Skip to main content
SpringerLink
Log in

Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application

  • Chapter
Fuzzy Cognitive Maps

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 247))

Abstract

This work investigates the yield and yield variability prediction in cotton crop. Cotton crop management is a complex process with interacting parameters like soil, crop and weather factors. The soft computing technique of fuzzy cognitive maps (FCMs) was used for modeling and representing experts’ knowledge. FCM, as a fusion of fuzzy logic and cognitive map theories, is capable of dealing with uncertain descriptions like human reasoning. It is a challenging approach for decision making especially in complex environments. The yield management in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. The developed FCM model consists of nodes that represent the main factors affecting cotton production linked by directed edges that show the cause-effect relationships between factors and cotton yield. Furthermore, weather factors and conditions were taken into consideration in this approach by categorizing springs as dry–wet and warm-cool. The methodology was evaluated for approximately 360 cases measured over 2001, 2003 and 2006 in a 5 ha cotton field. The results were compared with some benchmarking machine learning algorithms, which were tested for the same data set, with encouraging results. The main advantage of FCM is the simple structure and the easy handling of complex data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Adams, M.L., Cook, S.E., Caccetta, P.A., Pringle, M.J.: Machine learning methods in site–specific management research: An Australian case study. In: Robert, P.C., Rust, R.M., Larsen, W.E. (eds.) Proc. 4th Int. Conf. on Precision Agriculture, ASA, CSSA, SSSA, Madison, USA, pp. 1321–1333 (1999)

    Google Scholar 

  • Aggelopoulou, A.D., Bochtis, D., Koutsostathis, A., Fountas, S., Gemtos, T.A., Nanos, G.D.: Flower spatial variability in an apple orchard. In: JIAC Conference, Wageningen, July 5-8 (2009)

    Google Scholar 

  • Ambuel, J.R., Colvin, T.S., Karlen, D.L.: A Fuzzy logic yield simulator for prescription farming. Transactions of the ASAE 37(6), 1999–2009 (1994)

    Google Scholar 

  • Axelrod, R.: Structure of decision, the cognitive maps of political elites. Princeton University Press, Princeton (1976)

    Google Scholar 

  • Bishop, C.M.: Neural networks for pattern recognition, 1st edn. Oxford University Press, USA (1996)

    MATH  Google Scholar 

  • Berthold, M., Hand, D.J.: Intelligent Data Analysis, 2nd revised and extended edn. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  • Canteri, M., Avila, B.C., Dos Santos, E.L., Sanches, M.K., Kovaleschyn, D., Molin, J.P., Gimenez, L.M.: Application of Data Mining in Automatic Description of Yield Behavior in Agricultural Areas. In: Zazueta, F.S., Xin, J. (eds.) Proceedings of the World Congress of Computers in Agriculture and Natural Resources, Iguacu Falls, Brazil, March 13-15, pp. 183–189 (2002)

    Google Scholar 

  • Chiu, C., Norcio, A.F., Hsu, C.: Reasoning on domain knowledge level in human–computer interaction. Information Sciences 1, 31–46 (1994)

    Article  MATH  Google Scholar 

  • COTMAN, Cotton Management Expert System Software (2008), http://www.uark.edu/depts/cotman (last accessed: 20/9/2008)

  • Craiger, J.P., Weiss, R.J., Goodman, D.F., Butler, A.A.: Simulating organizational behavior with fuzzy cognitive maps. Int. J. Comp. Intell. Organ 1, 120–133 (1996)

    Google Scholar 

  • Dickerson, J.A., Kosko, B.: Virtual worlds in fuzzy cognitive maps. In: Kosko, B. (ed.) Fuzzy Engineering, pp. 499–528. Prentice-Hall, Simon & Schuster (1994)

    Google Scholar 

  • Drummond, S.T., Sudduth, K.A., Birrell, S.J.: Analysis and correlation methods for spatial data. Paper No. 95–1335, ASAE, St. Joseph, MI, USA (1995)

    Google Scholar 

  • Drummond, S.T., Sudduth, K.A., Joshi, A., Birrell, S.J., Kitchen, N.R.: Statistical and neural methods for site-specific yield prediction. Transactions of the ASAE 46(1), 5–14 (2003)

    Google Scholar 

  • Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., New York (2001)

    MATH  Google Scholar 

  • Fraisse, C.W., Sudduth, K.A., Kitchen, N.R.: Calibration of the CERES–MAIZE model for simulating site–specific crop development and yield on claypan soils. Applied Eng. Agriculture 17(4), 547–556 (2001)

    Google Scholar 

  • Froelich, W., Wakulicz-Deja, A.: Learning Fuzzy Cognitive Maps from the Web for the Stock Market Decision Support System. In: Wegrzyn-Wolska, K.M., Szczepaniak, P.S. (eds.) Adv. in Intel. Web, ASC, vol. 43, pp. 106–111 (2007)

