Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)

Leaf Identification Method Based on BP Neural Network

Authors
Peng Huang, Zhiliang Kang
Corresponding Author
Peng Huang
Available Online May 2018.
DOI
10.2991/snce-18.2018.70How to use a DOI?
Keywords
BP neural network; Gray-level co-occurrence matrix(GLCM); Texture features; Leaf shape features
Abstract

In view of the time-consuming and laborious disadvantages of traditional tree classification methods, the leaf identification method based on BP neural network is established in this paper. The leaves of osmanthus tree, ficus virens and ficus concinna were taken as objects. First, three leaf samples were collected, and then after image preprocessing, 19 feature parameters were extracted, including 3 leaf shape features and 16 texture features based on gray-level co-occurrence matrix(GLCM). Finally, the BP neural network was used to classify and identify the above three kinds of leaves. The simulation results showed that this method can classify and identify the leaves of osmanthus tree, ficus virens and ficus concinna quickly and accurately.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
Series
Advances in Computer Science Research
Publication Date
May 2018
ISBN
978-94-6252-505-4
ISSN
2352-538X
DOI
10.2991/snce-18.2018.70How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Peng Huang
AU  - Zhiliang Kang
PY  - 2018/05
DA  - 2018/05
TI  - Leaf Identification Method Based on BP Neural Network
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 348
EP  - 352
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.70
DO  - 10.2991/snce-18.2018.70
ID  - Huang2018/05
ER  -