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Statistical Modelling and Deconvolution of Yield Meter Data
Author(s) -
Tøgersen Frede Aakmann,
Waagepetersen Rasmus
Publication year - 2004
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2004.02-024.x
Subject(s) - deconvolution , mathematics , covariance function , impulse response , covariance , statistics , convolution (computer science) , gaussian , gaussian process , impulse (physics) , computer science , mathematical analysis , artificial intelligence , physics , quantum mechanics , artificial neural network
. This paper considers the problem of mapping spatial variation of yield in a field using data from a yield monitoring system on a combine harvester. The unobserved yield is assumed to be a Gaussian random field and the yield monitoring system data is modelled as a convolution of the yield and an impulse response function. This results in an unusual spatial covariance structure (depending on the driving pattern of the combine harvester) for the yield monitoring system data. Parameters of the impulse response function and the spatial covariance function of the yield are estimated using maximum likelihood methods. The fitted model is assessed using certain empirical directional covariograms and the yield is finally predicted using the inferred statistical model.