Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-22T17:12:09.305Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  01 June 2017

M. Dyrmann ,
R. N. Jørgensen  and
H. S. Midtiby
M. Dyrmann*
Affiliation:
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
R. N. Jørgensen
Affiliation:
Department of Engineering – Signal Processing, Faculty of Science and Technology, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
H. S. Midtiby
Affiliation:
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
*
†E-mail: [email protected]
Get access

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.

Keywords

Weed DetectionIntegrated Weed ManagementDeep LearningConvolutional Neural Network
Type
Agri-engineering
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 842 - 847
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andújar, D, Weis, M and Gerhards, R 2012. An ultrasonic system for weed detection in cereal crops. Sensors (Basel, Switzerland), 12 (12), 17343–17357.Google Scholar
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/ Google Scholar
Dyrmann, M and Christiansen, P 2014. “Automated Classification of Seedlings Using Computer Vision”. Technical report, Aarhus University, Aarhus, Denmark.Google Scholar
Dyrmann, M, Karstoft, H and Midtiby, HS 2016. “Plant Species Classification Using Deep Convolutional Neural Network.” Biosystems Engineering.Google Scholar
Eurostat 2016, “Crop statistics (from 2000 onwards)”, (retrieved: 2016-05-04),http://ec.europa.eu/eurostat/web/products-datasets/-/apro_acs_a Google Scholar
Giselsson, TM 2014. “Plant Object Classification in 2D Imagery”. PhD-thesis University of Southern Denmark, Denmark.Google Scholar
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, 637–659.Google Scholar
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.Google Scholar
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.CrossRefGoogle ScholarPubMed
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, 5157–5163.Google Scholar
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, 1717–1724.CrossRefGoogle Scholar
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), 197–212.Google Scholar
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.Google Scholar
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.Google Scholar
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, 1–12.Google Scholar