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Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Isobel Parkin, Jana Ebersbach, Ian Stavness
doi: https://doi.org/10.1101/557678
Jordan Ubbens
Department of Computer Science, University of Saskatchewan
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Mikolaj Cieslak
Department of Computer Science, University of Calgary
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Przemyslaw Prusinkiewicz
Department of Computer Science, University of Calgary
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Isobel Parkin
Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
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Jana Ebersbach
Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
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Ian Stavness
Department of Computer Science, University of Saskatchewan
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  • For correspondence: ian.stavness@usask.ca
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Abstract

Association mapping studies have enabled researchers to identify candidate loci for many important environmental resistance factors, including agronomically relevant resistance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of Sorghum (S. bicolor), and the founder panel for a nested association mapping population of Canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated resistance QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering complicated image processing pipelines.

Footnotes

  • This version of the paper includes two new datasets (Sorghum and Canola).

  • https://github.com/p2irc/lsplab

  • https://figshare.com/s/f710381c04c01e2ba319

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 28, 2019.
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Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies
Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Isobel Parkin, Jana Ebersbach, Ian Stavness
bioRxiv 557678; doi: https://doi.org/10.1101/557678
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Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies
Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Isobel Parkin, Jana Ebersbach, Ian Stavness
bioRxiv 557678; doi: https://doi.org/10.1101/557678

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