Prediction of root biomass in cassava based on ground penetrating radar phenomics
Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating rad...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/119869 |
| _version_ | 1855527823784017920 |
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| author | Agbona, A. Teare, B. Ruíz Guzman, H. Dobreva, I.D. Everett, M.E. Adams,T. Montesinos López, Osval A. Kulakow, Peter A. Hays, D.B. |
| author_browse | Adams,T. Agbona, A. Dobreva, I.D. Everett, M.E. Hays, D.B. Kulakow, Peter A. Montesinos López, Osval A. Ruíz Guzman, H. Teare, B. |
| author_facet | Agbona, A. Teare, B. Ruíz Guzman, H. Dobreva, I.D. Everett, M.E. Adams,T. Montesinos López, Osval A. Kulakow, Peter A. Hays, D.B. |
| author_sort | Agbona, A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield |
| format | Journal Article |
| id | CGSpace119869 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1198692025-08-15T13:22:06Z Prediction of root biomass in cassava based on ground penetrating radar phenomics Agbona, A. Teare, B. Ruíz Guzman, H. Dobreva, I.D. Everett, M.E. Adams,T. Montesinos López, Osval A. Kulakow, Peter A. Hays, D.B. radar cassava branching spectrum roots biomass Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield 2021-12-03 2022-06-17T10:11:43Z 2022-06-17T10:11:43Z Journal Article https://hdl.handle.net/10568/119869 en Open Access application/pdf MDPI Agbona, A., Teare, B., Ruiz-Guzman, H., Dobreva, I.D., Everett, M.E., Adams, T., ... & Hays, D.B. (2021). Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics. In Remote Sensing, 13(23): 4908, 1-18. |
| spellingShingle | radar cassava branching spectrum roots biomass Agbona, A. Teare, B. Ruíz Guzman, H. Dobreva, I.D. Everett, M.E. Adams,T. Montesinos López, Osval A. Kulakow, Peter A. Hays, D.B. Prediction of root biomass in cassava based on ground penetrating radar phenomics |
| title | Prediction of root biomass in cassava based on ground penetrating radar phenomics |
| title_full | Prediction of root biomass in cassava based on ground penetrating radar phenomics |
| title_fullStr | Prediction of root biomass in cassava based on ground penetrating radar phenomics |
| title_full_unstemmed | Prediction of root biomass in cassava based on ground penetrating radar phenomics |
| title_short | Prediction of root biomass in cassava based on ground penetrating radar phenomics |
| title_sort | prediction of root biomass in cassava based on ground penetrating radar phenomics |
| topic | radar cassava branching spectrum roots biomass |
| url | https://hdl.handle.net/10568/119869 |
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