Hostname: page-component-7479d7b7d-wxhwt Total loading time: 0 Render date: 2024-07-15T13:45:40.895Z Has data issue: false hasContentIssue false
  • English
  • Français

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]
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Apan, A, Held, A, Phinn, S and Markley, J 2003. Formulation and assessment of narrow-band vegetation indices from EO-1 Hyperion imagery for discriminating sugarcane disease. Paper presented at the Spatial Sciences Institute Biennial.Google Scholar
Kuester, M 2016. Radiometric use of worldview-3 imagery. DigitalGlobe: Longmont, CO, USA, 2016.Google Scholar
Kohne, S 1985. Yield estimation based on measureable parameters. South African Avocado Grower’s Association Yearbook 8, 103 -105. Retrieved from http://www.avocadosource.com/Journals/SAAGA/SAAGA_1985/SAAGA_1985_PG_103.pdf.Google Scholar
Mayer, DG and Stephenson, RA 2015. Statistical ensemble models to forecast the Australian macadamia crop. Proceedings of the 21st international congress on modelling and simulation. Gold Coast, Australia. 455-461.Google Scholar
Papageorgiou, EI, Aggelopoulou, KD, Gemtos, TA and Nanos, GD 2013. Yield prediction in apples using Fuzzy Cognitive Map learning approach. Computers and Electronics in Agricultur 91, 19–29.Google Scholar
Robson, AJ, Petty, J, Joyce, DC, Marques, JR and Hofman, PJ 2014. High resolution remote sensing, GIS and Google Earth for avocado fruit quality mapping and tree number auditing. Proceedings of the 29th International Horticultural Congress 2014. Brisbane Convention and Exhibition Centre, Brisbane, Queensland, Australia.Google Scholar
Rueda, CA, Greenberg, JA and Ustin, SL 2005. StarSpan: A tool for fast selective pixel extraction from remotely sensed data. Center for Spatial Technologies and Remote Sensing (CSTARS). University of California, Davis, California, USA.Google Scholar
Wang, Q, Nuske, S, Bergerman, M and Singh, S 2013. Automated Crop Yield Estimation for Apple Orchards. In PJ Desai, G Dudek, OKh atib and V Kumar, (Eds.), In Experimental Robotics: The 13th International Symposium on Experimental Robotics (pp. 745–758).CrossRefGoogle Scholar