DocumentCode :
3305635
Title :
Plant Species Identification Based on Neural Network
Author :
Zhang, Lei ; Kong, Jun ; Zeng, Xiaoyun ; Ren, Jiayue
Author_Institution :
Network Inf. Center, Northeast Normal Univ., Changchun
Volume :
5
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
90
Lastpage :
94
Abstract :
Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plant. This paper presents a new method for plant species identification using leaf image. It focuses on the stable features extraction of leaf, such as the geometrical features of shape and the texture features of venation. The 2-D moment invariants, Wavelet statistical features are used to extract leaf information. Self-organizing feature map (SOM) neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class pattern recognition, high dimension input vector and large quantity training data. So this paper use SOM neural network to identify the plant species. The experimental results illustrate the effectiveness of this method.
Keywords :
biology computing; botany; feature extraction; neural nets; self-organising feature maps; 2D moment invariants; SOM neural network; computer-aided plant species identification; features extraction; leaf image; multiclass pattern recognition; neural network; plant digital museum system; plant species identification; self-organizing feature map; systematic botany; texture features; wavelet statistical features; Artificial neural networks; Computer networks; Data mining; Educational institutions; Feature extraction; Military computing; Network topology; Neural networks; Research and development; Shape; SOM neural network; Wavelet statistical features; plant species identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.253
Filename :
4667403
Link To Document :
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