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2015 12th Conference on Computer and Robot Vision (CRV) (2015)
Halifax, NS, Canada
June 3, 2015 to June 5, 2015
ISBN: 978-1-4799-1986-4
pp: 277-284
In the field of precision agriculture (PA), Un-manned Aerial Vehicles (UAVs) are creating new opportunities for remotely assessing various characteristics of crops. In this paper, we present two main contributions that were evaluated on a novel application: mapping red clover ground cover (RCGC). First, we develop an integrated system for collecting, pre-processing and analyzing aerial data for the mapping of RCGC at a patch-level. Second, we collected, ground-trusted, and pre-processed a RCGC dataset that we make public for further analysis. We evaluated several different machine learning classifiers for mapping image patches to discrete clover coverage levels, reaching an accuracy of 91%.
Hyperspectral sensors, Agriculture, Support vector machines, Sensors, Global Positioning System, Accuracy, Data collection

A. M. Abuleil, G. W. Taylor and M. Moussa, "An Integrated System for Mapping Red Clover Ground Cover Using Unmanned Aerial Vehicles: A Case Study in Precision Agriculture," 2015 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, Canada, 2015, pp. 277-284.
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