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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<jats:italic>n<\/jats:italic>-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.<\/jats:p>","DOI":"10.1155\/2015\/164547","type":"journal-article","created":{"date-parts":[[2015,4,6]],"date-time":"2015-04-06T06:20:38Z","timestamp":1428301238000},"page":"1-8","source":"Crossref","is-referenced-by-count":13,"title":["A Curvelet-SC Recognition Method for Maize Disease"],"prefix":"10.1155","volume":"2015","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3950-8437","authenticated-orcid":true,"given":"Jing","family":"Luo","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin 300387, China"},{"name":"College of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuze","family":"Geng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, 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