Mads Dyrmann

1.8k total citations · 2 hit papers
32 papers, 1.2k citations indexed

About

Mads Dyrmann is a scholar working on Plant Science, Ecology and Ecological Modeling. According to data from OpenAlex, Mads Dyrmann has authored 32 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Plant Science, 13 papers in Ecology and 6 papers in Ecological Modeling. Recurrent topics in Mads Dyrmann's work include Smart Agriculture and AI (21 papers), Remote Sensing in Agriculture (11 papers) and Species Distribution and Climate Change (6 papers). Mads Dyrmann is often cited by papers focused on Smart Agriculture and AI (21 papers), Remote Sensing in Agriculture (11 papers) and Species Distribution and Climate Change (6 papers). Mads Dyrmann collaborates with scholars based in Denmark, France and United States. Mads Dyrmann's co-authors include Henrik Skov Midtiby, Henrik Karstoft, Rasmus Nyholm Jørgensen, Anders Krogh Mortensen, René Gislum, Solvejg K. Mathiassen, Morten Stigaard Laursen, Søren Skovsen, Henrik Karstoft and Toke T. Høye and has published in prestigious journals such as Sensors, Remote Sensing and Biosystems Engineering.

In The Last Decade

Mads Dyrmann

30 papers receiving 1.2k citations

Hit Papers

Plant species classification using deep convolutional neu... 2016 2026 2019 2022 2016 2023 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Plant species classification using deep convolutional neural network
    2016 466 citations Mads Dyrmann, Henrik Karstoft et al. Biosystems Engineering profile →
  2. Accurate detection and identification of insects from camera trap images with deep learning
    2023 60 citations Kim Bjerge, Jamie Alison et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mads Dyrmann Denmark 15 969 422 254 126 74 32 1.2k
Philipp Lottes Germany 17 1.3k 1.4× 569 1.3× 228 0.9× 251 2.0× 45 0.6× 19 1.6k
Masayuki Hirafuji Japan 17 788 0.8× 377 0.9× 167 0.7× 205 1.6× 23 0.3× 84 1.3k
Yeyin Shi United States 23 1.1k 1.2× 823 2.0× 235 0.9× 436 3.5× 32 0.4× 80 1.8k
Gercina Gonçalves da Silva Brazil 6 572 0.6× 220 0.5× 168 0.7× 66 0.5× 22 0.3× 9 720
G.W.A.M. van der Heijden Netherlands 18 620 0.6× 259 0.6× 378 1.5× 106 0.8× 24 0.3× 54 1.0k
João Camargo Neto Brazil 9 871 0.9× 760 1.8× 259 1.0× 393 3.1× 46 0.6× 14 1.3k
Mirwaes Wahabzada Germany 18 774 0.8× 548 1.3× 497 2.0× 85 0.7× 22 0.3× 22 1.2k
Huiqin Ma China 22 979 1.0× 714 1.7× 585 2.3× 173 1.4× 38 0.5× 44 1.5k
Kaihua Wu China 12 485 0.5× 350 0.8× 262 1.0× 84 0.7× 18 0.2× 56 840

Countries citing papers authored by Mads Dyrmann

Since Specialization
Citations

This map shows the geographic impact of Mads Dyrmann'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 Mads Dyrmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mads Dyrmann more than expected).

Fields of papers citing papers by Mads Dyrmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mads Dyrmann. 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 Mads Dyrmann. The network helps show where Mads Dyrmann may publish in the future.

Co-authorship network of co-authors of Mads Dyrmann

This figure shows the co-authorship network connecting the top 25 collaborators of Mads Dyrmann. A scholar is included among the top collaborators of Mads Dyrmann 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 Mads Dyrmann. Mads Dyrmann 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.
Lombardo, Jean‐Christophe, Hervé Goëau, Christophe Botella, et al.. (2025). Adapting a global plant identification model to detect invasive alien plant species in high-resolution road side images. Ecological Informatics. 89. 103129–103129.
2.
Bjerge, Kim, et al.. (2023). Accurate detection and identification of insects from camera trap images with deep learning. 2(3). e0000051–e0000051. 60 indexed citations breakdown →
3.
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
4.
Høye, Toke T., Mads Dyrmann, Christian Kjær, et al.. (2022). Accurate image-based identification of macroinvertebrate specimens using deep learning—How much training data is needed?. PeerJ. 10. e13837–e13837. 12 indexed citations
5.
Dyrmann, Mads, et al.. (2021). Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning. Sensors. 21(18). 6126–6126. 11 indexed citations
6.
Mathiassen, Solvejg K., et al.. (2020). Open Plant Phenotype Database of Common Weeds in Denmark. Remote Sensing. 12(8). 1246–1246. 52 indexed citations
7.
Skovsen, Søren, Morten Stigaard Laursen, Jim Rasmussen, et al.. (2020). Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors. 21(1). 175–175. 15 indexed citations
8.
Dyrmann, Mads, Rasmus Nyholm Jørgensen, Morten Stigaard Laursen, & Søren Skovsen. (2019). Overfitting a convolutional neural network to support annotations of weeds. 761–766. 6 indexed citations
9.
Dyrmann, Mads, et al.. (2019). Generating artificial images of plant seedlings using generative adversarial networks. Biosystems Engineering. 187. 147–159. 40 indexed citations
10.
Dyrmann, Mads, et al.. (2019). A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sensing. 11(8). 990–990. 71 indexed citations
11.
Jørgensen, Rasmus Nyholm, et al.. (2018). Spatial variability of optimized herbicide mixtures and dosages. 1 indexed citations
12.
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
13.
Dyrmann, Mads, et al.. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. 2 indexed citations
14.
Laursen, Morten Stigaard, et al.. (2017). Roboweedsupport-Sub Millimeter Weed Image Acquisition In Cereal Crops With Speeds Up Till 50 Km/H. Zenodo (CERN European Organization for Nuclear Research). 11(4). 317–321. 12 indexed citations
15.
Skovsen, Søren, Mads Dyrmann, Anders Krogh Mortensen, et al.. (2017). Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks. Sensors. 17(12). 2930–2930. 34 indexed citations
16.
Dyrmann, Mads, et al.. (2017). Roboweedsupport-Semi-Automated Unmanned Aerial System For Cost Efficient High Resolution In Sub-Millimeter Scale Acquisition Of Weed Images. Zenodo (CERN European Organization for Nuclear Research). 11(4). 835–839. 1 indexed citations
17.
Dyrmann, Mads, Henrik Skov Midtiby, & Rasmus Nyholm Jørgensen. (2016). Evaluation of intra variability between annotators of weed species in color images. University of Southern Denmark Research Portal (University of Southern Denmark). 1 indexed citations
18.
Dyrmann, Mads, Henrik Karstoft, & Henrik Skov Midtiby. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering. 151. 72–80. 466 indexed citations breakdown →
19.
Dyrmann, Mads & Rasmus Nyholm Jørgensen. (2015). RoboWeedSupport: Weed recognition for reduction of herbicide consumptionUkrudtsgenkendelse med henblik på reducering af herbicidforbrug. 571–578. 7 indexed citations
20.
Dyrmann, Mads & J. V. Stafford. (2015). Precision agriculture '15. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 19 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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