Binch, A and Fox, CW orcid.org/0000-0002-6695-8081 (2017) Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland. Computers and Electronics in Agriculture, 140. pp. 123-138. ISSN 0168-1699
Abstract
Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision methods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standardised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machine
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier B.V. All rights reserved. This is an author produced version of a paper published in Computers and Electronics in Agriculture. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Jun 2017 12:46 |
Last Modified: | 09 Jun 2018 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.compag.2017.05.018 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117717 |