Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Classification Method of Plant Leaf using DenseNet

DenseNet을 활용한 식물 잎 분류 방안 연구

  • Park, Young Min (Dept of Computer Science, Graduate School, Keimyung University) ;
  • Gang, Su Myung (Dept of Computer Science, Graduate School, Keimyung University) ;
  • Chae, Ji Hun (Dept of Computer Science, Graduate School, Keimyung University) ;
  • Lee, Joon Jae (Dept of Computer Engineering, Keimyung University)
  • Received : 2018.03.16
  • Accepted : 2018.04.30
  • Published : 2018.05.31
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Abstract

Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.

Keywords

References

  1. Y. Park, Plant Leaf Classification Based on DenseNet of Deep Learning Algorithm, Master's Thesis of Keimyung University, 2018.
  2. S. Gang, S. Yoon, and J. Lee, “Plant leaf Classification Using Orientation Feature Descriptions,” Journal of Korea Multimedia Society, Vol. 17, No. 3, pp. 300-311, 2014. https://doi.org/10.9717/kmms.2014.17.3.300
  3. ILSVRC2012, http://image-net.org/challenges/LSVRC/2012 (accessed Sept., 26, 2017).
  4. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
  5. G. Huang, Z. Liu, Kilian Q. Weinberger, L. van der Maaten, "Densely Connected Convolutional Networks," arXiv Preprint arXiv:168.06993, 2016.
  6. S.G. Wu, F.S. Bao, E.Y. Xu, Y. Wang, Y. Chang, and Q. Xiang, "A leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network," Signal Processing and Information Technology, Proceeding of 2007 IEEE International Symposium on, pp. 11-16, 2007.
  7. S. Sharma and C. Gupta, “Recognition of Plant Species Based on Leaf Images Using Multilayer Feed Forward Neural Network,” International Journal of Innovative Research in Advanced Engineering, Vol. 2, No. 6, pp. 104-110, 2015.
  8. S.H. Lee, C.S. Chan, P. Wilkin, and P. Remagnino, "Deep-plant: Plant Identification with Convolutional Neural Networks," Proceeding of 2015 IEEE International Conference on, pp. 452-456, 2015.
  9. W. Jeon and S. Rhee, “Plant Leaf Recognition Using a Convolution Neural Network,” International J ournal of Fuzzy Logic and Intelligent Systems, Vol. 17, No. 1, pp. 26-34, 2017. https://doi.org/10.5391/IJFIS.2017.17.1.26
  10. National Biological Species Information System, http://www.nature.go.kr/main/Main.do (accessed Feb., 20, 2018).

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