Automated Analysis of Disease Symptoms Using LemnaTec ScanalyzerHTS

Automated non-invasive phenotyping procedures have been established as valuable tools in plant science and breeding. Beyond measuring traits related to growth and/or architecture of plants, the analysis of foliar diseases increasingly gains importance in phenotyping. Image-based phenotypic studies on disease development have highlighted the potential to monitor and understand plant-pathogen interactions. This generates valuable knowledge both for basic and applied science with the aim to breed for resistant cultivars.

In a first case study we quantified foliar diseases on sugar beets, an important crop in central Europe. Beets are threatened by foliar diseases such as Cercospora beticola, Ramularia beticola, or Uromyces betae. We collected samples of diseased sugar beet leaves from a field trial at the Julius Kühn Institute in Elsdorf, Germany. Leaf discs of healthy and infected tissue were imaged in the Scanalyzer^HTS. Images were acquired with a Basler camera (pilot piA2400-17gc) using background illumination and analysed using LemnaGrid software. Image analysis yielded the relative infected leaf area, the number of detected disease symptoms, and their size distribution. Shape and colour parameters of symptoms were specific for the different diseases and enabled discrimination between them.

In a second case study we analysed Diplocarpon rosae leaf spots on roses, one of the most important ornamental plants worldwide. Image acquisition and analysis of healthy and diseased leaves was carried out as using the same imaging and analysis setup. Disease severity as percentage of infected leaf area could be derived from image analysis.

Both case studies demonstrate how automated phenotyping technology allows quantifying plant diseases. Versatility of image acquisition and analysis technology makes the approach suitable for analysing further plant-disease combinations.