Automatic plant branch segmentation and classification using vesselness measure

Mohammed Amean, Z. ORCID: https://orcid.org/0000-0003-1581-3883 and Low, T. ORCID: https://orcid.org/0000-0001-8666-0517 and McCarthy, C. and Hancock, N. (2013) Automatic plant branch segmentation and classification using vesselness measure. In: Australasian Conference on Robotics and Automation (ACRA 2013), 2-4 Dec 2013, Sydney, Australia.

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Abstract

Remote monitoring of plant vegetation is an effective method to save time and to improve production efficiency. Modern agriculture techniques utilise mobile robot and machine vision for automated image acquisition and analysis. The Identification of plant parts such as leaves, stem, branches and flowers is important for assessing plant growth, irrigation strategy and plant health. In this paper, automatic segmentation and counting of plant branches based on vesselness measure and Hough Transform techniques is presented. Frangi 2D filter, based on Hessian matrix eigenvalues has been used to classify image pixels as either tube-like or blob-like. First the input image was converted to the gray scale image and used as input to the Frangi 2D filter. Size filter was used to eliminate non-branches and small objects from the image. Hough Transform was applied to detect and draw lines on the stem and branches on the image. The developed method can detect and count the branches automatically and was applied on different sides of view and different illumination conditions for the same plant. The results show a high percentage of branches segmentation for clear side views of the plant. However, branch segmentation was affected by low illumination conditions.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Authors retain copyright. This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Date Deposited: 15 Jan 2014 05:15
Last Modified: 25 Oct 2021 12:50
Uncontrolled Keywords: remote monitoring; plant branch; modern agriculture; automatic segmentation; counting; stem; vesselness measure; Hough Transform; Frangi 2D filter
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision
09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400799 Control engineering, mechatronics and robotics not elsewhere classified
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9605 Ecosystem Assessment and Management > 960504 Ecosystem Assessment and Management of Farmland, Arable Cropland and Permanent Cropland Environments
URI: http://eprints.usq.edu.au/id/eprint/24529

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