This paper presents the results of a study into the use of the texture parameters of barley kernel images in varietal classification. A total of more than 270 textures have been calculated from the surface of single kernels and bulk grain. The measurements were performed in four channels from a 24 bit image. The results were processed statistically by variable reduction and general discriminant analysis. Classification accuracy was more than 99%.
Department of Agri-Food Process Engineering, University of Warmia and Mazury in Olsztyn, Heweliusza 14, 10-718 Olsztyn, Poland
Bibliografia
Bharati Manish H., Liu J.J., and MacGregor F., 2004. Image texture analysis: methods and comparisons. Chemometrics and Intelligent Laboratory Systems, 72, 57-71.
Chandraratne M.R., Samarasinghe S., Kulasiri D., and Bickerstaffe R., 2006. Prediction of lamb tenderness using image surface texture features. J. Food Eng., 77, 492-499.
Choudhary R., Paliwal J., and Jayas D.S., 2009. Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosys. Eng., 99, 330-337.
Courtois F., Faessel M., and Bonazzi C., 2010. Assessing breakage and cracks of parboiled rice kernels by image analysis techniques. Food Control, 21, 567-572.
Emadzadeh B., Razavi S.M.A., and Farahmandfar R., 2010. Monitoring geometric characteristics of rice during processing by image analysis system and micrometer measurement. Int. Agrophys., 24, 21-27.
Foley D.H., 1972. Considerations of sample and feature size. IEEE Trans. Information Theory, 18, 618-626.
Gancarz M., Konstankiewicz K., Pawlak K., and Zdunek A., 2007. Analysis of plant tissue images obtained by confocal tandem scanning reflected light microscope. Int. Agrophysics, 21, 49-53.
Jayas D.S., Paliwal J., and Visen N.S., 2000. Multi-layer neural networks for image analysis of agricultural products. J. Agric. Eng. Res., 77(2), 119-128.
Li J., Tan J., and Shatadal P., 2001. Classification of tough and tender beef by image texture analysis. Meat Sci., 57, 341-346.
Majumdar S. and Jayas D.S., 2000a. Classification of cereal grains using machine vision: I. Morphology models. Am. Soc. Agric. Eng., 43(6), 1669-1675.
Majumdar S. and Jayas D.S., 2000b. Classification of cereal grains using machine vision: II. Color Models. Morphology models. Am. Soc. Agric. Eng., 43(6), 1677-1680.
Majumdar S. and Jayas D.S., 2000c. Classification of cereal grains using machine vision: III. Texture Models. Morphology models. Am. Soc. Agric. Eng., 43(6), 1681-1687.
Materka A. and Strzelecki M., 1998. Texture Analysis Methods– A Review. Technical University of £ódź, Institute of Electronics, COST B11 report, Brussels, Belgium.
Neuman M., Sapristein H.D., Shwedyk E., and Bushuk W., 1989a. Wheat grain color analysis by digital image processing: I. Methodology. J. Cereal Sci., 10(3), 175-182.
Neuman M., Sapristein H.D., Shwedyk E., and Bushuk W., 1989b. Wheat grain color analysis by digital image processing: II. Wheat class determination. J. Cereal Sci., 10(3), 183-188.
Quevedo R., Carlos L.G., Aguilera J.M., and Cadoche L., 2002. Description of food surfaces and microstructural changes using fractal image texture analysis. J. Food Eng., 53, 361-371.
Tahir A.R., Neethirajan S., Jayas D.S., Shahin M.A., Symons S.J., and White N.D.G., 2007. Evaluation of the effect of moisture content on cereal grains by digital image analysis. Food Res. Int., 40, 1140-1145.
Zieliñski K. and Strzelecki M., 2002. Computerized Analysis of Biomedical Images (in Polish). PWN Press, Warsaw-Łódź, Poland.