    Google Scholar 

  • Gemtos, T.A., Markinos, A.T., Toulios, L., Pateras, D., Zerva, G.: Precision Farming applications in Cotton Fields of Greece. In: 2004 CIGR international conference, Beijing, China, October 11-14 (2004) (CD-ROM)

    Google Scholar 

  • Glykas, M., Xirogiannis, G.: A soft knowledge modeling approach for geographically dispersed financial organizations. Soft Computing 9(8), 579–593 (2004)

    Article  Google Scholar 

  • GRASS GIS (1999) http://grass.osgeo.org (Last updated: October 23, 2008)

  • Groumpos, P., Stylios, C.: Modelling Supervisory Control Systems using Fuzzy Cognitive Maps. Chaos Solit Fract 11(1-3), 329–336 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  • Irmak, A., Jones, J.W., Batchelor, W.D., Irmak, S., Boote, K.J., Paz, J.O.: Artificial neural network model as a data analysis tool in precision farming. Transactions of the ASABE 49(6), 2027–2037 (2006)

    Google Scholar 

  • Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy & Soft Computing. Prentice-Hall, Upper Saddle River (1997)

    Google Scholar 

  • Jang, L.: Soft Computing Techniques in Knowledge-Based Intelligent Engineering Systems: Approaches and Applications. Studies in Fuzziness and Soft Computing, vol. 10. Springer, Heidelberg (1997)

    Google Scholar 

  • Khakural, B.R., Robert, P.C., Huggins, D.R.: Variability of corn/soybean yield and soil / landscape properties across a southwestern Minnesota landscape. In: Robert, P.C., Rust, R.M., Larsen, W.E. (eds.) Proc. 4th Int. Conf. on Precision Agriculture, ASA, CSSA, SSSA, Madison, USA, pp. 573–579 (1999)

    Google Scholar 

  • Kang lI, Lee, S.: Using fuzzy cognitive map for the relationship management in airline service. Expert systems with applications 26(4), 545–555 (2004)

    Google Scholar 

  • Khan, M.S., Khor, S.W.: A framework for fuzzy rule-based cognitive maps. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 454–463. Springer, Heidelberg (2004)

    Google Scholar 

  • Kosko, B.: Fuzzy Cognitive Maps. Int. J. Man-Machine Studies 24, 65–75 (1986)

    Article  MATH  Google Scholar 

  • Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, New Jersey (1992)

    MATH  Google Scholar 

  • Kosko, B.: Fuzzy Engineering. Prentice-Hall, Upper Saddle River (1997)

    MATH  Google Scholar 

  • Kottas, T.L., Boutalis, Y.S., Christodoulou, M.A.: Fuzzy cognitive network: A general framework. Intelligent Decisions Technologies Journal 4, 183–196 (2007)

    Google Scholar 

  • Kravchenko, A.N., Bullock, D.G.: Correlation of corn and soybean grain yield with topography and soil properties. Agronomy J. 92(1), 75–83 (2000)

    Google Scholar 

  • Lee, K.C., Kin, J.S., Chung, N.H., Kwon, S.J.: Fuzzy cognitive map approach to web-mining inference amplification. Journal of Experts Systems with Applications 22, 197–211 (2002)

    Article  Google Scholar 

  • Lee, K.-C., Kwon, S.: CAKES-NEGO: Causal knowledge-based expert system for B2B negotiation. Expert Systems with Applications (2007)

    Google Scholar 

  • Lin, C.T., Lee, C.S.G.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, Upper Saddle River (1996)

    Google Scholar 

  • Liu, J., Goering, C.E., Tian, L.: A neural network for setting target corn yields. Transactions of the ASAE 44(3), 705–713 (2001)

    Google Scholar 

  • Liu, Z., Satur, R.: Contextual fuzzy cognitive map for decision support in geographic information systems. IEEE Trans. Fuzz Syst. 5, 495–507 (1999)

    Google Scholar 

  • Liu, Z.Q.: Fuzzy Cognitive Maps: Analysis and Extension. Springer, Tokyo (2000)

    Google Scholar 

  • Lund, E.D., Christy, C.D., Drummond, P.E.: Practical applications of soil electrical conductivity mapping. In: Stafford, J.V. (ed.) Proceedings of the 2nd European Conference on Precision Agriculture, pp. 771–779. Sheffield Academic Press, Sheffield (1999)

    Google Scholar 

  • Markinos, A.T., Gemtos, T.A., Pateras, D., Toulios, L., Zerva, G., Papaeconomou, M.: The influence of cotton variety in the calibration factor of a cotton yield monitor. In: Proc. of the International Conference of the Hellenic Association of Information and Communication Technology in Agriculture, Food and Environment (HAICTA 2004) on Information Systems & Innovative Technologies in Agriculture, Food and Environment, Thessaloniki, Greece, March 18-20, vol. 2, pp. 65–74 (2004)

