Feature Extraction Method for Wheat Diseases Based on Multi-Fractal Spectrum

Fei Yun Zhang

doi:10.4028/www.scientific.net/AMR.760-762.1491

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762Feature Extraction Method for Wheat Diseases Based...

Feature Extraction Method for Wheat Diseases Based on Multi-Fractal Spectrum

Abstract:

Wheat diseases image noise was effectively removed using lifting scheme multi-wavelet transform and multi-fractal analysis, and then it used multi-fractal theory to segment diseases image and extract eight multi-fractal spectrum values as wheat shape feature of diseases. Experiments showed that the shape characteristic value of different wheat diseases had great difference, and the shape characteristic value of similar diseases had certain regularity. Therefore, it could extract shape characteristic value to recognize wheat diseases.

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Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

1491-1494

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.1491

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Online since:

September 2013

Authors:

Fei Yun Zhang

Keywords:

Feature Extraction, Multi-Fractal Spectrum, Multi-Wavelet Transform, Shape Feature, Wheat Diseases

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References

[1] R. Pydipati, T.F. Burks, W. S Lee. Identification of citrus disease using color texture features and discriminant analysis. Computers and electronics in agriculture, 2006, (52): 49-59.

DOI: 10.1016/j.compag.2006.01.004

Google Scholar

[2] R. Pydipati, T.F. Burks, W. S Lee. Statistical and neural network classifiers for citrus disease detection using machine vision. Transactions of the ASAE，2005, 48(5): 2007-2014.

DOI: 10.13031/2013.19994

Google Scholar

[3] G.L. Xu, H.P. Mao, P.P. Li. Extracting color features of leaf color images. Transactions of CSAE, 2002, 18(4): 150-154. ( in Chinese ).

Google Scholar

[4] Y. Lu , H.O. Guan, B. Zhao, et al. Sdudy on the method of image pre-processing and feature extraction for rice diseases. Journal of agricultural mechanization research, 2011(8): 27-30. ( in Chinese ).

Google Scholar

[5] Y.W. Tian, Y. Niu. Research on recognition of cucumber disease based on image processing in sunlight greenhouse. Journal of agricultural mchanization rsearch, 2006(2): 151-160. ( in Chinese ).

Google Scholar

[6] B.Q. Chen, X.M. Guo, X.H. Li. Image diagnosis algorithm of diseased wheat. Transactions of the chinese society for agricultural machinery, 2009, 40(12): 190-195. ( in Chinese ).

Google Scholar

[7] Y.W. Tian, C.S. Zhang, C.H. Li. Application of support vector machine to shape recognition of plant disease spot. Transactions of the chinese society for agricultural machinery, 2004, 20(3): 134-136. ( in Chinese ).

Google Scholar

[8] H.S. Shang, F.K. Wang, X.L. Li, et al. Primary color atlas of wheat diseases and insect pests. Beijing: Golden Shield Press, 2007. ( in Chinese ).

Google Scholar

[9] X.Q. Pu. Image recognition based on multi-fractal. Northwestern University master's thesis, 2009. ( in Chinese ).

Google Scholar