Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling
The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop mod...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/11/12/2428 |
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| author | Yubin Yang Livia Paleari Lloyd T. Wilson Roberto Confalonieri Adriano Z. Astaldi Mirko Buratti Zongbu Yan Eric Christensen Jing Wang Stanley Omar P. B. Samonte |
| author_facet | Yubin Yang Livia Paleari Lloyd T. Wilson Roberto Confalonieri Adriano Z. Astaldi Mirko Buratti Zongbu Yan Eric Christensen Jing Wang Stanley Omar P. B. Samonte |
| author_sort | Yubin Yang |
| collection | DOAJ |
| description | The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding. |
| first_indexed | 2024-03-10T04:40:58Z |
| format | Article |
| id | doaj.art-d7b2c1a2cdbb4b839fbcee2f25c88ac1 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T04:40:58Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-d7b2c1a2cdbb4b839fbcee2f25c88ac12023-11-23T03:21:43ZengMDPI AGAgronomy2073-43952021-11-011112242810.3390/agronomy11122428Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data ModelingYubin Yang0Livia Paleari1Lloyd T. Wilson2Roberto Confalonieri3Adriano Z. Astaldi4Mirko Buratti5Zongbu Yan6Eric Christensen7Jing Wang8Stanley Omar P. B. Samonte9Texas A&M AgriLife Research Center, Beaumont, TX 77713, USACassandra Lab, Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, ItalyTexas A&M AgriLife Research Center, Beaumont, TX 77713, USACassandra Lab, Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, ItalyCassandra Lab, Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, ItalyCassandra Lab, Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, ItalyTexas A&M AgriLife Research Center, Beaumont, TX 77713, USATexas A&M AgriLife Research Center, Beaumont, TX 77713, USATexas A&M AgriLife Research Center, Beaumont, TX 77713, USATexas A&M AgriLife Research Center, Beaumont, TX 77713, USAThe quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.https://www.mdpi.com/2073-4395/11/12/2428rice<i>Oryza sativa</i> L.leaf architectural traitsleaf angle distributionlight extinction coefficient |
| spellingShingle | Yubin Yang Livia Paleari Lloyd T. Wilson Roberto Confalonieri Adriano Z. Astaldi Mirko Buratti Zongbu Yan Eric Christensen Jing Wang Stanley Omar P. B. Samonte Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling Agronomy rice <i>Oryza sativa</i> L. leaf architectural traits leaf angle distribution light extinction coefficient |
| title | Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling |
| title_full | Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling |
| title_fullStr | Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling |
| title_full_unstemmed | Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling |
| title_short | Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling |
| title_sort | characterizing genotype specific rice architectural traits using smart mobile app and data modeling |
| topic | rice <i>Oryza sativa</i> L. leaf architectural traits leaf angle distribution light extinction coefficient |
| url | https://www.mdpi.com/2073-4395/11/12/2428 |
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