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



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Ribeiro, Angela and Ranz, Juan and Burgos Artizzu, Xavier Paolo and Pajares Martinsanz, Gonzalo and Sánchez del Arco, María and Navarrete, Luis (2011) An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues. Sensors, 11 (6). pp. 6480-6492. ISSN 1424-8220

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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 quantific 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).ation obtained using template images. Moreover, the proposed method does not.

Item Type:Article
Uncontrolled Keywords:computer vision; conservation agriculture; estimation of coverage by crop residue; genetic algorithms; texture segmentation
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:68460
Deposited On:02 Nov 2021 12:19
Last Modified:03 Nov 2021 12:18

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