GSTAT: a program for geostatistical modelling, prediction and simulation
Computers & Geosciences
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
A probabilistic framework for representing and simulating uncertain environmental variables
International Journal of Geographical Information Science - Special Issue in Honour of the Contribution of Peter Burrough to Geographical Information Science
Spatial optimisation of cropped swaths and field margins using GIS
Computers and Electronics in Agriculture
Original paper: Path planning for in-field navigation-aiding of service units
Computers and Electronics in Agriculture
Managing uncertainty in integrated environmental modelling: The UncertWeb framework
Environmental Modelling & Software
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It has been argued that the upcoming targeted approach to managing field operations, or precision farming, requires that field boundaries are measured with cm level accuracy, thus avoiding losses such as wasted inputs, unharvested crops and inefficient use of the land. This paper demonstrates a method for verification of such claims, based on a statistical model that accounts for temporal correlation in positional measurement errors. Our implementation employs the Data Uncertainty Engine (DUE), which is free software that aids the user in defining probability distributions for uncertain spatial objects, and draws random samples from these distributions. A case study concerning the financial consequences of uncertain geometry for a farmer who uses a digital map to optimise field operations for 15ha of a potato crop is presented. The error model was parameterised on measurement scenarios representing (1) the Dutch registry of agricultural fields; (2) differential GPS-based field checks for verification of area declarations; and (3) special purpose Real Time Kinematic (RTK)-GPS surveys. We found that a farmer who has a manually digitised map of the study area would benefit from a RTK-GPS survey in a single crop year if the survey would cost less than @? 442. An independent test case showed that the results of the error model were consistent with field data.