Automatic plant branch segmentation and classification using vesselness measure
Paper
Paper/Presentation Title | Automatic plant branch segmentation and classification using vesselness measure |
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Presentation Type | Paper |
Authors | Mohammed Amean, Z. (Author), Low, T. (Author), McCarthy, C. (Author) and Hancock, N. (Author) |
Editors | Katupitiya, Jayantha, Guivant, Jose and Eaton, Ray |
Journal or Proceedings Title | Proceedings of the Australasian Conference on Robotics and Automation (ACRA 2013) |
Number of Pages | 9 |
Year | 2013 |
Place of Publication | Australia |
ISBN | 9780980740448 |
Web Address (URL) of Paper | http://www.araa.asn.au/acra/acra2013/papers/pap171s1-file1.pdf |
Conference/Event | Australasian Conference on Robotics and Automation (ACRA 2013) |
Event Details | Rank B B B B B B B B B B B |
Event Details | Australasian Conference on Robotics and Automation (ACRA 2013) Event Date 02 to end of 04 Dec 2013 Event Location Sydney, Australia |
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. |
Keywords | remote monitoring; plant branch; modern agriculture; automatic segmentation; counting; stem; vesselness measure; Hough Transform; Frangi 2D filter |
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 | |
300207. Agricultural systems analysis and modelling | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Faculty of Engineering and Surveying |
National Centre for Engineering in Agriculture | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2351/automatic-plant-branch-segmentation-and-classification-using-vesselness-measure
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