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Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry
Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expand...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948741/ https://www.ncbi.nlm.nih.gov/pubmed/29614039 http://dx.doi.org/10.3390/s18041077 |
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author | Andújar, Dionisio Calle, Mikel Fernández-Quintanilla, César Ribeiro, Ángela Dorado, José |
author_facet | Andújar, Dionisio Calle, Mikel Fernández-Quintanilla, César Ribeiro, Ángela Dorado, José |
author_sort | Andújar, Dionisio |
collection | PubMed |
description | Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants’ shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches. |
format | Online Article Text |
id | pubmed-5948741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59487412018-05-17 Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry Andújar, Dionisio Calle, Mikel Fernández-Quintanilla, César Ribeiro, Ángela Dorado, José Sensors (Basel) Article Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants’ shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches. MDPI 2018-04-03 /pmc/articles/PMC5948741/ /pubmed/29614039 http://dx.doi.org/10.3390/s18041077 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Andújar, Dionisio Calle, Mikel Fernández-Quintanilla, César Ribeiro, Ángela Dorado, José Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry |
title | Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry |
title_full | Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry |
title_fullStr | Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry |
title_full_unstemmed | Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry |
title_short | Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry |
title_sort | three-dimensional modeling of weed plants using low-cost photogrammetry |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948741/ https://www.ncbi.nlm.nih.gov/pubmed/29614039 http://dx.doi.org/10.3390/s18041077 |
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