Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology
Affiliation(s)
College of information science and engineering, ShenYang University of Tchnology, Shenyang 110870.
College of information science and engineering, ShenYang University of Tchnology, Shenyang 110870.
ABSTRACT
This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.
This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.
Cite this paper
D. Pixia and W. Xiangdong, "Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology," Open Journal of Applied Sciences, Vol. 3 No. 1, 2013, pp. 27-31. doi: 10.4236/ojapps.2013.31B006.
D. Pixia and W. Xiangdong, "Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology," Open Journal of Applied Sciences, Vol. 3 No. 1, 2013, pp. 27-31. doi: 10.4236/ojapps.2013.31B006.
References
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[1] ZhangYing,Mu-nan,XueqingZhang.A foreign facility status and trends of development to fagriculture [J]Agriculture and Technology, 2008 [2] A. H. AI-Abbas, R. Barr, J. D. Hall, F. L. Crane, and M. F. Baumgardner Spectra of Normal and Nu-trient-Deficient Maize Leaves Laboratory for Applica-tions of Remote Sensing [3] C. H. Bock, P. E. Parker, A. Z. Cook，T. R. Gottwald. plant diseaseAutomated Image Analysis of the Severity of Foliar Citrus Canker Symptoms. The American Phytopathological Society, 2009. [4] Yuan Yuan, Li Miao, Liang Qing, et al. Segmentation method for crop disease leaf images with complex background[J]. Transactions of the CSAE, 2011 [5] Tian Youwen,Wang Lide,Jiang Shuhua. Recognition Of maize disease based on image processing and SVM[J]. Chinese Journal Of Scientific Instrument, 2006 [6] Chen Bingqi,Guo Xuemei,Li Xiaohua. Image Diagnosis Algorithm of Diseased Wheat[J].Transactions of the Chinese Society for Agricultural Machi-nery ,2009 [7] Zhang Qiang,Wang Zhenglin Proficient in MATLAB image processing[M]. BeiJing Publishing House of Electronics Industry