Crop Row Detection in Maize Fields Inspired on the Human Visual Perception
This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are cap...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/2012/484390 |
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| author | J. Romeo G. Pajares M. Montalvo J. M. Guerrero M. Guijarro A. Ribeiro |
| author_facet | J. Romeo G. Pajares M. Montalvo J. M. Guerrero M. Guijarro A. Ribeiro |
| author_sort | J. Romeo |
| collection | DOAJ |
| description | This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, 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 two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection. |
| format | Article |
| id | doaj-art-ce81c5f91b314553a74be58c1c2609dd |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ce81c5f91b314553a74be58c1c2609dd2025-02-03T05:43:57ZengWileyThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/484390484390Crop Row Detection in Maize Fields Inspired on the Human Visual PerceptionJ. Romeo0G. Pajares1M. Montalvo2J. M. Guerrero3M. Guijarro4A. Ribeiro5Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense, 28040 Madrid, SpainDepartment of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense, 28040 Madrid, SpainDepartment of Computer Architecture and Automatic, Faculty of Informatics, University Complutense, 28040 Madrid, SpainDepartment of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense, 28040 Madrid, SpainDepartment of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense, 28040 Madrid, SpainArtificial Perception Group, Center for Automation and Robotics (CAR), CSIC-UPM, 28500, Arganda del Rey, Madrid, SpainThis paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, 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 two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.http://dx.doi.org/10.1100/2012/484390 |
| spellingShingle | J. Romeo G. Pajares M. Montalvo J. M. Guerrero M. Guijarro A. Ribeiro Crop Row Detection in Maize Fields Inspired on the Human Visual Perception The Scientific World Journal |
| title | Crop Row Detection in Maize Fields Inspired on the Human Visual Perception |
| title_full | Crop Row Detection in Maize Fields Inspired on the Human Visual Perception |
| title_fullStr | Crop Row Detection in Maize Fields Inspired on the Human Visual Perception |
| title_full_unstemmed | Crop Row Detection in Maize Fields Inspired on the Human Visual Perception |
| title_short | Crop Row Detection in Maize Fields Inspired on the Human Visual Perception |
| title_sort | crop row detection in maize fields inspired on the human visual perception |
| url | http://dx.doi.org/10.1100/2012/484390 |
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