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dc.contributor.authorShanmuganathan, S
dc.contributor.editorPiantadosi, J
dc.contributor.editorAnderssen, RS
dc.contributor.editorBoland, J
dc.date.accessioned2013-12-08T19:29:26Z
dc.date.accessioned2013-12-08T19:46:23Z
dc.date.accessioned2013-12-08T20:07:59Z
dc.date.available2013-12-08T19:29:26Z
dc.date.available2013-12-08T19:46:23Z
dc.date.available2013-12-08T20:07:59Z
dc.date.copyright2013-12-01
dc.date.issued2013-12-09
dc.identifier.citationThe 20th International Congress on Modelling and Simulation (MODSIM2013) held at Adelaide Convention Centre, Adelaide, Australia, 2013-12-01to 2013-12-06, published in: MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, pp.810 - 816 (7)
dc.identifier.isbn978-0-9872143-3-1
dc.identifier.urihttp://hdl.handle.net/10292/6151
dc.description.abstractThis paper describes an approach to pixel clustering using self-organising map (SOM) techniques in order to identify environmental factors that influence grape quality. The study area is the Kumeu grape wine region of northern New Zealand (NZ). SOM methods first introduced by Kohonen in the late 1980s, are based on two layered feed forward artificial neural networks (ANNs) with an unsupervised training algorithm. They are useful in projecting multidimensional input data onto low dimensional displays while preserving the intrinsic properties in the raw data by which the detection of previously unknown knowledge in the form of patterns, structures and relationships is enhanced. In modern day viticultural zoning approaches, factors that contribute to grape quality are typically categorised into three classes; terrior (climate, soil type, topography of a location), cultiva (the variety of the vine) and dependent factors such as berry quality indicators (e.g.: Brix and pH) and wine quality/market price. Many modern viticulturists rely on expert knowledge and intuition to establish viticultural zones in conjunction with Geographic Information Systems (GIS) to further subdivide a wine region and vineyards into zones. The most common scale for such zoning has been the “meso” scale and the factors used for the characterisation of vineyards, varies extensively. The most adopted factors used for zoning are grapevine growth phenology (growing degree days (GDD), frost days/timing, berry ripening temperature range) for which comprehensive knowledge on local viticulture and wine quality is essential. Hence, for characterising vineyards from the new world or wine regions with insufficient knowledge for zoning is considered as a challenging task. For such instances, the SOM approach discussed in this paper provides a means to resolving a lack of extensive historical knowledge especially, when establishing zoning systems. The case study presented demonstrates the advantages of the SOM approach to identifying the ideal discerning attributes for zoning between and within vineyard/s using available geocoded digital data. The results of the SOM based clustering and data mining approach show that water deficit, elevation (along with hill shade and aspect) and annual average/minimum temperatures, are the main contributory factors for zoning vineyards in the Kumeu wine region at the meso scale. Interestingly, the elevation, annual average- and minimum- temperatures, induration, drainage and monthly water ratio balance are found to be the discerning factors at the macro conforming some of the currently used factors in NZ.  Cluster  pixel count  Elevation  Ave Temp  A  min Temp  A sol Radiation Induratin  Exch Cation Acid sol P  Che limitaton Age Slope Drainage Wat  BR  Water deficit  1a&c  177191  128.59 12.04  1.57  14.92 3.11 1.97 3.79 1.00 1.87 0.06  4.34  1.62 219.95 1b  93607  62.37 11.62  1.09  14.07 3.31 2.01 3.86 1.00 1.16 0.03  4.88  1.70 208.26 2a  127694  36.85 13.35  3.20  14.72 1.23 2.21 2.46 1.07 1.37 0.04  3.28  1.76 179.55 2b  39396  93.84 13.74  4.59  14.89 2.28 1.42 1.62 0.94 1.71 0.06  3.74  2.67 54.10 Total  437888  Figure 1b: SOM cluster profiles, WatBR: monthly water balance ratio.
dc.publisherThe Modelling and Simulation Society of Australia and New Zealand Inc (MODSIM)
dc.relation.replaceshttp://hdl.handle.net/10292/6144
dc.relation.replaces10292/6144
dc.relation.replaceshttp://hdl.handle.net/10292/6148
dc.relation.replaces10292/6148
dc.relation.isreplacedby10292/6208
dc.relation.isreplacedbyhttp://hdl.handle.net/10292/6208
dc.relation.urihttp://www.mssanz.org.au/modsim2013/C5/shanmuganathan2.pdf
dc.rightsResponsibility for the contents of these papers rests upon the authors and not on the Modelling and Simulation Society of Australia and New Zealand Inc. Every effort has been made by the members of the editorial board and reviewers to assist the authors with improving their initial submissions when and where required.
dc.titlePixel clustering in spatial data mining; an example study with Kumeu wine region in New Zealand
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
aut.conference.typePaper Published in Proceedings
aut.relation.endpage816
aut.relation.pages7
aut.relation.startpage810
pubs.elements-id158949


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