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Self-supervised leaf segmentation under complex lighting conditions
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Lin, Xufeng, Li, Chang-Tsun, Adams, Scott, Kouzani, Abbas Z., Jiang, Richard, He, Ligang, Hu, Yongjian, Vernon, Michael, Doeven, Egan, Webb, Lawrence, Mcclellan, Todd and Guskich, Adam (2023) Self-supervised leaf segmentation under complex lighting conditions. Pattern Recognition, 135 . 109021. doi:10.1016/j.patcog.2022.109021 ISSN 0031-3203.
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WRAP-Self-supervised-leaf-segmentation-complex-lightning-conditions-22.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 17 September 2023. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (11Mb) |
Official URL: http://dx.doi.org/10.1016/j.patcog.2022.109021
Abstract
As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software S Agriculture > SB Plant culture |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Plant genetics, Phenotype, Learning, Psychology of, Neural networks (Computer science), Computer vision | ||||||||
| Journal or Publication Title: | Pattern Recognition | ||||||||
| Publisher: | Pergamon | ||||||||
| ISSN: | 0031-3203 | ||||||||
| Official Date: | March 2023 | ||||||||
| Dates: |
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| Volume: | 135 | ||||||||
| Article Number: | 109021 | ||||||||
| DOI: | 10.1016/j.patcog.2022.109021 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 7 December 2022 | ||||||||
| Date of first compliant Open Access: | 7 December 2022 |
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