Publications

Farkhani, S. (2022). Soft Regression based on Sparse Unbalanced Labels.

Gislum, R., Jørgensen, R., Mortensen, A. K. & Mikkelsen, B. (2022). Roboter im Ackerbau - Entwicklung aus der Nische. 39. Abstract fra DLG-Technikertagung 2022.

Kragh, M. F., Rimestad, J., Lassen, J. T., Berntsen, J. & Karstoft, H. (2022). Predicting embryo viability based on self-supervised alignment of time-lapse videos. IEEE Transactions on Medical Imaging, 41(2), 465-475. https://doi.org/10.1109/TMI.2021.3116986

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2022). Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data. Manuskript afsendt til publicering.

Christensen, S., Dyrmann, M., Nyholm Jørgensen, R. & Laursen, M. S. (2021). Sensing for Weed Detection. I Sensing Approaches for Precision Agriculture (s. 275-300). Springer. Progress in Precision Agriculture https://doi.org/10.1007/978-3-030-78431-7

Dyrmann, M., Mortensen, A. K., Linneberg, L., Høye, T. T. & Bjerge, K. (2021). Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning. Sensors (Switzerland), 21(18), [6126]. https://doi.org/10.3390/s21186126

Dyrmann, M., Skovsen, S. K. & Christiansen, P. H. (2021). Camera-based estimation of sugar beet stem points and weed cover using convolutional neural networks. I Precision agriculture ’21 (s. 307-313). Precision agriculture ’21 https://doi.org/10.3920/978-90-8686-916-9_36

Farkhani, S., Skovsen, S. K., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2021). Weed classification using explainable multi-resolution slot attention. Sensors, 21(20), [6705]. https://doi.org/10.3390/s21206705

Gislum, R., Thomsen, I. K., Hansen, E. M., Mortensen, A. K., Larsen, R. & Olesen, J. E., (2021). Analyser i pilotprojekt om biomasse på baggrund af data fra forsøgsår 2020, Nr. 2021-0206943, 18 s., maj 05, 2021. Rådgivningsnotat fra DCA – National Center for Fødevarer og Jordbrug

Gislum, R., Thomopoulos, S., Gyldengren, J. G., Mortensen, A. K. & Boelt, B. (2021). The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production. Nitrogen, 2(2), 229-243. https://doi.org/10.3390/nitrogen2020015

Kragh, M. F. & Karstoft, H. (2021). Embryo selection with artificial intelligence: how to evaluate and compare methods? Journal of Assisted Reproduction and Genetics, 38, 1675-1689. https://doi.org/10.1007/s10815-021-02254-6

Mortensen, A. K., Gislum, R., Jørgensen, J. R. & Boelt, B. (2021). The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research. Agriculture, 11(4), [301]. https://doi.org/10.3390/agriculture11040301

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3485-3494. https://doi.org/10.1109/JSTARS.2021.3062936

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). IrradianceNet: Spatiotemporal Deep Learning Model for Satellite-Derived Solar Irradiance Nowcasting. Solar Energy, 228, 659-669. https://doi.org/10.1016/j.solener.2021.09.073

Skovsen, S. K. (2021). Mixed Crop Mapping by Species Discrimination using Data Driven Computer Vision and Simulated Data. Aarhus Universitet.

Skovsen, S. K., Laursen, M. S., Kristensen, R. K., Rasmussen, J., Dyrmann, M., Eriksen, J., Gislum, R., Nyholm Jørgensen, R. & Karstoft, H. (2021). Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors, 21( 1), [175]. https://doi.org/10.3390/s21010175

Sørensen, R. A., Rosenberg, J. & Karstoft, H. (2021). Baggage Routing with Scheduled Departures using Deep Reinforcement Learning. I 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) (s. 13-19). IEEE. https://doi.org/10.1109/ISCSIC54682.2021.00014

Tan, S., Mortensen, A. K., Ma, X., Boelt, B. & Gislum, R. (2021). Assessment of grass lodging using texture and canopy height distribution features derived from UAV visual-band images. Agricultural and Forest Meteorology, 308-309, [108541]. https://doi.org/10.1016/j.agrformet.2021.108541

Farkhani, S., Skovsen, S. K., Mortensen, A. K., Laursen, M. S., Nyholm Jørgensen, R. & Karstoft, H. (2020). Initial evaluation of enriching satellite imagery using sparse proximal sensing in precision farming. I C. M. U. Neale & A. Maltese (red.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (Bind 11528). SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.2573626

Madsen, S. L. (2020). Advancing visual recognition of plant seedlings through generative modelling and data collection. Aarhus Universitet.

