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Mapping Red Clover Ground Cover Using Unmanned Aerial Vehicles

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Title: Mapping Red Clover Ground Cover Using Unmanned Aerial Vehicles
Author: Abuleil, Ammar
Department: School of Engineering
Program: Engineering
Advisor: Taylor, Graham W.Moussa, Medhat
Abstract: In the field of precision agriculture (PA), Unmanned Aerial Vehicles (UAVs) are creat- ing new opportunities for remotely assessing various characteristics of crops. This thesis presents two main contributions: an integrated system for collecting, preprocessing and analyzing aerial data for the novel application of mapping RCGC at a patch-level, and a collected, ground-truthed, and preprocessed RCGC dataset that will be made public for further analysis. Several different machine learning classifiers were evaluated for map- ping image patches to discrete clover coverage levels, reaching an accuracy of 91%. The best performing classifier was then tested for its robustness and adaptiveness and obtained satisfactory performance considering the size of the dataset being used.
URI: http://hdl.handle.net/10214/8865
Date: 2015-05
Rights: Attribution-NoDerivs 2.5 Canada
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Attribution-NoDerivs 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NoDerivs 2.5 Canada