Automatic expert system based on images for accuracy crop row detection in maize fields

Guerrero, Josep M.; Guijarro Mata-García, María; Montalvo Martínez, Martín; Romeo Granados, Juan; Emmi, Luis Alfredo; Ribeiro Seijas, Ángela y Pajares Martinsanz, Gonzalo (2013). Automatic expert system based on images for accuracy crop row detection in maize fields. "Expert Systems With Applications", v. 40 (n. 2); pp. 656-664. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2012.07.073.

Descripción

Título: Automatic expert system based on images for accuracy crop row detection in maize fields
Autor/es:
  • Guerrero, Josep M.
  • Guijarro Mata-García, María
  • Montalvo Martínez, Martín
  • Romeo Granados, Juan
  • Emmi, Luis Alfredo
  • Ribeiro Seijas, Ángela
  • Pajares Martinsanz, Gonzalo
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems With Applications
Fecha: 2013
Volumen: 40
Materias:
Palabras Clave Informales: Expert system; Crop row detection in maize fields; Image thresholding; Theil–Sen estimator; Machine vision; Image segmentation; Linear regression
Escuela: Centro de Automática y Robótica (CAR) UPM-CSIC
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.

Más información

ID de Registro: 32345
Identificador DC: http://oa.upm.es/32345/
Identificador OAI: oai:oa.upm.es:32345
Identificador DOI: 10.1016/j.eswa.2012.07.073
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0957417412009293
Depositado por: Memoria Investigacion
Depositado el: 26 Oct 2014 13:13
Ultima Modificación: 24 Feb 2017 17:26
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