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dc.contributor.authorTu, Yu-Hsuan
dc.contributor.authorJohansen, Kasper
dc.contributor.authorPhinn, Stuart
dc.contributor.authorRobson, Andrew
dc.date.accessioned2019-01-09T14:00:42Z
dc.date.available2019-01-09T14:00:42Z
dc.date.issued2018-12-29
dc.identifier.citationTu Y-H, Johansen K, Phinn S, Robson A (2018) Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Available: http://dx.doi.org/10.20944/preprints201812.0261.v1.
dc.identifier.doi10.20944/preprints201812.0261.v1
dc.identifier.urihttp://hdl.handle.net/10754/630772
dc.description.abstractTree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.
dc.description.sponsorshipFunding: This research was funded by Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program’s subproject “Multi-Scale Monitoring Tools for Managing Australia Tree Crops - Industry Meets Innovation”. Acknowledgments: The authors would like to acknowledge the support from local farmers, especially Chad Simpson; fieldwork assistance from Dan Wu; and technical supports from online forums.
dc.publisherMDPI AG
dc.relation.urlhttps://www.preprints.org/manuscript/201812.0261/v1
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleMeasuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment
dc.typePreprint
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.eprint.versionPre-print
dc.contributor.institutionRemote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, St Lucia, QLD, 4072 Australia.
dc.contributor.institutionPrecision Agriculture Research Group, School of Science and Technology, University of New England, Armidale, NSW, 2351 Australia.
kaust.authorJohansen, Kasper


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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.