Skip to main content

Advertisement

Springer Nature Link
Log in

Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Coconut is one of the major perennial food crops that has a long development phase of 44 months. The climatic and seasonal variations affect all stages of coconut’s long development cycle. Besides, the soil composition also plays a vital role in deciding the coconut yield behavior. The present study is focused on categorizing the coconut production level for the given set of agro-climatic conditions using the methodology of fuzzy cognitive map (FCM) enhanced by its learning capabilities. Additionally, an attempt is made to study the impact of climatic variations and weather parameters on the coconut yield behavior using the reasoning capabilities of FCM. Real coconut field data of different seasons for the period from 2009 to 2013 of Kerala state’s Malabar region were used for training and evaluation of the FCM. The present work demonstrates the classification and prediction capabilities of FCM for the described precision agriculture application, with the two most known and efficient FCM learning approaches, viz., nonlinear Hebbian (NHL) and data-driven nonlinear Hebbian (DDNHL). The DDNHL-FCM offers an overall classification accuracy of 96 %. The various case studies furnished in the paper demonstrate the power of NHL-FCM in effectively reasoning new knowledge pertaining to the presented precision agriculture application.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Chandrasekharan VG, Vasanthkumar VC, Preethakumari PV, Renu PV, Vinod ES (2013) Consolidated report on concurrent estimation of coconut production in Kerala 2012–2013. Coconut Development Board, Ministry of Agriculture, Government of India. http://www.coconutboard.nic.in

  2. WEKA, Toolbox (2003) http://www.cs.waikato.ac.nz/~ml/weka (last stable release 24/04/2014)

  3. Peiris TSG, Hansen JW, Zubair L (2008) Use of seasonal climate information to predict coconut production in Sri Lanka. Int J Climatol 28:103–110

    Article  Google Scholar 

  4. Saraswathi P, Mathew TP (1988) Forecasting coconut yield using monthly distribution of rainfall. In: Agrometerology of plantation crop, proceedings of the national seminar. Kerala Agricultural University Press, pp 138–143

  5. Kumar SN, Rajeev MS, Vinayan NagvekarDD, Venkitaswamy R, Rao DVR, Boraiah B, Gawankar MS, Dhanapal R, Patil DV, Bai KVK (2009) Trends in weather and yield changes in past in coconut growing areas in India. J Agrometerol 11:15–18

    Google Scholar 

  6. Balakrishnan M, Meena K (2010) ANN model for coconut yield prediction using optimal discriminant plane method at Bay Islands. IUP J Comput Sci 4(1):27–34

    Google Scholar 

  7. Kosko B (1992) Neural networks and fuzzy systems. Prentice-Hall, Englewood Cliffs, pp 29–32

    MATH  Google Scholar 

  8. Papageorgiou EI, Parsopoulos KE, Stylios CD, Groumpos PP, Vrahatis MN (2005) Fuzzy cognitive maps learning using particle swarm optimization. J Intell Inf Syst 25(1):95–121

    Article  Google Scholar 

  9. Papageorgiou EI, Stylios CD, Groumpos PP (2003) An integrated two-level hierarchical decision making system based on fuzzy cognitive maps. IEEE Trans Biomed Eng 50(12):1326–1339

    Article  Google Scholar 

  10. Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37(3):219–245

    Article  MATH  MathSciNet  Google Scholar 

  11. Papageorgiou EI (2012) Learning algorithms for fuzzy cognitive maps—a review study. IEEE Trans Syst Man Cybern Part C 42(2):150–163

    Article  Google Scholar 

  12. Papageorgiou EI, Markinos A, Gemptos Theofanis (2009) Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Syst Appl 36:12399–12413

    Article  Google Scholar 

  13. Papageorgiou EI, Aggelopoulou K, Gemptos T, Nanos G (2013) Υield prediction in apples related to precision agriculture using Fuzzy Cognitive Map learning approach. Comput Electron Agric 91:19–29

    Article  Google Scholar 

  14. Irmak A, Jones JW, Batchelor WD, Irmak S, Boote KJ, Paz JO (2006) Artificial neural network model as a data analysis tool in precision farming. Trans ASABE 49(6):2027–2037

    Article  Google Scholar 

  15. Lund ED, Christy CD, Drummond PE (1999) Practical applications of soil electrical conductivity mapping. In: Stafford JV (ed) Proceedings of the second European conference on precision agriculture. Sheffield Academic Press, Sheffield, pp 771–779

    Google Scholar 

  16. Shearer SA, Thomasson JA, Mueller TG, Fulton JP, Higgins SF, Samson S (1999) Yield prediction using a neural network classifier trained using soil and scape features and soil fertility data. ASAE Paper No. 993042. St. Joseph, Michigan, USA

