Falzon, Gregory

Lamb, David

Schneider, Derek

2011

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.

Book of Abstracts of the Biennial Conference of the Australian Society for Engineering in Agriculture (SEAg), p. 52-52

9780858259904

https://hdl.handle.net/1959.11/13369

en

Australian Society for Engineering in Agriculture

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

Conference Publication

Publication