Notice of Violation of IEEE Publication Principles: Survey on automatic segmentation of relevant textures in agricultural images
Notice of Violation of IEEE Publication Principles Survey on Segmentation of Relevant Textures in Agricultural Images by J. Sophia Joycy and Kethsy Prabavathy in the Proceedings of the International Conference on Advances in Engineering, Science and Management (ICAESM), March 2012 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: Automatic Segmentation of Relevant Textures in Agricultural Images by M. Guijarro, G. Pajares, I Riomoros, P.J. Herrera, X.P. Burgos-Artizzu, and A. Ribeiro in Computers and Electronics in Agriculture, Volume 75, Issue 1, January 2011, Pages 75-83 The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, sometimes it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows verifying its viability for automatic tasks in agriculture based on image processing.