Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass
Abstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.20076 |
| _version_ | 1797374368530038784 |
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| author | Sorawich Pongpiyapaiboon Hidenori Tanaka Masatsugu Hashiguchi Takuyu Hashiguchi Atsushi Hayashi Takanari Tanabata Sachiko Isobe Ryo Akashi |
| author_facet | Sorawich Pongpiyapaiboon Hidenori Tanaka Masatsugu Hashiguchi Takuyu Hashiguchi Atsushi Hayashi Takanari Tanabata Sachiko Isobe Ryo Akashi |
| author_sort | Sorawich Pongpiyapaiboon |
| collection | DOAJ |
| description | Abstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using a 3D model phenotyping system we developed, we reconstructed and analyzed 20 accessions of zoysiagrass (Zoysia spp.), including three species and their hybrid, over a period of 1 year. Artificial neural network with three hidden layers was able to effectively remove nonplant parts while retaining plant parts that were incorrectly removed using the cropping method, offering a robust and flexible approach for post‐processing of 3D models. The system also demonstrated its ability to accurately evaluate a range of traits, including height, area, and color using red green blue (RGB)‐based vegetation indices. The results showed a high correlation between the estimated volume obtained from voxel 3D model and dry weight, enabling its use as a non‐destructive method for measuring plant volume. In addition, we found that the green red normalized difference index from RGB‐based indices was similar to the commonly used normalized difference vegetation index in controlled illumination conditions. These results demonstrate the potential for three‐dimensional model phenotyping to facilitate plant breeding, particularly in the field of turfgrass and feed crops. |
| first_indexed | 2024-03-08T19:04:49Z |
| format | Article |
| id | doaj.art-8026472397b84b0ba78c25165a601b64 |
| institution | Directory Open Access Journal |
| issn | 2578-2703 |
| language | English |
| last_indexed | 2024-03-08T19:04:49Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Phenome Journal |
| spelling | doaj.art-8026472397b84b0ba78c25165a601b642023-12-28T02:10:32ZengWileyPlant Phenome Journal2578-27032023-12-0161n/an/a10.1002/ppj2.20076Development of a digital phenotyping system using 3D model reconstruction for zoysiagrassSorawich Pongpiyapaiboon0Hidenori Tanaka1Masatsugu Hashiguchi2Takuyu Hashiguchi3Atsushi Hayashi4Takanari Tanabata5Sachiko Isobe6Ryo Akashi7Graduate School of Agriculture University of Miyazaki Miyazaki JapanFaculty of Agriculture University of Miyazaki Miyazaki JapanFaculty of Regional Innovation University of Miyazaki Miyazaki JapanFaculty of Regional Innovation University of Miyazaki Miyazaki JapanSchool of Computer Science Tokyo University of Technology Tokyo JapanDepartment of Frontier Research and Development Kazusa DNA Research Institute Chiba JapanDepartment of Frontier Research and Development Kazusa DNA Research Institute Chiba JapanFaculty of Agriculture University of Miyazaki Miyazaki JapanAbstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using a 3D model phenotyping system we developed, we reconstructed and analyzed 20 accessions of zoysiagrass (Zoysia spp.), including three species and their hybrid, over a period of 1 year. Artificial neural network with three hidden layers was able to effectively remove nonplant parts while retaining plant parts that were incorrectly removed using the cropping method, offering a robust and flexible approach for post‐processing of 3D models. The system also demonstrated its ability to accurately evaluate a range of traits, including height, area, and color using red green blue (RGB)‐based vegetation indices. The results showed a high correlation between the estimated volume obtained from voxel 3D model and dry weight, enabling its use as a non‐destructive method for measuring plant volume. In addition, we found that the green red normalized difference index from RGB‐based indices was similar to the commonly used normalized difference vegetation index in controlled illumination conditions. These results demonstrate the potential for three‐dimensional model phenotyping to facilitate plant breeding, particularly in the field of turfgrass and feed crops.https://doi.org/10.1002/ppj2.20076 |
| spellingShingle | Sorawich Pongpiyapaiboon Hidenori Tanaka Masatsugu Hashiguchi Takuyu Hashiguchi Atsushi Hayashi Takanari Tanabata Sachiko Isobe Ryo Akashi Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass Plant Phenome Journal |
| title | Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass |
| title_full | Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass |
| title_fullStr | Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass |
| title_full_unstemmed | Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass |
| title_short | Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass |
| title_sort | development of a digital phenotyping system using 3d model reconstruction for zoysiagrass |
| url | https://doi.org/10.1002/ppj2.20076 |
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