A Curvelet-SC Recognition Method for Maize Disease. (5th April 2015)

• Record Type: Journal Article
• Authors: Luo, Jing; Geng, Shuze; Xiu, Chunbo; Song, Dan; Dong, Tingting
• Other Names: Sandham William Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2015(2015)
• Publication Date: 2015-04-05
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2015/164547
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new recognition algorithm based on Curvelet and Shape Context (SC). This method can improve the speed and accuracy of maize leaf disease recognition. Firstly, we use Seeded Regional Growing (SRG) algorithm to segment the maize leaf disease image. Secondly, Curvelet Modulus Correlation (CMC) method is put forward to extract the effective contour of maize leaf disease. Thirdly, we combine CMC with the SC algorithm to obtain the histogram features and then use these features we obtain to calculate the similarities between the template image and the target image. Finally, we adopt n -fold cross-validation algorithm to recognize diseases on maize leaf disease database. Experimental results show that the proposed algorithm can recognize 6 kinds of maize leaf diseases accurately and achieve the accuracy of 94.446%. Meanwhile this algorithm has guiding significance for other diseases recognition to an extent.