Selected skills

Data science

Expertise in data processing, image processing and geospatial data handling. Employ a data exploration approach to problem-solving, combining strong foundation in data visualization, statistical modeling, and machine learning.

Electrical Engineering

I routinely design, build, and set up sensor systems, cameras and processing platforms; including PCB-development and embedded programming. The right data, starts with the right sensor.

Programming Languages, Frameworks and tools

Python, Matlab, C, Linux, PyTorch, Scikit-learn, OpenCV, Scikit-image, ROS/ROS2, Pandas, Jupyter, Git, MongoDB

Links

• Google Scholar

Publications

2024

Dyrmann, M., Skovsen, S. K., Christiansen, P. H., Kragh, M. F., & Mortensen, A. K. (2024). High-speed camera system for efficient monitoring of invasive plant species along roadways (13:360). F1000Research. https://doi.org/10.12688/f1000research.141992.1

2023

Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C. E., Mann, H. M. R., & Høye, T. T. (2023). Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustainability and Transformation, 2(3). https://doi.org/10.1371/journal.pstr.0000051

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

2022

Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C. E., Mann, H. M. R., & Høye, T. T. (2022). Accurate detection and identification of insects from camera trap images with deep learning. bioRxiv. https://doi.org/10.1101/2022.10.25.513484

Høye, T. T., Dyrmann, M., Kjær, C., Nielsen, J., Bruus, M., Mielec, C. L., Vesterdal, M. S., Bjerge, K., Madsen, S. A., Jeppesen, M. R., & Melvad, C. (2022). Accurate image-based identification of macroinvertebrate specimens using deep learning — How much training data is needed? PeerJ, 10, artikel e13837. https://doi.org/10.7717/peerj.13837

2021

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

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), artikel 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 J. V. Stafford (red.), Precision agriculture ’21 (s. 307-313) 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), artikel 6705. https://doi.org/10.3390/s21206705

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

2020

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

2019

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

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 J. V. Stafford (red.), Precision Agriculture 2019 – Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (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

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), artikel 990. https://doi.org/10.3390/rs11080990

2018

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

Karimi, H., Skovsen, S., Dyrmann, M., & Nyholm Jørgensen, R. (2018). A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network. Sensors, 18 (5), artikel 1611. https://doi.org/10.3390/s18051611

Larsen, D., Steen, K. A., Skovsen, S., Grooters, K., Eriksen, J., Nyholm Jørgensen, R., Dyrmann, M., & Green, O. (2018). Semantic Segmentation of Clover-Grass Images using Images from Commercially Available Drones. I P. W. G. Groot Koerkamp, C. Lokhorst , A. H. Ipema, C. Kempenaar, C. M. Groenestein, C. G. van Oostrum, & N. J. Ros (red.), Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (s. 110). Wageningen University. https://doi.org/10.18174/471678

Madsen, S. L., Dyrmann, M., Laursen, M. S., Mathiassen, S. K., & Nyholm Jørgensen, R. (2018). Data Acquisition Platform for Collecting High-Quality Images of Cultivated Weed. I P. W. G. Groot Koerkamp, C. Lokhorst , A. H. Ipema, C. Kempenaar, C. M. Groenestein, C. van Oostrum, & N. Ros (red.), Proceedings of the European Conference on Agricultural Engineering: AgEng2018 (s. 360-369). Wageningen University. https://doi.org/10.18174/471679

Rydahl, P., Bojer, O. M., Jorgensen, R. N., Dyrmann, M., Andersen, P., Jensen, N., & Sorensen, M. (2018). Spatial variability of optimized herbicide mixtures and dosages. I Proceedings 14th International Conference on Precision Agriculture (ICPA2018) (s. 1-14). artikel 5040 International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=abstractid=5040

Skovsen, S., Dyrmann, M., Eriksen, J., Gislum, R., Karstoft, H., & Nyholm Jørgensen, R. (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. I Proceedings of the 14th International Conference on Precision Engineering artikel 5079 International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5079

Skovsen, S., Dyrmann, M., Eriksen, J., Gislum, R., Karstoft, H., & Nyholm Jørgensen, R. (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. Abstract fra International Conference on Precision Agriculture, Montréal, Quebec, Canada. https://ispag.org/proceedings/?action=abstract&id=5079&search=authors

Steen, K. A., Grooters, K., Høilund, C., Rasmussen, J., Nyholm Jørgensen, R., Dyrmann, M., & Green, O. (2018). Automated Weed Intensity Mapping. I G. K. P.W.G. , C. Lokhorst, A. H. Ipema, C. Kempenaar, C. M. Groenestein , C. G. van Oostrum, & N. J. Ros (red.), Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (s. 109). Wageningen University. https://doi.org/10.18174/471678

