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

  • Authors:

  • M. Montalvo;G. Pajares;J. M. Guerrero;J. Romeo;M. Guijarro;A. Ribeiro;J. J. Ruz;J. M. Cruz

  • Affiliations:

  • Dpto. Arquitectura Computadores y Automática, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Grupo de Percepción Artificial, Centro de Automática y Robótica, Consejo Superior de Investigaciones Científicas, Arganda del Rey, Madrid, Spain;Dpto. Arquitectura Computadores y Automática, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Dpto. Arquitectura Computadores y Automática, Facultad Informática, Universidad Complutense, Madrid 28040, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

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.