Fuzzy Sets in Agriculture

  • Elpiniki I. PapageorgiouEmail author
  • Konstantinos Kokkinos
  • Zoumpoulia Dikopoulou
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 341)


Agricultural modeling and management are complex conceptual processes, where a large number of variables are taken into consideration and interact for system analysis and decision making. Most of the processes in the agricultural sector include the uncertainty, ambiguity, incomplete information and human intuition characteristics. These processes are not only constrained by their environment (e.g., market, climate, seasons, consumer choices), but they are also highly influenced by human factors (stakeholders’ perceptions). Fuzzy sets are able to manage and represent uncertainty, assure that the incomplete information is valued and provide solutions to issues which are crucial in agriculture like fertilization, land degradation, soil erosion and climate variability during planting material selection in physiological analysis. Fuzzy sets have gained constantly increasing research interest in the last twenty years and have found great applicability in the agricultural domain, helping farmers to take right decisions for their cultivated.


Agriculture Fuzzy sets Irrigation Fuzzy cognitive maps Crop simulation 


  1. 1.
    Food and Agriculture Organization: Investing in food security, p. 3. I/I1230E/1/11.09/1000, Italy (2009)Google Scholar
  2. 2.
    Popa, C.: Adoption of artificial intelligence in agriculture. Bull. UASVM Agric. Electron. 68(1), 284–293 (2011)Google Scholar
  3. 3.
    Dengel, A.: Special issue on artificial intelligence in agriculture. Künstl Intell. 27, 309–311 (2013). doi  10.1007/s13218-013-0275-y
  4. 4.
    Center, B., Verma, B.P.: Fuzzy logic for biological and agricultural systems. Artif. Intell. Rev. 12, 213–225 (1998)CrossRefzbMATHGoogle Scholar
  5. 5.
    Dubey, S., Pandey, R.K., Gautam, S.S.: Literature review on fuzzy expert system in agriculture. Int. J. Soft Comput. Eng. (IJSCE) 2(6) (2013)Google Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision, processes. IEEE Trans. Syst. Man Cybern. 3, 28–44 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Azeem, M.F.: Fuzzy inference system—Theory and applications. InTech 518 (2012)Google Scholar
  9. 9.
    Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R.E.: Development of soft computing and applications in agricultural and biological engineering. Comput. Electron. Agric. 71, 107–127 (2010)CrossRefGoogle Scholar
  10. 10.
    Bosma, R., van den Berg, J., Kaymak, U., Udo, H., Verreth, J.: A generic methodology for developing fuzzy decision models. Expert Syst. Appl. 39, 1200–1210 (2012)CrossRefGoogle Scholar
  11. 11.
    Djatkov, D., Effenberger, M., Lehner, A., Martinov, M., Tesic, M.: New method for assessing the performance of agricultural biogas plants. Renew. Energy 40, 104–112 (2011)CrossRefGoogle Scholar
  12. 12.
    Djatkov, D., Effenberger, M., Martinov, M.: Method for assessing and improving the efficiency of agricultural biogas plants based on fuzzy logic and expert systems. Appl. Energy 134, 163–175 (2014)CrossRefGoogle Scholar
  13. 13.
    Murmu, S., Biswas, S.: Application of fuzzy logic and neural network in crop classification: a review. Aquat. Procedia 4, 1203–1210 (2015)CrossRefGoogle Scholar
  14. 14.
    Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A.: A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice. Measurement 66, 26–34 (2015)CrossRefGoogle Scholar
  15. 15.
    Pandey, A., Prasad, R., Singh, V.P., Jha, S.K., Shukla, K.K.: Crop parameters estimation by fuzzy inference system using X-band scatterometer data. Adv. Space Res. 51, 905–911 (2013)CrossRefGoogle Scholar
  16. 16.
    Li, Q., Yan, J.: Assessing the health of agricultural land with emergy analysis and fuzzy logic in the major grain-producing region. Catena 99, 9–17 (2012)CrossRefGoogle Scholar
  17. 17.
    da Silva, A.F., Barbosa, A.P., Zimback, C.R.L., Landim, P.M.B., Soares, A.: Estimation of croplands using indicator kriging and fuzzy classification. Comput. Electron. Agric. 111, 1–11 (2015)CrossRefGoogle Scholar
  18. 18.
    Abbaspour-Gilandeh, Y., Sedghi, R.: Predicting soil fragmentation during tillage operation using fuzzy logic approach. J. Terrramech. 57, 61–69 (2015)CrossRefGoogle Scholar
  19. 19.
