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Land usage classification: a hierarchical neural network approach

Published online by Cambridge University Press:  23 October 2013

E. J. PALOMO ,
D. ELIZONDO  and
G. BRUNSCHWIG
E. J. PALOMO*
Affiliation:
Department of Computer Science, University of Malaga, Malaga, Spain
D. ELIZONDO
Affiliation:
DIGITS, School of Computing and Informatics, Faculty of Technology, De Monfort University, Leicester, UK
G. BRUNSCHWIG
Affiliation:
Clermont Université, VetAgro Sup, UMR1213 Herbivores, BP 10448, F-63000 Clermont-Ferrand, France INRA, UMR1213 Herbivores, Theix, F-63122 Saint-Genès Champanelle, France
*
*To whom all correspondence should be addressed. Email: ejpalomo@lcc.uma.es
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Summary

The classification of land usage in mountain grassland bovine areas is important for the management of forage production and grazing in grass-based livestock systems. The present paper proposes a novel, hierarchical neural network-based approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massif Central (France). Information was gathered on geographical characteristics and cutting and/or grazing practices on three general groups of fields: cut only, cut and grazed and grazed only fields. To classify land usage, the data were clustered and visualized in a hierarchical fashion. This was done by using a novel method for the analysis and classification of data based on growing hierarchical self-organizing maps (GHSOM). Self-organizing maps (SOM) have been shown to be successful for the analysis of highly dimensional input data in data mining applications as well as for data visualization. Moreover, the hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, capturing inherent hierarchical relationships among them. Experimental results show the utility of this approach.

Type
Modelling Animal Systems Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 5 , October 2014 , pp. 817 - 828
Copyright
Copyright © Cambridge University Press 2013 

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