Study on the Fundamental of Crop Pest Image Recognition

Jie Zhang; Feng Jiang; Cheng Jun Xie; Rui Li

doi:10.4028/www.scientific.net/AMM.532.130

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 532Study on the Fundamental of Crop Pest Image...

Study on the Fundamental of Crop Pest Image Recognition

Abstract:

There are few literatures, among existing studies, about the fundamental of crop pest image recognition and these literatures are not systematic. In view of this situation, in this paper, the basic content of the recognition of crop pests image were studied. Firstly, two ways of acquiring the pest images have been discussed and compared; Secondly, preprocesses of these images have been studied and experimented, including image graying as well as image enhancement; Finally, seven principles and steps have been proposed to construct the pest image set.

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Periodical:

Applied Mechanics and Materials (Volume 532)

Pages:

130-133

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.532.130

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Online since:

February 2014

Authors:

Jie Zhang*, Feng Jiang, Cheng Jun Xie, Rui Li

Keywords:

Image Acquisition, Image Preprocessing, Image Recognition, Pest Control, Pest Image Set

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* - Corresponding Author

References

[1] H. Z. Yang, J. W. Zhang and X. T. Li. Remote automatic identification system based on insect image. Transactions of the Chinese Society of Agricultural Engineering, Vol. 24 (2008) No. 1, p.188.

Google Scholar

[2] W. H. Mao, Y. J. Zheng and Y. Q. Zhang. Grasshopper detection method based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, Vol. 24 (2008) No. 11, p.155.

Google Scholar

[3] A. T. Han, X. H. Guo and Z. Liao, et al. Classification of agricultural pests based on compressed sensing theory. Transactions of the Chinese Society of Agricultural Engineering, Vol. 37 (2011) No. 6, p.203.

Google Scholar

[4] S. Neethirajan, C. Karunakaran and D. S. Jayas. Detection techniques for stored-product insects in grain. Food Control, Vol. 18 (2007) No. 2, p.157.

DOI: 10.1016/j.foodcont.2005.09.008

Google Scholar

[5] L. Q. Zhu, Z. Zhang. Insect recognition based on integrated region matching and dual tree complex wavelet transform. Journal of Zhejiang University- SCIENCE C, (2011), p.243.

DOI: 10.1631/jzus.c0910740

Google Scholar

[6] Z. H. Diao: Research and Application of Intelligent System of Field Wheat Leaf Disease Detection (Ph.D., University of Science and Technology of China, China 2010).

Google Scholar

[7] J. S. Li: Digital image processing (Tsinghua University Press, China 2007).

Google Scholar

[8] X. F. Zhu: Introduction to computer graphics (Scientific and Technological Literature Publishing House, China 2002).

Google Scholar

[9] L. Li: Research of agricultural pests remote automatic monitoring system based on image recognition (MS., Zhejiang University of Technology, China 2001).

Google Scholar