J. Fang, Y. Jiang, and J. Yue, A hybrid approach for efficient detection of plastic mulching films in cotton, Mathematical and Computer Modelling, vol.58, issue.3-4, pp.58-61, 2013.
DOI : 10.1016/j.mcm.2012.12.018

M. Lieberman, C. K. Bragg, and S. Brennan, Determining Gravimetric Bark Content in Cotton with Machine Vision, Textile Research Journal, vol.68, issue.2, pp.6894-104, 1998.
DOI : 10.1177/004051759806800203

P. Tantaswadi, J. Vilainatre, and N. Tamaree, Machine vision for automated visual inspection of cotton quality in textile industries using color isodiscrimination contour, Computers & Industrial Engineering, vol.37, issue.1-2, pp.347-350, 1999.
DOI : 10.1016/S0360-8352(99)00090-X

M. P. Millman, M. Acar, and M. Jackson, Computer vision for textured yarn interlace (nip) measurements at high speeds, Mechatronics, vol.11, issue.8, pp.111025-1038, 2001.
DOI : 10.1016/S0957-4158(00)00056-8

URL : https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/22046/1/Computer%20Vision%20for%20Texture%20Yarn%20Interlace%20Measurements.pdf

A. Abouelela, H. M. Abbas, and H. Eldeeb, Automated vision system for localizing structural defects in textile fabrics, Pattern Recognition Letters, vol.26, issue.10, pp.261435-1443, 2005.
DOI : 10.1016/j.patrec.2004.11.016

W. Yang, D. Li, and L. Zhu, A new approach for image processing in foreign fiber detection, Computers and Electronics in Agriculture, vol.68, issue.1, pp.68-77, 2009.
DOI : 10.1016/j.compag.2009.04.005

D. Li, Y. W. Wang, and S. , Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine, Computers and Electronics in Agriculture, vol.74, issue.2, pp.74274-279, 2010.
DOI : 10.1016/j.compag.2010.09.002

C. Hua and S. , White Foreign Fibers Detection in Cotton Using Line Laser. Nongye Jixie Xuebao/transactions of the Chinese Society of Agricultural Machinery, pp.43181-185

F. Liu, Z. Su, and X. He, A laser imaging method for machine vision detection of white contaminants in cotton, Textile Research Journal, vol.7, issue.14, pp.841987-1994
DOI : 10.1016/1049-9652(92)90055-3

J. Xing, C. Bravo, and P. T. Jancsók, Detecting Bruises on ???Golden Delicious??? Apples using Hyperspectral Imaging with Multiple Wavebands, Biosystems Engineering, vol.90, issue.1, pp.9027-9063, 2005.
DOI : 10.1016/j.biosystemseng.2004.08.002

J. Qin, T. F. Burks, and M. S. Kim, Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing & Instrumentation for Food Quality & Safety, pp.168-177, 2008.
DOI : 10.1007/s11694-008-9043-3

J. Xing, S. Symons, and M. Shahin, Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging, Biosystems Engineering, vol.106, issue.2, pp.188-194, 2010.
DOI : 10.1016/j.biosystemseng.2010.03.010

J. Barbedo, C. S. Tibola, and J. Fernandes, Detecting Fusarium head blight in wheat kernels using hyperspectral imaging, Biosystems Engineering, vol.131, pp.65-76, 2015.
DOI : 10.1016/j.biosystemseng.2015.01.003

J. Li, X. Rao, and Y. Ying, Detection of common defects on oranges using hyperspectral reflectance imaging, Computers and Electronics in Agriculture, vol.78, issue.1, pp.38-48, 2011.
DOI : 10.1016/j.compag.2011.05.010

J. Guo, Y. Ying, and F. Cheng, Detection of foreign materials on the surface of ginned cotton by hyper-spectral imaging, Nongye Gongcheng Xuebao/transactions of the Chinese Society of Agricultural Engineering, issue.21, pp.28126-134, 2012.

J. Qin, T. F. Burks, and X. Zhao, Multispectral Detection of Citrus Canker Using Hyperspectral Band Selection, Transactions of the Asabe, issue.6, pp.542331-2341, 2011.

J. P. Wold, T. Jakobsen, and L. Krane, Atlantic Salmon Average Fat Content Estimated by Near-Infrared Transmittance Spectroscopy, Journal of Food Science, vol.57, issue.4, pp.6174-77, 1996.
DOI : 10.1016/0963-9969(92)90126-P