Madsen, S. L., Mathiassen, S. K., Dyrmann, M., Laursen, M. S., Paz, L-C. & Nyholm Jørgensen, R. (2020). Open Plant Phenotype Database of Common Weeds in Denmark. Remote Sensing, 12(8), [1246]. https://doi.org/10.3390/rs12081246

Nielsen, A. H., Wagner, A., Iosifidis, A. & Karstoft, H. (2020). Cloud Nowcasting on Satellite Images: A Novel Dataset and Experimental Comparisons. https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365696

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2020). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. Abstract fra European Conference on Computer Vision Workshop . https://drive.google.com/file/d/1_TV20JCIyBaetN6Y1mt7K5FeMx431Rr3/view

Skovsen, S. K., Haraldsson, H., Davis, A., Karstoft, H. & Belongie, S. (2020). Decoupled Localization and Sensing with HMD-based AR for Interactive Scene Acquisition. I 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (s. 167-171). [9288396] IEEE. https://doi.org/10.1109/ISMAR-Adjunct51615.2020.00053

Sørensen, R. A., Nielsen, M. & Karstoft, H. (2020). Routing in congested baggage handling systems using deep reinforcement learning. Integrated Computer-Aided Engineering, 27(2), 139-152. https://doi.org/10.3233/ICA-190613

Thomsen, I. K., Hansen, E. M., Jensen, J. L., Larsen, R., Mortensen, A. K. & Gislum, R., (2020). Revurdering af Landbrugsstyrelsens kontrolmodel 2019 (trappemodellen), Nr. 2020-0133641, 15 s., sep. 01, 2020.

Christiansen, P. H., Kragh, M. F., Brodskiy, Y. & Karstoft, H. (2019). UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor. arXiv.org. https://arxiv.org/abs/1907.04011

Eriksen, J., Frandsen, T. S., Knudsen, L., Skovsen, S., Nyholm Jørgensen, R., Steen, K. A., Green, O. & Rasmussen, J. (2019). Nitrogen fertilization of grass-clover leys. I O. Huguenin-Elie, B. Studer, R. Kölliker, D. Reheul, M. Probo, P. Barre, U. Feuerstein, I. Roldan-Ruiz, P. Mariotte & A. Hopkins (red.), Improving sown grasslands through breeding and management: Proceedings of the Joint 20th Symposium of the European Grassland Federation and the 33rd Meeting of the EUCARPIA Section "Fodder Crops and Amenity Grasses", Zürich, Switzerland, 24-27 June 2019 (s. 103-109). Wageningen Academic Publishers. Grassland Science in Europe Bind 24

Farkhani, S., Kragh, M. F., Christiansen, P. H., Nyholm Jørgensen, R. & Karstoft, H. (2019). Sparse-to-Dense Depth Completion in Precision Farming. I ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing [35] Association for Computing Machinery. https://doi.org/10.1145/3387168.3387230

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in Biology and Medicine, 115, [103494]. https://doi.org/10.1016/j.compbiomed.2019.103494

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic morphological grading of human blastocysts with time-lapse imaging and artificial intelligence. Abstract fra ESHRE, Vienna, Østrig. https://www.eshre.eu/ESHRE2019/Programme/Searchable#!abstractdetails/0000573820

Madsen, S. L., Mortensen, A. K., Nyholm Jørgensen, R. & Karstoft, H. (2019). Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN. Remote Sensing, 11(22), [2671]. https://doi.org/10.3390/rs11222671

Madsen, S. L., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2019). Generating artificial images of plant seedlings using generative adversarial networks. Biosystems Engineering, 187, 147-159. https://doi.org/10.1016/j.biosystemseng.2019.09.005

Mortensen, A. K., Laursen, M. S., Nyholm Jørgensen, R. & Gislum, R. (2019). Drone dataflow - a MATLAB toolbox for extracting plots from images captured by a UAV. I J. V. Stafford (red.), Precision Agriculture '19 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (s. 959-965). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_118

Mortensen, A. K. (Producent), Skovsen, S. (Producent), Karstoft, H. (Producent) & Gislum, R. (Producent). (2019). The Oil Radish Growth Dataset. Datasæt

Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation. Poster session præsenteret ved IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, California, USA.

Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation. I Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (s. 2703-2710). [9025602] IEEE. https://doi.org/10.1109/CVPRW.2019.00328

Nyholm Jørgensen, R. (Producent), Laursen, M. S. (Producent), Teimouri, N. (Producent), Madsen, S. L. (Producent), Dyrmann, M. (Producent), Somerville, G. J. (Producent) & Mathiassen, S. K. (Producent). (2019). RoboWeedMaps - Automated weed detection and mapping - Invited talk at SSWM 2019, SDU, Odense Denmark. Billeder, Video- og Lydoptagelser (digital), YouTube.

Skovsen, S., Laursen, M. S., Gislum, R., Eriksen, J., Dyrmann, M., Mortensen, A. K., Farkhani, S., Karstoft, H., Jensen, N-P. & Nyholm Jørgensen, R. (2019). Species distribution mapping of grass clover leys using images for targeted nitrogen fertilization. I Precision agriculture ’19 (s. 639-645). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_79

Skovsen, S. (Producent), Dyrmann, M. (Producent), Mortensen, A. K. (Producent), Laursen, M. S. (Producent), Gislum, R. (Producent), Eriksen, J. (Producent), Farkhani, S. (Producent), Karstoft, H. (Producent) & Nyholm Jørgensen, R. (Producent). (2019). The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture. Datasæt https://vision.eng.au.dk/grass-clover-dataset/

Skovsen, S., Dyrmann, M., Mortensen, A. K., Laursen, M. S., Gislum, R., Eriksen, J., Farkhani, S., Karstoft, H. & Nyholm Jørgensen, R. (2019). The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture. Poster session præsenteret ved IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, California, USA.

Skovsen, S., Dyrmann, M., Mortensen, A. K., Laursen, M. S., Gislum, R., Eriksen, J., Farkhani, S., Karstoft, H. & Nyholm Jørgensen, R. (2019). The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture. I The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops IEEE. http://openaccess.thecvf.com/content_CVPRW_2019/html/CVPPP/Skovsen_The_GrassClover_Image_Dataset_for_Semantic_and_Hierarchical_Species_Understanding_CVPRW_2019_paper.html

Somerville, G. J., Nyholm Jørgensen, R., Bojer, O. M., Rydahl, P., Dyrmann, M., Andersen, P., Jensen, N-P. & Green, O. (2019). Marrying futuristic weed mapping with current herbicide sprayer capacities. I J. V. Stafford (red.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (s. 231-237). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_28

Sørensen, R. A., Nielsen, M. & Karstoft, H. (2019). Deep Reinforcement Learning for Route Optimization in Baggage Handling Systems. I S. Y. Yurish (red.), Advances in Signal Processing and Artificial Intelligence: Proceedings of the 1st International Conference on Advances in Signal Processing and Artifical Intelligence (s. 29-33). [11] IFSA Publishing.

Teimouri, N., Dyrmann, M. & Nyholm Jørgensen, R. (2019). A novel spatio-temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sensing, 11(8), [990]. https://doi.org/10.3390/rs11080990

Wang, H., Mortensen, A. K., Mao, P., Boelt, B. & Gislum, R. (2019). Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectralcamera. International Journal of Remote Sensing, 40(7), 2467-2482. https://doi.org/10.1080/01431161.2019.1569783

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R., Skovsen, S. & Gislum, R. (2018). Ground Vehicle Mapping of Fields using LiDAR to Enable Prediction of Crop Biomass. I Proceedings of the 14th International Conference on Precision Agriculture [5145] International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5145

Dyrmann, M., Christiansen, P. & Midtiby, H. S. (2018). Estimation of plant species by classifying plants and leaves in combination. Journal of Field Robotics, 35(2), 202-212. https://doi.org/10.1002/rob.21734

Dyrmann, M., Skovsen, S., Sørensen, R. A., Nielsen, P. R. & Nyholm Jørgensen, R. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. Abstract fra International Conference on Precision Agriculture, Montréal, Quebec, Canada.

Dyrmann, M., Skovsen, S., Laursen, M. S. & Nyholm Jørgensen, R. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. I Proceedings of the 14th International Conference on Precision Agriculture International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=download&item=5081

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Revideret 12.01.2022 -

Henrik Karstoft