Sunflower diseases recognition algorithm based on wavelet domain feature dimension reduction

DOI

10.2991/jimet-15.2015.73How to use a DOI?

Keywords

Wavelet analysis; Feature Vector Dimension Reduction; Probabilistic neural network

Abstract

A new sunflower diseases recognition algorithm has been presented, which is based on image processing and pattern recognition. Firstly, sunflower diseases were collected for segmenting disease spot. The color histogram based on RGB space and color features based on HSI space of leaf for disease are extracted, based on gray level co-occurrence matrix texture features, these features are arranged for one dimensional vector. Dimension of characteristic vector is reduced by wavelet analysis, and the original vector is replaced by discrete approximation information of characteristic vector. Finally, the dimension reduction series and identify diseases were trained and automatically determined by probabilistic neural network. Experimental results show that the system not only can accurately identify these three diseases, sunflower powdery mildew, sunflower black rot and sunflower downy mildew, but also can make the feature vector have low characteristics dimension, in the mean time, ensure the recognition accuracy.

Copyright

© 2015, 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  - Zhu Zhongyang
AU  - Xiao Zhiyun
AU  - Guo Yi
PY  - 2015/12
DA  - 2015/12
TI  - Sunflower diseases recognition algorithm based on wavelet domain feature dimension reduction
BT  - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
PB  - Atlantis Press
SP  - 393
EP  - 398
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimet-15.2015.73
DO  - 10.2991/jimet-15.2015.73
ID  - Zhongyang2015/12
ER  -