Henrik Karstoft

1.6k total citations
48 papers, 1.0k citations indexed

About

Henrik Karstoft is a scholar working on Plant Science, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Henrik Karstoft has authored 48 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Plant Science, 14 papers in Computer Vision and Pattern Recognition and 9 papers in Aerospace Engineering. Recurrent topics in Henrik Karstoft's work include Smart Agriculture and AI (13 papers), Robotics and Sensor-Based Localization (7 papers) and Remote Sensing in Agriculture (6 papers). Henrik Karstoft is often cited by papers focused on Smart Agriculture and AI (13 papers), Robotics and Sensor-Based Localization (7 papers) and Remote Sensing in Agriculture (6 papers). Henrik Karstoft collaborates with scholars based in Denmark, Uganda and United States. Henrik Karstoft's co-authors include Rasmus Nyholm Jørgensen, Kim Arild Steen, Mikkel Fly Kragh, Peter Christiansen, Peter Ahrendt, Ole Green, Alexandros Iosifidis, Lars Nielsen, Jørgen Berntsen and Jens Rimestad and has published in prestigious journals such as Scientific Reports, Sensors and Solar Energy.

In The Last Decade

Henrik Karstoft

46 papers receiving 977 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Henrik Karstoft Denmark 17 246 214 140 125 116 48 1.0k
Ta‐Te Lin Taiwan 28 699 2.8× 124 0.6× 197 1.4× 58 0.5× 221 1.9× 102 2.1k
Hao Guo China 26 236 1.0× 150 0.7× 212 1.5× 47 0.4× 61 0.5× 107 2.0k
Taranjit Kaur India 19 109 0.4× 219 1.0× 270 1.9× 194 1.6× 77 0.7× 44 1.2k
Amin Nasiri Iran 15 440 1.8× 117 0.5× 68 0.5× 84 0.7× 43 0.4× 25 1.1k
Tilo Burghardt United Kingdom 19 120 0.5× 688 3.2× 366 2.6× 135 1.1× 18 0.2× 64 1.6k
Tim Wark Australia 26 291 1.2× 285 1.3× 387 2.8× 178 1.4× 15 0.1× 69 2.4k
Sook Yoon South Korea 23 1.4k 5.8× 606 2.8× 303 2.2× 268 2.1× 16 0.1× 74 2.7k
J. W. Nicholson United States 27 217 0.9× 42 0.2× 103 0.7× 28 0.2× 29 0.3× 157 2.2k
Abhishek Dutta United Kingdom 5 107 0.4× 287 1.3× 56 0.4× 94 0.8× 18 0.2× 14 735
Philip Valencia Australia 14 125 0.5× 111 0.5× 102 0.7× 109 0.9× 8 0.1× 32 1.5k

Countries citing papers authored by Henrik Karstoft

Since Specialization
Citations

This map shows the geographic impact of Henrik Karstoft's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Henrik Karstoft with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Henrik Karstoft more than expected).

Fields of papers citing papers by Henrik Karstoft

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Henrik Karstoft. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Henrik Karstoft. The network helps show where Henrik Karstoft may publish in the future.

Co-authorship network of co-authors of Henrik Karstoft

This figure shows the co-authorship network connecting the top 25 collaborators of Henrik Karstoft. A scholar is included among the top collaborators of Henrik Karstoft based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Henrik Karstoft. Henrik Karstoft is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bjerge, Kim, Paul Bodesheim, & Henrik Karstoft. (2025). Deep Image Clustering with Model-Agnostic Meta-Learning. 286–297.
2.
Bjerge, Kim, Henrik Karstoft, & Toke T. Høye. (2025). Towards edge processing of images from insect camera traps. Remote Sensing in Ecology and Conservation. 11(5). 573–589. 1 indexed citations
3.
Bjerge, Kim, Henrik Karstoft, Hjalte M. R. Mann, & Toke T. Høye. (2024). A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments. Ecological Informatics. 84. 102861–102861. 7 indexed citations
4.
Bjerge, Kim, et al.. (2023). Object Detection of Small Insects in Time-Lapse Camera Recordings. Sensors. 23(16). 7242–7242. 13 indexed citations
5.
Bjerge, Kim, Quentin Geissmann, Jamie Alison, et al.. (2023). Hierarchical classification of insects with multitask learning and anomaly detection. Ecological Informatics. 77. 102278–102278. 22 indexed citations
6.
Iosifidis, Alexandros, et al.. (2022). Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data. Scientific Reports. 12(1). 8395–8395. 6 indexed citations
7.
Kragh, Mikkel Fly & Henrik Karstoft. (2021). Embryo selection with artificial intelligence: how to evaluate and compare methods?. Journal of Assisted Reproduction and Genetics. 38(7). 1675–1689. 73 indexed citations
8.
Iosifidis, Alexandros, et al.. (2021). IrradianceNet: Spatiotemporal deep learning model for satellite-derived solar irradiance short-term forecasting. Solar Energy. 228. 659–669. 43 indexed citations
9.
Kragh, Mikkel Fly, Jens Rimestad, Jørgen Berntsen, & Henrik Karstoft. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in Biology and Medicine. 115. 103494–103494. 75 indexed citations
10.
Omid, Mahmoud, et al.. (2018). On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach. Biosystems Engineering. 167. 8–20. 34 indexed citations
11.
Skovsen, Søren, Mads Dyrmann, Jørgen Eriksen, et al.. (2018). Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds. 4 indexed citations
12.
Kragh, Mikkel Fly, et al.. (2018). Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 5. 28–28. 16 indexed citations
13.
Christiansen, Peter, Mikkel Fly Kragh, Kim Arild Steen, Henrik Karstoft, & Rasmus Nyholm Jørgensen. (2017). Platform for evaluating sensors and human detection in autonomous mowing operations. Precision Agriculture. 18(3). 350–365. 8 indexed citations
14.
Mortensen, Anders Krogh, Henrik Karstoft, Karen Søegaard, René Gislum, & Rasmus Nyholm Jørgensen. (2017). Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis. Journal of Imaging. 3(4). 59–59. 13 indexed citations
15.
Nielsen, Lars, et al.. (2016). DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field. Sensors. 16(11). 1904–1904. 99 indexed citations
16.
Kragh, Mikkel Fly, et al.. (2016). Multi-modal Obstacle Detection and Evaluation of Occupancy Grid Mapping in Agriculture. 2 indexed citations
17.
Kragh, Mikkel Fly, et al.. (2016). Multi-Modal Obstacle Detection and Evaluation of Occupancy Grid Mapping in Agriculture. Publikationen an der Universität Bielefeld (Universität Bielefeld). 1–8. 3 indexed citations
18.
Christiansen, Peter, Kim Arild Steen, Rasmus Nyholm Jørgensen, & Henrik Karstoft. (2014). Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors. 14(8). 13778–13793. 116 indexed citations
19.
Karstoft, Henrik, et al.. (2014). Map Building Based on a Xtion Pro Live RGBD and a Laser Sensors. 4(1). 5 indexed citations
20.
Karstoft, Henrik, et al.. (2012). Improvement of KinectTM Sensor Capabilities by Fusion with Laser Sensing Data Using Octree. Sensors. 12(4). 3868–3878. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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