Skip to main content Accessibility help

RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network

  • M. Dyrmann (a1), R. N. Jørgensen (a2) and H. S. Midtiby (a1)


This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on an all-terrain vehicle. Hereby, the network is able to automatically detect single weed instances in cereal fields despite heavy leaf occlusion.


Corresponding author



Hide All
Andújar, D, Weis, M and Gerhards, R 2012. An ultrasonic system for weed detection in cereal crops. Sensors (Basel, Switzerland), 12 (12), 1734317357.
Barker, J, Sarathy, S and Tao, A 2016. “DetectNet: Deep Neural Network for Object Detection in DIGITS”. Nvidia, (retrieved: 2016-11-30),
Dyrmann, M and Christiansen, P 2014. “Automated Classification of Seedlings Using Computer Vision”. Technical report, Aarhus University, Aarhus, Denmark.
Dyrmann, M, Karstoft, H and Midtiby, HS 2016. “Plant Species Classification Using Deep Convolutional Neural Network.” Biosystems Engineering.
Eurostat 2016, “Crop statistics (from 2000 onwards)”, (retrieved: 2016-05-04),
Giselsson, TM 2014. “Plant Object Classification in 2D Imagery”. PhD-thesis University of Southern Denmark, Denmark.
Herrmann, I, Shapira, U, Kinast, S, Karnieli, A and Bonfil, DJ 2013. Ground-level hyperspectral imagery for detecting weeds in wheat fields. Precision Agriculture 14, 637659.
Laursen, MS, Jørgensen, RN, Dyrmann, M and Poulsen, RN 2016. “RoboWeedSupport - Sub millimeter weed image acquisition in cereal crops with speeds up till 50 km/h”, European Conference of Precision Agriculture 2016.
Laursen, MS, Jørgensen, RN, Midtiby, HS, Jensen, K, Christiansen, MP, Giselsson, TM and Jensen, PK 2016. “Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops”. Sensors 16 (11), 1848.
Lottes, P, Hoeferlin, M, Sander, S, Müter, M, Schulze, P, Stachniss, LC and Stachniss, C 2016. “An Effective Classification System for Separating Sugar Beets and Weeds for Precision Farming Applications”. Proc. of the IEEE International Conference on Robotics and Automation (ICRA) 2016, 51575163.
Oquab, M, Bottou, L, Laptev, I and Sivic, J 2014. “Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks”. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 17171724.
Pérez, AJ, López, F, Benlloch, JV and Christensen, S 2000. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 25 (3), 197212.
Russakovsky, O, Deng, J, Su, H, Krause, J, Satheesh, S, Ma, S, Huang, Z, Karpathy, A, Khosla, A, Bernstein, M, Berg, AC and Fei-Fei, L. ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.
Rydahl, P, Jensen, N-P, Dyrmann, M, Nielsen, PH and Jørgensen, RN 2016. “RoboWeedSupport - Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems”, European Conference of Precision Agriculture 2016.
Szegedy, C, Liu, W, Jia, Y, Sermanet, P, Reed, S, Anguelov, D and Rabinovich, A 2014. “Going Deeper with Convolutions”. arXiv Preprint arXiv:1409.4842, 112.


Related content

Powered by UNSILO

RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network

  • M. Dyrmann (a1), R. N. Jørgensen (a2) and H. S. Midtiby (a1)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.