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

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.

Citation

Zhang, W., Hansen, M. F., Volonakis, T. N., Smith, M., Smith, L., Wilson, J., …Wright, G. (2018). Broad-leaf weed detection in pasture.

• Conference Name: 3rd International Conference on Image, Vision and Computing (ICIVC)
• Conference Location: Chongqing, China
• 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