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

Jordan Ubbens1, Mikolaj Cieslak2, Przemyslaw Prusinkiewicz2, Isobel Parkin3, Jana Ebersbach3, Ian Stavness1
1 University of Saskatchewan, Saskatoon, Canada
2 University of Calgary, Calgary, Canada
3 Agriculture and Agri-Food Canada, Saskatoon, Canada


Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance 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 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.


Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Isobel Parkin, Jana Ebersbach, and Ian Stavness. Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. Plant Phenomics vol. 2020, Article ID 5801869, 2020.

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