Machine vision-based weed spot spraying: a review and where next for sugarcane?

McCarthy, Cheryl and Rees, Steven and Baillie, Craig (2010) Machine vision-based weed spot spraying: a review and where next for sugarcane? In: 32nd Annual Conference of the Australian Society of Sugar Cane Technologists, 11-14 May 2010, Bundaberg, Australia.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: No evidence of copyright restrictions.
Depositing User: Ms Cheryl McCarthy
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - No Department
Date Deposited: 07 Dec 2010 12:10
Last Modified: 05 Sep 2014 05:23
Uncontrolled Keywords: machine vision; image analysis; Guinea grass; weed identification; precision agriculture
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
09 Engineering > 0910 Manufacturing Engineering > 091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)
Socio-Economic Objective (SEO2008): B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820304 Sugar
URI: http://eprints.usq.edu.au/id/eprint/7965

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