Preliminary evaluation of shape and colour image sensing for automated weed identification in sugarcane

McCarthy, Cheryl and Rees, Steven and Baillie, Craig (2012) Preliminary evaluation of shape and colour image sensing for automated weed identification in sugarcane. In: 34th Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2012), 1-4 May 2012, Cairns, Australia.

Abstract

Automated within-row weed spot spraying is expected to provide a mechanism for controlling difficult weeds in the sugarcane industry whilst reducing herbicide usage and the labour of manual weed spot spraying. However, technologies need to be developed that enable robust and automated in-field crop/weed discrimination. Weed identification is potentially achievable using machine vision, a technology that enables low-cost sensing and analysis of colour, shape, texture and depth (i.e. plant and leaf height) information.

The National Centre for Engineering in Agriculture (NCEA) has evaluated machine vision and image analysis approaches for colour and shape sensing for the purpose of automatic discrimination of sugarcane from in-field weeds. The approach involves application of line detection techniques to high quality in-field colour camera images. This follows on from research in which NCEA developed a colour-based image analysis system that was effective at discriminating in-field mature Panicum spp. (guinea grass) from sugarcane at night time. The current research has demonstrated that shape sensing in addition to colour sensing enhances sugarcane/weed discrimination. Preliminary image analysis results are presented for the evaluated machine vision approach on a range of weed species.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2012 by Australian Society of Sugar Cane Technologists. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without permission of the publisher.
Faculty / Department / School: Current - USQ Other
Date Deposited: 22 Apr 2013 02:47
Last Modified: 06 Sep 2017 05:51
Uncontrolled Keywords: machine vision; image analysis; line detection; spot spraying
Fields of Research : 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: B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820304 Sugar
URI: http://eprints.usq.edu.au/id/eprint/22995

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