Sunflower diseases recognition algorithm based on wavelet domain feature dimension reduction
- https://doi.org/10.2991/jimet-15.2015.73How to use a DOI?
- Wavelet analysis; Feature Vector Dimension Reduction; Probabilistic neural network
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/jimet-15.2015.73 ID - Zhongyang2015/12 ER -