Recognition of weed during cotton emergence based on principal component analysis and support vector machine

被引：0

作者：

Li, Hui ^{[1
]}

Qi, Lijun ^{[1
]}

Zhang, Jianhua ^{[1
]}

Ji, Ronghua ^{[2
]}

机构：

[1] College of Engineering, China Agricultural University

[2] College of Information and Electrical Engineering, China Agricultural University

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | 2012年 / 43卷 / 09期

关键词：

Cotton; Image processing; Principal component analysis; Support vector machine; Weed recognition;

D O I：

10.6041/j.issn.1000-1298.2012.09.034

中图分类号：

学科分类号：

摘要：

A method of recognition weeds during cotton emergence based on principal component analysis (PCA) and support vector machine (SVM) was developed. For the effective classification of the variety of weeds in cotton field, the dimension of feature variable space was reduced by extracting color, shape, texture characteristics and principal component analysis. The experiment of classification for 120 samples of cottons and weeds showed that it was able to reduce training time and increase classification accuracy effectively by the first three principal components obtained by PCA dimensionality reduction. It was found by comparison that the best classification and recognition result was obtained by using the combination of the first three principal components and RBF kernel function SVM. The training time is 91 ms and the average correct classification rate is 98.33%.

引用

页码：184 / 189+196

共 21 条

[1]

Li H., Yi F., Pattern of world cotton market and the development measures of Chinese cotton industry, Journal of International Trade, 7, pp. 30-34, (2005)

[2]

Tellaeche A., Pajares G., Burgos-Artizzu X.P., Et al., A computer vision approach for weeds identification through support vector machines, Applied Soft Computing, 11, 1, pp. 908-915, (2011)

[3]

Tillett N.D., Hague T., Miles S.J., A field assessment of a potential method for weed and crop mapping on the basis of crop planting geometry, Computers and Electronics in Agriculture, 32, 3, pp. 229-246, (2001)

[4]

Woebbecke D.M., Meyer G.E., von Bargen K., Et al., Shape feature for identifying young weeds using image analysis, Transactions of the ASAE, 38, 1, pp. 271-281, (1995)

[5]

Asnor J.I., Aini H., Mohd M.M., Et al., Weed image classification using Gabor wavelet and gradient field distribution, Computers and Electronics in Agriculture, 66, 1, pp. 53-61, (2009)

[6]

Alchanatis V., Ridel L., Hetzroni A., Et al., Weed detection in multi-spectral images of cotton fields, Computers and Electronics in Agriculture, 47, 3, pp. 243-260, (2005)

[7]

Glaucia M.B., Vilma A.O., Estevam R.H., Et al., Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop, Engineering Applications of Artificial Intelligence, 22, 4-5, pp. 579-592, (2009)

[8]

Wu L., Liu J., Wen Y., Image identification of corn and weed based on fractal dimension, Transactions of the Chinese Society for Agricultural Machinery, 40, 3, pp. 176-179, (2009)

[9]

Long M., He D., Weed identification from corn seedling based on computer visio, Transactions of the CSAE, 23, 7, pp. 139-144, (2007)

[10]

Li X., Zhu W., Ji B., Et al., Shape feature selection and weed recognition based on image processing and ant colony optimization, Transactions of the CSAE, 26, 10, pp. 178-182, (2010)

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