Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data

dc.creatorPacheco Gil, R. A.
dc.creatorVelasco Cruz, C.
dc.creatorPerez-Rodriguez, P.
dc.creatorBurgueño, J.
dc.creatorPerez-Elizalde, S.
dc.creatorRodrigues, F.
dc.creatorOrtiz-Monasterio, I.
dc.creatorValle-Paniagua, D.H. del
dc.creatorToledo, F.H.
dc.date2023-01-28T01:10:14Z
dc.date2023-01-28T01:10:14Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:17Z
dc.date.available2023-07-17T20:10:17Z
dc.identifierhttps://hdl.handle.net/10883/22476
dc.identifier10.1186/s13007-023-00980-9
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514220
dc.descriptionBackground: 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.
dc.languageEnglish
dc.publisherBioMed Central
dc.relationhttps://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-00980-9#Sec15
dc.relationPoverty reduction, livelihoods & jobs
dc.relationBreeding Resources
dc.relationResilient Agrifood Systems
dc.relationGenetic Innovation
dc.relationBill & Melinda Gates Foundation
dc.relationUnited States Agency for International Development
dc.relationCGIAR Research Program on Wheat
dc.relationCGIAR Research Program on Maize
dc.relationCGIAR Trust Fund
dc.relationhttps://hdl.handle.net/10568/128350
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source19
dc.source1746-4811
dc.sourcePlant Methods
dc.source6
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectBayesian Statistics
dc.subjectHyperspectral Reflectance
dc.subjectBAYESIAN THEORY
dc.subjectWHEAT
dc.subjectSPATIAL ANALYSIS
dc.subjectWAVELENGTH
dc.subjectPHOSPHORUS
dc.subjectGenetic Resources
dc.titleBayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data
dc.typeArticle
dc.typePublished Version
dc.coverageLondon (United Kingdom)


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