On-the-go forecasting of crop reflectance indices for controlling aerial prescription applications

Title
On-the-go forecasting of crop reflectance indices for controlling aerial prescription applications
Publication Date
2011
Author(s)
Falzon, Gregory
( author )
OrcID: https://orcid.org/0000-0002-1989-9357
Email: gfalzon2@une.edu.au
UNE Id une-id:gfalzon2
Lamb, David
Schneider, Derek
( author )
OrcID: https://orcid.org/0000-0002-1897-4175
Email: dschnei5@une.edu.au
UNE Id une-id:dschnei5
Editor
Editor(s): Thomas Banhazi and Chris Saunders
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Australian Society for Engineering in Agriculture
Place of publication
Barton, Australia
UNE publication id
une:13581
Abstract
Whole-paddock, fertiliser prescription maps derived from high spatial-frequency variations in canopy reflectance data requires spatial statistical procedures such as kriging which, in turn requires the complete 2-0 dataset. Recent successful trials of active crop reflectance sensors in low-level aircraft provide a realistic opportunity for real time sensing and control of fertiliser application rates at a single pass. However in-flight sensing and actuation requires a predictive map of crop reflectance variability, preferentially in zones, that can be created from a 1-dimensional data stream. Forecasting the required prescription zones ahead of the aircraft avoids actuation delays and mechanical loading on components associated with responding to high spatial frequency noise. A dynamic aerial survey algorithm (OAS) has been devised that utilises each transect flown to create a full-field predictive map. OAS uses a radial, basis function, kernel - support, vector machine regressor, which progressively updates its field estimate using the cumulative data from all previous transects. A fixed-cut, contouring algorithm then segments this current prediction into a predefined number of zones (prescriptions). A recent field trial comparing a full field NDVI map with the successive prediction maps from an aircraft demonstrates the error in the forecast map reduces linearly with increasing transect number.
Link
Citation
Book of Abstracts of the Biennial Conference of the Australian Society for Engineering in Agriculture (SEAg), p. 52-52
ISBN
9780858259904
Start page
52
End page
52

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