Leaf Identification Method Based on BP Neural Network

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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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  -