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Simulating yield datasets: an opportunity to improve data filtering algorithms

Published online by Cambridge University Press:  01 June 2017

C. Leroux ,
H. Jones ,
A. Clenet ,
B. Dreux ,
M. Becu  and
B. Tisseyre
C. Leroux*
Affiliation:
SMAG, Montpellier, France UMR ITAP, Montpellier SupAgro, Irstea, France
H. Jones
Affiliation:
UMR ITAP, Montpellier SupAgro, Irstea, France
A. Clenet
Affiliation:
SMAG, Montpellier, France
B. Dreux
Affiliation:
DEFISOL, Evreux, France
M. Becu
Affiliation:
DEFISOL, Evreux, France
B. Tisseyre
Affiliation:
UMR ITAP, Montpellier SupAgro, Irstea, France
*
†E-mail: cleroux@smag-group.com
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Abstract

Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.

Keywords

FilteringSimulationYield
Type
Data analysis and Geostatistics
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 600 - 605
Copyright
© The Animal Consortium 2017 

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