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 fra EGU General Assembly 2025, Vienna, Østrig. https://doi.org/10.5194/egusphere-egu25-9634

Bjerge, K., Bodesheim, P. & Karstoft, H. (2025). Deep Image Clustering with Model-Agnostic Meta-Learning. I In Proceedings of the 20th International Joint Conference on Computer Vision (Bind 2, s. 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. I 20TH EUROPEAN WEED RESEARCH SOCIETY SYMPOSIUM: Joint Approaches for Sustainable Weed Management (s. 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, Artikel 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), Artikel 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, Artikel 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, Artikel 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. Afhandling præsenteret på 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. Artikel 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 fra 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, Artikel 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 fra 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. I Book of Abstracts of the 75th Annual Meeting of the European Federation of Animal Science (s. 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 fra 2023 Butterfly Conservation Symposium, Bedfordshire , Storbritannien.

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, Artikel 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), Artikel 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, Nr. 2023-0543874, 8 s., 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), Artikel 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. I Sensing Approaches for Precision Agriculture (s. 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), Artikel 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. Artikel 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), Artikel 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. I Proceedings - 2021 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2021 (s. 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. I C. M. U. Neale & A. Maltese (red.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (Bind 11528). Artikel 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. I A. Trost, A. Zemva & A. Skavhaug (red.), 2020 23rd Euromicro Conference on Digital System Design (DSD) (s. 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. [Ph.d.-afhandling, Aarhus Universitet]. 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), Artikel 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 fra 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. Billeder, Video- og Lydoptagelser (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. I 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (s. 167-171). Artikel 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. Artikel 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. 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.

Farkhani, S., Kragh, M. F., Christiansen, P. H., Nyholm Jørgensen, R. & Karstoft, H. (2019). Sparse-to-Dense Depth Completion in Precision Farming. I 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 Artikel 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, Nr. 2019-760-001283, 3 s., 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, Artikel 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

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Revideret 13.11.2025

Henrik Karstoft