Publications

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Asif, M. R., Rafiei Foroushani, M., Jørgensen, R. N., Nørremark, M. & Teimouri, N. (2025). Assessing generalization of deep learning models for crop classification under climatic variability in Denmark. Abstract from EGU General Assembly 2025, Vienna, Austria. https://doi.org/10.5194/egusphere-egu25-9634

Bjerge, K., Bodesheim, P. & Karstoft, H. (2025). Deep Image Clustering with Model-Agnostic Meta-Learning. In In Proceedings of the 20th International Joint Conference on Computer Vision (Vol. 2, pp. 286-297). Imaging and Computer Graphics Theory and Applications - VISAPP 2025. https://doi.org/10.5220/0013114600003912

Bjerge, K., Karstoft, H. & Høye, T. T. (2025). Towards edge processing of images from insect camera traps. Remote Sensing in Ecology and Conservation, 11(5), 573-589. https://doi.org/10.1002/rse2.70007

Gentili, M., Tanaka, T., Nichols, V. A., Jørgensen, R. N., Jørgensen, J. R. & Gislum, R. (2025). Investigating weed and companion plant ecosystem services and disservices distribution maps using machine learning: Joint approaches for sustainable weed management. In 20TH EUROPEAN WEED RESEARCH SOCIETY SYMPOSIUM: Joint Approaches for Sustainable Weed Management (pp. 340-340). European Weed Research Society. https://doi.org/10.21001/20.weed.research.society.symposium.2025

Nawoya, S., Geissmann, Q., Karstoft, H., Bjerge, K., Akol, R., Katumba, A., Mwikirize, C. & Gebreyesus, G. (2025). Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning. Smart Agricultural Technology, 11, Article 100953. https://doi.org/10.1016/j.atech.2025.100953

Salavatidezfouli, S., Karstoft, H., Iosifidis, A. & Abkar, M. (2025). Dual guidance: Reduced-order model-informed field reconstruction with generative models. Physics of Fluids, 37(9), Article 093617. https://doi.org/10.1063/5.0285462

Ssemakula, F., Nawoya, S., Kunyanga, C. N., Akol, R., Nakimbugwe, D., Bett, R. C., Karstoft, H., Bjerge, K., Katumba, A., Mwikirize, C. & Gebreyesus, G. (2025). Emerging technologies for fast determination of nutritional quality and safety of insects for food and feed: A review. Computers and Electronics in Agriculture, 239, Article 111126. https://doi.org/10.1016/j.compag.2025.111126

Bjerge, K., Karstoft, H., Mann, H. M. R. & Høye, T. T. (2024). A deep learning pipeline for time-lapse camera monitoring of floral environments and insect populations. bioRxiv. https://doi.org/10.1101/2024.04.12.589205

Bjerge, K., Karstoft, H., Mann, H. M. R. & Høye, T. T. (2024). A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments. Ecological Informatics, 84, Article 102861. https://doi.org/10.1016/j.ecoinf.2024.102861

Bjerge, K., Bodesheim, P. & Karstoft, H. (2024). Few-Shot Learning with Novelty Detection. 340-363. Paper presented at DeLTA 2024 - 5th International Conference on Deep Learning Theory and Applications. https://doi.org/10.1007/978-3-031-66694-0_21

Bjerge, K., Karstoft, H. & Høye, T. T. (2024). Towards edge processing of images from insect camera traps. bioRxiv. https://doi.org/10.1101/2024.07.01.601488

Gebreyesus, G., Nawoya, S., Ssemakula, F., Karstoft, H., Bjerge, K., Kunyanga, C. N., Mwikirize, C., Akol, R., Katumba, A., Nakimbugwe, D. & Geissmann, Q. (2024). Innovative phenotyping systems to advance selective breeding in black soldier fly: status from the FlyGene project. Journal of Insects as Food and Feed, 10(Supplement 1 (2024) S1–S353), S250. Article Volume 10, Supplement 1 (2024). https://doi.org/10.1163/23524588-20241013

