Automatic detection of crop rows in maize fields with high weeds pressure

Montalvo Martínez, Martín; Pajares Martinsanz, Gonzalo; Guerrero Hernández, José Miguel; Romeo Granados, Juan; Guijarro Mata-García, María; Ribeiro Seijas, Ángela; Ruz Ortiz, José Jaime y Cruz García, Jesús Manuel de la (2012). Automatic detection of crop rows in maize fields with high weeds pressure. "Expert Systems with Applications", v. 39 (n. 15); pp. 11889-11897. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2012.02.117.

Descripción

Título: Automatic detection of crop rows in maize fields with high weeds pressure
Autor/es:
  • Montalvo Martínez, Martín
  • Pajares Martinsanz, Gonzalo
  • Guerrero Hernández, José Miguel
  • Romeo Granados, Juan
  • Guijarro Mata-García, María
  • Ribeiro Seijas, Ángela
  • Ruz Ortiz, José Jaime
  • Cruz García, Jesús Manuel de la
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems with Applications
Fecha: Noviembre 2012
Volumen: 39
Materias:
Palabras Clave Informales: Crop row detection; Vegetation index; Image thresholding; Linear regression; Machine vision; Precision agriculture
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 a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
FP7245986RHEASin especificarRobots fleets for highly effective agriculture and forestry management

Más información

ID de Registro: 21253
Identificador DC: http://oa.upm.es/21253/
Identificador OAI: oai:oa.upm.es:21253
Identificador DOI: 10.1016/j.eswa.2012.02.117
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0957417412003806
Depositado por: Memoria Investigacion
Depositado el: 06 Nov 2013 16:44
Ultima Modificación: 24 Feb 2017 17:37
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