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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]
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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 

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