An optimized enhanced Intuitionistic fuzzy cognitive maps for groundnut yield prediction

  • Authors

    • Malarkodi.K. P
    • Dr. Arthi .K
    https://doi.org/10.14419/ijet.v7i4.21564

  • Abstract

    Over the past decades, several types of crop yield prediction systems using different kinds of data mining algorithms have been developed in agriculture that supports cultivators to analyze the yield productivity. Among those techniques, Fuzzy Cognitive Map (FCM) based crop yield prediction has better efficiency, flexibility and ability to predict yield productivity. However, the performance of FCM was degraded due to some missing input data. Hence in this article, Intuitionistic Fuzzy Cognitive Map (IFCM) is initially used to improve the groundnut yield prediction with the aid of weather and soil parameters. The IFCM is built by considering the expert’s hesitancy in the computation of the causal relations between the concepts of a groundnut yield. On the other hand, the learning rate and stability of the IFCM are less due to fixed parameter based weight adaptation. As a result, a supervised multistep learning using the gradient method is proposed for enhancing weight adaptation of IFCM. The enhanced IFCM (EIFCM) estimate the current value of the weight matrix elements from the previous estimation history. Moreover, the learning parameters of the gradient method utilized in EIFCM are optimized by using Self-Organizing Migration Algorithm (SOMA) to reduce the iteration of the weight update. The experimental results prove the efficiency of the proposed OEIFCM in crop yield prediction in terms of accuracy, precision and recall.

  • References

    1. [1] Papageorgiou EI, Markinos AT, & Gemtos TA (2011), “Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture applicationâ€, Applied Soft Computing, 11(4), 3643-3657. https://doi.org/10.1016/j.asoc.2011.01.036.

      [2] Ramesh D, & Vardhan BV (2015), “Analysis of crop yield prediction using data mining techniquesâ€, International Journal of Research in Engineering and Technology, 4(1), 47-473. https://doi.org/10.15623/ijret.2015.0401071.

      [3] Gandge Y (2017), “A study on various data mining techniques for crop yield predictionâ€, in 2017 IEEE International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 420-423. https://doi.org/10.1109/ICEECCOT.2017.8284541.

      [4] Papageorgiou EI, Aggelopoulou KD, Gemtos TA, & Nanos GD (2013), “Yield prediction in apples using Fuzzy Cognitive Map learning approachâ€, Computers and electronics in agriculture, 91, 19-29. https://doi.org/10.1016/j.compag.2012.11.008.

      [5] Shastry A, Sanjay HA, & Hegde M (2015), “A parameter based ANFIS model for crop yield predictionâ€, in 2015 IEEE International Advance Computing Conference (IACC), pp. 253-257. https://doi.org/10.1109/IADCC.2015.7154708.

      [6] Paul M, Vishwakarma SK, & Verma A (2015), “Analysis of soil behaviour and prediction of crop yield using data mining approachâ€, in 2015 IEEE International Conference on Computational Intelligence and Communication Networks (CICN), pp. 766-771. https://doi.org/10.1109/CICN.2015.156.

      [7] Manjula A, & Narsimha G (2016), “Crop yield prediction with aid of optimal neural network in spatial data mining: new approachesâ€, International Journal of Information & Computation Technology, 6(1), 25-33.

      [8] Natarajan R, Subramanian J, & Papageorgiou EI (2016), “Hybrid learning of fuzzy cognitive maps for sugarcane yield classificationâ€, Computers and Electronics in Agriculture, 127, 147-157. https://doi.org/10.1016/j.compag.2016.05.016.

      [9] Balakrishnan N, & Muthukumarasamy G (2016), “Crop production-ensemble machine learning model for predictionâ€, International Journal of Computer Science and Software Engineering, 5(7), 148.

      [10] Silas NM, & Nderu L (2017), “Prediction of tea production in Kenya using clustering and association rule mining techniquesâ€, American Journal of Computer Science and Information Technology, 5(2), 1-7. https://doi.org/10.21767/2349-3917.100006.

      [11] Garg A, & Garg B (2017), “A robust and novel regression based fuzzy time series algorithm for prediction of rice yieldâ€, in 2017 IEEE International Conference on Intelligent Communication and Computational Techniques (ICCT), pp. 48-54. https://doi.org/10.1109/INTELCCT.2017.8324019.

      [12] Rajak RK, Pawar A, Pendke M, Shinde P, Rathod S, & Devare A (2017), “Crop recommendation system to maximize crop yield using machine learning techniqueâ€, International Research Journal of Engineering and Technology, 4(12), 950-953.

  • Downloads

  • How to Cite

    P, M., & .K, D. A. (2018). An optimized enhanced Intuitionistic fuzzy cognitive maps for groundnut yield prediction. International Journal of Engineering & Technology, 7(4), 3204-3208. https://doi.org/10.14419/ijet.v7i4.21564

    Received date: 2018-11-25

    Accepted date: 2018-11-25