Skip to main content
×
×
Home

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)
Abstract

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.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
      Available formats
      ×
Copyright
Corresponding author
E-mail: mady@mmmi.sdu.dk
References
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), https://devblogs.nvidia.com/parallelforall/detectnet-deep-neural-network-object-detection-digits/
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),http://ec.europa.eu/eurostat/web/products-datasets/-/apro_acs_a
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.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Advances in Animal Biosciences
  • ISSN: 2040-4700
  • EISSN: 2040-4719
  • URL: /core/journals/advances-in-animal-biosciences
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

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