2018

Mads Dyrmann, Søren Skovsen, Morten Stigaard Laursen, and Rasmus Nyholm Jørgensen. (2018,6). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. 14th International Conference on Precision Agriculture
(Paper)
Hadi Karimi, Søren Skovsen, Mads Dyrmann, and Rasmus Nyholm Jørgensen. (2018,5). A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network. Sensors
(Paper)
Nima Teimouri, Mads Dyrmann, Per Rydahl Nielsen, Solvejg Kopp Mathiassen, Gayle J. Somerville, and Rasmus Nyholm Jørgensen. (2018,5). Weed Growth Stage Estimator Using Deep Convolutional Neural Networks. Sensors
(Paper)
Nima Teimouri, M. Omid, K. Mollazade, H. Mousazadeh, R. Alimardani, H. Karstof. (2018,3). On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach. Biosystems Engineering
(Paper)

2017

Anders K. Mortensen, Henrik Karstoft, Karen Søegaard, René Gislum, and Rasmus N. Jørgensen. (2017,12). Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis. Journal of Imaging, special issue: Remote and Proximal Sensing Applications in Agriculture
(Paper)
Søren Skovsen, Mads Dyrmann, Anders Krogh Mortensen, Kim Arild Steen, Ole Green, Jørgen Erikse, René Gislum, Rasmus Nyholm Jørgensen, and Henrik Karstoft. (2017,12). Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks. Sensors, special issue: Sensors in Agriculture
(Paper)
Mikkel Fly Kragh, Peter Christiansen, Morten Stigaard Laursen, Morten Larsen, Kim Arild Steen, Ole Green, Henrik Karstoft, Rasmus Nyholm Jørgensen. (2017,11). FieldSAFE: Dataset for Obstacle Detection in Agriculture. Sensors
(Paper)
Thomas Mosgaard Giselsson, Rasmus Nyholm Jørgensen, Peter Kryger Jensen, Mads Dyrmann, Henrik Skov Midtiby. (2017,11). A Public Image Database for Benchmark of Plant Seedling Classification Algorithms. arXiv
(Paper)
Timo Korthals, Mikkel Fly Kragh, Peter Christiansen, Ulrich Rückert. (2017,8). Towards Inverse Sensor Mapping in Agriculture. International Conference on Intelligent Robots and Systems (IROS 2017) Workshop
(Paper)
Mads Dyrmann, Rasmus Nyholm Jørgensen, Henrik Skov Midtiby. (2017,7). Detection of Weed Locations in Leaf-occluded Cereal Crops using a Fully-Convolutional Neural Network. Advances in Animal Biosciences Volume 8, Issue 2 (Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017)
(Paper)
Per Rydahl, Niels-Peter Jensen, Mads Dyrmann, Poul Henning Nielsen, Rasmus Nyholm Jørgensen. (2017,7). Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems. Advances in Animal Biosciences Volume 8, Issue 2 (Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017)
(Paper)
Peter Christiansen, Mikkel Kragh, Kim A. Steen, Henrik Karstoft, Rasmus N. Jørgensen. (2017,6). Platform for evaluating sensors and human detection in autonomous mowing operations. Precision Agriculture
(Paper)
Mads Dyrmann. (2017,3). Automatic Detection and Classification of Weed Seedlings under Natural Light Conditions. University of Southern Denmark
(Paper)
Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Mads Dyrmann, Robert Poulsen. (2017,3). RoboWeedSupport – Sub Millimeter Weed Image Acquisition in Cereal Crops with Speeds up till 50 Km/h. International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering (ICSWTS 2017)
(Paper)
Mads Dyrmann, Peter Christiansen. (2017,2). Estimation of plant species by classifying plants and leaves in combination. Journal of Field Robotics
(Paper)
Mikkel Kragh, James Underwood. (2017,2). Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields. arXiv
(Paper)
Rasmus Nyholm Jørgensen, Mads Dyrmann. (2017,1). Automatisk ukrudtsgenkendelse er ikke længere science fiction. Plantekongres '17
(Paper)

2016

Mikkel Kragh, Kim Bjerge, Peter Ahrendt. (2016,12). 3D impurity inspection of cylindrical transparent containers. Measurement Science and Technology
(Paper)
M. S. Laursen, R. N. Jørgensen, H. S. Midtiby, K. Jensen, M. P. Christiansen, T. M. Giselsson, A. K. Mortensen and P. K. Jensen. (2016,11). Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. Sensors
(Paper)
Mads Dyrmann, Henrik Karstoft, Henrik Skov Midtiby. (2016,11). Plant species classification using deep convolutional neural network. Biosystems Engineering
(Paper)
Peter Christiansen, Lars N Nielsen, Kim A Steen, Rasmus N Jørgensen, Henrik Karstoft. (2016,11). DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field. Sensors
(Paper)
Mads Dyrmann, Anders Krogh Mortensen, Henrik Skov Midtiby, Rasmus Nyholm Jørgensen. (2016,6). Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network. 4th International Conference on Agricultural and Biosystems Engineering
(Paper) (Presentation, video)
Mads Dyrmann, Henrik Skov Midtiby, Rasmus Nyholm Jørgensen. (2016,6). Evaluation of intra variability between annotators of weed species in color images. 4th International Conference on Agricultural and Biosystems Engineering
(Paper)
Mikkel Fly Kragh, Peter Christiansen, Timo Korthals, Thorsten Jungeblut, Henrik Karstoft, Rasmus Nyholm Jørgensen. (2016,6). Multi-modal Obstacle Detection and Evaluation of Occupancy Grid Mapping in Agriculture. International Conference on Agricultural Engineering 2016
(Paper)
A. K. Mortensen, P. Lisouski and P. Ahrendt. (2016,3). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture
(Paper)

2015

Mads Dyrmann. (2015,8). Fuzzy C-means based plant segmentation with distance dependent threshold. Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP)
(Paper)
Mikkel Fly Kragh, Rasmus Nyholm Jørgensen, Henrik Pedersen. (2015,7). Object Detection and Terrain Classification in Agricultural Fields using 3D Lidar Data. 10th International Conference on Computer Vision Systems
(Paper)
Peter Christiansen, Mikkel Fly Kragh, Kim Arild Steen, Henrik Karstoft, Rasmus Nyholm Jørgensen. (2015,7). Advanced sensor platform for human detection and protection in autonomous farming. 10th European Conference on Precision Agriculture (ECPA)
(Paper)

2014

Mads Dyrmann, Peter Christiansen. (2014,1). Automated Classification of Seedlings Using Computer Vision: Pattern Recognition of Seedlings Combining Features of Plants and Leaves for Improved Discrimination. Aarhus University
(Paper)

2012

M.R. Andersen, T. Jensen, P. Lisouski, A.K. Mortensen, M.K. Hansen, T. Gregersen, P. Ahrendt. (2012,2). Kinect Depth Sensor Evaluation for Computer Vision Applications. Technical Report, Electronics and Computer Engineering, Department of Engineering, Aarhus University
(Paper)