Gebreyesus, G., Cheruiyot Bett , R., Nakimbugwe, D., Hansen, L. S., Nielsen, H. M., Karstoft, H., Bjerge, K., Nkirote Kunyanga , C., MBI Tanga, C., Mwikirize, C., Akol, R., Katumba, A., Khamis, F., Kinyua, J., Walusimbi, S., Geoffrey, S., Roos, N. & Sahana, G. (2024). Prospects of implementing black soldier fly (BSF) selective breeding in Kenya and Uganda: Status from the FlyGene Project. Abstract from Insects for the Green Economy: Sustainable Food
Systems and Livelihoods in Africa, Nairobi, Kenya. https://qgg.au.dk/fileadmin/site_files/mb/QGG/Billeder/FLYgene/book-of-abstracts-insects-for-the-green-economy-conference-feb2024.pdf

Nawoya, S., Ssemakula, F., Akol, R., Geissmann, Q., Karstoft, H., Bjerge, K., Mwikirize, C., Katumba, A. & Gebreyesus, G. (2024). Computer vision and deep learning in insects for food and feed production: A review. Computers and Electronics in Agriculture, 216, Article 108503. https://doi.org/10.1016/j.compag.2023.108503

Nawoya, S., Geissmann, Q., Karstoft, H., Bjerge, K., Akol, R., Katumba, A., Mwikirize, C. & Gebreyesus, G. (2024). Computer-vision based prediction of body traits and larval sex in black soldier fly. Abstract from Insects for the Green Economy: Sustainable Food
Systems and Livelihoods in Africa, Nairobi, Kenya. https://qgg.au.dk/fileadmin/site_files/mb/QGG/Billeder/FLYgene/book-of-abstracts-insects-for-the-green-economy-conference-feb2024.pdf

Sahana, G., Gebreyesus, G., Cheruiyot Bett , R., Kinyua, J., Roos, N., MBI Tanga, C., Mwikirize, C., Akol, R., Khamis, F. M., Karstoft, H., Bjerge, K., Nkirote Kunyanga , C., Hansen, L. S., Nielsen, H. M., Lund, M. S., Geoffrey, S., Walusimbi, S. & Nakimbugwe, D. (2024). FLYgene: Advancing Sustainable Breeding Programs and Genomic Tools for Black Soldier Fly (Hermetia illucens) in Kenya and Uganda. In Book of Abstracts of the 75th Annual Meeting of the European Federation of Animal Science (pp. 633-633) https://docs.eaap.org/boa/2024_Florence_EAAP_Book_Abstracts.pdf

Bjerge, K., Pinoy, N., Karstoft, H. & Høye, T. T. (2023). Field trials with automated moth monitoring in three different habitats of Denmark. Abstract from 2023 Butterfly Conservation Symposium, Bedfordshire , United Kingdom.

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical classification of insects with multitask learning and anomaly detection. Ecological Informatics, 77, Article 102278. https://doi.org/10.1016/j.ecoinf.2023.102278

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection. bioRxiv. https://doi.org/10.1101/2023.06.29.546989

Bjerge, K., Frigaard, C. E. & Karstoft, H. (2023). Object Detection of Small Insects in Time-Lapse Camera Recordings. Sensors, 23(16), Article 7242. https://doi.org/10.3390/s23167242

Gislum, R., Larsen, R., Thomsen, I. K., Greve, M. B., Hansen, E. M. & Nyholm Jørgensen, R., (2023). Betingelse for indsamling af data til brug for omregningsfaktor mellem NDV/NDRE og planteoptag af kvælstof, No. 2023-0543874, 8 p., Aug 23, 2023. Rådgivningsnotat fra DCA - Nationalt Center for Fødevarer og Jordbrug

Bjerge, K., Frigaard, C. E. & Karstoft, H. (2022). Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings. arxiv.org. https://arxiv.org/abs/2212.00423

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. Scientific Reports, 12(1), Article 8395. https://doi.org/10.1038/s41598-022-12167-8

Christensen, S., Dyrmann, M., Nyholm Jørgensen, R. & Laursen, M. S. (2021). Sensing for Weed Detection. In Sensing Approaches for Precision Agriculture (pp. 275-300). Springer. https://doi.org/10.1007/978-3-030-78431-7, https://doi.org/10.1007/978-3-030-78431-7_10

