On-the-go machine vision sensing of cotton plant geometric parameters: first results
Paper
Paper/Presentation Title | On-the-go machine vision sensing of cotton plant geometric parameters: first results |
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Presentation Type | Paper |
Authors | McCarthy, Cheryl (Author), Hancock, Nigel (Author) and Raine, Steven (Author) |
Editors | Billingsley, John and Bradbeer, Robin |
Journal or Proceedings Title | Mechatronics and machine vision in practice |
Page Range | 305-312 |
Number of Pages | 8 |
Year | 2008 |
Place of Publication | Germany |
ISBN | 9783540740261 |
9783540740278 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-540-74027-8_26 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-540-74027-8_26 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-540-74027-8 |
Conference/Event | 13th Annual Conference on Mechatronics and Machine Vision in Practice. (M2VIP 2006) |
Event Details | 13th Annual Conference on Mechatronics and Machine Vision in Practice. (M2VIP 2006) Parent International Conference on Machine Vision and Mechatronics in Practice Delivery In person Event Date 05 to end of 07 Dec 2006 Event Location Toowoomba, Australia |
Abstract | Plant geometrical parameters such as internode length (i.e. the distance between successive branches on the main stem) indicate water stress in cotton. This paper describes a machine vision system that has been designed to measure internode length for the purpose of determining real-time cotton plant irrigation requirement. The imaging system features an enclosure which continuously traverses the crop canopy and forces the flexible upper main stem of individual plants against a glass panel at the front of the enclosure, hence allowing images of the plant to be captured in a fixed object plane. Subsequent image processing of selected video sequences enabled detection of the main stem in 88% of frames. However, node detection was subject to a high false detection rate due to leaf edges present in the images. Manual identification of nodes in the acquired imagery enabled measurement of internode lengths with 3% standard error. |
Keywords | machine vision systems; cotton; geometric parameters |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 310806. Plant physiology |
460304. Computer vision | |
400707. Manufacturing robotics | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Book Title | Mechatronics and machine vision in practice |
Chapter Number | 26 |
Byline Affiliations | National Centre for Engineering in Agriculture |
https://research.usq.edu.au/item/9yx90/on-the-go-machine-vision-sensing-of-cotton-plant-geometric-parameters-first-results
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