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Survey on Automatic Segmentation of Relevant Textures in Agricultural Images

J. Sophia Joycy, A. Kethsy Prabavathy


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.


Machine Vision, Image Segmentation, Texture Identification in Crops, Automatic Tasks in Agriculture.

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