@inproceedings{9af4141684294aa19b5a1f0c5bc1f22c,
title = "UAV-Based Hyperspectral Sensing for Yield Prediction in Winter Barley",
abstract = "In this study we evaluated the potential of the hyperspectral sensor 'Cubert UHD 185 Firefly' for yield prediction in 76 plots of a field trial with different varieties of winter barley at K{\"o}nigslutter (Lower-Saxony, Germany) in 2017. An UAV was used as carrier platform for the sensor. In 2017 we used 63 channels in a wavelength range of 450 to 700 nm. Predicted yield using PLSR and reference yield closely agreed with \mathrm R 2 =0.78. We also calculated the NDVIRGB and evaluated its suitability for yield prediction in the same field trial. NDVIRGB and reference yield were less well related to each other with \mathrm R 2 =0.46. The results show that using additional information from hyperspectral datasets allowed for a better yield prediction compared to RGB data alone. In 2018 a field trial with 76 plots of winter barley at Poppenburg (Lower-Saxony, Germany) was assessed on June 6, using the complete wavelength range of the sensor from 450 to 950 nm. In 2018, predicted yield using PLSR and reference yield agreed with \mathrm R 2 =0,81.",
keywords = "UAV, barley, field experiment, hyperspectral, phenomics, phenotyping, yield prediction",
author = "J. Oehlschlager and U. Schmidhalter and Noack, {P. O.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018 ; Conference date: 23-09-2018 Through 26-09-2018",
year = "2018",
month = sep,
doi = "10.1109/WHISPERS.2018.8747260",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2018 9th Workshop on Hyperspectral Image and Signal Processing",
}