Machine vision-based weed spot spraying: a review and where next for sugarcane?
Paper
Paper/Presentation Title | Machine vision-based weed spot spraying: a review and where next for sugarcane? |
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Presentation Type | Paper |
Authors | McCarthy, Cheryl (Author), Rees, Steven (Author) and Baillie, Craig (Author) |
Editors | Bruce, R. C. |
Journal or Proceedings Title | Proceedings of the 32nd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2010) |
Journal Citation | 32, pp. 424-432 |
Number of Pages | 9 |
Year | 2010 |
Place of Publication | Australia |
ISBN | 9781617388279 |
Conference/Event | 32nd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2010) |
Event Details | 32nd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2010) Event Date 11 to end of 14 May 2010 Event Location Bundaberg, Australia |
Abstract | Automated precision weed spot spraying in the sugarcane industry has potential to increase production while reducing herbicide usage. However, commercially-available technologies based on sensing of weed optical properties are typically restricted to detecting weeds on a soil background (i.e. detection of green on brown) and are not suited to detecting weeds amongst a growing crop. Machine vision and image analysis technology potentially enables leaf colour, shape and texture to achieve discrimination between vegetation species. The National Centre for Engineering in Agriculture (NCEA) has developed a machine vision-based weed spot spraying demonstration unit to target the weed Panicum spp. (Guinea Grass) in a sugarcane crop, which requires discrimination of a green grass weed from a green grass crop. The system operated effectively at night time for mature Guinea Grass but further work is required for the system to operate under a greater range of conditions (e.g. different times of day and crop growth stages). Techniques such as multispectral imaging and shape analysis may potentially be required to achieve more robust weed identification. The implications for machine vision detection of Guinea Grass and other weed species in sugarcane crops are considered. |
Keywords | machine vision; image analysis; Guinea grass; weed identification; precision agriculture |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460304. Computer vision |
400799. Control engineering, mechatronics and robotics not elsewhere classified | |
400707. Manufacturing robotics | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | National Centre for Engineering in Agriculture |
https://research.usq.edu.au/item/9zx5w/machine-vision-based-weed-spot-spraying-a-review-and-where-next-for-sugarcane
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