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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*
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: mady@mmmi.sdu.dk
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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.

Type
Agri-engineering
Copyright
© The Animal Consortium 2017 

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References

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