DocumentCode :
2615338
Title :
A Research of Maize Disease Image Recognition of Corn Based on BP Networks
Author :
Kai, Song ; Zhikun, Liu ; Hang, Su ; Chunhong, Guo
Author_Institution :
Info. Sci. & Eng. Coll., ShenYang Ligong Univ., Shenyang, China
Volume :
1
fYear :
2011
fDate :
6-7 Jan. 2011
Firstpage :
246
Lastpage :
249
Abstract :
A Research of maize disease image recognition of corn leaf based on image processing and analysis, which is to study diseases of image classification. According to the texture characteristics of corn diseases, it uses YCbCr color space technology to segment disease spot, and uses the cooccurrence matrix spatial gray level layer to extract disease spot texture feature, and uses BP neural network to class the maize disease. Application YCbCr color space technology segmented disease spot, and using the co-occurrence matrix spatial gray level layer extracted disease spot texture feature of using BP neural network, on maize disease classification identification. On VC++ platform, do experiments for the study design recognition algorithm, the experimental results show that the algorithm can effectively identify the disease image, the accuracy was as high as 98% or more, the study provided the theoretical basis to cognition of maize leaf disease. the image re of maize leaf disease image recognition to provide a theoretical basis.
Keywords :
backpropagation; crops; diseases; image classification; image colour analysis; image segmentation; neural nets; BP neural network; VC++ platform; YCbCr color space technology; cooccurrence matrix spatial gray level layer; corn leaf; image classification; maize disease image recognition; Diseases; Feature extraction; Image color analysis; Image segmentation; Lesions; Pixel; Presses;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on
Conference_Location :
Shangshai
Print_ISBN :
978-1-4244-9010-3
Type :
conf
DOI :
10.1109/ICMTMA.2011.66
Filename :
5720767
Link To Document :
بازگشت