    Petkovic, D., Gocic, M., Trajkovic, S., Shamshirband, S., Motamedi, S., Hashim, R., Bonakdari, H.: Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Comput. Electron. Agric. 114, 277–284 (2015)CrossRefGoogle Scholar
  20. 20.
    Prakash, C., Thakur, G.S.M.: Fuzzy based agriculture expert system for Soya-bean. In: International Conference on Computing Sciences WILKES100-ICCS2013, Jalandhar, Punjab, India (2013)Google Scholar
  21. 21.
    Zhang, J., Su, Y., Wu, J., Liang, H.: GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Comput. Electron. Agric. 114, 202–211 (2015)CrossRefGoogle Scholar
  22. 22.
    Coulon-Leroy, C., Charnomordic, B., Thiollet-Scholtus, M., Guillaume, S.: Imperfect knowledge and data-based approach to model a complex agronomic feature—Application to vine vigor. Comput. Electron. Agric. 99, 135–145 (2013)CrossRefGoogle Scholar
  23. 23.
    Ceballos, M.R., Gorricho, J.L., Gamboa, O.P., Huerta, M.K., Rivas, D., Rodas, M.E.: Fuzzy system of irrigation applied to the growth of Habanero Pepper (Capsicum chinense Jacq.) under protected conditions in Yucatan, Mexico. Int. J. Distrib. Sens. Netw. 2015, 124–137 (2015). doi: 10.1155/2015/123543 CrossRefGoogle Scholar
  24. 24.
    Islam, N., Sadiq, R., Rodriguez, M.J., Francisque, A.: Evaluation of source water protection strategies: a fuzzy-based model. J. Environ. Manage. 121, 191–201 (2013)CrossRefGoogle Scholar
  25. 25.
    Giusti, E., Marsili-Libelli, S.: A Fuzzy Decision Support System for irrigation and water conservation in agriculture. Environ. Model Softw. 63, 73–86 (2015)CrossRefGoogle Scholar
  26. 26.
    Rossi, F., Nardino, M., Mannini, P., Genovesi, R.: IRRINET Emilia Romagna: online decision support on irrigation. Online agrometeological applications with decision support on the farm level. Cost Action 718, 99–102 (2004)Google Scholar
  27. 27.
    Binte Zinnat, S., Abdullah, D.: Design of a fuzzy logic based automated shading and irrigation system. In: 17th International Conference on Computer and Information Technology, 22–23 Dec 2014, Daffodil International University, Dhaka, Bangladesh (2014)Google Scholar
  28. 28.
    Trono, E.M., Guico, M.L., Labuguen, R., Navarro, A., Libatique, N.G., Tangonan, G.: Design and development of an integrated web-based system for tropical rainfall monitoring. Procedia Environ. Sci. 20, 305–314 (2014)CrossRefGoogle Scholar
  29. 29.
    Almeida, G., Vieira, J., Marques, A.S., Kiperstok, A., Cardoso, A.: Estimating the potential water reuse based on fuzzy reasoning. J. Environ. Manage. 128, 883–892 (2013)CrossRefGoogle Scholar
  30. 30.
    Liu, Y., Jiao, L., Liu, Y., He, J.: A self-adapting fuzzy inference system for the evaluation of agricultural land. Environ. Model Softw. 40, 226–234 (2013)CrossRefGoogle Scholar
  31. 31.
    Mawale, M.V., Chavan, V.: Fuzzy Inference System for productivity and fertility of soil. Int. J. Eng. Dev. Res. 2(3), 2321–9939 (2014)Google Scholar
  32. 32.
    Papadopoulos, A., Kalivas, D., Hatzichristos, T.: Decision support system for nitrogen fertilization using fuzzy theory. Comput. Electron. Agric. 78, 130–139 (2011)CrossRefGoogle Scholar
  33. 33.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Machine Stud. 24, 65–75 (1986)CrossRefzbMATHGoogle Scholar
  34. 34.
    Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)zbMATHGoogle Scholar
  35. 35.
    Papageorgiou, E.I. (ed.): Fuzzy Cognitive Maps for Applied Sciences and Engineering—From Fundamentals to Extensions and Learning Algorithms, Intelligent Systems Reference Library 54. Springer, Berlin (2014)Google Scholar
  36. 36.
    Papageorgiou, E.I., Aggelopoulou, K., Gemptos, T., Nanos, G.: Yield prediction in apples related to precision agriculture using fuzzy cognitive map learning approach. In: Computers and Electronics in Agriculture, vol. 91, pp. 19–29, December 2012 (2013)Google Scholar
  37. 37.
    Papageorgiou, E.I., Markinos, A., Gemptos, T.: Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Syst. Appl. 36, 12399–12413 (2009)CrossRefGoogle Scholar
  38. 38.
    Papageorgiou, E.I., Markinos, A., Gemptos, T.: Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Appl. Soft Comput. 11(4), 3643–3657 (2011)CrossRefGoogle Scholar
  39. 39.
    Jayashree, S., Nikhil P., Papageorgiou E.I., Papageorgiou K.: Application of fuzzy cognitive maps in precision agriculture: a case study of coconut yield prediction in India. Neural Comput. Appl. (2015). doi: 10.1007/s00521-015-1864-5
