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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Arnhold, Jessica Ispizua Yamati, Facundo R. Kage, Henning Mahlein, Anne-Katrin Koch, Heinz-Josef Grunwald, Dennis |
| Abstract | Background Root growth is most commonly determined with the destructive soil core method, which is very labor-intensive and destroys the plants at the sampling spots. The alternative minirhizotron technique allows for root growth observation throughout the growing season at the same spot but necessitates a high-throughput image analysis for being labor- and cost-efficient. In this study, wheat root development in agronomically varied situations was monitored with minirhizotrons over the growing period in two years, paralleled by destructive samplings at two dates. The aims of this study were to (i) adapt an existing CNN-based segmentation method for wheat minirhizotron images, (ii) verify the results of minirhizotron measurements with root growth data obtained by the destructive soil core method, and (iii) investigate the effect of the presence of the minirhizotron tubes on root growth. Results The previously existing CNN could successfully be adapted for wheat root images. The minirhizotron technique seems to be more suitable for root growth observation in the subsoil, where a good agreement with destructively gathered data was found, while root length results in the topsoil were dissatisfactory in comparison to the soil core method in both years. The tube presence was found to affect root growth only if not installed with a good soil-tube contact which can be achieved by slurrying, i.e. filling gaps with a soil/water suspension. Conclusions Overall, the minirhizotron technique in combination with high-throughput image analysis seems to be an alternative and valuable technique for suitable research questions in root research targeting the subsoil. |
| Related Links | https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-024-01313-0.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17464811 |
| DOI | 10.1186/s13007-024-01313-0 |
| Journal | Plant Methods |
| Issue Number | 1 |
| Volume Number | 20 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-12-17 |
| Access Restriction | Open |
| Subject Keyword | Plant Sciences Biological Techniques Root sampling Root length estimation Convolutional neural network Root image segmentation Soil core sampling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Plant Science Biotechnology Genetics |
| Journal Impact Factor | 4.7/2023 |
| 5-Year Journal Impact Factor | 5.6/2023 |
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