| Plant species classification using deep convolutional neural network M Dyrmann, H Karstoft, HS Midtiby Biosystems engineering 151, 72-80, 2016 | 622 | 2016 |
| RoboWeedSupport-Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network M Dyrmann, RN Jørgensen, HS Midtiby Advances in Animal Biosciences 8 (2), 842-847, 2017 | 152 | 2017 |
| A public image database for benchmark of plant seedling classification algorithms TM Giselsson, RN Jørgensen, PK Jensen, M Dyrmann, HS Midtiby arXiv preprint arXiv:1711.05458, 2017 | 145 | 2017 |
| Semantic segmentation of mixed crops using deep convolutional neural network. AK Mortensen, M Dyrmann, H Karstoft, RN Jørgensen, R Gislum | 114 | 2016 |
| Weed growth stage estimator using deep convolutional neural networks N Teimouri, M Dyrmann, PR Nielsen, SK Mathiassen, GJ Somerville, ... Sensors 18 (5), 1580, 2018 | 103 | 2018 |
| A novel spatio-temporal FCN-LSTM network for recognizing various crop types using multi-temporal radar images N Teimouri, M Dyrmann, RN Jørgensen Remote Sensing 11 (8), 990, 2019 | 76 | 2019 |
| Pixel-wise classification of weeds and crop in images by using a fully convolutional neural network. M Dyrmann, AK Mortensen, HS Midtiby, RN Jørgensen | 74 | 2016 |
| The GrassClover image dataset for semantic and hierarchical species understanding in agriculture S Skovsen, M Dyrmann, AK Mortensen, MS Laursen, R Gislum, J Eriksen, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 56 | 2019 |
| Open plant phenotype database of common weeds in Denmark S Leminen Madsen, SK Mathiassen, M Dyrmann, MS Laursen, LC Paz, ... Remote Sensing 12 (8), 1246, 2020 | 52 | 2020 |
| Estimation of the botanical composition of clover-grass leys from RGB images using data simulation and fully convolutional neural networks S Skovsen, M Dyrmann, AK Mortensen, KA Steen, O Green, J Eriksen, ... Sensors 17 (12), 2930, 2017 | 50 | 2017 |
| Generating artificial images of plant seedlings using generative adversarial networks SL Madsen, M Dyrmann, RN Jørgensen, H Karstoft Biosystems Engineering 187, 147-159, 2019 | 30 | 2019 |
| Estimation of plant species by classifying plants and leaves in combination M Dyrmann, P Christiansen, HS Midtiby Journal of Field Robotics 35 (2), 202-212, 2018 | 26 | 2018 |
| Accurate detection and identification of insects from camera trap images with deep learning K Bjerge, J Alison, M Dyrmann, CE Frigaard, HMR Mann, TT Høye PLOS Sustainability and Transformation 2 (3), e0000051, 2023 | 24 | 2023 |
| Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields M Dyrmann, S Skovsen, MS Laursen, RN Jørgensen The 14th International Conference on Precision Agriculture, 1-7, 2018 | 24 | 2018 |
| RoboWeedSupport-Sub millimeter weed image acquisition in cereal crops with speeds up till 50 km/h MS Laursen, RN Jørgensen, M Dyrmann, R Poulsen International Journal of Agricultural and Biosystems Engineering 11 (4), 317-321, 2017 | 22 | 2017 |
| Automatic Detection and Classification of Weed Seedlings under Natural Light Conditions M Dyrmann University of Southern Denmark, 2017 | 16 | 2017 |
| Automated Classification of Seedlings Using Computer Vision: Pattern Recognition of Seedlings Combining Features of Plants and Leaves for Improved Discrimination M Dyrmann, P Christiansen Aarhus University, 2014 | 14 | 2014 |
| Robust species distribution mapping of crop mixtures using color images and convolutional neural networks SK Skovsen, MS Laursen, RK Kristensen, J Rasmussen, M Dyrmann, ... Sensors 21 (1), 175, 2020 | 13 | 2020 |
| 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 S Skovsen, M Dyrmann, J Eriksen, R Gislum, H Karstoft, RN Jørgensen Proceedings of the 14th International Conference on Precision Engineering, 5079, 2018 | 12 | 2018 |
| RoboWeedSupport: weed recognition for reduction of herbicide consumption M Dyrmann, RN Jørgensen Precision agriculture'15, 571-578, 2015 | 9 | 2015 |
