (2010)

    Article  Google Scholar 

  6. Kumar, P., Singh, R.K., Shankar, R.: Efficiency measurement of fertilizer-manufacturing organizations using Fuzzy data envelopment analysis. J. Manag. Anal. (2017)

    Google Scholar 

  7. Sundaravalli, N., Geetha, A.: A Study & Survey on Rainfall Prediction and Production of Crops Using Data Mining Techniques (2016)

    Google Scholar 

  8. Jawad, F., et al.: Analysis of Optimum Crop Cultivation using Fuzzy System. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE (2016)

    Google Scholar 

  9. Murmu, S., Biswas, S.: Application of fuzzy logic and neural network in crop classification: A review. Aquati. Procedia 4, 1203–1210 (2015)

    Article  Google Scholar 

  10. Dahikar, S.S., Rode, S.V.: Agricultural crop yield prediction using artificial neural network approach. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 2(1), 683–686 (2014)

    Google Scholar 

  11. Singh, H., Sharma, N.: A Review of Fuzzy Based Expert System in Agriculture. Int. J. Eng. Sci. Res. Technol.

    Google Scholar 

  12. Mansourifar, M., et al.: Optimization crops pattern in variable field ownership. World Appl. Sci. J. 21(4), 492–497 (2013)

    Google Scholar 

  13. Waongo, M., et al.: A crop model and fuzzy rule based approach for optimizing maize planting dates in Burkina Faso, West Africa. J.Appl. Meteorol. Climatol. 53(3), 598–613 (2014)

    Article  Google Scholar 

  14. Houshyar, E., et al.: Sustainable and efficient energy consumption of corn production in Southwest Iran: combination of multi-fuzzy and DEA modeling. Energy 44(1), 672–681 (2012)

    Article  Google Scholar 

  15. Naderloo, L., et al.: Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45(6), 1406–1413 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prabira Kumar Sethy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sethy, P.K., Panigrahi, G.R., Barpanda, N.K., Behera, S.K., Rath, A.K. (2018). Application of Soft Computing in Crop Management. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_64

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7566-7_64

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7565-0

  • Online ISBN: 978-981-10-7566-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics