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International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 282–289Cite as

Plant Species Recognition Based on Deep Convolutional Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

Abstract

A number of the existing leaf based plan leaf recognition methods rely on the hand-crafted features of color, texture and shape, and other various features. One drawback of these methods is poor convergence and generalization. To overcome this problem, a deep convolutional neural network (DCNN) is applied to plant species recognition. The proposed method is different from the existing feature extraction based recognition approaches. The high-level features can be extracted by DCNN. The experimental results clearly demonstrate the effectiveness and efficiency of the proposed model in leaf identification in comparison with current state-of-the-art.

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References

  1. Souza, M.M.S.D., Medeiros, F.N.S., Ramalho, G.L.B., et al.: Evolutionary optimization of a multiscale descriptor for leaf shape analysis. Expert Syst. Appl. 63, 375–385 (2016)

    Article  Google Scholar 

  2. Xu, Q., Varadarajan, S., Chakrabarti, C., Karam, L.J.: A distributed canny edge detector: algorithm and FPGA implementation. IEEE Trans. Image Process. 23(7), 2944–2960 (2014)

    Article  MathSciNet  Google Scholar 

  3. Zhao, C., Chan, S.S.F., Cham, W.K., et al.: Plant identification using leaf shapes-A pattern counting approach. Pattern Recogn. 48(10), 3203–3215 (2015)

    Article  Google Scholar 

  4. Hu, R., Jia, W., Ling, H., et al.: Multiscale distance matrix for fast plant leaf recognition. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 21(11), 4667 (2012)

    MathSciNet  Google Scholar 

  5. Chaki, J., Parekh, R.: Plant Leaf recognition using shape based features and neural network classifiers. Int. J. Adv. Comput. Sci. Appl. 2(10), 41–47 (2011)

    Google Scholar 

  6. Hasim, A., Herdiyeni, Y., Douady, S.: Leaf shape recognition using centroid contour distance. IOP Conf. Ser. Earth Environ. Sci. 31, 1–8 (2016)

    Article  Google Scholar 

  7. Kadir, A., Nugroho, L.E., Susanto, A., et al.: Leaf classification using shape, color, and texture features. Int. J. Comput. Trends Technol. 1(3), 225–230 (2013)

    Google Scholar 

  8. Jin, T., Hou, X., Li, P., et al.: A novel method of automatic plant species identification using sparse representation of leaf tooth features. Plos One 10(10), e0139482 (2015)

    Article  Google Scholar 

  9. Atabay, H.A.: A convolutional neural network with a new architecture applied on leaf classification. IIOABJ 7(5), 326–331 (2016)

    Google Scholar 

  10. Wu, Y.H., Shang, L., Huang, Z.K., Wang, G., Zhang, Xiao-Ping: Convolutional neural network application on leaf classification. In: Huang, D.S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 12–17. Springer, Cham (2016). doi:10.1007/978-3-319-42291-6_2

    Chapter  Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)

    Google Scholar 

  12. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  13. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53

    Google Scholar 

  14. Schmidhuber, J.: Deep learning in neural networks: An overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  15. Sladojevic, S., Arsenovic, M., Anderla, A., et al.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016(6), 1–11 (2016)

    Article  Google Scholar 

  16. The experimental result shows that the method for classification gives average accuracy of 93.75% when it was tested on Flavia dataset, that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work

    Google Scholar 

  17. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004)

    Article  Google Scholar 

  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2014)

  19. Szegedy, C. et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

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Acknowledgments

This work is partially supported by the China National Natural Science Foundation under grant No. 61473237. It is also supported by Tianjin Research Program of Application Foundation and Advanced Technology under grant No. 14JCYBJC42500, and Tianjin science and technology correspondent project 16JCTPJC47300. The authors would like to thank all the editors and anonymous reviewers for their constructive advices

Author information

Authors and Affiliations

  1. Department of Information Engineering, Xijing University, Xi’an, 710123, China

    Shanwen Zhang

  2. Tianjin University of Science and Technology, Tianjin, 300222, China

    Chuanlei Zhang

Authors
  1. Shanwen Zhang
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  2. Chuanlei Zhang
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Corresponding author

Correspondence to Chuanlei Zhang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Zhang, S., Zhang, C. (2017). Plant Species Recognition Based on Deep Convolutional Neural Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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