Steen, K. A., Delebasse, S., Grooters, K., Høilund, C., Dyrmann, M., Skovsen, S., Nyholm Jørgensen, R., & Green, O. (2018). In-field Potato Diseases Detection. I P. W. G. Groot Koerkamp, C. Lokhorst, A. H. Ipema, C. Kempenaar, C. M. Groenestein, C. G. van Oostrum , & N. J. Ros (red.), Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (s. 111). Wageningen University. https://doi.org/10.18174/471678

Teimouri, N., Dyrmann, M., Nielsen, P. R., Mathiassen, S. K., Somerville, G. J., & Jørgensen, R. N. (2018). Weed Growth Stage Estimator Using Deep Convolutional Neural Networks. Sensors, 18(5), artikel 1580. https://doi.org/10.3390/s18051580

2017

Dyrmann, M. (2017). Automatic Detection and Classification of Weed Seedlings under Natural Light Conditions.

Dyrmann, M., Nyholm Jørgensen, R., & Midtiby, H. S. (2017). Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. Advances in Animal Biosciences, 8(2), 842-847. https://doi.org/10.1017/S2040470017000206

Giselsson, T. M., Nyholm Jørgensen, R., Jensen, P. K., Dyrmann, M., & Midtiby, H. S. (2017). A Public Image Database for Benchmark of Plant Seedling Classification Algorithms. arXiv.org. https://arxiv.org/pdf/1711.05458

Jensen, K., Dyrmann, M., & Midtiby, H. S. (2017). Increasing the motivation of high school students to pursue engineering careers through an application-oriented active learning boot-camp. Abstract fra Exploring Teaching for Active Learning in Engineering Education, Odense, Danmark. https://doi.org/10.13140/RG.2.2.11323.64808

Jørgensen, R. N., & Dyrmann, M. (2017). Automatisk ukrudtsgenkendelse er ikke længere science fiction. Abstract fra plantekongres 2017, Herning, Danmark. https://www.landbrugsinfo.dk/Planteavl/Plantekongres/Sider/pl_plk_2017_samlet_sammendrag.pdf

Laursen, M. S., Nyholm Jørgensen, R., Dyrmann, M., & Poulsen, R. (2017). RoboWeedSupport – Sub Millimeter Weed Image Acquisition in Cereal Crops with Speeds up till 50 Km/h. International Journal of Agricultural and Biosystems Engineering, 11(4), 311-315. http://waset.org/publications/10007027/roboweedsupport-sub-millimeter-weed-image-acquisition-in-cereal-crops-with-speeds-up-till-50-km-h

Nielsen, P. R., Jensen, N-P., Dyrmann, M., Nielsen, P-H., & Nyholm Jørgensen, R. (2017). RoboWeedSupport – Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems. Advances in Animal Biosciences, 8(2), 860-864. https://doi.org/10.1017/S2040470017001054

Nyholm Jørgensen, R. (Producent), & Dyrmann, M. (Producent). (2017). Automatisk genkendelse af ukrudt. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=xw9p3S-GZG8&t=794s

Skovsen, S., Dyrmann, M., Mortensen, A. K., Steen, K. A., Green, O., Eriksen, J., Gislum, R., Nyholm Jørgensen, R., & Karstoft, H. (2017). Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks. Sensors, 17(12), artikel 2930. https://doi.org/10.3390/s17122930

2016

Dyrmann, M., Midtiby, H. S., & Nyholm Jørgensen, R. (2016). Evaluation of intra variability between annotators of weed species in color images. Paper præsenteret ved International Conference on Agricultural Engineering 2016, Aarhus, Danmark. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_annotation.pdf

Dyrmann, M., Mortensen, A. K., Midtiby, H. S., & Nyholm Jørgensen, R. (2016). Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network. Paper præsenteret ved International Conference on Agricultural Engineering 2016, Aarhus, Danmark. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_semanticsegmentation.pdf

Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151(November), 72-80. https://doi.org/10.1016/j.biosystemseng.2016.08.024

2015

Dyrmann, M. (2015). Fuzzy C-means based plant segmentation with distance dependent threshold. I S. A. Tsaftaris, H. Scharr, & T. Pridmore (red.), Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) (s. 5.1-5.11). BMVA Press. https://doi.org/10.5244/C.29.CVPPP.5

2014

Dyrmann, M., & Christiansen, P. (2014). Automated Classification of Seedlings Using Computer Vision: Pattern Recognition of Seedlings Combining Features of Plants and Leaves for Improved Discrimination. Aarhus University.

Dyrmann, M. (2014). Weed detection by UAV with camera guided landing sequence. Abstract fra NJF Seminar, Herning, Denmark.