    Google Scholar 

  • Markinos, A.T., Papageorgiou, E.I., Stylios, C.D., Gemtos, T.A.: Introducing Fuzzy Cognitive Maps for decision making in precision agriculture. In: Stafford, J.V. (ed.) Proceedings of 6th European Conference on Precision Agriculture (6th ECPA), pp. 77–86. Wageningen Academic Publishers, Netherlands (2007)

    Google Scholar 

  • Mathews, R., Blackmore, S.: Using crop simulation models to determine optimum management practices in precision agriculture. In: Stafford, J.V. (ed.) Proceedings of the 1st European Conference on Precision Agriculture, pp. 413–420. BIOS Scientific Publishers, Oxford (1997)

    Google Scholar 

  • McKinion, J.M., Wagner, T.L.: GOSSYM/COMAX: A decision support system for precision application of nitrogen and water. In: Yuanchun, S., Xu, C. (eds.) Integrated Resources Management for Sustainable Agriculture, pp. 32–38. Agricultural University Press, Beijing (1994)

    Google Scholar 

  • McKinion, J.M., Jenkins, J.N., Akins, D., Turner, S.B., Willers, J.L., Jallas, E., Whisler, F.D.: Analysis of a precision agriculture approach to cotton production. Computers and Electronics in Agriculture 32, 213–228 (2001)

    Article  Google Scholar 

  • Mendoza, G.A., Prabhu, R.: Participatory modeling and analysis for sustainable forest management: Overview of soft system dynamics models and applications. Forest Policy and Economics 9(2), 179–196 (2006)

    Article  Google Scholar 

  • Miao, Y., Liu, Z.: On Causal Inference in Fuzzy Cognitive Maps. IEEE Trans. Fuzz Syst. 8, 107–119 (2000)

    Article  Google Scholar 

  • Miao, Y., Mulla, D.J., Robert, P.C.: Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture 7, 117–135 (2006)

    Article  Google Scholar 

  • Neapolitan, R.E.: Learning Bayesian Networks, 1st edn. Prentice Hall Publishers, Upper Saddle River (2003)

    Google Scholar 

  • Papageorgiou, E., Stylios, C., Groumpos, P.: An Integrated Two-Level Hierarchical Decision Making System based on Fuzzy Cognitive Maps (FCMs). IEEE Trans. Biomed. Engin. 50(12), 1326–1339 (2003)

    Article  Google Scholar 

  • Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Active Hebbian Learning to Train Fuzzy Cognitive Maps. Int. J. Approx. Reasoning 37, 219–249 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  • Papageorgiou, E.I., Groumpos, P.P.: A weight adaptation method for fine-tuning Fuzzy Cognitive Map causal links. Soft Computing 9, 846–857 (2005a)

    Article  MATH  Google Scholar 

  • Papageorgiou, E.I., Groumpos, P.P.: A new hybrid learning algorithm for Fuzzy Cognitive Maps learning. Applied Soft Computing 5, 409–431 (2005b)

    Article  Google Scholar 

  • Papageorgiou, E.I., Spyridonos, P., Ravazoula, P., Stylios, C.D., Groumpos, P.P., Nikiforidis, G.: Advanced Soft Computing Diagnosis Method for Tumor Grading. Artificial Intelligence in Medicine 36(1), 59–70 (2006)

    Article  Google Scholar 

  • Papageorgiou, E.I., Groumpos, P.P.: Neuro-fuzzy, fuzzy decision tree and association rule based methods for fuzzy cognitive map grading process. In: Proceedings of International Conference on Computational Intelligence in MEDicine, CIMED 2007, Plymouth, UK, July 25-27 (2007) (CD-ROM)

    Google Scholar 

  • Papageorgiou, E.I., Markinos, A.T., Gemtos, T.A.: Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Systems with Applications 48(1) (2009) (in press)

    Google Scholar 

  • Papageorgiou, E.I.: A novel approach on designing augmented Fuzzy Cognitive Maps using fuzzified decision trees. In: Cai, Z., et al. (eds.) Proceedings at 4th international Symposium of Intelligence Computations and Applications, ISICA 2009, Computers in Communication and Intelligent Systems-CCIS 51, China, October 23-25, pp. 266–275. Springer, Heidelberg (2009)

    Google Scholar 

  • Peláez, C.E., Bowles, J.B.: Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Information Sciences 88, 177–199 (1996)

    Article  Google Scholar 

  • Quinlan, J.R.: Decision trees and decision making. IEEE Trans. System, Man and Cybernetics 20(2), 339–346 (1990)

