A GA_FFNN algorithm applied for classification in diseased plant leaf system

Article
  • 7 Downloads

Abstract

In order to solve the problems of conventional neural network when it is applied to the diseased plant leaf system such as making itself for better classification, Genetic algorithm-based feed forward neural network (GA_FFNN) hybrid technique is proposed. Besides, Particle swarm optimization (PSO)-based segmented hybrid features were used for the analysis of classification of diseased leaf and its severity. The main contribution of this paper incorporates Genetic weight optimization-based neural network systems of diseased plant leaf classification for better classification accuracy. Various diseased plant leaves such as bitter gourd (Brown Leaf Spot), beans (Pest leaf minor), chilly (Pest), Cotton (Mineral Deficiency), pigeon pea (Blight Leaf minor) and tomato (Leaf spot) were used. In the proposed work, attributes are combined as a single vector for hybrid features. Five attributes, namely contrast, correlation, energy, homogeneity and area of the leaf were used as features. Initially, the features were extracted from the segmented image after preprocessing. Genetic-based Feed Forward Neural network architecture is constructed for the classification of diseased plant leaf. The weight of the neural network is updated by Genetic algorithm for specified iterations. Finally, the performance is analyzed in different classes (class 2, class 3 and class 6) of diseased plant leaves using classification accuracy.

Keywords

PSO-based segmentation Genetic algorithm Weight optimization Feed forward neural network Leaf severity analysis 

Notes

Acknowledgements

The authors would like to thank the reviewers for their valuable suggestions which have helped in improving the quality of this paper. We would like to thank the Tamilnadu Agricultural University for their continuous encouragement and support.

References

  1. 1.
    Abdullah NE, Rahim AA, Hashim H, Kamal MM (2007) Classification of rubber tree leaf diseases using multilayer perceptron neural network. In: SCRD 2007, proceedings of the 5th student conference on Research and Development, pp 1–6Google Scholar
  2. 2.
    Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M, ALRahamneh Z (2011) Fast and Accurate Detection and Classification of Plant Diseases. Int J Comput Appl, Wageningen Academic publishers 17:31–38Google Scholar
  3. 3.
    Beichel R, Sonka M (2006) Computer vision approaches to medical image analysis, vol 4241. Lecture Notes in Computer Science, Springer, Berlin, pp 37–48Google Scholar
  4. 4.
    Ding SF, Xu L, Su CY, Zhou H (2010) Using genetic algorithm to optimize artificial neural networks. J Converg Inf Technol 5:54–62Google Scholar
  5. 5.
    Fraser AS (1957) Simulation of genetic systems by automatic digital computers, introduction. Aust J Biol Sci 10:484–491CrossRefGoogle Scholar
  6. 6.
    Horn J, Nafpliotis N, Goldberg DE (1994) A niched pareto genetic algorithm for multiobjective optimization. Evolutionary Computation Paper presented at the IEEE world congress on Computational Intelligence, Orlando, FL, USA, pp 82–87Google Scholar
  7. 7.
    Hornik KM, Stinchcombe M, White H (1989) Multilayer feed-forward networks are universal approximators. Neural Netw 2:359–366CrossRefGoogle Scholar
  8. 8.
    Kai S, Zhikun L, Hang S, Chunhong G (2011) A research of maize disease image recognition of corn based on BP networks. Third International Conference on Measuring Technology and Mechatronics Automation, Shenyang, China, pp 246–249Google Scholar
  9. 9.
    Kutty SB, Abdullah NE (2013) Classification of watermelon leaf diseases using neural network analysis. BEIAC, IEEE, pp 459–464Google Scholar
  10. 10.
    Liu L, Nie F, Zhang T, Wiliem A, Lovell BC (2016) Unsupervised automatic attribute discovery method via multi-graph clustering. ICPR, pp 1713–1718Google Scholar
  11. 11.
    Liu L, Wiliem A, Chenand S, Lovell BC (2017) What is the best way for extracting meaningful attributes from pictures? Pattern Recogn 64:314–326CrossRefGoogle Scholar
  12. 12.
    Lung Huang C, Jen Wang C (2006) A GA based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240CrossRefGoogle Scholar
  13. 13.
    Muthukannan K, Latha P (2015) A pso model for disease pattern detection on leaf surfaces. IAS 34:209–216MathSciNetMATHGoogle Scholar
  14. 14.
    Revathi P, Hemalatha M (2014) Identification of cotton diseases based on cross information gain deep forward neural network classifier with pso feature selection. Int J Eng Technol 5:4637–4642Google Scholar
  15. 15.
    Schwefel HP (1995) Evolution and optimum seeking. Wiley, New YorkMATHGoogle Scholar
  16. 16.
    Shi ZZ (2009) Neural networks. Higher Education Press, BeijingGoogle Scholar
  17. 17.
    Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4:41–49Google Scholar
  18. 18.
    Zhang T, Wiliem A, Hemson G, Lovell B (2015) Detecting kangaroos in the wild: the first step towards automated animal surveillance. ICASSPGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECEEinstein College of EngineeringTirunelveliIndia
  2. 2.Department of CSEGovernment College of EngineeringTirunelveliIndia

Personalised recommendations