Zhang J.,China Agricultural University |
Zhang J.,Key Laboratory of Optimal Design of Modern Agricultural Equipment in Beijing |
Qi L.,China Agricultural University |
Qi L.,Key Laboratory of Optimal Design of Modern Agricultural Equipment in Beijing |
And 8 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012
In order to improve the recognition rate of cotton diseases, an identification method of cotton diseases based on rough sets and BP neural network under natural environmental conditions was presented. In this method, Otsu method was used to get the threshold of H, a* and b* components from four cotton diseases colored images in the HSI and L* a* b* color spaces, and diseased regions of cotton were extract by intersection with H+a*+b* component and original image. Color moments and GLCM were used to extract texture features and color features from diseased regions. Features were then used as inputs to a cotton disease recognition model with rough set theory and a BP neural network classifier. The comparison test showed that rough set theory could cut down the dimension of features from sixteen to five and reduce training time of BP neural network to 25% of that without rough set, and the average recognition accuracy rate could reach up to 92.72%. The results of this study showed that the proposed classification method could accurately identify four cotton diseases, which can provide a technical support for cotton diseases prevention.