An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues
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...
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MDPI AG
2011-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/11/6/6480/ |
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| author | Luis Navarrete Xavier P. Burgos-Artizzu Gonzalo Pajares Maria J. Sanchez del Arco Angela Ribeiro Juan Ranz |
| author_facet | Luis Navarrete Xavier P. Burgos-Artizzu Gonzalo Pajares Maria J. Sanchez del Arco Angela Ribeiro Juan Ranz |
| author_sort | Luis Navarrete |
| collection | DOAJ |
| description | 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). |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:44:53Z |
| publishDate | 2011-06-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-98b8434c5bbf4e6c88de08f959f5003f2022-12-22T04:21:07ZengMDPI AGSensors1424-82202011-06-011166480649210.3390/s110606480An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop ResiduesLuis NavarreteXavier P. Burgos-ArtizzuGonzalo PajaresMaria J. Sanchez del ArcoAngela RibeiroJuan RanzDetermination 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).http://www.mdpi.com/1424-8220/11/6/6480/computer visionconservation agricultureestimation of coverage by crop residuegenetic algorithmstexture segmentation |
| spellingShingle | Luis Navarrete Xavier P. Burgos-Artizzu Gonzalo Pajares Maria J. Sanchez del Arco Angela Ribeiro Juan Ranz An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues Sensors computer vision conservation agriculture estimation of coverage by crop residue genetic algorithms texture segmentation |
| title | An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues |
| title_full | An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues |
| title_fullStr | An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues |
| title_full_unstemmed | An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues |
| title_short | An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues |
| title_sort | image segmentation based on a genetic algorithm for determining soil coverage by crop residues |
| topic | computer vision conservation agriculture estimation of coverage by crop residue genetic algorithms texture segmentation |
| url | http://www.mdpi.com/1424-8220/11/6/6480/ |
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