Image‐based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato

Abstract

A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.

Plant phenotyping relevance

画像解析パイプラインを開発し、植物形態から病害進展を定量化する方法が研究の中心であるため含める。

Code and data availability

The paper's data availability statement points to a public Purdue-hosted repository containing the raw plant images and genotype data used for the image-based disease phenotyping and QTL analysis. The analysis code, however, is only available upon request from an author, so it is not a public asset.