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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.

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Corresponding author

E-mail: mady@mmmi.sdu.dk

References

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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)

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