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Expedited generation of terrain digital classes in flat areas from UAV images for precision agriculture purposes

Published online by Cambridge University Press:  01 June 2017

M. C. Pineda ,
C. Perdomo ,
R. Caballero ,
A. Valera ,
J. A. Martínez-Casasnovas  and
J. Viloria
M. C. Pineda*
Affiliation:
Instituto de Edafología, Facultad de Agronomía, Universidad Central de Venezuela, Maracay, Aragua, Venezuela
C. Perdomo
Affiliation:
Instituto de Edafología, Facultad de Agronomía, Universidad Central de Venezuela, Maracay, Aragua, Venezuela
R. Caballero
Affiliation:
Instituto de Edafología, Facultad de Agronomía, Universidad Central de Venezuela, Maracay, Aragua, Venezuela
A. Valera
Affiliation:
Universidad Nacional Experimental de los Llanos Centrales Rómulo Gallegos. San Juan de los Morros, Guárico, Venezuela
J. A. Martínez-Casasnovas
Affiliation:
Universidad de Lleida, Research Group on AgroICT and Precision Agriculture, Agrotecnio Center, Rovira Roure 191, 25198 Lleida, Spain
J. Viloria
Affiliation:
Instituto de Edafología, Facultad de Agronomía, Universidad Central de Venezuela, Maracay, Aragua, Venezuela
*
†E-mail: [email protected]
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Abstract

Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.

Keywords

unmanned aerial vehicleneuro-fuzzy networkkrigingsoil propertiesterrain variables
Type
UAV applications
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. 828 - 832
Copyright
© The Animal Consortium 2017 

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