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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorKooistra, L.
dc.contributor.authorMeij, R.P. van der
dc.date.accessioned2016-08-22T17:01:13Z
dc.date.available2016-08-22T17:01:13Z
dc.date.issued2016
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/23664
dc.description.abstractAlthough they have been effectively around for several decades, the societal interest for Unmanned Aerial Vehicles (UAVs) has recently taken off dramatically.It is anticipated that (precision) agriculture (PA) in particular will represent the largest client of UAV technology in the coming decade.practitioners of PA consider the presence of complex in field variability and require accurate and repeated information regarding crop statuses on a detailed small scale to adjust their intervening practices accordingly. A UAV flight was conducted in the 2015 growing season over a field comprising of seventy separate plots cultivated with oats, each having received different treatments. Spectral data was acquired in the visible and near-infrared range (450-915nm) over 94 adjacent wavebands by a hyperspectral push broom scanner. The acquired hyperspectral data was related to in situ measured crop parameters in an independent calibration procedure through univariate regression analysis over individual wavebands, existing vegetation indices (VIs), new optimized indices and partial least squares (PLS) regression. A selection of the best performing indices and models found during calibration was evaluated with respect to their precision and predictive accuracies on an independent validation set. Validation displayed considerably varied results, indicated by relatively high prediction capabilities for models estimating crop height (CVRMSE = 5.12%, R2 = 0.79) followed by leaf chlorophyll content (CVRMSE = 14.5%. R2 = 0.79). The best models related to prediction of N content (CVRMSE = 21.6%. R2 = 0.68), fresh biomass (CVRMSE = 20.8%. R2 = 0.56) and C content (CVRMSE = 20.8%. R2 = 0.52) exhibited larger prediction inaccuracies and lower precision. Furthermore, the outcomes suggest that predictions through remotely sensed UAV data for height, leaf Chl content, N content and, to a lesser degree, fresh biomass may be effectively further discriminated for different cultivars and their associated treatments
dc.description.sponsorshipUtrecht University
dc.format.extent7479340
dc.format.extent7479340
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleMeasuring the legacy of plants and plant traits using UAV-based optical sensors
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsUAVs, Remote Sensing, precision agriculture, crop traits, treatment effects, height, nitrogen, leaf chlorophyll, biomass, carbon
dc.subject.courseuuGeographical Information Management and Applications (GIMA)


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