A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best means of disease control is plant resistance, but the identification of genes that promote resistance has been limited by the subjective quantification of disease, which is typically scored by the human eye. We hypothesized that image-based quantification of disease phenotypes would enable the identification of new disease resistance loci. We tested this using the interaction between tomato and Ralstonia solanacearum , a soilborne pathogen that causes bacterial wilt disease. We acquired over 40,000 time-series images of disease progression in a tomato recombinant inbred line population, and developed an image analysis pipeline providing a suite of ten traits to quantify wilt disease based on plant shape and size. Quantitative trait loci (QTL) analyses using image-based phenotyping identified QTL that were both unique and shared compared with those identified by human assessment of wilting. When shared loci were identified, image-based phenotyping could detect some QTL several days earlier than human assessment. Thus, expanding the phenotypic space of disease with image-based phenotyping allowed both earlier detection and identified new genetic components of resistance.

COLLECTION DE FIGURES POUR EN FACILITER L'USAGE. Ces figures sont disposées ici dans Tordre systématique le plus généralement adopté : celui du règne animal de G. Cuvier, pour la partie Zoologique, et celui des familles naturelles pour la Botanique. Les planches n'ont point été numérotées parle graveur, afin que chacun pût les placer à son gré si un ordre différent lui semblait préférable. PI. XX. Ilippopoiamedii Cap; Hippopolanms Capenais.