Skip to main content
SpringerLink
Log in

A cloud computing framework for analysis of agricultural big data based on Dempster–Shafer theory

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

This paper aims to extract optimal location for cultivating orange trees. In order to reach this goal, a combination of Dempster-Shafer theory (DST) and cloud computing is proposed. The DST method is applied to make weights for input parameters, and cloud computing is used for creating a cost-effective integrated solution on collected information of different geographic regions. To do this, eight parameters including minimum and maximum temperatures, aspect, elevation, growing degree days, rainfall, relative humidity, solar radiation and slope are incorporated to determine the most optimal region for orange cultivation. Moreover, interpolation maps for each parameter are determined with using the inverse distance weighting model in a geographic information system software. The DST model as a novel method for the determination of land suitability is eventually applied in the MATLAB software environment to complete the performance evaluation. Three confidence levels are set as 99.5%, 99% and 95% such that the final results for each confidence level are compared accordingly. It will be shown that the proposed method is successful in predicting suitable locations for the cultivation of oranges by generating different maps at various degrees of confidence.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

DST:

Dempster-Shafer theory (DST)

GDD:

Growing degree days

IDW:

Inverse distance weighting

GIS:

Geographic information system

MCDM:

Multi-criteria decision-making

ANP:

Analytic network process

AHP:

Analytic hierarchy process

OWA:

Ordered weighted average

RMSE:

Root mean square error

References

  1. Amazon EC2 (2019). https://aws.amazon.com/ec2/. Accessed August 24 2019

  2. Armbrust M, Stoica I, Zaharia M, Fox A, Griffith R, Joseph AD et al (2010) A view of cloud computing. Commun ACM 53:50. https://doi.org/10.1145/1721654.1721672

    Article  Google Scholar 

  3. Glenis V, McGough AS, Kutija V, Kilsby C, Woodman S (2013) Flood modeling for cities using Cloud computing. J Cloud Comput Adv Syst Appl 2:7. https://doi.org/10.1186/2192-113X-2-7

    Article  Google Scholar 

  4. Aragonés-Beltrán P, Chaparro-González F, Pastor-Ferrando J-P, Pla-Rubio A (2014) An AHP (Analytic Hierarchy Process)/ANP (Analytic Network Process)-based multi-criteria decision approach for the selection of solar-thermal power plant investment projects. Energy 66:222–238. https://doi.org/10.1016/J.ENERGY.2013.12.016

    Article  Google Scholar 

  5. Dokht Ghasemian S, Yavari G, Majed V, Mohmoodi A, Javadian A (2018) Evaluation and ranking of citrus Gardens’ risks using topsis method (case study: East of Mazandaran Province) 4 Associate Professor and Member of Direction Board for Agricultural Insurance Fund in Iran, vol 8

  6. Yang JL, Tzeng G-H (2011) An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method. Expert Syst Appl 38:1417–1424. https://doi.org/10.1016/J.ESWA.2010.07.048

    Article  Google Scholar 

  7. Zabihi H, Ahmad A, Vogeler I, Said MN, Golmohammadi M, Golein B et al (2015) Land suitability procedure for sustainable citrus planning using the application of the analytical network process approach and GIS. Comput Electron Agric 117:114–126. https://doi.org/10.1016/J.COMPAG.2015.07.014

    Article  Google Scholar 

  8. Elsheikh R, Rashid A, Shariff BM, Amiri F, Ahmad NB, Kumar Balasundram S et al (2013) Agriculture Land Suitability Evaluator (ALSE): a decision and planning support tool for tropical and subtropical crops. Comput Electron Agric 93:98–110. https://doi.org/10.1016/j.compag.2013.02.003

    Article  Google Scholar 

  9. Jaafari A, Najafi A, Melón MG (2015) Decision-making for the selection of a best wood extraction method: an analytic network process approach. For Policy Econ 50:200–209. https://doi.org/10.1016/J.FORPOL.2014.09.010

    Article  Google Scholar 

  10. Jung U, Seo DW (2010) An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. Decis Support Syst 49:335–342. https://doi.org/10.1016/j.dss.2010.04.005

