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

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 and consistently 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. Using two synthetically generated image datasets, we first show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We then demonstrate an example application of an interspecific cross of the model C4 grass Setaria. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling association mapping studies without the need for engineering complex image processing pipelines.

Plant phenotyping relevance

画像から処理応答を自動検出・定量する新規フェノタイピング手法を提案し、合成データで検証した方法開発研究。

Code and data availability

The paper provides two paper-specific public assets: the LSP-Lab implementation of the Latent Space Phenotyping method on GitHub, and a figshare deposit containing the full datasets and utility scripts needed to reproduce the paper's results and figures. Setaria and sorghum image datasets are third-party (Baxter group)