Hybrid feature- selection using group search particle swarm optimizer for plant- leaf classification

  • Authors

    • A. M. Ravishankar Department of CSE, Karpagam Academy of Higher Education, Karpagam University
    • P. Amudhavalli Department of CSE, Karpagam Academy of Higher Education, Karpagam University
    • G. K. D. Prasanna Venkatesan
    2019-04-21
    https://doi.org/10.14419/ijet.v7i4.22670
  • Plant Leaf Classification, Feature Selection, Artificial Neural Networks (ANN), Multilayer Perceptron Neural Network (MLPNN), Particle Swarm Optimization (PSO) and Group Search Optimizer (GSO).

  • Abstract

    Plants are considered to be the important sources for food and medicine. They are critical for the protection of the environment as well. The plant leaves carry information on the species of the plant. This kind of work can describe an approach that is optimal for the selection of the feature subset in classifying the leaves on the basis of a Group Search Optimizer (GSO). Owing to the high level of complexity in selecting optimal features, data classification has become now an important task to analyse the data of leaf images. Here for this work, there is a hybrid algorithm known as the Group Search Particle Swarm Optimization (GSPSO) which is based upon Particle Swarm Optimization (PSO) and here the GSO has been proposed wherein a PSO model along with the GSO model is made use of. A GSPSO combines all advantages in both the algorithms, the high speed of computing in the PSO and the good performance in the GSO. The Fuzzy classifier is that form of the many-valued logic which is derived from the theory of a fuzzy set. A Multilayer Perceptron Neural Network (MLPNN) concept is used for classification. Such techniques are selected as they can provide a training that is faster to solve the problems of pattern recognition by making use of the technique of numerical optimization.

     

     


  • References

    1. [1] Chaki, J. (2017). Template based shape feature selection for plant leaf classification. International Journal of Engineering and Technology (IJET), 9 (3), 1992-2002. https://doi.org/10.21817/ijet/2017/v9i3/1709030122.

      [2] Sumathi, C. S., & Kumar, A. S. (2012). Edge and texture fusion for plant leaf classification. International Journal of Computer Science and Telecommunications, 3(6), 6-9.

      [3] Gaber, T., Tharwat, A., Snasel, V., & Hassanien, A. E. (2015). Plant identification: Two dimensional-based vs. one dimensional-based feature extraction methods. In 10th international conference on soft computing models in industrial and environmental applications (pp. 375-385). Springer International Publishing. https://doi.org/10.1007/978-3-319-19719-7_33.

      [4] Pream Sudha, V. (2017). Feature Selection Techniques for the Classification of Leaf Diseases in Turmeric. International Journal of Computer Trends and Technology (IJCTT), 43 (3), 138-142. https://doi.org/10.14445/22312803/IJCTT-V43P121.

      [5] Sainin, M. S., Alfred, R., Ahmad, F., &Lammasha, M. A. (2017). An Evaluation of Feature Selection Methods on Multi-Class Imbalance and High Dimensionality Shape-Based Leaf Image Features. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-2), 57-61.

      [6] Zhao, X., Liu, X., Li, D., Chen, H., Liu, S., Yang, X., ...& Zhao, W. (2015, September). Comparative Study on Metaheuristic-Based Feature Selection for Cotton Foreign Fibers Recognition. In International Conference on Computer and Computing Technologies in Agriculture (pp. 8-18). Springer, Cham. https://doi.org/10.1007/978-3-319-48357-3_2.

      [7] Keerthika, D., Nandhinipriya, K., & Sivaranjani, S. (2018). An Efficient Classification of Leaves and Based On Leaf Architecture Using MLPNN Algorithm. International Journal of Engineering Development and Research, 6 (1), 305-310.

      [8] Eid, H. F., & Abraham, A. (2018). Plant species identification using leaf biometrics and swarm optimization: A hybrid PSO, GWO, SVM model. International Journal of Hybrid Intelligent Systems, (Preprint), 1-11. https://doi.org/10.3233/HIS-180248.

      [9] Muthukannan, K., & Latha, P. (2018). A GA_FFNN algorithm applied for classification in diseased plant leaf system. Multimedia Tools and Applications, 1-17.

      [10] Chuanlei, Z., Shanwen, Z., Jucheng, Y., Yancui, S., & Jia, C. (2017). Apple leaf disease identification using genetic algorithm and correlation-based feature selection method. International Journal of Agricultural and Biological Engineering, 10(2), 74.

      [11] Valliammal, N., & Geethalakshmi, S. N. (2012). An optimal feature subset selection for leaf analysis. International Journal of Computer and Communication Engineering, 6.

      [12] Magatrao, D., Ghosh, S., Valadi, J., &Siarry, P. (2013, July). Simultaneous gene selection and cancer classification using a hybrid group search optimizer. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation (pp. 7-8). ACM. https://doi.org/10.1145/2464576.2464579.

      [13] Vashishtha, J. (2016). Particle Swarm Optimization based Feature Selection. International Journal of Computer Applications, 146(6). https://doi.org/10.5120/ijca2016910789.

      [14] Xue, B., Zhang, M., & Browne, W. N. (2014). Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied Soft Computing, 18, 261-276. https://doi.org/10.1016/j.asoc.2013.09.018.

      [15] Yan, X., & Shi, H. (2011, July). A hybrid algorithm based on particle swarm optimization and group search optimization. In Natural computation (ICNC), 2011 seventh international conference on (Vol. 1, pp. 13-17). IEEE. https://doi.org/10.1109/ICNC.2011.6022076.

      [16] Rani, C., & Deepa, S. N. (2010, February). Design of optimal fuzzy classifier system using particle swarm optimization. In Innovative Computing Technologies (ICICT), 2010 International Conference on (pp. 1-6). IEEE. https://doi.org/10.1109/ICINNOVCT.2010.5440088.

      [17] Ramchoun, H., Amine, M., Idrissi, J., Ghanou, Y., & Ettaouil, M. (2016). Multilayer Perceptron: Architecture Optimization and Training. IJIMAI, 4(1), 26-30. https://doi.org/10.9781/ijimai.2016.415.

      [18] Muthevi, A., &Uppu, R. B. (2017, January). Leaf classification using completed local binary pattern of textures. In Advance Computing Conference (IACC), 2017 IEEE 7th International (pp. 870-874). IEEE. https://doi.org/10.1109/IACC.2017.0178.

      [19] Chaki, J., Parekh, R., & Bhattacharya, S. (2015). Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognition Letters, 58, 61-68. https://doi.org/10.1016/j.patrec.2015.02.010.

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  • How to Cite

    M. Ravishankar, A., Amudhavalli, P., & K. D. Prasanna Venkatesan, G. (2019). Hybrid feature- selection using group search particle swarm optimizer for plant- leaf classification. International Journal of Engineering & Technology, 7(4), 5909-5916. https://doi.org/10.14419/ijet.v7i4.22670

    Received date: 2018-12-01

    Accepted date: 2018-12-01

    Published date: 2019-04-21