Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.)
Crop phenotyping is a key process used to accelerate breeding programs in the era of high-throughput genotyping. However, most rapid phenotyping methods developed to date have focused on major cereals or legumes, and their application to minor crops has been delayed. In this study, we developed a no...
| Autores principales: | , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Informa UK Limited
2019
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/103494 |
| _version_ | 1855517405163290624 |
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| author | Iseki, K. Matsumoto, R. |
| author_browse | Iseki, K. Matsumoto, R. |
| author_facet | Iseki, K. Matsumoto, R. |
| author_sort | Iseki, K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Crop phenotyping is a key process used to accelerate breeding programs in the era of high-throughput genotyping. However, most rapid phenotyping methods developed to date have focused on major cereals or legumes, and their application to minor crops has been delayed. In this study, we developed a non-destructive method to predict shoot biomass by measuring spectral reflectance in staking yam (Dioscorea rotundata). The normalized difference vegetation index (NDVI) was evaluated using a handheld sensor that was vertically scanned from the top to the bottom of a plant alongside the stake. A linear regression model was constructed to predict shoot biomass through Bayesian analysis using NDVI as a parameter. The model well predicted the observed values of shoot biomass, irrespective of the growth stage and genotypes. Conversely, the model tended to underestimate the shoot biomass when the actual shoot biomass exceeded 150 g plant−1; this was compensated for when the parameter green area, calculated from plant image, was included in the model. This method reduced the time, cost, effort, and field space needed for shoot biomass evaluation compared with that needed for the sampling method, enabling shoot biomass phenotyping for a large population of plants. A total of 210 cross-populated plants were evaluated, and a correlation analysis was performed between the predicted shoot biomass and tuber yield. In addition to the prediction of tuber yield, this method could also be applied for the evaluation of crop models and stress tolerance, as well as for genetic analyses. |
| format | Journal Article |
| id | CGSpace103494 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Informa UK Limited |
| publisherStr | Informa UK Limited |
| record_format | dspace |
| spelling | CGSpace1034942025-11-11T10:06:12Z Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) Iseki, K. Matsumoto, R. yams dioscorea rotundata phenotypes biomass nigeria Crop phenotyping is a key process used to accelerate breeding programs in the era of high-throughput genotyping. However, most rapid phenotyping methods developed to date have focused on major cereals or legumes, and their application to minor crops has been delayed. In this study, we developed a non-destructive method to predict shoot biomass by measuring spectral reflectance in staking yam (Dioscorea rotundata). The normalized difference vegetation index (NDVI) was evaluated using a handheld sensor that was vertically scanned from the top to the bottom of a plant alongside the stake. A linear regression model was constructed to predict shoot biomass through Bayesian analysis using NDVI as a parameter. The model well predicted the observed values of shoot biomass, irrespective of the growth stage and genotypes. Conversely, the model tended to underestimate the shoot biomass when the actual shoot biomass exceeded 150 g plant−1; this was compensated for when the parameter green area, calculated from plant image, was included in the model. This method reduced the time, cost, effort, and field space needed for shoot biomass evaluation compared with that needed for the sampling method, enabling shoot biomass phenotyping for a large population of plants. A total of 210 cross-populated plants were evaluated, and a correlation analysis was performed between the predicted shoot biomass and tuber yield. In addition to the prediction of tuber yield, this method could also be applied for the evaluation of crop models and stress tolerance, as well as for genetic analyses. 2019-04-03 2019-09-04T15:29:09Z 2019-09-04T15:29:09Z Journal Article https://hdl.handle.net/10568/103494 en Open Access application/pdf Informa UK Limited Iseki, K. & Matsumoto, R. (2018). Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking yam (Dioscorea rotundata Poir.). Plant Production Science, 1-10. |
| spellingShingle | yams dioscorea rotundata phenotypes biomass nigeria Iseki, K. Matsumoto, R. Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) |
| title | Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) |
| title_full | Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) |
| title_fullStr | Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) |
| title_full_unstemmed | Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) |
| title_short | Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.) |
| title_sort | non destructive shoot biomass evaluation using a handheld ndvi sensor for field grown staking yam dioscorea rotundata poir |
| topic | yams dioscorea rotundata phenotypes biomass nigeria |
| url | https://hdl.handle.net/10568/103494 |
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