  17. Kosko B (1992) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, Englewood Cliffs, NJ. ISBN 0-13-611435-0

    MATH  Google Scholar 

  18. Papageorgiou E (2014) Fuzzy cognitive maps for applied sciences and engineering—from fundamentals to extensions and learning algorithms. Intelligent Systems Reference Library 54, Springer 2014. ISBN 978-3-642-39738-7

  19. Motlagh O, Tang SH, Homayouni SM, Grozev G, Papageorgiou EI (2014) Development of application-specific adjacency models using fuzzy cognitive map. J Comput Appl Math 270:178–187

    Article  Google Scholar 

  20. Zhang H, Shen Z, Miao C (2011) Train fuzzy cognitive maps by gradient residual algorithm. IEEE international conference on fuzzy systems, June 27–30, 2011, Taipel, Taiwan

  21. Papakostas GA, Poiydoros AS, Koulouritois DE, Tourassis VD (2011) Training fuzzy cognitive maps by Hebbian learning algorithm: a comparative study. IEEE international conference on fuzzy systems, 27–30 June 2011, Taipel, Taiwan

  22. Papageorgiou EI, Salmeron JL (2013) A review of fuzzy cognitive maps research during the last decade. IEEE Trans Fuzzy Syst 21(1):66–79

    Article  Google Scholar 

  23. Motlagh O, Tang SH, Ramli AR, Nakhaeinia D (2012) An FCM modeling for using a priori knowledge: application study in modeling quadruped walking. Neural Comput Appl 21(5):1007–1015

    Article  Google Scholar 

  24. Papageorgiou EI, Salmeron JL (2012) Learning fuzzy grey cognitive maps using nonlinear Hebbian-based approach. Int J Approx Reason 53(1):54–65

    Article  MATH  MathSciNet  Google Scholar 

  25. Papageorgiou EI, Froelich W (2012) Application of evolutionary fuzzy cognitive maps for prediction of pulmonary infections. IEEE Trans Inf Technol Biomed 16(1):143–149

    Article  Google Scholar 

  26. Stach W, Kurgan L, Pedrycz W (2008) Data driven nonlinear Hebbian learning method for fuzzy cognitive maps. In: IEEE international conference on fuzzy systems, pp 1975–1981

  27. Papageorgiou EI, Groumpos PP (2005) A weight adaptation method for fine tuning fuzzy cognitive map causal links. Soft Comput J 9:846–857

    Article  MATH  Google Scholar 

  28. Papageorgiou EI, Stylios CD, Groumpos PP (2006) Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. Int J Hum Comput Stud 64:727–743

    Article  Google Scholar 

  29. Cohen SCM, Castro LN (2006) Data clustering with particle swarms. In: Proceedings of the world congress on computational intelligence, pp 6256–6262

  30. Duda RO, Hart PE, Strok DG (2001) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  31. Quinlan JR (1990) Decision trees and decision making. IEEE Trans Syst Man Cybern 20(2):339–346

    Article  Google Scholar 

  32. Witten IH, Frank E (1999) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers, San Mateo

    Google Scholar 

  33. Arthi K, Tamilarasi A, Papageorgiou EI (2011) Analyzing the performance of fuzzy cognitive maps with non-linear Hebbian learning algorithm in predicting autistic disorder. Expert Syst Appl 38(3):1282–1292

    Article  Google Scholar 

  34. Krishna Kumar KN (2011) Coconut phenology and yield response to climate variability and change, Ph.D. Thesis. Department of Atmospheric Sciences Cochin University of Science and Technology, Kochi, India, October 2011

  35. Prasada Rao GSLHV, Ram Mohan HA, Gopakumar CS, Rishnakumar KN (2008) Climatic change and cropping system over Kerala in the humid tropics. J Agrometerol (special issue part 2) 286–291

Download references

Acknowledgments

We thank Dr. P. Rajendran, Professor, Horticulture, Agriculture Research Station (ARS), Anakkayam, Kerala Agriculture University (KAU); Dr. Mustafa Kunnathady, Assistant Professor, Agronomy, ARS Anakkayam, KAU; Mr. E. Jubail and Mrs. K. Shahida, Farm Officer, ARS, Anakkayam, KAU, for their support in providing the necessary guidance related to the interpretation of the coconut harvest and climatologically data.

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India

    L. S. Jayashree & Nidhil Palakkal

  2. Department of Computer Engineering, Technological Educational Institute of Central Greece, Lamia, Greece

    Elpiniki I. Papageorgiou

  3. Ministry of Education and Religious Affairs, Athens, Greece

    Konstantinos Papageorgiou

Authors
  1. L. S. Jayashree
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nidhil Palakkal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elpiniki I. Papageorgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Konstantinos Papageorgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elpiniki I. Papageorgiou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jayashree, L.S., Palakkal, N., Papageorgiou, E.I. et al. Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region. Neural Comput & Applic 26, 1963–1978 (2015). https://doi.org/10.1007/s00521-015-1864-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-1864-5

Keywords