Dr Wenhao Zhang Wenhao.Zhang@uwe.ac.uk
Associate Professor of Computer Vision and Machine Learning
Broad-leaf weed detection in pasture
Zhang, Wenhao; Hansen, Mark F; Volonakis, Timothy N; Smith, Melvyn; Smith, Lyndon; Wilson, Jim; Ralston, Graham; Broadbent, Laurence; Wright, Glynn
Authors
Mark Hansen Mark.Hansen@uwe.ac.uk
Professor of Machine Vision and Machine Learning
Timothy N Volonakis
Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof
Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine
Jim Wilson
Graham Ralston
Laurence Broadbent
Glynn Wright
Abstract
Weed control in pasture is a challenging problem that can be expensive and environmentally unfriendly. This paper proposes a novel method for recognition of broad-leaf weeds in pasture such that precision weed control can be achieved with reduced herbicide use. Both conventional machine learning algorithms and deep learning methods have been explored and compared to achieve high detection accuracy and robustness in real-world environments. In-pasture grass/weed image data have been captured for classifier training and algorithm validation. The proposed deep learning method has achieved 96.88% accuracy and is capable of detecting weeds in different pastures under various representative outdoor lighting conditions.
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | 3rd International Conference on Image, Vision and Computing (ICIVC) |
Start Date | Jun 27, 2018 |
End Date | Jun 29, 2018 |
Acceptance Date | May 11, 2018 |
Online Publication Date | Oct 18, 2018 |
Publication Date | Nov 1, 2018 |
Deposit Date | Dec 11, 2018 |
Publicly Available Date | Dec 12, 2018 |
Peer Reviewed | Peer Reviewed |
ISBN | 9781538649916 |
Keywords | weed detection, machine learning, deep learning, support vector machine |
Public URL | https://uwe-repository.worktribe.com/output/857686 |
Publisher URL | http://dx.doi.org/10.1109/ICIVC.2018.8492831 |
Additional Information | Additional Information : (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Title of Conference or Conference Proceedings : 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Corporate Creators : SoilEssentials, Aralia Systems Ltd |
Contract Date | Dec 11, 2018 |
Files
ICIVC revised - Zhang.pdf
(658 Kb)
PDF
You might also like
Gender recognition from facial images: Two or three dimensions?
(2016)
Journal Article
Eye center localization and gaze gesture recognition for human-computer interaction
(2016)
Journal Article
Eye centre localisation with convolutional neural network based regression
(2020)
Presentation / Conference Contribution
Weed classification in grasslands using convolutional neural networks
(2019)
Presentation / Conference Contribution
2D and 3D face analysis for ticketless rail travel
(2018)
Presentation / Conference Contribution
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search