Journal article
Statistical evaluation of data from tractor guidance systems
Precision Agriculture, Vol.7(3), pp.179-192
2006
Abstract
Statistical tools are discussed for the analysis of data collected from tractor guidance systems. The importance of both accuracy and precision is discussed, and statistical tools for analysis are considered which incorporate important features of the data. In particular, accuracy is modelled using a generalized least squares model incorporating autocorrelation, and variances (inverse of precision) using a gamma generalized linear model. The methods are applied to data collected during an experiment conducted with a Trimble receiver used with a Beeline tractor guidance system. Three different scenarios are considered, then compared: a tractor simulating ploughing a field; the tractor pulling a plough with the receivers on the tractor; the tractor pulling a plough with the Trimble receiver on the plough. The change in the precision and accuracy between the scenarios is discussed. Data were recorded over repeated swaths for each scenario. After discussing specific statistical techniques for analysis of this type of data, the collected data are analysed; major conclusions are: The data from the Trimble receiver showed evidence of autocorrelation in the offsets; the plough recorded a variance about three times that recorded by the tractor.
Details
- Title
- Statistical evaluation of data from tractor guidance systems
- Authors
- Peter K Dunn (Author) - University of Southern QueenslandA P Powierski (Author) - University of Southern QueenslandR Hill (Author) - Goulburn-Murray Water
- Publication details
- Precision Agriculture, Vol.7(3), pp.179-192
- Publisher
- Springer New York LLC
- Date published
- 2006
- DOI
- 10.1007/s11119-006-9007-8
- ISSN
- 1385-2256
- Copyright note
- Copyright © 2006 Springer New York LLC. The author's accepted version is reproduced here in accordance with the publisher's copyright policy. The final publication is available at Springer via http://dx.doi.org/10.1007/s11119-006-9007-8
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99449796402621
- Output Type
- Journal article
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