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

• Published in: The Plant journal : for cell and molecular biology 2023-03, Vol.113 (5), p.887-903
• Main Authors: Méline, Valérian, Caldwell, Denise L., Kim, Bong‐Suk, Khangura, Rajdeep S., Baireddy, Sriram, Yang, Changye, Sparks, Erin E., Dilkes, Brian, Delp, Edward J., Iyer‐Pascuzzi, Anjali S.
• Format: Article
• Language: English
• Subjects: bacterial wilt; Crop production; digital phenotyping; Disease Progression; Disease resistance; Disease Resistance - genetics; Gene mapping; Genotypes; Humans; Image acquisition; Image analysis; Image processing; Medical imaging; Pathogens; Phenotyping; Plant bacterial diseases; Plant Breeding; Plant diseases; Plant Diseases - genetics; Plant Diseases - microbiology; Plant morphology; Quantitative trait loci; Quantitative Trait Loci - genetics; Ralstonia solanacearum; Solanum lycopersicum; Solanum lycopersicum - genetics; tomato; Tomatoes; Wilt; Wilting
• ISSN: 0960-7412; 1365-313X
• DOI: 10.1111/tpj.16101

SUMMARY 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. Significance Statement Plant diseases cause complex, quantitative phenotypes that can be difficult to assess accurately by visual assessment, which leads to challenges in detecting disease resistance loci. We developed a non‐destructive disease imaging and phenotyping pipeline and used it to capture disease progression in tomato (Solanum lycopersicum) plants inoculated with the bacterial pathogen Ralstonia solanacearum. We phenotyped for 10 traits from these images and visually assessed disease over time. Quantitative trait locus mapping with image‐based phenotyping for single and multi‐traits identified new genetic loci for resistance and detected loci earlier than those detected with visual assessment.