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

Montalvo Martínez, Martín and Pajares Martinsanz, Gonzalo and Guerrero Hernández, José Miguel and Romeo Granados, Juan and Guijarro Mata-García, María and Ribeiro Seijas, Ángela and Ruz Ortiz, José Jaime and 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.

Description

Title: Automatic detection of crop rows in maize fields with high weeds pressure
Author/s:
  • 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
Item Type: Article
Título de Revista/Publicación: Expert Systems with Applications
Date: November 2012
ISSN: 0957-4174
Volume: 39
Subjects:
Freetext Keywords: Crop row detection; Vegetation index; Image thresholding; Linear regression; Machine vision; Precision agriculture
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7245986RHEAUnspecifiedRobots fleets for highly effective agriculture and forestry management

More information

Item ID: 21253
DC Identifier: http://oa.upm.es/21253/
OAI Identifier: oai:oa.upm.es:21253
DOI: 10.1016/j.eswa.2012.02.117
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417412003806
Deposited by: Memoria Investigacion
Deposited on: 06 Nov 2013 16:44
Last Modified: 24 Feb 2017 17:37
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