    Article  Google Scholar 

  11. Mendas A, Delali A (2012) Integration of MultiCriteria Decision Analysis in GIS to develop land suitability for agriculture: application to durum wheat cultivation in the region of Mleta in Algeria. Comput Electron Agric 83:117–126. https://doi.org/10.1016/J.COMPAG.2012.02.003

    Article  Google Scholar 

  12. Nguyen TT, Verdoodt A, Van YT, Delbecque N, Tran TC, Van Ranst E (2015) Design of a GIS and multi-criteria based land evaluation procedure for sustainable land-use planning at the regional level. Agric Ecosyst Environ 200:1–11. https://doi.org/10.1016/j.agee.2014.10.015

    Article  Google Scholar 

  13. Saaty TL, Vargas LG (2013) Decision making with the analytic network process, vol 195. Springer, Boston. https://doi.org/10.1007/978-1-4614-7279-7

    Book  Google Scholar 

  14. Shiue Y-C, Lin C-Y (2012) Applying analytic network process to evaluate the optimal recycling strategy in upstream of solar energy industry. Energy Build 54:266–277. https://doi.org/10.1016/j.enbuild.2012.07.032

    Article  Google Scholar 

  15. Wang ZZ, Zhang Z (2013) Seismic damage classification and risk assessment of mountain tunnels with a validation for the 2008 Wenchuan earthquake. Soil Dyn Earthq Eng 45:45–55. https://doi.org/10.1016/J.SOILDYN.2012.11.002

    Article  Google Scholar 

  16. Das PT, Sudhakar S (2014) Land suitability analysis for orange & pineapple: a multi criteria decision making approach using geo spatial technology. J Geogr Inf Syst 6:40–44. https://doi.org/10.4236/jgis.2014.61005

    Article  Google Scholar 

  17. Mondal MAH, Denich M (2010) Assessment of renewable energy resources potential for electricity generation in Bangladesh. Renew Sustain Energy Rev 14:2401–2413

    Article  Google Scholar 

  18. Kang HM, Choi SI, Kim H (2016) Identification of suitable sites for mountain ginseng cultivation using GIS and geo-temperature. Springerplus 5:394. https://doi.org/10.1186/s40064-016-2031-x

    Article  Google Scholar 

  19. Liu D, Wan F, Guo R, Li F, Cao H, Sun G (2011) GIS-based modeling of potential yield distributions for different oat varieties in China. Math Comput Model 54:869–876. https://doi.org/10.1016/J.MCM.2010.11.008

    Article  Google Scholar 

  20. Mokarram M, Mirsoleimani A (2018) Using Fuzzy-AHP and order weight average (OWA) methods for land suitability determination for citrus cultivation in ArcGIS (Case study: fars province, Iran). Phys A Stat Mech Its Appl 508:506–518. https://doi.org/10.1016/J.PHYSA.2018.05.062

    Article  Google Scholar 

  21. Van Chuong H (2011) Land suitability analysis and evaluation for production of fruit trees using GIS technology a case study at Thua Thien Hue. Hue Univ J Sci 67:1

    Google Scholar 

  22. Bozdağ A, Yavuz F, Günay AS (2016) AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environ Earth Sci 75:813. https://doi.org/10.1007/s12665-016-5558-9

    Article  Google Scholar 

  23. Elaalem M, Comber A, Fisher P (2011) A Comparison of fuzzy AHP and ideal point methods for evaluating land suitability. Trans GIS 15:329–346. https://doi.org/10.1111/j.1467-9671.2011.01260.x

    Article  Google Scholar 

  24. Achadi E, Low W, Ramdhan D, Wispriyono B, Susanna D, Njoto et al (2018) A brief economic evaluation of breastfeeding in Australia. KnE Life Sci 3:1

    Google Scholar 

  25. Kahsay A, Haile M, Gebresamuel G, Mohammed M (2018) Land suitability analysis for sorghum crop production in northern semi-arid Ethiopia: application of GIS-based fuzzy AHP approach. Cogent Food Agric 4:1–24. https://doi.org/10.1080/23311932.2018.1507184

    Article  Google Scholar 

  26. Helton JC (1997) Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. J Stat Comput Simul 57:1. https://doi.org/10.1080/00949659708811803

    Article  MATH  Google Scholar 

  27. Fidler F, Cumming G (2005) Teaching Confidence Intervals: Problems and Potential Solutions. Int Stat Institute, Voorburg, pp 1–5