  40. 40.
    Halbrendt, J., Steven, A., Gray, Β., Crow, S., Radovich, T., Kimura, A.H., Tamang, B.B.: Differences in farmer and expert beliefs and the perceived impacts of conservation agriculture. Glob. Environ. Change 28, 50–62 (2014)CrossRefGoogle Scholar
  41. 41.
    Christen, B., Kjeldsen, C., Dalgaard, T., Martin-Ortega, J.: Can fuzzy cognitive mapping help in agricultural policy design and communication? Land Use Policy 45, 64–75 (2015)CrossRefGoogle Scholar
  42. 42.
    Zimmermann, H.J.: Advanced Review: Fuzzy set theory. Wiley, New York (2010). doi: 10.1002/wics.82
  43. 43.
    Ross, T.: Fuzzy Logic in Engineering Applications. McGraw-Hill, New York (1995)zbMATHGoogle Scholar
  44. 44.
    Yager, R., Filev, D.: Essentials of Fuzzy Modeling and Control. Wiley, New York (1994)Google Scholar
  45. 45.
    Tagarakis, A., Koundouras, S., Papageorgiou, E.I., Dikopoulou, Z., Fountas, S., Gemtos, T.A.: A fuzzy inference system to model grape quality in vineyards. Precis. Agric. Int. J. Adv. Precis. Agric. 15(5), 555–578 (2014)Google Scholar
  46. 46.
    Vitoriano, B., Montero, J., Ruan, D.: Decision Aid Models for Disaster Management and Emergencies, p. 325. Springer Science & Business Media (2013)Google Scholar
  47. 47.
    Arabacioglu, B.C.: Using fuzzy inference system for architectural space analysis. Appl. Soft Comput. 10(3), 926–937 (2010)CrossRefGoogle Scholar
  48. 48.
    Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps—A review study. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(2), 150–163 (2012)Google Scholar
  49. 49.
    Dai, Z.W., Ollat, N., Gomès, E., Decroocq, S., Tandonnet, J.P., Bordenave, L., Pieri, P., Hil-bert, G., Kappel, C., van Leeuwen, C., Vivin, P., Delrot, S.: Ecophysiological, genetic, and molecular causes of variation in grape berry weight and composition. Am. J. Enol. Viticulture 62, 413–425 (2011)CrossRefGoogle Scholar
  50. 50.
    Ribéreau-Gayon, P., Dubourdieu, D., Donèche, B., Lonvaud, A.: Handbook of Enology, Microbiology of Wine and Vinification. Wiley, West Sussex (2006)Google Scholar
  51. 51.
    Ruffner, H.P.: Metabolism of tartaric and malic acids in Vitis: a review. Part B Vitis 21, 346–358 (1982)Google Scholar
  52. 52.
    van Leeuwen, C., Tregoat, O., Choné, X., Bois, B., Pernet, D., Gaudillère, J.-P.: Vine water status is a key factor in grape ripening and vintage quality for red Bordeaux wine. How can it be assessed for vineyard management purposes? Journal International des Sciences de la Vigne et du Vin 43, 121–134 (2009)Google Scholar
  53. 53.
    Bramley, R.G.V., Trought, M.C.T., Praat, J.P.: Vineyard variability in Marlborough, New Zeland: characterizing variation. Aust. J. Grape Wine Res. 17, 72–78 (2011)CrossRefGoogle Scholar
  54. 54.
    Koundouras, S., Marinos, V., Gkoulioti, A., Kotseridis, Y., van Leeuwen, C.: Influence of vineyard location and vine water status on fruit maturation of nonirrigated cv. Agiorgitiko (Vitis vinifera L.). Effects on wine phenolic and aroma components. J. Agric. Food Chem. 54, 5077–5086 (2006)CrossRefGoogle Scholar
  55. 55.
    Kennedy, J.A., Saucier, C., Glories, Y.: Grape and wine phenolics: history and perspective. Am. J. Enol. Viticulture 57, 239–248 (2006)Google Scholar
  56. 56.
    Mazza, G., Francis, F.J.: Anthocyanins in grapes and grape products. Crit. Rev. Food Sci. Nutr. 35, 341–371 (1995)CrossRefGoogle Scholar
  57. 57.
    Sannakki, S.S., Rajpurohit, V.S., Arunkumar, R.: A survey on applications of fuzzy logic in agriculture. J. Comput. Appl. (JCA) 4(1) (2011)Google Scholar
  58. 58.
    Salleh, M., Nawi, N., Ghazali, R.: Uncertainty analysis using fuzzy sets for decision support system. Efficient Decision Support Systems—Practice and Challenges in Multidisciplinary Domains. InTech, pp. 273–290 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Elpiniki I. Papageorgiou
    • 1
    • 3
    Email author
  • Konstantinos Kokkinos
    • 2
  • Zoumpoulia Dikopoulou
    • 3
  1. 1.Department of Computer EngineeringTechnological Educational Institute (TEI) of Central GreeceLamiaGreece
  2. 2.Centre for Research and Technology HellasThessalonikiGreece
  3. 3.Faculty of Business EconomicsHasselt UniversityDiepenbeekBelgium

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