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Evaluating satellite remote sensing as a method for measuring yield variability in Avocado and Macadamia tree crops

Published online by Cambridge University Press:  01 June 2017

A. Robson ,
M. M. Rahman ,
J. Muir ,
A. Saint ,
C. Simpson  and
C. Searle
A. Robson*
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
M. M. Rahman
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
J. Muir
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
A. Saint
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
C. Simpson
Affiliation:
Simpson Farms, Childers Qld 4660Australia
C. Searle
Affiliation:
Suncoast gold macadamia, Gympie, Qld 4570Australia
*
†E-mail: [email protected]
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Abstract

This paper evaluates the potential of very high resolution multispectral (Worldview-3) satellite imagery for mapping yield parameters in avocado and macadamia orchards. An evaluation of 18 structural and pigment based vegetation indices (VIs) derived from Worldview-3 imagery identified a positive relationship to nut/ fruit weight (kg/tree) R2>0.69 for macadamia and R2>0.68 for avocado; and nut/ fruit number (per tree) R2>0.6 for macadamia and R2>0.61 for avocado. Using the algorithms derived between the optimal VI and the measured parameter, yield and nut/ fruit number maps were derived for each block. In the absence of a commercial yield monitor, the resulting yield maps offer significant benefit to growers for improving orchard management, harvest scheduling, and forward selling decisions.

Keywords

Remote sensingyield forecastingyield mappingavocadomacadamia
Type
Precision Horticulture and Viticulture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 498 - 504
Copyright
© The Animal Consortium 2017 

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