|Authors: ||E.M. Perry, M. Bluml, I. Goodwin, D. Cornwall, N.D. Swarts|
|Keywords: ||canopy chlorophyll concentration index (CCCI), normalized difference vegetation index (NDVI), normalized difference red-edge vegetation index (NDRE), unmanned aerial vehicle (UAV), multi-spectral camera|
Innovative use of sensor technology offers the potential to provide better feedback techniques of nutrient status, resulting in the lowered expenditure on fertiliser, increased capacity to meet market specification and reduced loss of nutrients.
In this study we evaluated the use of remote sensing to assess canopy N status through the canopy chlorophyll concentration index (CCCI) which has been applied to cereal crops, but was untested for apple and pear.
Initial analysis suggested that 2.5 m resolution satellite imagery was not sufficient to separate the tree canopy from orchard floor vegetation.
To remove the influence of the orchard understorey, test datasets of high spatial resolution imagery (<0.05 m pixel) were acquired over the canopy in a research pear orchard and commercial apple orchards.
A multispectral camera was deployed in hand held mode to generate side views of the canopy, and above the canopy using an unmanned aerial vehicle (UAV). CCCI determined from side viewing imagery within commercial apple orchards was compared with corresponding leaf N values taken from the same plots.
The results suggest a linear relationship between CCCI and leaf N. Imagery acquired from a UAV platform validated the use of a UAV in commercial and research orchard settings to provide maps of CCCI.
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