Testing the ability of Unmanned Aerial Systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds).

Lambert, J.P.T. orcid.org/0000-0001-7034-7219, Childs, D.Z. orcid.org/0000-0002-0675-4933 and Freckleton, R.P. orcid.org/0000-0002-8338-864X (2019) Testing the ability of Unmanned Aerial Systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds). Pest Management Science. ISSN 1526-498X

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 Society of Chemical Industry. This is an author-produced version of a paper subsequently published in Pest Management Science. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Convolutional Neural Networks; Unmanned Aerial Systems; black-grass; management; weed mapping
Dates:
  • Accepted: 8 April 2019
  • Published (online): 10 April 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Biological Sciences (Sheffield) > Department of Animal and Plant Sciences (Sheffield)
Funding Information:
FunderGrant number
BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL (BBSRC)BB/L001489/1
Depositing User: Symplectic Sheffield
Date Deposited: 03 May 2019 09:33
Last Modified: 10 Apr 2020 00:39
Status: Published online
Publisher: Wiley
Refereed: Yes
Identification Number: https://doi.org/10.1002/ps.5444
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