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

Kragh, M. F. & Karstoft, H. (2021). Embryo selection with artificial intelligence: how to evaluate and compare methods? Journal of Assisted Reproduction and Genetics, 38(7), 1675-1689. https://doi.org/10.1007/s10815-021-02254-6

Lati, R. N., Rasmussen, J., Andujar, D., Dorado, J., Berge, T. W., Wellhausen, C., Pflanz, M., Nordmeyer, H., Schirrmann, M., Eizenberg, H., Neve, P., Jørgensen, R. N. & Christensen, S. (2021). Site-specific weed management—constraints and opportunities for the weed research community: Insights from a workshop. Weed Research, 61(3), 147-153. https://doi.org/10.1111/wre.12469

Lindahl Petersen , K., Ladegaard Jensen, K., Back Nielsen, M., Pas, L.-C., Jensen, N.-P., Nielsen, P. R., Bøjer, O. M., Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N. & Hartmann, B. (2021). Analyse af mulige herbicidbesparelser ved brug af erfaringer og data fra RoboWeedMaPS. Datalogisk Institut. https://datalogisk.dk/wp-content/uploads/2021/03/MST_20rapport_version_2_2002092021.pdf

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. Article 9366908. 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., 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), Article 175. https://doi.org/10.3390/s21010175

Somerville, G. J., Mathiassen, S. K., Melander, B., Bøjer, O. M. & Nyholm Jørgensen, R. (2021). Analysing the number of images needed to create robust variable spray maps. Precision Agriculture, 22(5), 1377-1396. https://doi.org/10.1007/s11119-021-09800-3

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

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. In C. M. U. Neale & A. Maltese (Eds.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (Vol. 11528). Article 115280A SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.2573626

Jeppesen, J. H., Jacobsen, R. H. & Jorgensen, R. N. (2020). Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data. In A. Trost, A. Zemva & A. Skavhaug (Eds.), 2020 23rd Euromicro Conference on Digital System Design (DSD) (pp. 557-564). IEEE. https://doi.org/10.1109/DSD51259.2020.00092

Madsen, S. L. (2020). Advancing visual recognition of plant seedlings through generative modelling and data collection. [PhD dissertation, Aarhus University]. 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), Article 1246. https://doi.org/10.3390/rs12081246

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

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

Nyholm Jørgensen, R. (2020). Plantekongres 2020 - RoboWeedMaps præsenterer: Flere muligheder med digital ukrudtsgenkendelse. Pictures, Video and sound recordings (digital) https://www.youtube.com/watch?v=HEsJ9k1SJYM

Skovsen, S. K., Haraldsson, H., Davis, A., Karstoft, H. & Belongie, S. (2020). Decoupled Localization and Sensing with HMD-based AR for Interactive Scene Acquisition. In 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 167-171). Article 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

Christiansen, M. P., Teimouri, N., Laursen, M. S., Mikkelsen, B. F., Jorgensen, R. N. & Sorensen, C. A. G. (2019). Preprocessed sentinel-1 data via a web service focused on agricultural field monitoring. IEEE Access, 7(1), 65139-65149. Article 8715769. https://doi.org/10.1109/ACCESS.2019.2917063

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. In O. Huguenin-Elie, B. Studer, R. Kölliker, D. Reheul, M. Probo, P. Barre, U. Feuerstein, I. Roldan-Ruiz, P. Mariotte & A. Hopkins (Eds.), 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 (pp. 103-109). Wageningen Academic Publishers.

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

Gislum, R., Nyholm Jørgensen, R., Thomsen, I. K., Hansen, E. M. & Olesen, J. E., (2019). Beskrivelse af setup for vidensindsamling og måling af NDVI-værdi i pilotprojekt om biomasse, No. 2019-760-001283, 3 p., Jul 03, 2019.

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, Article 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 from ESHRE, Vienna, Austria. https://www.eshre.eu/ESHRE2019/Programme/Searchable#!abstractdetails/0000573820

Displaying results 1 to 50 out of 321

Revised 13.11.2025

Henrik Karstoft