Automatic segmentation of relevant textures in agricultural images

Guijarro, Maria; Pajares, Gonzalo; Riomoros, I.; Herrera, P.J.; Burgos Artizzu, Xavier y Ribeiro Seijas, Ángela (2011). Automatic segmentation of relevant textures in agricultural images. "Computers and Electronics in Agriculture", v. 75 (n. 1); pp. 75-83. ISSN 01681699. https://doi.org/10.1016/j.compag.2010.09.013.

Descripción

Título: Automatic segmentation of relevant textures in agricultural images
Autor/es:
  • Guijarro, Maria
  • Pajares, Gonzalo
  • Riomoros, I.
  • Herrera, P.J.
  • Burgos Artizzu, Xavier
  • Ribeiro Seijas, Ángela
Tipo de Documento: Artículo
Título de Revista/Publicación: Computers and Electronics in Agriculture
Fecha: Enero 2011
Volumen: 75
Materias:
Escuela: Otros Centros UPM
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevantimage 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. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times 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. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. 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 to verify its viability for automatic tasks in agriculture based on image processing

Más información

ID de Registro: 13699
Identificador DC: http://oa.upm.es/13699/
Identificador OAI: oai:oa.upm.es:13699
Identificador DOI: 10.1016/j.compag.2010.09.013
Depositado por: Memoria Investigacion
Depositado el: 20 Nov 2012 10:53
Ultima Modificación: 24 Feb 2017 17:51
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