Study of Stored Product Insects Identification Based on near Infrared Spectroscopy (NIRS)

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Abstract:

The harm of stored product pests are very serious, which caused huge losses to the national economy. It is important to strengthen the research on the insects, for the protection of reservoir storage and quarantine. In the recognition of stored product insects, Some insects have anabiosis, once it is missed, if the external environment is favorable, it will flourish and spread, there will be great harm to the storage of food. This paper briefly introduces the basic principle of the near infrared spectroscopy technology combined with the basic principle of digital image processing and recognition technology. The basic principle of automatic identification technology of dead and live insects, it is based on the difference of absorption and reflection of near-infrared light for the identification of insect pests. This paper presents a new idea of research on near infrared spectroscopy technology and digital image processing technology for automatic identification of direction in the dead and live insects.

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2706-2709

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Zayas I Y, Flinn P W. Detection of insects in bulk wheat samples with machine vision[J]. Transactions of the ASAE, 1998, 41(3): 883~888.

DOI: 10.13031/2013.17206

Google Scholar

[2] Ridgway C, Davies E R, Chambers J. Rapid machine vision method for the detection of insects and other particulate biocontaminants of bulk grain in transit[J]. Biosystems Engineering, 2002, 83(1): 21~30.

Google Scholar

[3] Mao Hanping, Zhang Hongtan. Research progress and prospect for image recognition of stored-grain pests[J]. Transactions of the Chinese Society for Agricultural Machinery, 2008, 39(4): 175~179.

Google Scholar

[4] Zhang H M, Wang J. Detection of age and insect damage incurred by wheat with an electronic nose[J]. Journal of Stored Products Research, 2007, 43(4): 489~495.

DOI: 10.1016/j.jspr.2007.01.004

Google Scholar

[5] Neethirajan S, Karunakaran C, Jayas D S, et al. Detection techniques for stored-product insects in grain[J]. Food Control, 2007, 18(2): 157~162.

DOI: 10.1016/j.foodcont.2005.09.008

Google Scholar

[6] Singh C B, Jayas D S, Paliwal J, et al. Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging[J]. Journal of Stored Products Research, 2009, 45(3): 151~158.

DOI: 10.1016/j.jspr.2008.12.002

Google Scholar

[7] H Bay, T Tuytelaars, L V Gool. SURF: Speeded Up Robust Features. Proceedings of the ninth European Conference on Computer Vision, (2006).

DOI: 10.1007/11744023_32

Google Scholar