    Article  Google Scholar 

  • Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  • Sadiq, R., Kleiner, Y., Rajani, B.: Interpreting fuzzy cognitive maps (FCMs) using fuzzy measures to evaluate water quality failures in distribution networks. National Research Council of Canada (2006), http://irc.nrc-cnrc.gc.ca

  • Schneider, M., Kandel, A., Chew, G.: Automatic construction of FCMs. Fuzzy Sets and System 93, 161–172 (1998)

    Article  Google Scholar 

  • Schultz, A., Wieland, R., Lutze, G.: Neural networks in agroecological modeling-stylish application or helpful tool? Computers and Electronics in Agriculture 29, 73–97 (2000)

    Article  Google Scholar 

  • Shearer, S.A., Thomasson, J.A., Mueller, T.G., Fulton, J.P., Higgins, S.F., Samson, S.: Yield prediction using a neural network classifier trained using soil landscape features and soil fertility data. Paper No. 993042., ASAE, St. Joseph, MI, USA (1999)

    Google Scholar 

  • Skov, F., Svenning, J.C.: Predicting plant species richness in a managed forest. Forest and Ecological Management 620, 1–11 (2006)

    Google Scholar 

  • Sowa, J.F.: Principles of Semantic Networks: Explorations in the Representation of Knowledge. Morgan Kaufmann Publishers, San Mateo (1991)

    MATH  Google Scholar 

  • Stach, W., Kurgan, L., Pedrycz, W.: Parallel Learning of Large Fuzzy Cognitive Maps. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17 (2007)

    Google Scholar 

  • SSToolbox for agriculture, User’s Guide. N. Country Club Rd., 824, Stillwater, OK, USA (2004)

    Google Scholar 

  • Stylios, C.D., Groumpos, P.P.: Modeling Fuzzy Cognitive Maps. IEEE Transactions on Systems, Man, and Cybernetics, Part A 34, 155–162 (2004)

    Article  Google Scholar 

  • Taber, R.: Knowledge processing with Fuzzy Cognitive Maps. Expert Systems with Applications 2, 83–87 (1991)

    Article  Google Scholar 

  • Taber, R.: Fuzzy cognitive maps. AI Expert 9, 19–23 (1994)

    Google Scholar 

  • Taber, R., Yager, R.R., Helgason, C.: Quantization Effects on the Equilibrium Behavior of Combined Fuzzy Cognitive Maps. International Journal of Intelligent Systems 22, 181–202 (2007)

    Article  MATH  Google Scholar 

  • Tsadiras, A.: Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences (2008) Press, Corrected Proof. (Available online: May 23 )

    Google Scholar 

  • Tsadiras, A.K., Margaritis, K.G.: Cognitive mapping and certainty neuron Fuzzy Cognitive Maps. Information Sciences 101, 109–130 (1997)

    Article  Google Scholar 

  • WEKA, Toolbox (2003), http://www.cs.waikato.ac.nz/~ml/weka (last accessed – 30/11/08)

  • Wendroth, O., Jurschik, P., Nielsen, D.R.: Spatial crop yield prediction from soil and land surface state variables using an autoregressive state–space approach. In: Precision Agriculture 1999, Proceedings of the 2nd European Conference on Precision Agriculture, JV Stafford ed, pp. 419–428. Sheffield Academic Publishers, Sheffield (1999)

    Google Scholar 

  • Werner, A., Doelling, S., Jarfe, A., Kuhn, J., Pauly, S., Roth, R.: Deriving maps of yield-potentials through the use of crop growth models, site information and remote sensing. In: Proceedings of Fifth International Conference on Precision Agriculture (CD), Minneapolis, MN, July 16-19, ASA, CSSA, and SSSA, Madison, WI, USA (2000)

    Google Scholar 

  • Witten, I., Frank, E.: Data mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Mateo (1999)

    Google Scholar 

  • Xirogiannis, G., Stefanou, J., Glykas, M.: A fuzzy cognitive map approach to support urban design. Expert Systems with Applications 26(2), 257–268 (2004)

    Article  Google Scholar 

  • Xirogiannis, G., Glykas, M.: Intelligent Modeling of e-Business Maturity. Expert Systems with Applications 32/2, 687–702 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics and Computer Technology, Technological Educational Institute (TEI) of Lamia, 3rd Km Old National Road Lamia-Athens, 35100, Lamia, Greece

    E. I. Papageorgiou

  2. Laboratory of Farm Mechanization, Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece

    A. T. Markinos & T. A. Gemtos

Authors
  1. E. I. Papageorgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. T. Markinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. A. Gemtos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Aegean, Chios, Greece

    Michael Glykas

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Papageorgiou, E.I., Markinos, A.T., Gemtos, T.A. (2010). Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application. In: Glykas, M. (eds) Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, vol 247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03220-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03220-2_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03219-6

  • Online ISBN: 978-3-642-03220-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation