Next Article in Journal
Correction: Yoshie, T. et al. Optical Microcavity: Sensing downto Single Molecules and Atoms. Sensors 2011, 11, 1972-1991
Next Article in Special Issue
Geosensors to Support Crop Production: Current Applications and User Requirements
Previous Article in Journal / Special Issue
Enviro-Net: From Networks of Ground-Based Sensor Systems to a Web Platform for Sensor Data Management
Sensors 2011, 11(6), 6480-6492; doi:10.3390/s110606480
Article

An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues

1,* , 1
, 1
, 2
, 3
 and 3
1 Centre for Automation and Robotics (CAR), CSIC-UPM, 28500 Arganda del Rey, Madrid, Spain 2 Department of Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain 3 Madrid Institute for Research and Rural Development, Agriculture and Food (IMIDRA), Finca El Encín, 28800 Alcalá de Henares, Madrid, Spain
* Author to whom correspondence should be addressed.
Received: 29 April 2011 / Revised: 29 May 2011 / Accepted: 7 June 2011 / Published: 17 June 2011
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
View Full-Text   |   Download PDF [719 KB, uploaded 21 June 2014]   |   Browse Figures

Abstract

Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest. The RGB images used come from a sample of images in which sections of terrain were photographed with a conventional camera positioned in zenith orientation atop a tripod. The images were taken outdoors under uncontrolled lighting conditions. Up to 92% similarity was achieved between the images obtained by the segmentation process proposed in this paper and the templates made by an elaborate manual tracing process. In addition to the proposed segmentation procedure and the fine tuning procedure that was developed, a global quantification of the soil coverage by residues for the sampled area was achieved that differed by only 0.85% from the quantification obtained using template images. Moreover, the proposed method does not depend on the type of residue present in the image. The study was conducted at the experimental farm “El Encín” in Alcalá de Henares (Madrid, Spain).
Keywords: computer vision; conservation agriculture; estimation of coverage by crop residue; genetic algorithms; texture segmentation computer vision; conservation agriculture; estimation of coverage by crop residue; genetic algorithms; texture segmentation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Export to BibTeX |
EndNote


MDPI and ACS Style

Ribeiro, A.; Ranz, J.; Burgos-Artizzu, X.P.; Pajares, G.; Sanchez del Arco, M.J.; Navarrete, L. An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues. Sensors 2011, 11, 6480-6492.

View more citation formats

Related Articles

Article Metrics

Comments

Citing Articles

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert