Rasmus Nyholm Jørgensen
Rasmus Nyholm Jørgensen
Senior Scientist, Aarhus University
Verified email at - Homepage
Cited by
Cited by
Site‐specific weed control technologies
S Christensen, HT Søgaard, P Kudsk, M Nørremark, I Lund, ES Nadimi, ...
Weed Research 49 (3), 233-241, 2009
Designing and testing a UAV mapping system for agricultural field surveying
MP Christiansen, MS Laursen, RN Jørgensen, S Skovsen, R Gislum
Sensors 17 (12), 2703, 2017
Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks
ES Nadimi, RN Jørgensen, V Blanes-Vidal, S Christensen
Computers and electronics in agriculture 82, 44-54, 2012
Automated detection and recognition of wildlife using thermal cameras
P Christiansen, KA Steen, RN Jørgensen, H Karstoft
Sensors 14 (8), 13778-13793, 2014
DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field
P Christiansen, LN Nielsen, KA Steen, RN Jørgensen, H Karstoft
Sensors 16 (11), 1904, 2016
RoboWeedSupport-Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
M Dyrmann, RN Jørgensen, HS Midtiby
Advances in Animal Biosciences 8 (2), 842-847, 2017
A public image database for benchmark of plant seedling classification algorithms
TM Giselsson, RN Jørgensen, PK Jensen, M Dyrmann, HS Midtiby
arXiv preprint arXiv:1711.05458, 2017
Semantic segmentation of mixed crops using deep convolutional neural network.
AK Mortensen, M Dyrmann, H Karstoft, RN Jørgensen, R Gislum
Monitoring and modeling temperature variations inside silage stacks using novel wireless sensor networks
O Green, ES Nadimi, V Blanes-Vidal, RN Jørgensen, IMLD Storm, ...
Computers and Electronics in Agriculture 69 (2), 149-157, 2009
Estimation of leaf area index in cereal crops using red–green images
K Kirk, HJ Andersen, AG Thomsen, JR Jørgensen, RN Jørgensen
Biosystems Engineering 104 (3), 308-317, 2009
Weed growth stage estimator using deep convolutional neural networks
N Teimouri, M Dyrmann, PR Nielsen, SK Mathiassen, GJ Somerville, ...
Sensors 18 (5), 1580, 2018
Using deep learning to challenge safety standard for highly autonomous machines in agriculture
KA Steen, P Christiansen, H Karstoft, RN Jørgensen
Journal of Imaging 2 (1), 6, 2016
A novel spatio-temporal FCN-LSTM network for recognizing various crop types using multi-temporal radar images
N Teimouri, M Dyrmann, RN Jørgensen
Remote Sensing 11 (8), 990, 2019
Pixel-wise classification of weeds and crops in images by using a fully convolutional neural network
M Dyrmann, AK Mortensen, HS Midtiby, RN Jørgensen
Proceedings of the International Conference on Agricultural Engineering …, 2016
Object detection and terrain classification in agricultural fields using 3D lidar data
M Kragh, RN Jørgensen, H Pedersen
International conference on computer vision systems, 188-197, 2015
Fieldsafe: dataset for obstacle detection in agriculture
MF Kragh, P Christiansen, MS Laursen, M Larsen, KA Steen, O Green, ...
Sensors 17 (11), 2579, 2017
N2O emission from energy crop fields of Miscanthus “Giganteus” and winter rye
RN Jørgensen, BJ Jørgensen, NE Nielsen, M Maag, AM Lind
Atmospheric Environment 31 (18), 2899-2904, 1997
Towards an open software platform for field robots in precision agriculture
K Jensen, M Larsen, SH Nielsen, LB Larsen, KS Olsen, RN Jørgensen
Robotics 3 (2), 207-234, 2014
Modelling nitrogen and phosphorus content at early growth stages in spring barley using hyperspectral line scanning
LK Christensen, BS Bennedsen, RN Jørgensen, H Nielsen
Biosystems Engineering 88 (1), 19-24, 2004
N2O emission immediately after rainfall in a dry stubble field
RN JØrgensen, BJ JØrgensen, NE Nielsen
Soil Biology and Biochemistry 30 (4), 545-546, 1998
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