Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction
Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modelling. However, the construction of accurate 3D plant models is challenging as plants are complex objects with an intricate leaf structur...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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American Society of Plant Biologists
2018
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| Online Access: | https://eprints.nottingham.ac.uk/54780/ |
| _version_ | 1848799072466501632 |
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| author | Pridmore, Tony P. Gibbs, Jonathon Pound, Michael P. French, Andrew P. Wells, Darren M. Murchie, Erik H. |
| author_facet | Pridmore, Tony P. Gibbs, Jonathon Pound, Michael P. French, Andrew P. Wells, Darren M. Murchie, Erik H. |
| author_sort | Pridmore, Tony P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modelling. However, the construction of accurate 3D plant models is challenging as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition. Rather than use the same camera positions for all plants, our technique is to acquire the images needed to reconstruct the target plant, tuning camera placement to match the plant’s individual structure. Our method also combines volumetric- and surface-based reconstruction methods and determines the necessary images based on the analysis of voxel clusters. We describe a fully automatic plant modelling/phenotyping cell (or module) comprising a six-axis robot and a high-precision turntable. By using a standard colour camera, we overcome the difficulties associated with laser-based plant reconstruction methods. The 3D models produced are compared with those obtained from fixed cameras and evaluated by comparison with data obtained by X-ray μ-computed tomography across different plant structures. Our results show that our method is successful in improving the accuracy and quality of data obtained from a variety of plant types. |
| first_indexed | 2025-11-14T20:29:51Z |
| format | Article |
| id | nottingham-54780 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:29:51Z |
| publishDate | 2018 |
| publisher | American Society of Plant Biologists |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-547802018-09-13T10:23:55Z https://eprints.nottingham.ac.uk/54780/ Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction Pridmore, Tony P. Gibbs, Jonathon Pound, Michael P. French, Andrew P. Wells, Darren M. Murchie, Erik H. Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modelling. However, the construction of accurate 3D plant models is challenging as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition. Rather than use the same camera positions for all plants, our technique is to acquire the images needed to reconstruct the target plant, tuning camera placement to match the plant’s individual structure. Our method also combines volumetric- and surface-based reconstruction methods and determines the necessary images based on the analysis of voxel clusters. We describe a fully automatic plant modelling/phenotyping cell (or module) comprising a six-axis robot and a high-precision turntable. By using a standard colour camera, we overcome the difficulties associated with laser-based plant reconstruction methods. The 3D models produced are compared with those obtained from fixed cameras and evaluated by comparison with data obtained by X-ray μ-computed tomography across different plant structures. Our results show that our method is successful in improving the accuracy and quality of data obtained from a variety of plant types. American Society of Plant Biologists 2018-07-27 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/54780/1/pp.18.00664.full.pdf Pridmore, Tony P., Gibbs, Jonathon, Pound, Michael P., French, Andrew P., Wells, Darren M. and Murchie, Erik H. (2018) Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction. Plant Physiology . ISSN 1532-2548 (In Press) http://www.plantphysiol.org/content/early/2018/08/10/pp.18.00664 doi:10.1104/pp.18.00664 doi:10.1104/pp.18.00664 |
| spellingShingle | Pridmore, Tony P. Gibbs, Jonathon Pound, Michael P. French, Andrew P. Wells, Darren M. Murchie, Erik H. Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| title | Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| title_full | Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| title_fullStr | Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| title_full_unstemmed | Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| title_short | Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| title_sort | plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction |
| url | https://eprints.nottingham.ac.uk/54780/ https://eprints.nottingham.ac.uk/54780/ https://eprints.nottingham.ac.uk/54780/ |