    Google Scholar 

  28. Dempster AP (2008) A generalization of Bayesian inference. Class. Work. Dempster-Shafer theory belief Funct. Springer, Berlin, pp 73–104

  29. Varma VK, Ferguson I, Wild I (2000) Decision support system for the sustainable forest management. For Ecol Manag 128:49–55. https://doi.org/10.1016/S0378-1127(99)00271-6

    Article  Google Scholar 

  30. Fars Meteorological Bureau n.d. http://www.farsmet.ir/Default.aspx. Accessed 28 Aug 2018

  31. Burrough PA (1989) Fuzzy mathematical methods for soil survey and land evaluation. J Soil Sci 40:477–492. https://doi.org/10.1111/j.1365-2389.1989.tb01290.x

    Article  Google Scholar 

  32. Malpica JA, Alonso MC, Sanz MA (2007) Dempster–Shafer Theory in geographic information systems: a survey. Expert Syst Appl 32:47–55. https://doi.org/10.1016/j.eswa.2005.11.011

    Article  Google Scholar 

  33. Shoyaib M, Abdullah-Al-Wadud M, Chae O (2012) A skin detection approach based on the Dempster–Shafer theory of evidence. Int J Approx Reason 53:636–659. https://doi.org/10.1016/j.ijar.2012.01.003

    Article  Google Scholar 

  34. Dempster AP (2008) Upper and lower probabilities induced by a multivalued mapping. Stud Fuzziness Soft Comput 219:57–72. https://doi.org/10.1007/978-3-540-44792-4_3

    Article  Google Scholar 

  35. Dempster AP (1968) A generalization of bayesian inference. J R Stat Soc Ser B 30:205–247. https://doi.org/10.2307/2984504

    Article  MathSciNet  MATH  Google Scholar 

  36. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton

    MATH  Google Scholar 

  37. Chaabane S Ben, Sayadi M, Fnaiech F, Brassart E (2008) Color image segmentation based on Dempster-Shafer evidence theory. In: MELECON 2008—14th IEEE Mediterr. Electrotech. Conf. IEEE, pp 862–826. https://doi.org/10.1109/melcon.2008.4618544

  38. Shafer G (1976) Dempster-Shafer Theory. Int J Approx Reason 21:1–2. https://doi.org/10.1016/S0888-613X(99)00011-0

    Article  Google Scholar 

  39. Ackerman EA (1938) Influences of climate on the cultivation of citrus fruits. Geogr Rev 28:289. https://doi.org/10.2307/210476

    Article  Google Scholar 

  40. Stenzel NMC, Neves CSVJ, Marur CJ, dos Scholz MB, Gomes JC (2006) Maturation curves and degree-days accumulation for fruits of “Folha Murcha” orange trees. Sci Agric 63:219–225. https://doi.org/10.1590/S0103-90162006000300002

    Article  Google Scholar 

  41. Coops N, Loughhead A, Ryan P, Hutton R (2001) Development of daily spatial heat unit mapping from monthly climatic surfaces for the Australian continent. Int J Geogr Inf Sci 15:345–361. https://doi.org/10.1080/13658810010011401

    Article  Google Scholar 

  42. Mann KK, Schumann AW, Obreza TA (2011) Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data. Precis Agric 12:457–472. https://doi.org/10.1007/s11119-010-9189-y

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editor and reviewers for providing valuable and insightful comments on write-up and technical aspects of the research.

Funding

The authors would like to thank Shiraz University for providing financial support (238726-121) for this study.

Author information

Authors and Affiliations

  1. Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran

    Marzieh Mokarram

  2. Department of Computer Engineering, Persian Gulf University, Bushehr, Iran

    Mohammad R. Khosravi

  3. Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran

    Mohammad R. Khosravi

Authors
  1. Marzieh Mokarram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad R. Khosravi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marzieh Mokarram.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mokarram, M., Khosravi, M.R. A cloud computing framework for analysis of agricultural big data based on Dempster–Shafer theory. J Supercomput 77, 2545–2565 (2021). https://doi.org/10.1007/s11227-020-03366-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03366-z

Keywords

Search

Navigation