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

Guerrero, Josep M. and Guijarro Mata-García, María and Montalvo Martínez, Martín and Romeo Granados, Juan and Emmi, Luis Alfredo and Ribeiro Seijas, Ángela and 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.

Description

Title: Automatic expert system based on images for accuracy crop row detection in maize fields
Author/s:
  • Guerrero, Josep M.
  • Guijarro Mata-García, María
  • Montalvo Martínez, Martín
  • Romeo Granados, Juan
  • Emmi, Luis Alfredo
  • Ribeiro Seijas, Ángela
  • Pajares Martinsanz, Gonzalo
Item Type: Article
Título de Revista/Publicación: Expert Systems With Applications
Date: 2013
ISSN: 0957-4174
Volume: 40
Subjects:
Freetext Keywords: Expert system; Crop row detection in maize fields; Image thresholding; Theil–Sen estimator; Machine vision; Image segmentation; Linear regression
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 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.

More information

Item ID: 32345
DC Identifier: http://oa.upm.es/32345/
OAI Identifier: oai:oa.upm.es:32345
DOI: 10.1016/j.eswa.2012.07.073
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417412009293
Deposited by: Memoria Investigacion
Deposited on: 26 Oct 2014 13:13
Last Modified: 24 Feb 2017 17:26
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