Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data
Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of i...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/128350 |
| _version_ | 1855524250309361664 |
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| author | Pacheco Gil, Rosa Angela Velasco Cruz, Ciro Pérez Rodriguez, Paulino Burgueño, Juan Pérez Elizalde, Sergio Rodrigues, Francelino Ortíz Monasterio, Jose Iván Valle Paniagua, David Hebert del Toledo, Fernando H. |
| author_browse | Burgueño, Juan Ortíz Monasterio, Jose Iván Pacheco Gil, Rosa Angela Pérez Elizalde, Sergio Pérez Rodriguez, Paulino Rodrigues, Francelino Toledo, Fernando H. Valle Paniagua, David Hebert del Velasco Cruz, Ciro |
| author_facet | Pacheco Gil, Rosa Angela Velasco Cruz, Ciro Pérez Rodriguez, Paulino Burgueño, Juan Pérez Elizalde, Sergio Rodrigues, Francelino Ortíz Monasterio, Jose Iván Valle Paniagua, David Hebert del Toledo, Fernando H. |
| author_sort | Pacheco Gil, Rosa Angela |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative. |
| format | Journal Article |
| id | CGSpace128350 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1283502025-11-06T13:05:28Z Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data Pacheco Gil, Rosa Angela Velasco Cruz, Ciro Pérez Rodriguez, Paulino Burgueño, Juan Pérez Elizalde, Sergio Rodrigues, Francelino Ortíz Monasterio, Jose Iván Valle Paniagua, David Hebert del Toledo, Fernando H. bayesian theory wheat spatial analysis wavelength phosphorus Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative. 2023-01-20 2023-01-31T09:32:32Z 2023-01-31T09:32:32Z Journal Article https://hdl.handle.net/10568/128350 en Open Access application/pdf Springer Pacheco-Gil, R. A., Velasco-Cruz, C., Pérez-Rodríguez, P., Burgueño, J., Pérez-Elizalde, S., Rodrigues, F., Ortiz-Monasterio, I., del Valle-Paniagua, D. H., & Toledo, F. (2023). Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data. Plant Methods, 19(1). https://doi.org/10.1186/s13007-023-00980-9 |
| spellingShingle | bayesian theory wheat spatial analysis wavelength phosphorus Pacheco Gil, Rosa Angela Velasco Cruz, Ciro Pérez Rodriguez, Paulino Burgueño, Juan Pérez Elizalde, Sergio Rodrigues, Francelino Ortíz Monasterio, Jose Iván Valle Paniagua, David Hebert del Toledo, Fernando H. Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| title | Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| title_full | Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| title_fullStr | Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| title_full_unstemmed | Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| title_short | Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| title_sort | bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data |
| topic | bayesian theory wheat spatial analysis wavelength phosphorus |
| url | https://hdl.handle.net/10568/128350 |
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